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 U.S. DEPARTMENT OF ENERGY

 + + + + +

 THE ENERGY CONSERVATION STANDARDS FOR

 AUTOMATIC COMMERCIAL ICE

 MAKERS

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 PUBLIC MEETING

 + + + + +

 THURSDAY

 JUNE 19, 2014

 + + + + +

 The Public Meeting met in Room GH-019, 1000

Independence Avenue, S.W., Washington, D.C., at

9:00 a.m., Ashley Armstrong and John Cymbalsky,

Meeting Leads, presiding.
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PRESENT

ASHLEY ARMSTRONG, Department of Energy

JOHN CYMBALSKY, Department of Energy

SCOTT BINGHAM, Follett Corporation

JAVIER BURGOS, Navigant Consulting

SARAH BUTLER, Department of Energy

TOM ECKMAN, Northwest Power and Conservation

 Council

DARYL ERBS, Manitowoc Foodservice

CHRISTOPHER KENT, U.S. Environmental

 Protection Agency

MICHAEL KIDO, Department of Energy

TOM LASECKI, Scotsman Ice Systems

EMILY MARCHETTI, Energetics Inc.

JOANNA MAUER, Appliance Standards Awareness

 Project

ANNIE McCARTHY, North American Association of

 Food Equipment Manufacturers

ELIZABETH NOLL, Natural Resources Defense

 Council

LAURA PETRILLO-GROH, Air-Conditioning,

 Heating, and Refrigeration Institute

DOUG RAWALD, Department of Energy

MICHAEL RIVEST, Navigant Consulting

CHRIS SALATINO, Scotsman Ice Systems *

STEPHEN SCHAEFER, Hoshizaki America

AMY SHEPHERD, AHRI

RON SOBAN, The Legacy Companies

ADAM SPITZ, ICF International

LOUIS STARR, Northwest Energy Efficiency

 Alliance

DETLEF WESTPHALEN, Navigant Consulting

*Participated via teleconference
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 T-A-B-L-E O-F C-O-N-T-E-N-T-S

Welcome, Introductions 4

Background and Energy Model Overview

 John Cymbalsky 6

Use of the Energy Model in DOE Analysis

 Detlef Westphalen 9

Energy Model Demonstration

 Javier Burgos 42

Next Steps and Closing Remarks 201

Adjourn 202
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1 P-R-O-C-E-E-D-I-N-G-S

2 9:05 a.m.

3 MR. CYMBALSKY: Okay. Let's try to

4 get this show on the road here.

5 Thanks everyone for coming. I'm John

6 Cymbalsky. I am the Program Manager for

7 Appliance Standards.

8 So based on concerns from our last

9 public meeting on the proposal for ice makers, we

10 decided it was in the best interest for everyone

11 to hold another public meeting, get together, get

12 into the details of the engineering model, give

13 the stakeholders a chance to see how the model

14 works, hopefully do some analysis. I think here

15 today we'll do our best to do what we can on the

16 fly, but I think obviously we'll take home some

17 data and run some of the models post-meeting. We

18 still have some time here to get the final rule

19 out at the end of the year. So I think we're in

20 a good place.

21 So let's start off by going around the

22 table, everybody introducing themselves. So we
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1 have the court reporter. And as we go through

2 the meeting please speak loudly so that we can

3 record your comments and please identify your

4 name each time you wish to speak. So let's start

5 over here with Sarah.

6 MS. BUTLER: Sarah Butler, DOE.

7 MR. KENT: Christopher Kent, U.S.

8 Environmental Protection Agency, ENERGY STAR

9 Program.

10 MR. SPITZ: Adam Spitz, ICF

11 International, supporting the EPA ENERGY STAR

12 Program.

13 MS. McCARTHY: Amy McCarthy, North

14 American Association of Food Equipment

15 Manufacturers.

16 MR. SCHAEFER: Stephen Schaefer,

17 Hoshizaki America.

18 MS. NOLL: Elizabeth Noll, Natural

19 Resources Defense Council.

20 MR. BURGOS: Javier Burgos, Navigant.

21 MR. WESTPHALEN: Detlef Westphalen,

22 Navigant.
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1 MR. RIVEST: Mike Rivest, Navigant.

2 MR. ERBS: Daryl Erbs, Manitowoc

3 Foodservice.

4 MR. BINGHAM: Scott Bingham, Follett

5 Corporation.

6 MR. LASECKI: Tom Lasecki, Scotsman

7 Ice.

8 MS. PETRILLO-GROH: Laura Petrillo9 Groh, AHRI.

10 MS. SHEPHERD: Amy Shepherd, AHRI.

11 MR. CYMBALSKY: Okay. Great. And we

12 have a Webinar going.

13 MS. MARCHETTI: Yes.

14 MR. CYMBALSKY: How many do we have

15 today?

16 MS. MARCHETTI: We have 11 so far.

17 MR. CYMBALSKY: Okay. And we're going

18 to allow them to raise their hands as well?

19 MS. MARCHETTI: Yes.

20 MR. CYMBALSKY: Okay. So for those of

21 you on the Webinar, there's a process to raise

22 your hand if you want to make a comment and we
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1 will recognize you at that time.

2 So let's start with the slide deck.

3 We can skip the slide 2. I think we all know

4 what to do here in terms of announcing your name

5 and giving your remarks.

6 We have an agenda here. We'll try to

7 follow it the best we can, but if we get done

8 sooner, that's great. If not, we'll stick to the

9 agenda here.

10 Next slide. Just again we have the

11 Webinar going on. Again raise your hand if you'd

12 like to be recognized here at the table.

13 And then the next slide just shows you

14 the purpose of having this meeting.

15 So again, the Department was pleased

16 we can set aside some time to do this. We really

17 want this to be transparent. We want the

18 stakeholders to be comfortable with the analysis.

19 So that is our goal today is to get everyone to

20 that spot where they feel comfortable. And then

21 again as we move through to the final rule, what

22 we found out today and whatever data could be
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1 provided to be used in the final rule will be

2 very helpful to the Department.

3 So at this time I'll turn it over if

4 anyone has some opening remarks that they'd like

5 to make before we get started.

6 Okay. Seeing none, we'll get right

7 down to the content.

8 So Slide 9, just giving some

9 background on this rulemaking. So we have a

10 statutory requirement to finish this rulemaking

11 by the end of this year, so actually January 1st,

12 2015. So we still plan on obviously making that

13 date. We're here in June, so I think we're still

14 in a good spot to do that.

15 As you all know, our NOPR published on

16 March 17th, 2014. We had the public meeting in

17 April and this public meeting is a natural

18 outgrowth from the comments we heard here. So

19 here we sit at June 19th and we are going to have

20 a meeting about the energy and engineering model.

21 Okay. So the next slide kind of gives

22 you a flow chart of the whole analytical
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1 framework of the rulemaking. We've circled the

2 part here that we're going to focus on today,

3 which is the engineering analysis. That is from

4 the NOPR meeting. That is what we heard you all

5 wanted to talk about and get familiar with. So

6 basically we have this model that the purpose is

7 to do a cost efficiency curve. So that's -- you

8 see the little example down in the lower left of

9 the chart.

10 And so what we're going to do here

11 today is talk about the inputs and the outputs of

12 the model. And we'll actually get right into it,

13 and I'm going to turn it over to the Technical

14 Team. Detlef?

15 MR. WESTPHALEN: All right. Thanks,

16 John. I guess I'll talk about the energy model

17 and going through this section, the energy model

18 description.

19 Just to touch on a little bit about

20 the previous slide that John presented, the

21 engineering analysis, the energy model focuses on

22 the efficiency level side of the engineering.
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1 There's obviously a cost model as well, but we

2 have prepared material that focuses on the

3 efficiency side rather than the cost side. And

4 that's something that was -- what we got from the

5 intent of what the meeting was going to be about.

6 So I'm not going to spend a lot of

7 time on the description of the model here. We

8 can come back to some of this if there are some

9 questions. I get the sense that the

10 demonstration of the model is probably more

11 important. So I'll try to go through these

12 slides fairly quickly here.

13 MR. BINGHAM: I have a question. This

14 is Scott Bingham with Follett Corporation. Has

15 the model been modified by either the comments

16 and data that was provided by stakeholders?

17 MR. WESTPHALEN: Since the NOPR phase?

18 MR. BINGHAM: Since the May 16th

19 public comment deadline.

20 MR. WESTPHALEN: I don't think so. I

21 think we're still waiting for input on -- all the

22 inputs to come in to put all of those
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1 modifications together.

2 MR. BINGHAM: Okay.

3 MR. RIVEST: This is Mike Rivest from

4 Navigant. I want to draw a distinction which may

5 be obvious, but in case it's not I do want to say

6 that the model is a tool that is used to derive a

7 cost efficiency curve. In other analyses, like

8 the CRE with the Commercial Refrigeration, when

9 we talk about the model, we're really talking

10 about a spreadsheet that shows design options and

11 the end efficiency improvement of each design

12 option.

13 Here this analytical tool, you input

14 a series of design options and out comes an

15 efficiency rating for those design options. So

16 it's a part of the overall analysis. So from

17 this is derived -- this is used multiple times

18 with multiple designs to derive that cost

19 efficiency curve. So there isn't a model that

20 spits out a cost efficiency curve. Was that

21 clear to everyone that that's not what this does?

22 MS. PETRILLO-GROH: Yes, that was very
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1 clear in thinking.

2 MR. RIVEST: Okay.

3 MS. PETRILLO-GROH: This is Laura

4 Petrillo, AHRI. Could you talk a little bit

5 about some of the ice makers that the machine can

6 model or some of the limitations that it may have

7 in terms of does it model all the different types

8 of ice makers that are in this rulemaking?

9 MR. WESTPHALEN: I think some of that

10 will become more apparent as we go through the

11 description and some of the modeling

12 demonstrations.

13 MS. PETRILLO-GROH: Okay. Thank you.

14 And could you also -- when you're going over that

15 would you also talk about any updates that have

16 been made to the model since 1995?

17 MR. WESTPHALEN: Yes, that's part of

18 what we'll be talking about.

19 MS. PETRILLO-GROH: Thank you.

20 MR. ERBS: This is Daryl Erbs at

21 Manitowoc. Just something I want to throw out on

22 the table. Would we consider this model to be
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1 something that DOE would accept as a AEDM?

2 MR. WESTPHALEN: Well, I would think

3 that that would depend on -- there are

4 requirements for AEDMs and --

5 (Simultaneous speaking)

6 (Laughter)

7 MR. ERBS: It just popped in my mind

8 because we've always -- you know, we've said we

9 can't use AEDM because we don't think it's really

10 possible to do a good job of modeling the ice

11 machine, but now that I think about it, if we

12 reach consensus that this is a good way to model

13 efficiency for purposes of rulemaking, then I

14 think manufacturers might want to look at it as a

15 way to reduce the regulatory burden of all the

16 testing we currently have to do.

17 MS. ARMSTRONG: So let's get through

18 the discussion on what the model is and what it

19 encompasses and then perhaps towards the end of

20 the day we can circle back to that.

21 MR. ERBS: Okay. I just wanted to get

22 it on the table.
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1 MS. ARMSTRONG: Your point is clear

2 and well taken.

3 MR. ERBS: Okay.

4 MS. ARMSTRONG: How about that?

5 MR. SCHAEFER: Stephen Schaefer with

6 Hoshizaki America. I see here that it was first

7 modeled for refrigerators. I'd also like to see

8 how this was modeled separately for ice machines,

9 because refrigerators and ice makers work totally

10 different. So I hope that we can see that here

11 today.

12 MR. WESTPHALEN: Yes, I think we can,

13 but -- all right. Well, let me get into the

14 description. As mentioned in the NOPR, the

15 energy modeling for the rulemaking was conducted

16 with the help of this model called FREEZE. It

17 was first developed in the mid-'90s by Anthony

18 Varone. Sort of parallel development, the EPA

19 Refrigerator Analysis Program was developed by

20 Richard Merriam, and that model was used for

21 refrigerator rulemaking that had a final rule in

22 '97. And that model was also modified more
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1 recently in support of the 2011 refrigerator

2 rulemaking.

3 Varone and Merriam got together to

4 support the rulemaking and developed a Windows

5 version of FREEZE. Now this is not a

6 refrigerator model. It uses some aspects of the

7 refrigerator model. For instance, how do you

8 model an air-cooled condenser? That is a

9 component that's present in an ice maker and it's

10 also a component present in a refrigerator. And

11 so, basically uses the tools that were developed

12 already for the refrigerator with updated

13 routines to solve heat transfer equations and

14 then using them in the FREEZE model.

15 They also upgraded the user interface

16 for the model to make it a Windows-based version

17 as you can see the picture down below here in

18 this slide. And this is the model that we've

19 been using, the FREEZE model to support the

20 energy calculations, and Navigant has permission

21 to use it.

22 So going to the next page, basically
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1 the model calculates the energy input of the

2 power-using components and it calculates the ice

3 production for a full ice making cycle. Some of

4 these components, as you know, have constant

5 power input. A condenser fan, a pump,

6 potentially an auger motor controls. A lot of

7 the power and energy obviously go into the

8 compressor, and the power input to the compressor

9 obviously is much more complicated than power

10 input to a fan or a pump. And calculating that

11 power input involves figuring out what the

12 thermal load is in the evaporator that the

13 compressor is trying to cool. And so the model

14 includes the thermal analysis associated with the

15 warm water coming in, cooling down the drain

16 water going out at a low temperature and then the

17 ice going out, also frozen at a lower

18 temperature, obviously.

19 And then there is the harvest energy.

20 And we've had discussions about how the model

21 handles harvest energy and the model does not

22 directly calculate that. That's an input to the
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1 model. And you'll see that directly when we get

2 into it later.

3 Also to mention about the harvest, as

4 you know the harvest also warms up the

5 evaporator. And so part of the heat that is put

6 into the system in harvest must be then removed

7 again and that then also adds to the thermal load

8 that the evaporator needs to take care of. And

9 the model takes that into account as well.

10 So looking at the next page, for a

11 batch ice maker the freeze cycle calculation is

12 conducted a number of times. You've got small

13 time steps representing the progression of the

14 cool down of the sump water temperature and then

15 the formation of ice. And those charts sort of

16 give a visual representation. The one on the top

17 represents the amount of ice formed. And you can

18 see that initially there is no ice being formed

19 because the water is being cooled down and then

20 slowly the ice formation starts. And then the

21 lower chart shows progression of some of the

22 temperatures cooling down. And so at each time
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1 step there's a balance of the refrigeration

2 system performance and the water evaporator

3 system which represents where the thermal load is

4 removed from the water and ice.

5 And so at each time step there is a

6 calculation of what's the rate of heat being

7 removed at the evaporator? And there's a

8 calculation of either how much colder does the

9 water in the sump get or how much ice is being

10 formed. And that then sets the condition for the

11 next time step. And then these time steps

12 continue until you have as much ice as the input

13 parameter that represents the pre-harvest ice

14 weight.

15 And then going to the next page --

16 MR. ERBS: Can we interrupt --

17 MR. WESTPHALEN: Sure.

18 MR. ERBS: -- with questions? So this

19 is Daryl Erbs, Manitowoc. Just when you're doing

20 the modeling, how do you set the pre-harvest ice

21 weight for the model? Do you go to our technical

22 documents and look at what we tell you the slab
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1 weight should be, or what's the input to kind of

2 get a representative pre-harvest weight when

3 you're doing the modeling?

4 MR. WESTPHALEN: Well, that is an

5 input parameter.

6 MR. ERBS: Right.

7 MR. WESTPHALEN: And so through

8 testing we know what the harvest weight is.

9 MR. ERBS: Okay.

10 MR. WESTPHALEN: And then there's a

11 little bit of assumption work associated with how

12 much ice meltage is there between pre-harvest and

13 harvest. And we'll get into some of the

14 parameters that we've been using.

15 MR. ERBS: So you took experimental

16 data as representative of what the -- the

17 example? Yes. Okay.

18 MS. PETRILLO-GROH: Could you speak

19 up, please?

20 MR. ERBS: Oh, so I was just saying so

21 they're taking experimental measurements off of

22 whatever machines they tested as a basis for
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1 doing modeling for those classes of ice machines

2 in terms of what's the right pre-harvest weight

3 to input into the model.

4 MS. PETRILLO-GROH: Thanks, Daryl.

5 MR. ERBS: Sorry about that.

6 MR. BINGHAM: This is Scott Bingham at

7 Follett. I don't want to get us off track, and

8 so don't need an answer to this question right

9 now, but I would like -- this is a modeling of a

10 batch-type ice maker, and at some point I'd like

11 to understand the nuances and differences of the

12 model for continuous-type ice makers.

13 MR. WESTPHALEN: Okay. Yes, we can

14 get into that. We do discuss that a little bit

15 more.

16 MR. BINGHAM: Right.

17 MR. CYMBALSKY: Not to jump ahead, but

18 I mean can we go to slides 22 and 23, because I'm

19 sure we're not going to read these off in great

20 detail, but you can see all the testing that DOE

21 has done in this space, both batch and continuous

22 ice makers.
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1 MR. WESTPHALEN: So moving to page 15,

2 after the full cycle the freeze cycle has been

3 calculated. Then the program basically just does

4 the counting of how much energy was used for each

5 of the components. For the condenser fan, the

6 power input times the cycle time. Similarly for

7 pump and controls. For the compressor it is an

8 energy calculation for each time step and then

9 those energy calculations are summed throughout

10 the series of steps.

11 And as mentioned, the harvest is an

12 input, and that includes operation of all the

13 components. Compressor isn't listed there, but

14 that obviously is the key part of it. But it

15 also includes the pump, the controls, the

16 solenoid valve which is energized during harvest,

17 but not during the freeze typically. The per

18 cycle ice production is just the pre-harvest ice

19 weight minus the harvest ice loss. And then the

20 energy use in kilowatt hours for weight of ice is

21 just divide the one number by the other.

22 So going into the way the model deals
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1 with some of the components, the compressor

2 performance comes directly from performance maps.

3 There's a performance map for a Copeland

4 compressor taken right from their Web site based

5 on evaporating, condensing, superheating and sub6 cooling conditions. And the model adjusts for

7 non-standard superheating and sub-cooling. These

8 parameters are inputs to the model, and you'll

9 see that.

10 And clearly with some ice makers you

11 may have some variation in those parameters over

12 the course of the freeze cycle, but those are

13 inputs at this point.

14 Condensing and evaporating conditions

15 are set based on the balance of the refrigeration

16 circuit, the compressor with the condenser and

17 the evaporator making sure that the heat transfer

18 characteristics of the heat exchangers are

19 considered properly.

20 Also I'll mention this here; and

21 Javier will get into a little bit when we

22 demonstrate the model, for batch ice makers we
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1 found that obtaining a good model test match

2 involves putting in a derate of the compressor

3 capacity. Not the power input. Just the

4 capacity.

5 MR. ERBS: Are you going to comment --

6 this is Daryl Erbs, Manitowoc. Are you going to

7 give us some idea of how much the derating

8 factors --

9 MR. WESTPHALEN: Oh, yes.

10 MR. ERBS: You're going to go through

11 all that? Okay. Thanks.

12 MR. WESTPHALEN: Moving on to the next

13 slide, the heat exchangers, the evaporator and

14 condenser. basically the amount of heat transfer

15 must be consistent with the typical heat transfer

16 coefficients and the surface area involved. And

17 heat transfer rate also is equal to the

18 refrigerant mass flow passing through the heat

19 exchangers times the refrigerant enthalpy change.

20 For condensers or continuous ice maker

21 evaporators the heat transfer rate also equals

22 the fluid mass flow, be it water changing to ice
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1 or water used to cool a condenser or air times

2 the enthalpy change. Obviously for continuous

3 ice maker evaporator, the enthalpy is based on

4 removing the heat effusion from that water/ice

5 mix. For batch ice makers the heat transfer rate

6 determines how much ice forms and basically sets

7 up the next step of the calculation.

8 So this slide addresses what parts of

9 the refrigerator model were used in the upgraded

10 version of FREEZE. The air-cooled condenser

11 modeling, the refrigerant-side modeling for the

12 evaporator. And details of that are in Appendix

13 5-B of the refrigerator final rule TSD. But as

14 an example here, the air-cooled condenser air15 side heat transfer was model based on the Wang

16 correlations. And here's the reference for that.

17 There's been a lot of work done since

18 the '90s. It's possible that there's additional

19 work that we could use to update some of this

20 material, but generally these correlations have

21 worked fairly well.

22 Moving on to the next slide, the
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1 evaporator side for a batch ice maker. Most of

2 our modeling focused on what we call waffle plate

3 evaporators. These are the evaporators that

4 you'd see in like a Manitowoc, Scotsman or Ice-O5 Matic. And so they have a back plate and then

6 they have these slots that are -- or these, I

7 guess, plates that are vertical and horizontal

8 that form the cubic spaces and the ice forms in

9 those cavities.

10 And so in order to model the heat

11 transfer from the refrigerant to the outer layer

12 of ice we set up a conduction model, and it has

13 pieces associated with it. For instance, this

14 thermal resistance associated with the braze

15 that's between the tube and the back plate. Then

16 conduction through the back plate. Obviously not

17 much resistance there through a thin plate of

18 copper. Conduction through the back piece of

19 ice, a fin analysis to figure out how much heat

20 is transferred along the sides to the ice forming

21 on the insides of the cavities, and then all the

22 way to the bridge. And there's the additional
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1 thermal resistance of the water film layer when

2 we're initially just cooling the water rather

3 than forming the ice.

4 And basically what's happening here is

5 that as the ice layer forms, the thermal

6 resistance from the tube to the surface layer

7 gets much greater. And so the UA, the thermal

8 conductance, you can see in the plot, starts high

9 and then winds up leveling out. And, you know,

10 this is an example for a 126-cube evaporator.

11 MS. PETRILLO-GROH: What size model

12 unit would a 126-cube evaporator match up with?

13 MR. WESTPHALEN: I think that's

14 something like 300 pounds a day.

15 MS. PETRILLO-GROH: Three hundred

16 pounds a day?

17 MR. WESTPHALEN: Yes. I mean, it

18 could vary because you can put bigger compressors

19 on them and get more out.

20 MS. PETRILLO-GROH: Okay.

21 MR. SCHAEFER: This is Steve Schaefer,

22 Hoshizaki America. You showed an example for the
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1 Ice-O-Matic, Manitowoc and Scotsman, but what

2 about continuous-type for the Hoshizaki plates?

3 MR. WESTPHALEN: So for a continuous4 type; I think we have on the next page, we use a

5 constant UA value and we develop what that value

6 should be based on the test data for a given

7 machine. So we can get into the details of the

8 progression of -- you know, for baseline up to

9 high efficiency, but when we calibrate to a

10 machine, we're basically using the UA that gets

11 us the right number.

12 MR. SCHAEFER: Okay. So was there one

13 for other plates that are separate from the

14 waffle like the Hoshizaki plates?

15 MR. WESTPHALEN: We did do modeling in

16 the preliminary analysis phase of the Hoshizaki,

17 and obviously it's a very different heat

18 conduction problem. It has quite a higher UA

19 value at the start, but then quite a bit less at

20 the end because it doesn't have those plates that

21 go horizontally and vertically toward the ice

22 surface the way the waffle plate does. So
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1 there's much more -- the ice blocks off the heat

2 transfer much more.

3 But as part of the discussion of the

4 technology status of the Hoshizaki evaporator, we

5 decided that we were going to consider that as a

6 design option, and through the analysis that we

7 had done we concluded that there may very well be

8 an efficiency advantage associated with that

9 design and that in order to have a representative

10 analysis that would be fair across most of the

11 industry, the waffle plate would be more

12 appropriate.

13 MS. PETRILLO-GROH: Did you use those

14 preliminary models moving forward or did you --

15 how did you treat those proprietary designs?

16 MR. WESTPHALEN: For the NOPR phase

17 analysis it was a waffle plate batch ice maker

18 analysis. We didn't consider the technology, the

19 Hoshizaki technology at all.

20 MS. PETRILLO-GROH: Thank you.

21 MR. ERBS: This is Daryl Erbs from

22 Manitowoc. One of the things you haven't really
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1 I don't think talked about explicitly is when you

2 change like air-cooled condenser size, how did

3 you adjust the fan energy to compensate I guess

4 for perhaps increased air flow resistance with a

5 larger condenser core?

6 MR. WESTPHALEN: Okay. That's a good

7 question. We looked at this -- and, Javier, you

8 can correct me if I'm wrong on some of the later

9 phase analyses. We looked at whether, okay, do

10 we want to just keep the same air flow and

11 thereby have lower velocity, reducing the heat

12 transfer coefficient with the high area, or do we

13 want to keep the same faced area? And so we up14 sized the power accordingly. We didn't make

15 comparative measurements to exactly nail that,

16 but we used some scaling parameters in order to

17 appropriately adjust upwards to power the fan.

18 MR. ERBS: Okay.

19 MR. WESTPHALEN: Anything to add on

20 that, Javier?

21 MR. BURGOS: No, I mean, that's pretty

22 accurate.
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1 MR. WESTPHALEN: Okay.

2 MR. BURGOS: It's Javier Burgos.

3 MR. WESTPHALEN: So on this page other

4 energy model details. For water-cooled

5 condensers we used a constant condensing

6 temperature assumption and that was -- for a

7 calibrated ice maker, that was just basically

8 based on the measurement.

9 We also implemented refrigerant flow

10 limit for TXV in the NOPR phase for the start of

11 the freeze cycle for batch ice makers, whether

12 that's an MOP or just a physical orifice size

13 restriction. Clearly that was happening in the

14 test data, so we implemented that in order to get

15 better representation. The refrigerant

16 properties were calculated using a REFPROP 8.0,

17 well-known algorithms.

18 MR. BINGHAM: This is Scott Bingham at

19 Follett. Going back to the continuous ice maker

20 model, did you take any flushing into account

21 with the cube units type ice maker?

22 MR. WESTPHALEN: No, and it is our
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1 understanding that that wouldn't be taken into

2 consideration in the test either.

3 MR. BINGHAM: Okay.

4 MR. WESTPHALEN: We're trying to

5 replicate the test.

6 So moving on to the next page, this

7 addresses some of the adjustments made to the

8 model. I already mentioned the condenser

9 capacity derating. Adjustment of harvest cycle

10 inputs for condenser temperature reduction in

11 batch ice makers. And we did that based on some

12 very limited testing, testing that DOE funded.

13 We also had some little bit of

14 information also on condenser swap, what happens

15 with a condenser swap and we applied the

16 adjustments that we developed to that data and

17 had very good agreement with the results of that

18 data in terms of what the energy would do when

19 the condenser gets quite a bit larger. I already

20 mentioned the addition of the refrigerant mass

21 flow limit. That's another adjustment.

22 And just to get back to your other
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1 question, these modifications were all made

2 during the NOPR phase. We haven't started re3 running -- redoing all the runs because we don't

4 have all the inputs yet as to how all the other

5 adjustments might have to be made.

6 Pages 22 and 23 was all the ice makers

7 that were purchased, tested and reverse8 engineered. John already mentioned that. I

9 don't think I need to dwell on any of these.

10 And so that's what we put together for

11 the model description. Before we get into

12 illustrating its use, I don't know if anybody has

13 any questions at this point.

14 MS. PETRILLO-GROH: Were all of these

15 the waffle-type fin pattern?

16 MR. WESTPHALEN: Well, for the batch

17 ones. I don't know if you have some continuous

18 also to show?

19 MR. BURGOS: Yes.

20 MR. WESTPHALEN: Yes. Okay.

21 MS. PETRILLO-GROH: For both?

22 MR. WESTPHALEN: Continuous and
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1 waffle-plate batch.

2 MS. PETRILLO-GROH: Okay.

3 MR. WESTPHALEN: Yes.

4 MR. ERBS: This is Daryl Erbs from

5 Manitowoc. When you did the testing were you

6 able to instrument and capture information,

7 particularly on the evaporator over time, the

8 trend of the refrigerant saturation temperature

9 and other things to help with validation of the

10 model?

11 MR. WESTPHALEN: Yes, we asked for

12 pressure transducer output at what, 15 second

13 intervals. Is that what they tested as?

14 MR. BURGOS: Yes.

15 MR. WESTPHALEN: And thermocouples all

16 around the refrigeration circuit, the evaporator.

17 MR. ERBS: The evaporator? Okay.

18 MR. WESTPHALEN: Yes.

19 MR. ERBS: Great.

20 MR. BURGOS: This is Javier. We have

21 some test data we can illustrate that with.

22 MR. ERBS: It would be interesting to
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1 look at your data and see if it looks familiar.

2 (Laughter)

3 MR. CYMBALSKY: Yes, this is a good

4 time to mention that all of your data would be

5 good to have and we can sign the NDA forms.

6 MR. ERBS: Well, we have the NDA in

7 place and we can probably get this on -- right

8 now we're trying to fill out this matrix.

9 MR. CYMBALSKY: Yes.

10 MR. ERBS: But anyway --

11 MR. CYMBALSKY: He was not very

12 detailed when he asked the question, so I'm sure

13 there's a couple of data --

14 MR. ERBS: There's like 194 columns.

15 (Laughter)

16 MR. CYMBALSKY: That's all? He asked

17 me to do a 300-column one.

18 (Laughter)

19 MR. ERBS: He did eventually come back

20 and say if you can't do all the

21 columns --

22 MR. WESTPHALEN: We can talk about
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1 prioritizing in order to get things done on time.

2 PARTICIPANT: Yes, we call them Dr.

3 Detlef.

4 MR. WESTPHALEN: Yes.

5 MR. ERBS: No, I understand that. You

6 should always ask for everything you would like

7 to have and then --

8 MR. WESTPHALEN: Exactly?

9 MS. PETRILLO-GROH: This is Laura

10 Petrillo-Groh, AHRI. So I know I can go back and

11 calculate this, but how did these energy uses

12 calculate -- or compare to where the units fall?

13 Are these are baseline units? Are they higher

14 efficiency units? Is it all over the map?

15 MR. WESTPHALEN: It's more or less all

16 over the map, yes. I mean, if I wanted to get a

17 baseline, mid and high for all the capacity

18 ranges that are representative for all the

19 equipment classes and I went to John asking for

20 all of that, he would shoot me.

21 MR. CYMBALSKY: I shot him the first

22 time he asked.
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1 MR. ERBS: Yes, that ain't too much

2 like what we have to do.

3 (Simultaneous speaking)

4 (Laughter)

5 MR. ERBS: But we don't have the labs.

6 MR. CYMBALSKY: I understand. I

7 understand.

8 MS. ARMSTRONG: Which is why you

9 should share your test data and we could work on

10 this process together.

11 MR. ERBS: Yes, I'm not saying that

12 wouldn't be very helpful. And we're working on

13 that.

14 MR. ARMSTRONG: Thank you.

15 MS. PETRILLO-GROH: We're really open

16 to negotiation, so --

17 MR. ERBS: Well, part of the issue is

18 we don't always run carefully paired comparisons

19 when we're doing development because we're just

20 trying to get to an end goal and not trying to

21 understand -- it probably could for us if we

22 understood better what the influence of
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1 individual design options are, taking one at a

2 time, but we don't always operate that way. So

3 we're actually trying to go back and see if we

4 can separate out of our data some comparable

5 models where it's not multiple factors all thrown

6 in together, because then you really can't

7 decompose them.

8 MS. PETRILLO-GROH: Thank you.

9 MR. CYMBALSKY: When you're developing

10 a new product line, I mean you have some

11 simulation tool, I'm sure some engineering

12 principles that you're using when you're matching

13 these things together to build an ice machine

14 that at the end of the day you have to certify a

15 rating.

16 MR. ERBS: Right.

17 MR. CYMBALSKY: So either you're doing

18 it through -- I mean, testing is obviously the --

19 you're doing some level of testing when you're

20 doing product development, so to the extent that

21 that data --

22 MR. ERBS: So for our condensers, for
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1 an air-cooled condenser there are good tools that

2 we can use to match an air-cooled condenser to

3 the expected heat rejection load and we can

4 understand how that might affect efficiency.

5 When it comes down to something like a

6 compressor; and we'll get into that, it's -- we

7 haven't figured out how to model it in a way that

8 is very useful, so that becomes largely

9 experimental. But depends on the component how

10 well we can -- and really I'm serious about maybe

11 this becomes a tool for all of us if we can get

12 more comfortable with it.

13 But historically, speaking for

14 Manitowoc, it's been largely experimental with

15 again some individual subsystem or component

16 level, you know, simple analysis. I mean, I came

17 out of the air-conditioning industry and we had

18 very sophisticated simulation programs because it

19 was possible to do that given the steady state

20 nature of how they operated. We're not there on

21 ice machines.

22 MR. SCHAEFER: This is Stephen
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1 Schaefer with Hoshizaki America. I agree with

2 Daryl on why there is experimental. We go

3 through compressors, we go through fans, we go

4 through fan blades. We're trying to see what

5 gets us the best end result, as you said. We're

6 trying to get most energy-efficient, most water7 efficient. Trying out the water valves. So we

8 can share whatever data we can to help you guys

9 and come to the best negotiated rulemaking that

10 we can seeing what's actually technologically

11 feasible, economically available that we can do.

12 And agree with him, there really isn't a tool out

13 there that works well.

14 MR. CYMBALSKY: So just to clarify a

15 point, we're not in a negotiated rulemaking here.

16 And you may have just used that word, but this is

17 -- we're going to take all the data inputs from

18 you guys, and obviously it's a standard notice

19 and comment rulemaking.

20 MR. SCHAEFER: Just a hope. I'm

21 sorry.

22 MR. CYMBALSKY: Yes, and but we do
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1 agree. We hope we get the answer right.

2 MS. PETRILLO-GROH: On that vein;

3 Laura Petrillo, AHRI, are you going to open the

4 comment period back up after this meeting or --

5 MR. CYMBALSKY: Well, we're going to

6 think about our next steps. Let's just, I

7 think, get through today and see where we're all

8 at the end of the day.

9 MS. ARMSTRONG: I mean, to that point

10 though I think it in part depends on: (A) the

11 level of feedback we get; and (B) the level of

12 participation we get in terms of sharing data and

13 the analysis that we need to revise. So we hope

14 that we can work with you guys to get some of

15 that data to inform our decision about whether we

16 are going to reopen the comment period, whether

17 the analysis was sufficiently revised and that

18 kind of stuff.

19 MS. PETRILLO-GROH: And just one more

20 question before we move past the 41 ice makers

21 that we used. So between the preliminary phase

22 and the NOPR phase it seems like testing was done
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1 and I guess calibration of the model -- you guys

2 calibrated the model to some extent based on the

3 test data. Is that correct?

4 MS. ARMSTRONG: We did it for both

5 phases.

6 MS. PETRILLO-GROH: Both? Separately

7 or was it --

8 MS. ARMSTRONG: Well, they're

9 different phases, right? So we pulled a group of

10 models we put out for preliminary analysis. We

11 got some feedback. We pulled another group of

12 models. We did additional calibration. And

13 you've seen the NOPR analysis.

14 MS. PETRILLO-GROH: Right.

15 MS. ARMSTRONG: So you've seen the

16 analysis resulting from inclusion of test data

17 across the board.

18 MS. PETRILLO-GROH: Right. Okay. So,

19 but it was the model -- the model was built on

20 the test data first from the preliminary phase

21 and then further in the NOPR phase?

22 MS. ARMSTRONG: And in response to
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1 comments and in response to feedback we've

2 gotten, sure. So you've seen what we have to

3 date prior to us getting comments after the NOPR.

4 MS. PETRILLO-GROH: Thank you.

5 MS. ARMSTRONG: Yes.

6 MR. WESTPHALEN: Okay. So if there

7 are no other questions on the initial model

8 description, why don't we move into the energy

9 model demonstration, which Javier will step us

10 through.

11 MR. BURGOS: All right. I'm Javier

12 Burgos. I'm here to basically walk through the

13 actual FREEZE model. I think you saw some of the

14 screenshots of the software. I'm here to kind of

15 walk through the actual software as it stands.

16 This is the FREEZE model, like Detlef

17 mentioned, developed recently in order to

18 basically be able to provide ice maker testing.

19 There are a number of inputs that go into here.

20 They're basically all segregated into different

21 tabs and within each input there is also a series

22 of inputs. Some of the inputs are based on test
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1 data, some of them are based on physical

2 measurements, some of them are based on

3 calculations that we have to make such as the UA.

4 This model is not the end-all-be-all

5 because all good models -- and we're only as good

6 as the inputs that go into them. So we also have

7 a series of models as well to calculate the

8 inputs to for this model.

9 So without further ado, let's start

10 stepping through it.

11 MS. ARMSTRONG: So if it's hard to see

12 -- I definitely can't read that from back here,

13 especially without my glasses. But feel free to

14 move forward, because the screen is pretty -- and

15 I think, Javier, that's probably the largest you

16 can make it.

17 MR. BURGOS: Yes, I think so.

18 MS. ARMSTRONG: Yes. So you guys

19 should feel free to move forward if you need to.

20 MR. BURGOS: All right. Well, I'll

21 try to describe it pretty well.

22 So we have basically a couple of ways
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1 we can model this. There's the batch process.

2 This model can also model continuous process.

3 There's separate types of inputs for both. As

4 you'll see, if we select continuous process, a

5 number of these tabs drop away. And so it's

6 designed to do both batch and continuous.

7 The first amount of inputs that we're

8 going to discuss are pretty much sort of the

9 batch weight. This basically limits the growth

10 of the UA until the amount of -- until sort of

11 the ice weight is reached. And also as Detlef

12 mentioned, the harvest energy and the harvest

13 time are inputs to the model, so these two inputs

14 are based strictly off of test data.

15 Melting during harvest. This melting

16 percentage multiplied by this pre-harvest rate

17 sort of gives you your post-harvest batch weight

18 which is then used to calculate what the overall

19 harvest rate would be for the rating. There's

20 also a set here for controls as well if there's

21 any auxiliary power controls. And then some of

22 the more intangible aspects of the model start
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1 here all around the solution parameters. There's

2 the evaporator temperature at start and the sump

3 temperature at start.

4 These are kind of going to define how

5 the starting points throughout the thermal

6 analysis begins. As we mentioned, the evaporator

7 kind of heats up during harvest rate, so this is

8 basically going to provide a starting point for

9 the sump and the evaporator. There's also a time

10 step that we can set. Different time steps,

11 different increments. Get more or less accurate,

12 more or less precision.

13 And down here is the maximum mass flow

14 rate that Detlef mentioned. This is kind of a

15 setting designed to in essence help us match to

16 the test data that we were seeing. I think we

17 were seeing some limitations due to the TXVs in

18 terms of what the mass flow was, which is then

19 affecting the evaporator temperatures that we saw

20 in our test data. This input basically serves to

21 help kind of address that and it basically is a

22 pounds per hour limit that the program will set.
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1 Moving on to the next step, we pretty

2 much have a curve set to calculate what the UAs

3 of the evaporator -- right as the freeze period

4 starts and right at the end of the freeze period.

5 So the first UA, the water-side UA is pretty much

6 the UA of the evaporator right before any ice has

7 kind of freezed on it, so just metal, maybe a

8 little bit of water. And the ice-side UA

9 basically includes the ice as well as the

10 evaporator, so it's going to be a lot more

11 difficult to get heat transferred through that

12 ice sheet. And all these coefficients, these six

13 coefficients over here are then basically served

14 as inputs, basically serve to find the curve from

15 going from the water-side UA to the ice-side UA.

16 MS. PETRILLO-GROH: Javier?

17 MR. BURGOS: Sure?

18 MS. PETRILLO-GROH: Laura Petrillo,

19 AHRI. How did you get that data from the ice

20 makers you tested?

21 MR. BURGOS: So, and maybe I'll jump

22 into it. So Detlef referring to -- in the
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1 presentation to a -- basically a model to sort of

2 model the ice film as it was increasing. So

3 basically sort of having a calculation of, all

4 right, this is the geometry of the evaporator and

5 the ice sheet as it increased in different sort

6 of time frames and we developed a separate

7 spreadsheet model in order to basically calculate

8 what those UAs were going to be at different

9 discreet points.

10 MR. WESTPHALEN: This is not something

11 that can easily be directly measured.

12 MR. BURGOS: Right.

13 MR. WESTPHALEN: It could be, but

14 you'd have to think about how to do it.

15 MR. BURGOS: Right. And so --

16 MR. ERBS: It's a conduction

17 calculation essentially.

18 MR. BURGOS: Right. And so what we

19 have here is we have a separate spreadsheet model

20 that basically gives us for the different

21 evaporators based off our reverse engineering --

22 basically it sort of gives us kind of little
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1 different points at which -- taking into account

2 what the ice thickness was, what the layers were,

3 what some of the UA models are. All these

4 basically give you basically points on this line

5 which we sort of calculated and drew a curve in

6 between the two. And that's all based off of (A)

7 looking at their -- the start point and the end

8 points are set because we did the reverse

9 engineering. We did look at what's the geometry

10 of the evaporator and we also did the testing.

11 And we know how much of an ice sheet you're going

12 to get, how much weight of the ice you're going

13 to have. And the real question is kind of

14 drawing the line in between the two. So this

15 spreadsheet is sort of our modeling. Does that

16 answer your question?

17 MS. PETRILLO-GROH: Yes.

18 MR. BURGOS: All right. Kind of a

19 deceptively simple set of inputs for what's

20 actually kind of a complex analysis. And I think

21 when we look at the condenser, that's something a

22 little bit more straightforward that's sort of
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1 been modeled already. And there are coefficients

2 for it, and so we're able to input on the

3 condenser side. And maybe as kind of a contrast

4 I'll show the condenser.

5 MR. ERBS: Can I interrupt?

6 MR. BURGOS: Sure.

7 MR. ERBS: So on the evaporator model

8 if you increase the surface area of an

9 evaporator, do you just multiply it by the ratio

10 of areas for the new UA?

11 MR. BURGOS: That's essentially what

12 we do, yes.

13 MR. ERBS: So you're assuming that the

14 use side of it, right, the convection

15 coefficient, the falling film coefficients and

16 the conduction pass and everything stay

17 relatively constant and the impact is one of

18 increased surface area?

19 MR. BURGOS: Yes.

20 MR. ERBS: Okay. And then when you

21 increase to a larger area, when you run the

22 simulation do you then go to a larger slab
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1 weight? We call it slab weight, but the ice mass

2 before harvest. Would that increase again with

3 the ratio of surface area?

4 MR. BURGOS: You mean this input here,

5 the pre-harvest --

6 MR. ERBS: Yes, does that get --

7 MR. BURGOS: Yes.

8 MR. ERBS: If you raise the surface

9 area 50 percent, that would go up 50 percent?

10 MR. BURGOS: Well, yes.

11 MR. ERBS: So I'm just trying to get

12 --

13 MR. BURGOS: Right. Exactly.

14 MR. ERBS: -- the assumptions of your

15 model --

16 MR. BURGOS: Right.

17 MR. ERBS: -- because that's actually

18 how we do it. We would keep the bridge thickness

19 relatively constant. If you have a heavier slab,

20 then you would hopefully freeze faster, right?

21 Do more cycles in time and that's how --

22 MR. BURGOS: Right, right. And --
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1 MR. WESTPHALEN: You mean you take

2 longer, but you get more per cycle?

3 MR. ERBS: I mean longer, yes. I'm

4 sorry. Yes. I had it backwards. Yes.

5 MR. BURGOS: And in the case of

6 adjusting for this pre-harvest batch weight, I

7 think we did it for three of the -- in terms of

8 looking at what the cost efficiency curves are,

9 we did it for three of the efficiency curves.

10 And essentially when we changed this, we also had

11 test data for sort of the bigger -- for the unit

12 that used the bigger evaporator. And so we were

13 able to also look at what the pre-harvest batch

14 weights were in case there was a thinning or

15 increase in thickness of the --

16 MR. ERBS: Okay.

17 MR. BURGOS: -- ice layer.

18 MR. WESTPHALEN: So here's a question

19 for you, Daryl. Have you ever correlated harvest

20 weight versus number of cells in an evaporator

21 and is a linear approximation --

22 MR. ERBS: I don't think we've ever
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1 drawn that curve, to be honest.

2 MR. WESTPHALEN: Okay.

3 MR. ERBS: But if we put in a larger

4 evaporator trying to get to higher efficiency --

5 we would just do an optimization, but we have

6 some pretty -- for a grid-style we have limits to

7 how much we can change the thickness of the

8 bridge, because if it gets too thin, then you end

9 up with cubes getting stuck in the grid. You

10 don't harvest the whole slab is a potential

11 problem. If you make it too thick, then (1) it

12 hurts efficiency typically, but the other side is

13 then it doesn't break up easily enough when it

14 falls into the bin and then people will be

15 dissatisfied with not having something that's

16 relatively close to individual cubes. You have

17 to really work at breaking it up.

18 MR. WESTPHALEN: Yes, I was just

19 trying to --

20 MR. ERBS: And that's unique to

21 waffle.

22 MR. WESTPHALEN: I was just trying to
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1 get to like in your questioning about the

2 proportionality there of whether there might be

3 problems with non-uniformity that would make this

4 not a good assumption.

5 MR. ERBS: That's always a challenge,

6 but we work really hard at -- I mean, the

7 uniformity of the water flow is critical just

8 because if you don't have an even -- and that's

9 probably even true for the individual cube,

10 right? You want all the cubes to be roughly the

11 same thickness. And so that tends to force

12 evening out the water flow. As you go larger and

13 larger evaporators it gets more challenging to

14 have good refrigerant flow distribution, right,

15 because you -- and but that does sometimes change

16 a little bit. But I would say that's a secondary

17 level impact.

18 MS. McCARTHY: This is Annie with

19 NAAFEM, and I just have a question. You

20 mentioned that the time step parameter is

21 variable and you use some and some are more

22 accurate than others. Do you have a range that
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1 you always use? And then how do you choose which

2 one becomes the model and which one is most --

3 which time frame is most accurate?

4 MR. BURGOS: I don't know if accurate

5 is the right -- well, the --

6 MS. McCARTHY: That's the word you

7 used.

8 MR. BURGOS: That's fair. But I mean

9 I can tell you for the purposes of this modeling

10 that we did for cost efficiency analysis we

11 pretty much ran it at the most precise settings

12 at the deepest time step.

13 MS. McCARTHY: So what is that?

14 MR. BURGOS: That is 0.010. And

15 that's in minutes.

16 MS. McCARTHY: Okay.

17 MR. BURGOS: And I can tell you the

18 model takes about six minutes to run each

19 iteration, so it's definitely doing a lot of

20 calculations in terms of stepping through it.

21 So, and I think the data it outputs is every 30

22 seconds. So, yes, essentially when it prints out
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1 the map of evaporator temperatures, temperate

2 condensing temperatures, evaporator, sort of what

3 evaporator mass and the evaporator temperature

4 were. That's kind of what -- that's what you

5 get. You get it in about 15-second, 30-second

6 increments, which you can then basically compare

7 it to the test data that you have.

8 MS. McCARTHY: Okay. This is Annie

9 again.

10 MR. BURGOS: Sure.

11 MS. McCARTHY: I have another

12 question. So you have said "test data" a few

13 times, and is that test data that the

14 manufacturers have submitted or that you're

15 taking from ENERGY STAR numbers, or is that your

16 own test data from another spreadsheet?

17 MR. BURGOS: Yes, it's our own DOE

18 test data.

19 MS. McCARTHY: Okay.

20 MR. BURGOS: So, yes.

21 (Simultaneous speaking)

22 MS. ARMSTRONG: DOE's purchased
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1 products off the market and had them tested at

2 third party laboratories pursuant to the DOE

3 trials procedure.

4 MS. McCARTHY: Okay. Okay.

5 MS. ARMSTRONG: So the 41 was we had

6 41 different sets of test data.

7 MS. McCARTHY: Okay.

8 MS. PETRILLO-GROH: Which labs did you

9 test with?

10 MS. ARMSTRONG: The same labs you test

11 at. I don't know that. I don't want to get into

12 that here.

13 MS. PETRILLO-GROH: Why?

14 MR. ERBS: They're ISO-compliant

15 laboratories.

16 MS. ARMSTRONG: Yes, they're --

17 MR. ERBS: Right? So we trust them.

18 MR. CYMBALSKY: We all use the same

19 one.

20 (Simultaneous speaking)

21 MR. ERBS: I have another question.

22 To me, when you do modeling assumptions and
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1 boundary conditions and things like that that can

2 be more important than the models themselves are.

3 So when you change evaporator size do you make

4 any adjustment to the harvest time?

5 MR. BURGOS: If our test data

6 indicates that we should, then we do. Because we

7 do --

8 MR. ERBS: What did your test data

9 indicate? Did you analyze that?

10 MR. BURGOS: We did take a look at it

11 and I think we did maybe increase it, but we do

12 have -- I mean, we have the steps there, so we

13 can --

14 MR. ERBS: Right. I mean, you would

15 expect from physics that you might have a longer

16 harvest time with a larger evaporator if

17 everything else stays the same just because the

18 thermal mass that you have to heat up grows.

19 MR. WESTPHALEN: Right. But that is

20 one item where certainly we could get more data.

21 We could just feed that into the model and adjust

22 it.
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1 MR. ERBS: Okay.

2 MR. BURGOS: All right.

3 MR. BINGHAM: This is Scott Bingham

4 with Follett.

5 MR. BURGOS: Sure?

6 MR. BINGHAM: For the continuous

7 process I don't see -- or can you explain where

8 the auger motor is taken into account? I don't

9 see that as one of the components on the left

10 side.

11 MR. BURGOS: Oh, sure.

12 MR. WESTPHALEN: I mean, we'll get to

13 that. Maybe step through the batch

14 first --

15 MR. BINGHAM: Okay. Sure.

16 MR. WESTPHALEN: -- if that's okay?

17 MR. BINGHAM: Sure.

18 (Simultaneous speaking)

19 MR. BURGOS: Yes, I guess to finish

20 off the evaporator, we also have to calculate

21 heat transfer for the refrigerant side,

22 especially looking at the evaporator dynamic
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1 properties. We look at the mass of the

2 evaporator. We look at what's the approximate

3 specific heat based off all of the metal that's

4 inside of it and all the heat transfer. We're

5 also looking at blanks and superheat, things like

6 that. Ultimately this is another input that goes

7 in that affects the thermal mass. And so

8 especially for the evaporator mass if we increase

9 the size of the evaporator, this thermal mass is

10 also going to increase.

11 All right. I'll just go through them

12 one by one. We also have water system

13 calculations, so a lot of the modeling of -- a

14 lot of what the model does is basically account

15 for how much energy is going into the water, how

16 is it freezing and what the mass of the water is

17 inside. So we have a water mass that is what

18 sits in the sump itself. The potable water usage

19 is an actual input to the model. And then we

20 also have a purge water temperature to know how

21 much heat is coming in and out of the sump water.

22 If there's a continuous valve, sort of
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1 a float valve on the ice makers, you can kind of

2 set what the minimum water level will be within

3 the unit, and that's on a per-pound basis. We

4 also list out the supply temperature so you know

5 what the enthalpy of the water is coming in. And

6 because these units are tested 90/70, that's

7 generally 70 percent 78 degrees.

8 As well we also do some of the thermal

9 modeling in terms of what the sump itself is.

10 It's generally a sort of piece of plastic where

11 the water lies that then gets pumped over to the

12 evaporator. We have the pump power. We model

13 the heat into the water. We also model the mass

14 of that sump, and that's part of the thermal

15 calculations.

16 MR. SCHAEFER: Stephen Schaefer,

17 Hoshizaki America. How did you determine the

18 water usage? Was that from the test data

19 or --

20 MR. BURGOS: Yes, test data.

21 MR. SCHAEFER: Okay.

22 MR. BURGOS: Cold compartment. We're
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1 also interested in looking at the thermal

2 analysis, how much heat is entering into the ice

3 maker. And so in our reverse engineering we

4 actually do basically tear-downs of these units,

5 and that involves looking at how much insulation

6 is there, looking at what's the metal, what's all

7 the -- and trying to look at it holistically

8 around the evaporator basically in order to

9 figure out how much heat might be coming into the

10 model. That's a UA input and that's also

11 something we calculate separately generally using

12 standard Q dot delta T equations.

13 All right. We covered that

14 evaporator. Now onto the condenser. We can

15 actually model four different kinds of

16 condensers: tube and fin, microchannel, water17 cooled and user-specified. Detlef kind of

18 alluded to these a little bit earlier. There are

19 a couple of different fin configurations for tube

20 and fin: plain fin, smooth and wavy, herringbone,

21 slit fin. Again, these are based off of the

22 correlations that are developed and used within
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1 the ERA refrigerator model. This is also where

2 you specify what the sub-cooling should be.

3 As well, we also have fan power

4 ratings and the air flow rate as well. So fan

5 power is something we can get sort of off the

6 testing that we do within our lab when we're

7 doing reverse engineering. We can hook up the

8 fan, see how much power it's drawing. For the

9 air flow rate we do have a hot wire and the

10 monitor that we do try to measure the cfm that's

11 coming through. That is one of the inputs that

12 we can vary to get a better match to the test

13 data.

14 There are a number of inputs here.

15 Most of them are physical inputs. So we're

16 looking at tube OD, tube wall thickness, width

17 and tube row, circuits, pitch, number of tube

18 rows. And this all goes into the correlations

19 for what the behavior and what the UA of that

20 condenser is. So for the evaporator we have to

21 calculate the UA ourselves. For the condensers

22 the program does that for us using these physical
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1 inputs.

2 Fin data as well. Fin thickness goes

3 in there, fins per inch, fin thermal

4 conductivity, which is basically just the

5 property of the metal it is, usually aluminum.

6 And so the fraction of the tube is actually

7 finned.

8 The compressor. We have a number of

9 compressor maps that we can basically add to the

10 program. Basically what it looks like. Kind of

11 what Detlef alluded to, it's pretty much just a

12 map of capacity versus power at different

13 evaporating and condensing temperatures. However

14 big of a map we get is however big we can plug it

15 in. So if we get data all the way down to minus

16 5, minus 10, we can plug it into model and the

17 model can basically use that in order to

18 determine what's going to be the capacity and

19 power sort of at the different items.

20 We mentioned a couple of multipliers.

21 We have a power multiplier and a capacity

22 multiplier in case we need to adjust the
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1 performance of the compressor based off our test

2 data. And we did have a multiplier here for

3 trying to get a better match to our test data.

4 And in this case the capacity multiplier that we

5 used was 0.7.

6 MR. ERBS: 0.7?

7 MR. BURGOS: 0.7. Yes. So do you

8 have any hypothesis as to why the -- an NSD

9 rating by 30 percent --

10 MR. ERBS: Yes.

11 MR. BURGOS: -- the capacity of the

12 compressor -- do you have any ideas for why those

13 manufacturers' data are so far from what the

14 model actually has to put in to match test data?

15 MR. WESTPHALEN: We don't have

16 hypotheses that we think are really solid. If we

17 knew that, we would fix this. The start of my

18 hypothesis has something to do with the fact that

19 it is a very dynamic situation, which we discuss,

20 and that the compressor just does not perform --

21 it takes awhile to get back to its steady state.

22 MR. ERBS: Yes, I figured that's part
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1 -- so then let me ask another question. Since

2 you tested more than one system and you modeled

3 more than one system, did you do a scatter plot

4 of capacity multipliers? I mean, if it's always

5 0.7 for a whole different range of sizes and

6 types of compressors, then you could say, well,

7 0.7 is a good number always. If it goes between

8 1.3 and 0.7, depending on whose compressor and

9 what size and so on and so forth, then there's

10 some uncertainty when you go from a lower

11 efficiency compressor to a higher efficiency

12 compressor, unless you've tested that range of

13 models to know what capacity multiplier to plug

14 into the model.

15 MR. BURGOS: And actually that's what

16 we found, is that 0.7 is actually gives us -- is

17 the number that gives us a very good match for

18 our model versus our test data.

19 MR. ERBS: Okay.

20 MR. BURGOS: So we used 0.7 across the

21 board.

22 MR. ERBS: So it's relatively
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1 constant?

2 MR. BURGOS: Right. Yes.

3 MR. WESTPHALEN: I think, yes, looking

4 at the scatter plot, the 0.7 looks like the best

5 estimate to use. And granted, things might be

6 different if you have -- like if somebody tried a

7 rotary compressor, for instance, not that I'm

8 suggesting that, but I'm saying for instance.

9 MR. ERBS: And you're really just

10 doing the comparison on the freeze part of the

11 cycle?

12 MR. BURGOS: Yes, when we model this,

13 this model is --

14 MR. ERBS: Because I think that's

15 where we find big differences is when we test a

16 complete cycle. We are often surprised by what

17 on paper the compressor looks like it should do

18 versus what it does in the system, but it may

19 have very little to do with the pre-cycle and a

20 lot more to do with the harvest.

21 MR. WESTPHALEN: Well, you can isolate

22 the harvest fairly easily by measuring how much
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1 power you draw during the harvest. And I think

2 it's more than what's happening in the harvest,

3 and hence the 0.7.

4 MR. ERBS: Right. Okay.

5 MR. BURGOS: Good questions. Anyone

6 else?

7 (No audible response.)

8 MR. BURGOS: All right. So the

9 compressor basically has its own program where

10 you can go in and based off any compressor that

11 you might have you can type in the rating

12 conditions, suction temperatures, superheat, sub13 cooling, liquid temperature, add the manufacturer

14 make and model. We also do a number of different

15 refrigerants as well. So for models that use

16 134a, we can model them using 134a as well. And

17 then this is pretty much the map of where you

18 would add in the different capacity and power

19 measurements for what the compressor map would

20 give you. And then the model basically spits out

21 kind of a curve based off those inputs that

22 you're given.
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1 And maybe I'll just open one up.

2 Let's pick one of Laura's.

3 MS. PETRILLO-GROH: It's not mine.

4 MR. BURGOS: Yes, so you get these

5 curves for capacity versus operating temperature

6 versus condensing temperature here at the top and

7 you can plot -- look at what the power is, look

8 at what the efficiency is. And also you can

9 basically observe methods of sort of the data or

10 the efficiency. So, yes, as mentioned, the

11 better data we can have on these compressors, the

12 better the model will ultimately run.

13 All right. And last but not least, we

14 also model a suction line heat exchanger, if

15 there is one present, effectiveness, heat gain,

16 pressure drop. Pretty standard variables. So

17 and then basically this guy -- well, let's find

18 the one that we know runs. This guy. Once you

19 put in all your inputs, you start running the

20 model. So you select your compressor. This guy

21 uses the Danfoss. As you can see, we have our

22 0.7 multiplier here, so basically just taking
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1 this off one of the cost efficiency curve files

2 we have.

3 It will ask you if you want to save

4 your changes and it will give you what the sum of

5 the UAI polynomial coefficients. It's there's a

6 couple of basically sort of QC checks to make.

7 It will tell you what your compressor is doing.

8 In case your compressor map is horribly wrong,

9 you can take a look at it. Yes, if you model

10 restricted flow steps, it tends to want run it

11 sort of at the lowest time step, so at the 0.010.

12 And then you're off. Basically it starts

13 modeling it.

14 Then it went to zero. Most of these

15 ice machines kind of have cycle times of 0 to 15

16 to 20 minutes, depending on who they are. If you

17 have a different type of evaporator, maybe it

18 will take a little longer. This takes about six

19 minutes to run. I was kind of timing it last

20 night. So six minutes later, ta da --

21 (Laughter)

22 MR. BURGOS: -- you get your
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1 production summary. Cycle time, harvest rate,

2 production rate, energy consumption. Most of

3 these are kind of calculated off what some of the

4 -- the harvest mass rate and the production rate

5 are going to be -- the production rate is

6 basically going to calculate off the cycle time

7 that the model calculates it to take because the

8 harvest ice mass is already an input. Same with

9 the water input.

10 Energy consumption. It will give you

11 the heat balance in terms of all the heats that

12 are going in, going out in total. On the

13 refrigerant side as well it will break down the

14 energies in terms of what the compressor was

15 versus what the condenser fan was versus the

16 pump, auxiliary power and the ice harvester.

17 Yes, we do also have an input for power in case

18 there's a harvest desist as well.

19 Yes, you can calculate your Carnot

20 cycles. It will give you detailed data in terms

21 of what the mass of the ice was at each point,

22 what the average temperature of the evaporator
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1 was, the temperature of the ice, temperature of

2 the water as it's flowing over and kind of what

3 the UA was of the evaporator plus the ice at each

4 step moving forward. And you can see that the

5 ice doesn't form until about sort of three,

6 three-and-a-half minutes in once that water kind

7 of starts getting cold enough.

8 Cycle summary. This is sort of cycle

9 data for at the end of the freeze cycle. Of

10 course this is going to change throughout the

11 course, but it sort of gives a good picture of

12 what it is at least at the end.

13 Cycle details. So it will give you

14 the evaporating temperature, the condensing

15 temperature, Q evaporator, the power and sort of

16 the heats going in and out. There's also sort of

17 a small diagnostic tool basically that says how

18 many durations it's taking to run in terms of

19 matching the evaporator with the condenser, what

20 some of the errors are, what the UA area is.

21 This is a bit more of a diagnostic tool. And it

22 will also give you plots so you can look at what
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1 the ice mass was over time and you can look at

2 what the evaporator temperatures that are

3 calculated, the evaporator UA values.

4 MR. ERBS: Could you go back to the --

5 MR. BURGOS: Yes, sure. Sure.

6 MR. ERBS: In terms of the ice --

7 MR. BURGOS: Yes.

8 MR. ERBS: Why is it linear? That

9 doesn't make sense to me. It says as you build

10 up ice on the evaporator the resistance

11 increases, the saturated suction condition of the

12 compressor goes down, the compressor capacity

13 decreases. It should asymptote. It shouldn't

14 have a convex downward shape to it.

15 MR. BURGOS: Yes, something a little

16 probably a bit more like these guys where the UA

17 kind of drops down. Yes.

18 MR. ERBS: The question is is that

19 some time past their time in their model or B

20 MR. BURGOS: Yes, this is the whole

21 cycle, so about 15 minutes. This is

22 the --
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1 (Simultaneous speaking)

2 MR. ERBS: So you're going to look at

3 your plot for the refrigerant states, right?

4 You're going to look at the suction.

5 MR. BURGOS: Right.

6 MR. ERBS: And I think you've got --

7 based on what I saw, you actually had pretty good

8 starting and ending points for saturated suction

9 temperature. You can match it pretty close to

10 what we would typically see.

11 MR. BURGOS: This guy?

12 MR. ERBS: Yes, where's your saturated

13 -- yes, so you're starting in the 20s. About the

14 time you start freezing, you'd be typically

15 around 20 degrees. You're kind of there. And we

16 would typically end somewhere in the low single

17 digits with the current designs. And I don't

18 know how different yours are, but that will make

19 sense. But I don't get the -- why the Q of --

20 maybe it's right. I'll have to go look. We

21 don't obviously have the instrumentation to

22 measure ice.
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1 (Laughter)

2 MR. WESTPHALEN: That would be a Ph.D.

3 project.

4 MR. BURGOS: Right.

5 MR. ERBS: Okay.

6 MR. BURGOS: And we are able to I

7 guess kind of as we go through -- well, it's

8 there. Condenser temperatures, mass flows,

9 compressor parameters, what the pressure ratio

10 is, what the COP of the compressors are over time

11 as well. And all these data inputs can also be

12 sort of output as an Excel model, which makes it

13 a lot easier to graph and compare to real test

14 data.

15 Before I jump too much ahead, should

16 get to the -- this guy's still running, as you

17 can see. But I'll just open a new instance.

18 MR. WESTPHALEN: Do you want to step

19 through continuous perhaps?

20 MR. BURGOS: Yes, I think so.

21 MS. PETRILLO-GROH: Before we get to

22 that, I have a question.
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1 MR. BURGOS: Sure.

2 MS. PETRILLO-GROH: Is this just the

3 freeze part of the process or does it also

4 include the defrost?

5 MR. BURGOS: Just the freeze.

6 MS. PETRILLO-GROH: Just the freeze?

7 MR. BURGOS: Just the freeze.

8 MR. ERBS: They can model the

9 harvesting.

10 MS. PETRILLO-GROH: Yes.

11 MR. ERBS: It's an input.

12 MS. PETRILLO-GROH: Yes.

13 MR. BURGOS: It's an input, right.

14 MR. ERBS: But you've looked at data.

15 You've got to pick a reasonable input.

16 MR. BURGOS: Right.

17 MS. PETRILLO-GROH: So when you get

18 the outputs for this model, what then happens to

19 get from these outputs to the cost efficiency

20 curves that are in the NOPR?

21 MR. WESTPHALEN: We'll go into more

22 detail of the whole, but basically we have the
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1 cost efficiency curve. This gets us the

2 efficiency for a particular design option mix.

3 MS. PETRILLO-GROH: So you would take

4 maybe the same ice maker and then either increase

5 the evaporator area, increase the -- you'd model

6 a different compressor in it? But would there be

7 test data for all that?

8 MR. WESTPHALEN: Yes, look at

9 different designs. And, I mean, as I mentioned,

10 if we wanted to get test data for all those

11 configurations, we'd -- John would shoot me if I

12 asked for that. We can't get it. So we try to

13 pin the curve at the bottom end and at the top

14 end, and if we have something in the middle end.

15 And we'll get into the details of how to do that.

16 MR. BURGOS: Yes, I mean, we can

17 definitely talk a little bit about the data that

18 we have and all the calibration that kind of goes

19 on. This is a model and it will spit out a

20 number. And you can't just take the number we

21 have at face value. You kind of have to go back

22 and look at -- like Daryl mentioned, look at the
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1 evaporating temperatures, look at the mass of the

2 device and make sure it's right and kind of make

3 alterations as needed, especially because -- and

4 like Daryl said, not all these values can be

5 measured.

6 MS. PETRILLO-GROH: Thank you. I

7 appreciate that. It just seems like so much of

8 this discussion is based on building the model,

9 not --

10 MR. BURGOS: Not the design option

11 part, yes.

12 MR. WESTPHALEN: And I mean, we've

13 gone through an hour-and-a-half, but --

14 MR. BURGOS: Right.

15 MR. WESTPHALEN: -- I'm sure we'll get

16 to that.

17 MR. BURGOS: Right.

18 (Laughter)

19 MR. CYMBALSKY: We've got to

20 understand the model first.

21 MR. BURGOS: Right.

22 (Simultaneous speaking)
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1 MR. BURGOS: Right. And that -- I

2 mean --

3 MR. ERBS: If you remember, you gave

4 us the model once. We could never get it to run,

5 so we just sort of just -- because, I don't know,

6 something about the version of Windows or

7 something. It was so far behind what we --

8 (Laughter)

9 MR. BURGOS: Well, we finally have a

10 Windows 7 version.

11 MR. RIVEST: It's not really shrink12 wrapped.

13 MR. ERBS: Well, maybe you should --

14 yes, would you share it again now that you have a

15 Windows 7?

16 MR. WESTPHALEN: And now that we've

17 thought a little bit harder about the

18 sensitivities of --

19 (Laughter)

20 MR. ERBS: Oh, okay.

21 MR. BURGOS: Yes, and we can

22 definitely talk a little bit about kind of --
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1 there's almost two steps to this. The first step

2 that we do is really trying to calibrate to the

3 test data that we have. And so we're trying to

4 match up to the different models that we test.

5 So if we have a high-efficiency model, we're

6 really trying to see if we can model that

7 particular unit correctly. And then if we have

8 baseline models, we really try to see if we can

9 model those correctly and make sure our inputs

10 are consistent and our assumptions are

11 consistent. That's really the first step of the

12 process.

13 And then you can go ahead and try to

14 model and predict and make predictive assumptions

15 in terms of what the design options might come

16 out, because that's kind of more of the sphere of

17 really trying to predict what could happen. Of

18 course, we validate it with as much test data as

19 we can.

20 But I know the continuous guys are a

21 bit impatient to look at the continuous stuff,

22 too. So before we do that, we'll just quickly go
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1 over the continuous.

2 So this is the process --

3 MR. CYMBALSKY: We'll take as much

4 time as necessary.

5 MR. BURGOS: Yes, we're ahead of

6 schedule, guys.

7 So this is the continuous process.

8 Here's where you input the auger motor power.

9 There's also a motor shaft efficiency. Ice

10 quality is an input to the model. Again, you

11 sort of have an input for auxiliary power. And

12 as well we also basically have an inlet water

13 temperature. And again, because this is DOE

14 testing, generally modeling this we -- that's

15 pretty much 90/70, so 78 degrees is going in.

16 That really simplifies the evaporator side of

17 things.

18 What we would do on the evaporator

19 side is we have just an ICUA input. Again, this

20 is mostly based off our tear-down data. So we

21 tear down a number of different continuous

22 models, look at the evaporators, the augers, look
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1 at the geometry and sort of try to come up with

2 an estimate of what the outside UA is. And then

3 that's something that can be adjusted to get a

4 closer match to the data.

5 This is where we specify the pressure

6 drop, the exit superheat, the external UA as

7 well. So this is kind of controlling how much

8 heat is coming into the system from the outside.

9 So we'll look at how much insulation is there, is

10 there any metal, kind of what's the combined

11 factor doing to -- and then sort of the exterior

12 temperature, which again sort of this being a DOE

13 test, 90/70, so it's generally 90.

14 MR. BINGHAM: This is Scott Bingham.

15 Excuse me. On the evaporator materials did you

16 model any different metals or materials that the

17 evaporator is made out of?

18 MR. WESTPHALEN: In order to get a UA

19 calculation?

20 MR. BINGHAM: Correct.

21 MR. WESTPHALEN: For the UA for the

22 continuous we tried to make sure that the UA that
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1 we put in had some basis on the size of the

2 evaporator, but mostly it was based on

3 calibrating with the data and not just the final

4 energy result, but looking at what evaporating

5 temperature we're running and how much heat has

6 to be transferred. So in order to transfer that

7 amount of heat with that evaporating temperature

8 what is it we would have to do? So there are

9 different evaporator designs, but in our NOPR

10 analysis we recognize that there's much more

11 limitation and variability in those designs. You

12 won't see a whole lot of up-sizing of evaporator

13 design options in the analysis for that reason.

14 MR. BURGOS: I mean, I'll just help

15 illustrate. I have some of the spreadsheets that

16 we used to kind of basically calculate some of

17 the inputs to the model. So this is basically

18 data from one of our reverse engineered models.

19 We took a lot of specifications in terms of what

20 the geometries are, what the turns are, what the

21 bends are, what the UAs are, pretty much kind of

22 first-level thermodynamics really trying to
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1 calculate the UA for each one of the pieces and

2 then add them all up at the end.

3 MR. WESTPHALEN: But those are

4 preliminary calculations for our initial calcs.

5 MR. BURGOS: Right.

6 MR. WESTPHALEN: There's so much going

7 on on the refrigerant side there that's just

8 spiraling around, that I don't know if there are

9 any good models for that.

10 MR. ERBS: I mean are these

11 calculations more of a sanity check, just to make

12 sure that --

13 MR. WESTPHALEN: Exactly.

14 MR. BURGOS: Right.

15 MR. ERBS: Yes.

16 MR. BURGOS: All right. Condenser

17 remains about the same. Nothing here changes.

18 I'll step through I guess -- we do have a

19 microchannel option as well. It's not something

20 we've used for this rulemaking with a set of

21 different inputs. We also have water cooled, as

22 Detlef mentioned, there. We're not calculating
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1 the UA. We're just basically adding the

2 condensing temperature in straight.

3 So, and then finally if we had a --

4 want to, we could also sort of enter in our own

5 UA, but we're already doing some UA calculations.

6 That's nice to use tube and fin. And for all of

7 these models that we've looked at pretty much

8 they're all tube and fin.

9 Compressor. Nothing changes there.

10 Same map, same looking at everything.

11 And then interchanger. Again, nothing

12 has changed.

13 A lot simpler model. It's also not a

14 dynamic model. We run it and it's sort of a

15 static -- this runs something that actually

16 works. Okay. This one takes about two seconds

17 to run because you're only trying to basically

18 capture one cycle as opposed to the batch machine

19 where you're basically capturing snapshots of the

20 cycle as it changes here. We're assuming it's

21 pretty much unchanging.

22 MR. ERBS: The ice quality is an
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1 input?

2 MR. BURGOS: Yes.

3 MR. ERBS: Okay.

4 MR. BURGOS: Yes, and so that's based

5 off of basically most of our test data.

6 (Simultaneous speaking)

7 MR. CYMBALSKY: It's basically which

8 brand of hotel you go to.

9 (Laughter)

10 MR. CYMBALSKY: The fancy new cocktail

11 places, they make their own ice. They actually

12 spend a lot of time to make the perfect ice cube

13 for like a gin and tonic.

14 MR. ERBS: There's actually a company

15 in Chicago that takes blocks of ice and saws them

16 into rough cubes and sells them in five-pound

17 bags. Artisan ice, yes.

18 MR. BURGOS: All right. Yes, that's

19 pretty much most of the same standard inputs for

20 ice quality. You still get your refrigerant

21 calculations, how much refrigeration heat went in

22 and went out, energy use of the compressor, the
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1 auger motor, the condenser fan, auxiliary power,

2 and what the comparison is for the Carnot cycle.

3 And for the cycle data this doesn't represent the

4 end of the cycle. It's just the basically

5 overall cycle that you add.

6 MR. BINGHAM: It assumes that there's

7 -- because the start-up period is so short that

8 it just models kind of steady state?

9 MR. BURGOS: Right. Again, this is a

10 DOE test as well.

11 MR. WESTPHALEN: That's the way the

12 DOE test is run.

13 MR. BURGOS: Yes.

14 MR. BINGHAM: Right.

15 MR. BURGOS: Right, you got to

16 stabilize first in order to run it.

17 I mean, in a nutshell that's the piece

18 of software that we use. We also have

19 calculation in terms of looking at power

20 consumption. So for something like -- we can

21 start getting into some of the discussion of what

22 the design options are, but for something like a
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1 condenser fan the power input is an input into

2 the system. So we'll go back and look at -- and

3 compare the efficiencies of the fan that we're

4 currently using towards maybe the ECM or the PSC

5 that we're trying to look at. And so the input

6 for the condenser fan will go down. And we

7 basically scale that kind of as a ratio of the

8 efficiencies. For something like evaporator UA

9 we'll go back, recalculate all the UAs and sort

10 of plug that into the model as well.

11 MR. WESTPHALEN: So the next section

12 we have on the agenda here is going into use of

13 the energy model and the engineering analysis. I

14 don't know whether --

15 MS. McCARTHY: Can I ask a question

16 first?

17 MR. WESTPHALEN: Sure.

18 MS. McCARTHY: This is Annie with

19 NAAFEM. So earlier when we were talking about

20 using more models and types and you referenced

21 shooting each other --

22 (Laughter)
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1 MS. McCARTHY: -- just for a reference

2 point; and forgive my naïvete, but other than it

3 would be time consuming -- you have the same

4 testing from DOE and you have the same models

5 which take, what did you say like two seconds and

6 six minutes. So why does -- is that crazy?

7 MR. CYMBALSKY: So the Government pays

8 to buy and test the units.

9 MS. McCARTHY: Right.

10 MR. CYMBALSKY: So it's the cost.

11 It's the sheer cost. Like I'm not going to --

12 MS. McCARTHY: But don't you have them

13 because you buy the full gamut and test all of

14 them?

15 MR. CYMBALSKY: No.

16 MS. ARMSTRONG: No, are you asking for

17 verification do we buy every product out there

18 and verify its performance?

19 MS. McCARTHY: Yes.

20 MS. ARMSTRONG: No. Currently DOE

21 does not do that. We do select models for

22 verification purposes, but anything that I
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1 selected for verification purposes we have them

2 passed to the Rulemaking Team to be used in the

3 analysis.

4 MS. McCARTHY: Okay.

5 MR. CYMBALSKY: So we do use -- every

6 test we run we use the data for rulemaking.

7 MS. McCARTHY: Okay.

8 MR. CYMBALSKY: Whether we use it for

9 verification or -- but we have a budget, right?

10 MS. ARMSTRONG: We don't test every --

11 MR. CYMBALSKY: We do not --

12 MS. ARMSTRONG: -- product

13 distributed. I mean, we have --

14 MS. McCARTHY: So I guess how do you

15 choose what you test and do you choose a little

16 bit based on what would be useful for this?

17 MS. ARMSTRONG: Yes.

18 MS. McCARTHY: Okay.

19 MS. ARMSTRONG: So typically we would

20 choose together and we would say -- maybe next

21 year I want to pull X percent of models. It just

22 depends. And I would say to Detlef and Javier
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1 what would be most

2 helpful --

3 MS. McCARTHY: Okay.

4 MS. ARMSTRONG: -- to ground your

5 model --

6 MS. McCARTHY: Okay.

7 MS. ARMSTRONG: -- in terms of certain

8 model attributes or that kind of stuff.

9 MS. McCARTHY: Okay.

10 MS. ARMSTRONG: Absolutely.

11 MS. McCARTHY: All right. Thank you.

12 MR. CYMBALSKY: And then what the

13 shooting me and him refers to is --

14 (Laughter)

15 MR. CYMBALSKY: -- when we're getting

16 quality products, we may develop a test plan

17 because when we do the rulemaking we want the

18 breadth of the market. So Detlef and whoever the

19 team is would look at what the market is for that

20 product, do research and say we need to test this

21 many. And because there's capacity differences,

22 efficiency, we're trying to optimize all of that
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1 but not spend a billion dollars either, because I

2 mean we don't have that kind of money. And so

3 he'll suggest like 40 units and I'll come back

4 and say --

5 MS. ARMSTRONG: Well, Detlef will come

6 in and suggest like 80 and then we get --

7 MR. CYMBALSKY: Yes, so there's --

8 (Laughter)

9 MS. ARMSTRONG: Or 60.

10 MR. CYMBALSKY: So we come to a place

11 where we think we've got a representative sample

12 and at the same time we get good results out of

13 it.

14 MS. ARMSTRONG: Okay. And if it gets

15 sufficient comment that says we've missed part of

16 the market or we missed an attribute or we need

17 to learn a couple more, we might pick up a couple

18 more and test them --

19 MS. McCARTHY: Okay.

20 MS. ARMSTRONG: -- at some different

21 point. Like you saw we tested some for the

22 preliminary phase. You saw we tested a second
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1 batch for the NOPR phase.

2 MS. McCARTHY: Right. Right. Okay.

3 MR. CYMBALSKY: And we don't want to

4 duplicate testing where we don't have to do that,

5 so I know we're cognizant of not doing that

6 across Government agencies and things like that.

7 MS. McCARTHY: And so what model years

8 do you use? Like are there -- is it this year or

9 the last five years, or like what's the --

10 MS. ARMSTRONG: So the testing was

11 probably done roughly a year ago, maybe.

12 MS. McCARTHY: Okay.

13 MS. ARMSTRONG: Right? Is that --

14 MR. BURGOS: Well, two years ago, yes.

15 MS. ARMSTRONG: Two years ago for some

16 of them?

17 MR. WESTPHALEN: Well, the first round

18 was 2011. The second round was 2012.

19 MS. McCARTHY: Okay.

20 MR. WESTPHALEN: Yes, and those are

21 still valid models. I mean, that's --

22 MS. McCARTHY: Yes, I just was --
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1 MR. WESTPHALEN: The psychics hasn't

2 changed.

3 MS. McCARTHY: -- asking the basics.

4 MR. ERBS: Yes, you probably had the

5 opportunity to compare lower-efficiency and

6 higher-efficiency models because over that time

7 frame the EPA implemented the ENERGY STAR Program

8 for ice machines. So we went out and redesigned

9 a number of ice machines to meet those higher

10 levels.

11 MR. WESTPHALEN: So some of the

12 selections do reflect that.

13 MR. BURGOS: All right. And maybe

14 I'll kind of help illustrate that a little bit.

15 In terms of looking at something like the IMH-A16 Small-B, we'll look at test data -- kind of the

17 next step in terms of modeling these guys is

18 looking at sort of a baseline unit if we can,

19 looking at kind of a higher-efficiency unit if we

20 have one and also trying to look at what the top

21 of the market is. In our discussions with

22 manufacturers we have a relatively good
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1 understanding of at least the mix of technologies

2 that are and aren't being used kind of at the

3 top. So and we're looking at this.

4 We can go and calibrate the model very

5 precisely for each individual unit that we've

6 tested. We can kind of see are -- for this set

7 of design options we're pretty close to baseline.

8 For this other set of design options, we can then

9 basically pick out -- and for this other set of

10 design options we'll be at a high-efficiency

11 level and we can basically look at that and sort

12 of see, all right, this is generally the suite of

13 design options you need in order to go from

14 baseline to high-efficiency.

15 And when we have that data that serves

16 as a very good check in terms of that cost

17 efficiency curve as in did we start off to where

18 we had the test data and we modeled all the

19 design options correctly? And towards the top

20 did we look at the data and did we model those

21 design options correctly? So we do as much

22 testing at the component side as we can, but even
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1 at the system level we do have all this test data

2 that kind of helps kind of calibrate for these

3 sets of design options.

4 MR. WESTPHALEN: So the next section

5 here is use of the energy model and the

6 engineering analysis. And I don't know, if

7 everybody's still going strong, we'll just dive

8 right into it.

9 MR. CYMBALSKY: The consensus is to

10 take a quick 15-minute break. Then we'll get

11 back to the content. Sound good?

12 MS. ARMSTRONG: Okay.

13 MR. CYMBALSKY: Okay. 10:40, so 10:55

14 come back.

15 (Whereupon, the above-entitled matter

16 went off the record at 10:41 a.m. and resumed at

17 11:00 a.m.)

18 MR. CYMBALSKY: Okay, let's go.

19 MR. WESTPHALEN: So, Javier, I guess

20 we are going to start this section.

21 MR. BURGOS: All right. Yes. So, we

22 are going to take a step back after the model.
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1 This is Javier from Navigant.

2 Just basically go over, I guess, a bit

3 more of kind of the process of kind of how the

4 analysis gets stepped through. This will kind of

5 help feed into the discussion of the design

6 option analysis.

7 Really, in the end, kind of the energy

8 model is the tool that we're using to kind of

9 predict energy use and energy use reductions.

10 You know, we are trying to base it as much as we

11 can on certain test data and designs that are

12 kind of available out there in the real world for

13 pretty much all of these.

14 Pretty much for all of the batch

15 options that we ran, they were based on ice

16 makers using kind of the typical waffle-plated

17 operator. So, certainly not any of the low-mass

18 designs, but kind of full-cube ice. We are

19 trying not to look at stuff like, things like

20 half-cube as well. We are really trying to keep

21 it apples-to-apples, and in the end what we did

22 is kind of tried to select models as close to the
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1 baseline efficiency level as possible, trying to

2 model those, and then, model the design options

3 on top of that.

4 In some cases, we will get models that

5 aren't quite at the baseline level. And so, we

6 will have to kind of model a pathway downward to

7 the actual standard level. Because, in the end,

8 what we need for the full cost-efficiency curve

9 is costs for the zero percent point all the way

10 up to max tech.

11 You know, there's pretty much sort of

12 a couple of different steps here. The first one,

13 which we just discussed sort of in the last half14 hour, is developing the baseline model. We are

15 kind of calibrating the models that you saw based

16 off all the physical measurements that we took

17 over the reverse engineering, as well as third18 party test data that we did using DOE test

19 conditions.

20 We also did our own measurements of

21 fan powers, pump powers, controls, auger motors.

22 And we are really trying to get a match for per-
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1 cycle ice production. The cycle time is a big

2 one, per-cycle energy use, and, also, the

3 refrigerant conditions when we have data

4 available. So, looking at de-evaporating

5 temperature and the condensing temperature.

6 And like I mentioned, most of the time

7 we're trying to sort of develop models for units

8 at baseline as much as we can. You know, in some

9 cases some of these models are going to be

10 basically mid-tier models because that's what is

11 available on the market. And so, we will have to

12 go back and sort of adjust downwards in order to

13 get to the baseline. And that can consist of

14 either a change in compressor, maybe a change to

15 go from batch potable water to continuous potable

16 water. In most cases, it basically consists of

17 going to a less-efficient compressor.

18 And then, finally, once we have the

19 calibration for all these baseline models, you

20 know, we are able to model some of the

21 alternative designs and the additional design

22 options. And so, in some cases we are able to
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1 basically look at two sets of reverse-engineered

2 units, look at design options within the baseline

3 units, compare that to design options available

4 in the high-efficiency unit, and so, provide a

5 direct comparison using both test data to kind of

6 calibrate it within those two points.

7 We will also try to look at what the

8 market is in terms of high-efficiency

9 compressors, looking at kind of what the size is

10 for packages, and increase the exchangers. And

11 also, in the cases of motors like ECM motors and

12 PSC motors, we are trying to basically increase

13 the efficiency of those consistent with what is

14 available on the motor market.

15 Any questions on this or anything that

16 we have covered so far? I think we are open to

17 all sorts of suggestions. I think we have

18 brought up a couple of good ones today.

19 If not, I'll try to step through a

20 couple of the cost-efficiency curves, how they

21 stand in NOPR. All right.

22 Just to refresh everyone's memory, we
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1 did post a version of the spreadsheet with the

2 official cost-efficiency curves on the DOE

3 website. So, that is available to everyone. It

4 basically shows the step-through of each design

5 option, the efficiency gain they get for each

6 one, as well as the estimated cost of each design

7 option. And that results ultimately in a cost8 efficiency curve.

9 Today we will be going over the

10 IMH-A-Small-B and the IMH-A-Large-C, so one batch

11 unit, one continuous unit. But, depending on how

12 much time we have, I think we are open to go

13 through as much as we can.

14 In terms of looking at the

15 IMH-A-Small-B, we have some compressor EER

16 changes. We have condenser width. In terms of

17 looking at the design options that were modeled,

18 you know, we have an increase in compressor EER;

19 we have an increase in condenser width; we have

20 an increase in evaporator area. We have an

21 increase in the efficiency of the PSC fan motor

22 from shaded pole to PSC and, then, from PSC to an
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1 ECM fan motor. We also have a condenser increase

2 as well as a switch to an ECM pump motor. And

3 finally, we have a drain water heat exchanger.

4 All the first eight options or the

5 first seven, I guess the first eight, including

6 the baseline, were modeled using the energy

7 model. The drain water heat exchanger was not

8 modeled. That percent energy use was calculated

9 based off the testing that we did, batch ice

10 makers using a drain water heat exchanger.

11 And so, we have a set of -- this goes

12 to the IMH-A-Small. These are the files that we

13 used to run them. You know, we kind of started

14 out with our baseline curve, basically,

15 calculated all the inputs off our test data.

16 So, we plugged in from the test data

17 what the pre-batch weight was, what our harvest

18 time was, what our energy rate was, sort of

19 estimated what the evaporator and some

20 temperature would be; and, also, kind of applied

21 a mass flow limiter based off the evaporator

22 temperatures that we're seeing, in order to get a
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1 better match for the evaporators for the test

2 data that we had acquired.

3 And maybe this is a good time to open

4 up our test data. So, we did testing of ice

5 makers. Sort of as we have mentioned before, DOE

6 does lots of it. And so, we were able to get

7 sort of all sorts of test data, evaporator

8 temperatures, condensing temperatures, discharge

9 temperatures, liquid suction lines. This is kind

10 of what we were trying to match in terms of

11 performance, in terms of that one baseline unit.

12 The unit that we selected in order to

13 sort of model this curve off of was the baseline

14 unit; basically, sort of .2 percent above the

15 standard. And so, no adjustment was needed to go

16 downward.

17 And so, the first change that we

18 modeled was an increase in compressor EER. And

19 so, we have two options here when it comes to

20 compressor EER changes. If we have the data

21 available -- so, DOE, what we do is we look at

22 the market for available compressors. And that
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1 is something we have gotten feedback on from the

2 manufacturers under NDA, as to which compressors

3 on the market are acceptable for use within ice

4 makers.

5 And so, during the NOPR analysis, we

6 took our list of compressors and basically tried

7 to select out which ones were sort of -- you

8 know, basically tried to rank them in terms of

9 efficiency, what was currently in the baseline,

10 and was there anything with higher efficiency.

11 In some cases for some manufacturers

12 all the data is available. So, we are able to

13 basically apply the compressor map. We are

14 basically able to apply a new compressor map to

15 the model.

16 In some cases that EER, that

17 compressor map isn't available to us. We are not

18 able to get our hands on it. And in those cases,

19 what we do is we do an adjustment of the

20 compressor coefficients as modeled within. So,

21 if we have our baseline compressor, that is

22 modeled at the 4.86 EER, and we want to go to the
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1 5.25, but we do not have the compressor map for

2 that. What we will do is we will scale-down the

3 power consistent with the change from 4.86 to

4 5.25.

5 So, as you will see, this is kind of

6 our starting-out compressor map that we have.

7 Here is the .7 multiplier that we found that we

8 applied to all our batch units.

9 The compressor map that we have here

10 is basically a very similar compressor. We

11 weren't able to get the data for a specific

12 compressor. So, what we did is we had a sort of

13 multiplier in order to get the actual model. And

14 this was adjusted in order to calibrate to the

15 actual test data that we had at the baseline.

16 And then, stepping up to the bigger

17 compressor, in this case we did not have the

18 compressor map available for the more efficient

19 compressor. And so, we applied the power

20 multiplier to that unit. So, instead of being

21 the .975, now we have the .903, and that is

22 consistent with the power you would get if you
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1 compared the COPs of the 4.86 versus the 5.25 EER

2 compressor.

3 I can run this, if you like.

4 Ultimately, what we will get is we will get the

5 energy consumption basically at the baseline

6 reduction.

7 Yes?

8 MS. PETRILLO-GROH: Laura Petrillo9 Groh for AHRI.

10 Did you make any other changes to the

11 model other than the increase in the compressor

12 size?

13 MR. BURGOS: No, not for this design

14 option. What you will find is that the models,

15 the compressors, what you will find is when we

16 model this, we are modeling the actual units.

17 So, the harvest rates are going to be at 280;

18 they are going to be at maybe 310, maybe 320.

19 And so, kind of when we look at the efficiency

20 gains, we are comparing kind of the baseline

21 model with the higher design options. And that

22 is where we get this percent energy reduction.
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1 That is what this is consistent with.

2 For this energy consumption, this

3 overall number is based off the actual harvest

4 rate, representative harvest rate that we are

5 looking at. So, these numbers and this standard

6 level is consistent with the 300 pounds per day.

7 That is considered representative for this unit.

8 MR. ERBS: This is Daryl Erbs,

9 Manitowoc.

10 When you put in the more efficient

11 compressor, do you make any adjustment to the

12 harvest energy?

13 MR. BURGOS: Not in this case, no.

14 MR. ERBS: All right. And I think, of

15 all the technical options that you're looking at,

16 the higher-efficiency compressors is the one area

17 that I believe is the most potential for error,

18 just because when manufacturers redesign their

19 compressors to achieve higher EERs, it typically

20 involves two steps. And there may be some other

21 things. But the two most common things they do

22 -- and you have illuminated the direct suction
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1 compressors out of there. So, we appreciate

2 that.

3 But, when they are indirect suction,

4 they are still doing things to change the routing

5 of suction gas through the compressor shell into

6 the cylinder, because that is a major loss

7 component in a hermetic compressor.

8 And as soon as you do that, you will

9 affect the ability to transfer heat to that

10 suction stream, which is not so much gas anymore

11 during harvest; it is more of a liquid. But that

12 impacts the energy that gets delivered. There is

13 a hot gas to the harvest.

14 And then, of course, the other thing

15 they do is they improve motor efficiency.

16 Higher-efficiency motors run cooler. And so,

17 again, there is less heat stored in that block of

18 steel that is the motor. And so, typically, we

19 see some percentage of the EER as an improvement,

20 but not all of it.

21 And in extreme cases -- and I know it

22 was in my comments; I am not sure if we shared
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1 the data, but we can probably share the data --

2 where we went from a recent to a scroll where

3 there is very different internal dynamics. And

4 that is all we changed.

5 On paper the difference in EER of

6 those two compressors, you know, at kind of the

7 midpoint of our freeze cycle on paper was a huge

8 improvement, and in testing it was a really small

9 improvement.

10 And then, if you start looking at

11 other data like discharge temperature from the

12 compressor during harvest, a huge difference

13 between those two compressors. And it is all

14 about that scroll is running very cool, and there

15 is a relatively -- you know, they have isolated

16 the motor. Because with a scroll, they can take

17 liquid into the scroll plate, so they don't care

18 anymore, and we don't care. It's reliable. But

19 it has a big impact on how much energy that

20 compressor delivers during harvest.

21 And I wouldn't expect you to be able

22 to model that. I mean, that is another one of
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1 those PhD or probably about 10 PhD student

2 projects.

3 So, I think that is an opportunity for

4 improvement, and I know it complicates things.

5 But I don't think you are capturing that.

6 MR. WESTPHALEN: Yes, understood. And

7 we saw the comment. You know, there was the data

8 provided in the AHRI comment.

9 The more such data we have, the more

10 accurately we can reflect that when we make

11 adjustments here.

12 MR. ERBS: Understood.

13 MR. SCHAEFER: So, this is Stephen

14 Schaefer, Hoshizaki America.

15 You have the percent reduction here

16 theoretically, but was it ever validated to see

17 whether it was close or accurate with actual

18 testing?

19 MR. BURGOS: We did not test

20 compressor swaps. So, we did not take a

21 compressor, a higher-efficiency compressor, and

22 swap it into an existing model. We are limited
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1 for the tests we are able to run on the existing

2 models.

3 MR. WESTPHALEN: Yes, there probably

4 isn't a model out there that represents that

5 design.

6 MR. SCHAEFER: You said you didn't do

7 that for the compressors, but what about the

8 fans? Were you able to do that for any of the

9 fans?

10 MR. BURGOS: No, we did not do any

11 specific swaps of fans within the existing --

12 MS. ARMSTRONG: Let's take a step

13 back, all right? I think that the question -- we

14 are not modifying manufacturers' products and,

15 then, retesting them, if that is what you are

16 getting at. We are not changing-out components

17 to an actual physical machine and, then,

18 retesting that.

19 What we did do, I think, is try to

20 round the model across a range of efficiencies,

21 and you guys can step in here. But that is not

22 to say that we don't have specific test data at
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1 those levels. It just may not be representative

2 of that specific design because what we are not

3 doing is modifying manufacturers' designs.

4 MR. ERBS: And we wouldn't want you --

5 MS. ARMSTRONG: Exactly.

6 MR. ERBS: -- to do that.

7 MS. ARMSTRONG: We are not in that

8 business.

9 MR. ERBS: Don't take this wrong. We

10 don't think you would do a very good job of it.

11 (Laughter.)

12 And it's not --

13 MS. ARMSTRONG: I would much prefer

14 you do it. Let's put it that way.

15 MR. ERBS: And it's not just about --

16 because this happens all the time with our

17 compressor suppliers. Sometimes they will take

18 one of our ice machines and they will modify it

19 for us. And then, they will get better results.

20 And then, we will say, okay, but run it at 40

21 degrees and 40 degrees, and does the ice fall

22 off. "Oh, no, it doesn't," right?
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1 So, you know, we have another set of

2 criteria to satisfy beyond just the efficiency of

3 the rating point. And that is why I think -- so,

4 my only point is compressors are not just apples,

5 right? They are apples, bananas, and oranges,

6 and the rules that apply to apples don't apply to

7 bananas and oranges. And, yes, we need to try to

8 help you with that somehow in the way that

9 doesn't -- my concern about any of this modeling

10 is the extrapolation of what you have been able

11 to validate and tune the model. But now you

12 start projecting out beyond where you have been

13 able to test any models because we haven't built

14 any of them yet, because the rulemaking isn't in

15 effect.

16 I mean, I have got some data that we

17 can talk about later. But sometimes things are

18 going at this curve and, then, they flatten off

19 and we don't want to get into a place where the

20 rule is placed on that curve continuing forever

21 when it actually doesn't

22 MR. WESTPHALEN: Yes.
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1 MR. ERBS: Because, then, that will

2 put us on an awkward position of being able to

3 actually achieve those efficiencies.

4 MR. WESTPHALEN: Yes, and we

5 understand. And we will get some of the slides,

6 step through the next step of what kind of data

7 we think.

8 If you have other specific suggestions

9 as to, well, I could tell you that a rule of

10 thumb that would be appropriate would be that you

11 would get this kind of a result or apply this to

12 the input to get this right, and here's the data

13 that shows it, you know, we are open to that.

14 MR. ERBS: Right.

15 MR. WESTPHALEN: I mean, what we are

16 trying to do here is step through so you can see

17 each step. We realize, because of the question

18 about the compressors, there may be questions

19 about the fans. But let's step through all of

20 them.

21 MR. ERBS: Yes. That was just a

22 point, to make that comment.
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1 MR. BURGOS: Yes, right. And I guess

2 maybe to help illustrate your point as to sort of

3 some of the testing of the components, I think,

4 especially for this curve, we did take a look at

5 a baseline unit and a high-efficiency unit. And

6 so, while we didn't test all the individual

7 components that are within that, when we take a

8 look at what's in that high-efficiency unit

9 versus what's in that baseline unit, what we

10 found reflected is there's an evaporator increase

11 of, basically, 50 percent. There is a condenser

12 width growth of about 2 inches. There's the

13 compressors at least comparing one, the baseline

14 one to the high-efficiency one.

15 As we point out, there's probably need

16 for test data, so you know how much of that, you

17 know, what that actually entails. But we did

18 look and see that there's a higher-efficiency EER

19 compressor in that high-efficiency model.

20 So, while all these individual points

21 may not exist as a model, I think for this, at

22 least our 18 percent or 19 percent high-
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1 efficiency model kind of reflects the combination

2 of all these things. And they won't reflect

3 something like a ECM motor or sort of a high4 efficiency PSC motor.

5 So, some of these, kind of once you

6 get to the top end of the curve, that is kind of

7 when you are looking sort of outside the bounds

8 of what we looked at on the market. Then, in

9 that case I think we are trying to limit it to at

10 least something a little basic, like a motor --

11 MR. RIVEST: So, Javier, for this

12 class best of market is --

13 MR. BURGOS: When we took a look at

14 this during the NOPR phase, this is what we had.

15 We had our test data for our two points, baseline

16 and high-efficiency, and then, this was the max

17 tech that was out on the market today.

18 MR. RIVEST: Twenty-three?

19 MR. BURGOS: Yes.

20 MR. RIVEST: And do you know what the

21 bundle of design options was at this point?

22 MR. WESTPHALEN: This was a trick
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1 example.

2 MR. BURGOS: No, not your question.

3 MR. WESTPHALEN: It was a trick

4 example because -- and correct me if I'm wrong --

5 but this is the example where the unit that was

6 out there at 23.6 is now rated at what?

7 MR. BURGOS: Fifteen percent.

8 MR. RIVEST: But it tested out at 23?

9 MR. WESTPHALEN: We didn't buy that

10 one.

11 MR. RIVEST: Oh, okay, Which did you

12 test?

13 MR. WESTPHALEN: This one.

14 MR. RIVEST: Okay. Well, let's forget

15 those figures. What was it in the 17.5 in terms

16 of design options and how does it compare to what

17 you have?

18 MR. BURGOS: That's pretty much a

19 straight comparison. Bigger evaporator, bigger

20 condenser. I think the compressor EER for that

21 guy was about --

22 MR. RIVEST: Which design option,
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1 though?

2 MR. BURGOS: This one. This one, the

3 compressor EER was about 5.1, I believe.

4 MR. RIVEST: Five point one on the

5 right?

6 MR. BURGOS: Right.

7 MR. RIVEST: It's No. 4 or 3?

8 MR. BURGOS: Yes, it's around 4.

9 MR. ERBS: My only comment would be,

10 well, I think your model might be conservative on

11 the impact of evaporator and overly optimistic on

12 the impact of compressor. So that those three

13 changes net out to the same, but you are not

14 modeling each one of them quite right.

15 And then, when you take that model and

16 use it to pick design options, you know, for

17 other sizes, other models, and if you are relying

18 more on the compressor EER increase and some of

19 those design options could be off, and, also, the

20 cost because eventually you have to bring cost

21 in, that the cost to get to a certain efficiency

22 level, if it means we have to actually use more
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1 of the evaporator increase because we can't count

2 as much on the compressor, based on those, then

3 the total cost analysis to get to that efficiency

4 level could be off because of that.

5 MR. WESTPHALEN: We weren't planning

6 to get into the costs very much.

7 MR. ERBS: No, I know, and I don't

8 want to. I don't want to, but that's the

9 consequence of validating the model with multiple

10 changes as opposed to by one-by-one and

11 understanding that more accurately.

12 MR. WESTPHALEN: Oh, yes. Point

13 mapping, and if we understand better how to

14 adjust those steps --

15 MR. ERBS: I know it is going to come

16 back to when are we give you some data.

17 (Laughter.)

18 MR. BURGOS: You know, I think we are

19 discussing this a little bit, but if I kind of

20 highlight the point, maybe we will step up to the

21 evaporator area increase. So, we are kind of

22 looking at Design Option 3. We are looking at --
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1 there we go -- so, we are looking at kind of at

2 sort of an evaporator mass of 6.51. That comes

3 straight from our reverse engineering, you know,

4 kind of specified our super heat, our hydraulic.

5 We have sort of a set of UAs

6 associated with this that would calculate it

7 using our waffle sheets. So, we are kind of

8 calculating, as the UA grows, what is the total

9 UA from the start to the finish? And we mapped

10 some coefficients between that to kind of show

11 the progression.

12 And we also have our initial pre13 harvest batch weight as well, and that comes from

14 our test data, as well as some manufacturers'

15 spec sheets. You know, it all kind of serves to

16 validate all these points.

17 Then, I believe it is four. We will

18 grow the evaporator. That will include a change

19 in the mass. This is pretty much weighed because

20 we also took a look at this unit as well.

21 But, you know, it is something we can

22 -- if you look at it in the gross, about 51
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1 percent, that growth of mass from 6.5 to about

2 9.8, you know, is about roughly sort of the same.

3 So, that's what gets plugged into here. You

4 know, here we have the 9.76, and, yes, making UAs

5 also go up, 939 and 166, and that is kind of

6 consistent with the growth in ice cubes. So, you

7 go from 1.6 ice cubes to 190 of them.

8 That's kind of basically you're

9 looking at the grid. And so, each individual

10 box. So, going into here, plugging in the 90,

11 gives you your sort of water UAs and your ice

12 UAs. And again, as Daryl pointed out, that is

13 basically assuming that it is a fixed increase

14 for each individual UA ice cube. So, that goes

15 into here.

16 Process and harvest rate. So, we will

17 go back, put in the pre-harvest batch weight. We

18 do have test data for this. So, you know, in

19 case the evaporator gets bigger, but perhaps the

20 ice sheet gets a little thinner, at least in the

21 case of this model we were able to sort of

22 validate that.
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1 Harvested ice, harvested energy.

2 Let's answer Daryl's question. We have sort of

3 18 energy watt hours for smaller. And then, for

4 the two, we had 11.9. So, the harvest energy did

5 go up, based off the test data that we had

6 basically found for the unit using the smaller

7 evaporator and the larger evaporator.

8 And so, you run that. You run that

9 and get your increase in evaporator area. In

10 this case, this percentage represents the sort of

11 total combination of all the options. So, we

12 will go through it, run them separately, run the

13 evaporator area increase separately from the

14 compressor EER increase from the condenser width,

15 and then, bring cost into the equation in order

16 to sort of help order the different design

17 options, because we are trying to go from the

18 most cost-efficient, so the most bang for your

19 buck, the most efficiency for the lowest-cost

20 increase. And then, kind of stagger it up as you

21 go along.

22 For something like a PS fan motor
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1 change or like an ECM motor change, that is

2 reflected in the condenser. We pretty much have

3 sort of rated fan powers that we go off. What we

4 will do is we will do testing within our own lab

5 for the reverse-engineered products. And then,

6 when looking at design options for PSCs and for

7 ECMs, what we have is basically models that let

8 us kind of basically extrapolate out.

9 So, we will know what the output power

10 is of the motor. We will have the input power

11 measurement. And so, we are able to basically

12 kind of extrapolate out what the power decrease

13 should be for each fan power. And we will do

14 that for the condenser fan and we will do that

15 for the pump as well.

16 MR. WESTPHALEN: And these

17 percentages, the 25, 45, 65, and 75, are

18 representative ratios for the different styles --

19 MR. BURGOS: Yes.

20 MR. WESTPHALEN: -- that would be put

21 out for Request for Comment in our preliminary

22 phase. But we think that those could potentially
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1 be adjusted with better data as well.

2 MR. BURGOS: Yes. So, for ECMs, we

3 are currently sort of estimating 75 percent of

4 efficiency compared to an SPM, which is about 25

5 percent, compared to a PSC, which is going around

6 45 to 65 percent. And I think, as we collect

7 data and kind of adjust to see what the

8 efficiency is of these baseline SPMs versus what

9 they might be for the PMs, you know, that

10 increase could go up or down, you know, depending

11 on what the data says.

12 MR. ERBS: Those are pretty

13 straightforward. As long as you get the

14 efficiencies where they are and could be, then it

15 is just a matter of integrating over time.

16 MR. BURGOS: Yes.

17 MR. WESTPHALEN: If you know what the

18 motor efficiencies are, but we find that it is

19 harder for us -- you know, motor vendors don't --

20 MR. ERBS: They won't share that with

21 you.

22 MR. WESTPHALEN: They won't share that
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1 with us.

2 MR. BURGOS: Right. We're not

3 purchasing the motors on our own.

4 MR. ERBS: But you have ice machines.

5 Of course, maybe none of them have ECMs.

6 MR. BURGOS: Yes, some of them do have

7 PSCs, but --

8 MR. ERBS: Yes, well, I meant PSCs

9 then.

10 MR. BURGOS: Yes.

11 MR. ERBS: But, yes, the ECM is not

12 out there. Because you could measure the input

13 of a motor on a dynamometer.

14 MR. BURGOS: Right.

15 MR. ERBS: You don't have a dynamo.

16 MR. WESTPHALEN: We don't have --

17 MR. BURGOS: But, yes, I mean, for the

18 motors --

19 MR. WESTPHALEN: You know, there may

20 be test labs that could do that for us. And we

21 ought to, for future rulemakings --

22 MR. BURGOS: Yes, I mean, for the fan
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1 motors, we are talking increases of about 3

2 percent, 1 percent, yes. Yes, about 2.3 percent.

3 For the condensers, you know, what we

4 will do, because we have our condenser model, is

5 we will go from having this kind of smaller

6 evaporator, we will go from having, let's see, we

7 will go from having kind of our condenser with

8 kind of the two growths, the circuits, the wall

9 thicknesses. If we predict that the number of

10 tube rows is going to change, here's where we

11 change it. We put in four, and then, the program

12 will sort of extrapolate out what the increase in

13 UAs should be.

14 If we think the width is going to

15 increase, then, you know, here is kind of where

16 -- where is it? Width, right here. Yes, this

17 width of the tube row is pretty much what

18 constitutes the increase. And that is pretty

19 much from hairpin to return bend, so kind of from

20 end-to-end.

21 When we do that, we have to go back

22 and recalculate out the fraction, the tube-fin
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1 fraction. Generally, I guess they will look for

2 an increase because you have pretty much the same

3 size on the outside, or really it is the inside,

4 fin increase that will go up. And again, that is

5 kind of relatively straightforward now.

6 Yes. And then, for the rated fan

7 power, generally, what we like to do is kind of

8 rely on the models. So, for some models, going

9 to a bigger condenser, they will pair that with,

10 say, going to a bigger condenser motor.

11 And in the case of this unit, when we

12 looked at the baseline and we looked at the high13 efficiency unit, we saw that the pan increased

14 from 16 watts to 50 watts. So, that is something

15 we incorporate into the increase as well.

16 And then, sort of apply kind of -- and

17 then, we will apply that. We will apply the

18 gains in terms of fan energy consumption both to

19 the 16 size motor and to the 50-percent motor.

20 So, we will start looking at, you know, instead

21 of looking at the ECM being at about 23 watts,

22 they will be about 71 watts because you are
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1 getting more of the airflow from that bigger

2 motor to take advantage of the bigger condenser.

3 Is there anything I haven't talked

4 about for this first one?

5 MS. McCARTHY: This is Annie with

6 NAAFEM.

7 MR. BURGOS: Sure.

8 MS. McCARTHY: Could you run through

9 for me probably about the price changes?

10 MR. BURGOS: Sure. I mean, I guess we

11 sort have a set of separate models to basically

12 calculate the price. The energy model only

13 calculates reduction in energy use.

14 MS. McCARTHY: Right.

15 MR. BURGOS: But, for something like

16 the compressor EER, you know, we published in the

17 NOPR kind of our formula for trying to calculate

18 what the cost is for a higher-efficiency

19 compressor EER.

20 I know we have gotten some feedback on

21 that. So, we know we are open to as much data as

22 we can for what that cost of a higher compressor
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1 EER is. But, generally, there is kind of well2 known relations, and the manufacturers have a

3 good idea of what those compressors cost.

4 MS. McCARTHY: So, what specific data

5 points would you want? I just want to be able to

6 take requests back to our members.

7 MR. BURGOS: Sure. I mean, kind of --

8 MR. WESTPHALEN: Well, give us a list

9 of the compressors that you buy and what you pay

10 for them on an NDA.

11 MR. BURGOS: Yes.

12 MR. WESTPHALEN: That would be the

13 ideal "ask". Then, we can look at what EERs they

14 are, and then, do the correlation and figure out

15 what the trend looks --

16 MR. BURGOS: Yes, the same with

17 condenser sizes, evaporator sizes. You know,

18 generally, for these units there's only two or

19 three different evaporator sizes. So, knowing

20 the cost for each one of those data points is

21 really kind of what we are getting at.

22 The way this analysis tends to go is
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1 we have our kind of cost model that models all

2 the processes that go into making something like

3 a condenser. So, looking at all the stamping,

4 looking at all the tube bending and the brazing,

5 I mean, we can come up and have our own estimate

6 of what that cost is. And then, we will go back

7 to the manufacturers and ask them for validation

8 of what the cost is for them. And there's

9 generally back-and-forth in terms of that.

10 But, yes, the more data points, the

11 better.

12 MS. McCARTHY: Yes. So, as far as

13 with your starting assumption level, what is your

14 starting point, I guess?

15 MR. BURGOS: We have a cost model that

16 we basically can take the weight of all the

17 different, basically, all the weights, all the

18 material costs, and then, all the processing

19 costs.

20 And then, this is the model we use for

21 all of our rulemakings. So, it has been honed

22 over time in terms of knowing what is the
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1 depreciation associated with certain pieces of

2 equipment.

3 That is kind of what we use to develop

4 our costs. And then, we go out and ask people

5 for data to see if we are right or wrong. If it

6 is a purchased part that needs to have a markup

7 or if it is something that is made in-house, and

8 thereby, it is more about the materials than the

9 process cost.

10 MS. McCARTHY: Okay. And that is in

11 the NOPR?

12 MR. WESTPHALEN: The process is

13 described.

14 MS. McCARTHY: Okay. So, would we be

15 able to get like what points you're --

16 MR. WESTPHALEN: The incremental costs

17 are all reported for the different efficiency

18 levels, the estimated incremental costs. I don't

19 know if we reported all the baseline costs.

20 MR. BURGOS: No.

21 MR. WESTPHALEN: It's not broken down.

22 It is just the total number, I think.
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1 MR. BURGOS: Yes, we have the number

2 --

3 MR. WESTPHALEN: It is not broken down

4 in terms of this much more for the compressor,

5 this much more for the -- and frankly, because

6 our manufacturing cost models do rely on a lot of

7 non-disclosure-agreement-type information, there

8 is some sensitivity in showing all the details.

9 MR. BURGOS: Yes.

10 MR. WESTPHALEN: And so, what we

11 generally do is we come up with our estimates of

12 the costs for a particular model, and then, under

13 a non-disclosure agreement, have a discussion

14 with the manufacturer and say, "Okay, are these

15 breakdowns of the costs representative for that

16 model?" And try to get that feedback and feed it

17 back in.

18 MR. BURGOS: Right.

19 MS. McCARTHY: Okay. And so, this has

20 been adjusted from, I guess, the preliminary

21 phase, but not from the costs that were just

22 submitted?
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1 MR. WESTPHALEN: Correct.

2 MS. McCARTHY: Okay.

3 MR. WESTPHALEN: And based on some of

4 the NOPR-based comments, we have to do some

5 calibration on the cost side as well.

6 MS. McCARTHY: Okay. Thank you.

7 MR. BURGOS: Yes. Yes, and, I mean,

8 I think we referenced the rows and rows and rows

9 of data.

10 MR. RIVEST: The thing to keep in mind

11 is that your members' cost, individual member's

12 costs may not be these costs.

13 MS. McCARTHY: Right.

14 MR. RIVEST: For a variety of reasons.

15 They may not be manufacturing all these products.

16 They may also be starting from a different point

17 on this efficiency curve.

18 MS. McCARTHY: So, yes, I know there

19 is a discrepancy between our larger members and

20 our smaller specialized members.

21 MR. RIVEST: That's also true.

22 MS. McCARTHY: So, I guess, do you all
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1 break that down at all?

2 MR. RIVEST: I think most important

3 for us would be to know what is it that they have

4 to do to reach these efficiency levels and how

5 much does it cost --

6 MS. McCARTHY: Okay.

7 MR. RIVEST: -- with as much detail as

8 possible. If they told us, to go from baseline

9 to level 4, we have to implement these changes

10 and it will cost us this much --

11 MS. McCARTHY: Okay.

12 MR. RIVEST: Rather than tell us what

13 it would theoretically cost to do these things.

14 MS. McCARTHY: Okay. And maybe later

15 you all can outline what the NDA says

16 specifically would share/not share, because I

17 know that also is a sensitive issue.

18 MR. RIVEST: We share nothing.

19 MS. McCARTHY: Okay.

20 MR. CYMBALSKY: I never see it. I can

21 tell you that. The government -- the whole idea

22 of being shared --
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1 MS. McCARTHY: Okay.

2 MR. CYMBALSKY: It keeps it with them,

3 and they roll it up to an industry number. And

4 that is the stuff that the public gets and me

5 gets to see.

6 MS. McCARTHY: Okay. Okay.

7 MS. SHEPHERD: This is Amy Shepherd

8 from AHRI.

9 Can I ask a question about that? So,

10 when you talk about certain things that covered

11 by NDAs that can't be disclosed, if it is

12 aggregated and rolled-up like that, where does

13 that issue come up?

14 MR. CYMBALSKY: So, the NDAs --

15 MS. ARMSTRONG: I don't quite

16 understand the --

17 MR. CYMBALSKY: The individual

18 companies sign the NDA.

19 MS. SHEPHERD: Right.

20 MR. CYMBALSKY: And we don't see that

21 then.

22 MS. SHEPHERD: We can talk about this
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1 later, but I am not sure whether there is in this

2 specific rulemaking, but there are some instances

3 where like, say, cost assumptions and things like

4 that, it says it is covered by an NDA. And so,

5 even though, like in response to questions and in

6 NOPR or something, you can't disclose it.

7 So, my confusion is just, if it is

8 rolled-up and aggregated like that such that it

9 is basically de-identified, what is the issue

10 there, then, with disclosing it?

11 MR. WESTPHALEN: Well, that would be

12 like the incremental costs, like the cost

13 efficiency curves, and those are in the NOPR. And

14 so, you see them right there.

15 MS. ARMSTRONG: But you don't do the

16 individual costs for each manufacturer.

17 MR. ERBS: Or even at an aggregate.

18 So, we manufacture an ice machine. We sell it to

19 a distributor. They sell them to a dealer. They

20 sell them to an end-customer.

21 And if they assumptions of cost aren't

22 accurate, we are not sure we want the general
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1 public to know how far they are off because, you

2 know, we have a business to run. We are trying

3 to maintain the margins we make. And so, if some

4 of that information gets out there, it affects

5 our ability to maintain the profits that we make.

6 Okay? So, you may not care, but we,

7 as a company, definitely care, where we have

8 gotten ourselves in a competitive industry and we

9 want to try to avoid releasing information that

10 might compromise our ability to maintain the

11 margin.

12 I mean, that is confidential

13 information. Even if it gets aggregated, you

14 know, it is a small enough industry that I don't

15 think any of us really want that to get out

16 there.

17 MS. PETRILLO-GROH: Are these

18 industry-specific or is cost data taken from the

19 larger HVAC industry?

20 MR. ERBS: I think you built the cost

21 models.

22 MS. PETRILLO-GROH: The cost model is
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1 specific --

2 MR. ERBS: These product costs --

3 MS. SHEPHERD: Okay. So, it wasn't

4 just the larger cost? It would be a motor for

5 other equipment that --

6 MS. ARMSTRONG: We get industry

7 specific from these guys.

8 MR. RIVEST: Yes, I mean, to disclose

9 the exact cost of a compressor makes sense if it

10 is a specific compressor from a specific

11 manufacturer.

12 MR. CYMBALSKY: Right. That is the

13 real issue I see, is that Daryl's company could

14 be getting it from the same compressor guy for

15 half the cost of the other company. We don't

16 want to --

17 MS. SHEPHERD: What is the basis for

18 that being the cost assumption?

19 MR. CYMBALSKY: That's what they pay,

20 I mean, right?

21 MS. SHEPHERD: So, do you have like a

22 -- do you seek data from a certain level or a
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1 certain number of manufacturers?

2 MS. ARMSTRONG: Anyone who will talk

3 to us.

4 MR. CYMBALSKY: Anyone who wants to

5 give us the data. I mean, that is the whole

6 point of --

7 MS. ARMSTRONG: Are you suggesting

8 that the data they don't give us is accurate?

9 MS. SHEPHERD: No, I'm just curious.

10 That's my question.

11 MR. CYMBALSKY: It's the sample is as

12 big as the people who want to sign the NDA and

13 help us out. We reach out to as many as we know

14 --

15 MR. ERBS: The parts manufacturers are

16 screaming, right, because they are trying to sell

17 compressors for less than they can get away with

18 selling it for? That's where they are always

19 going to start. So, people are protecting --

20 MR. RIVEST: We don't even want to get

21 their sample size.

22 MR. ERBS: Right. Yes. It's
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1 difficult, is all I can say. It really is.

2 MR. CYMBALSKY: Yes, I think the

3 history of the program is such that this method

4 has been the one that has provided the strongest

5 data --

6 MR. ERBS: Right.

7 MR. CYMBALSKY: -- to support the

8 analysis. So, I think through the years this is

9 the one method that has worked the best.

10 MR. RIVEST: I want to point something

11 out also. It hasn't come up today, but it has

12 come out in other industries about these types of

13 models.

14 In a similar situation where we had a

15 model like this, and industry had made a request

16 that the model be made public and reversed its

17 decision. And the reason that was given was they

18 realized that a lot of them had proprietary

19 models that they had developed over the years

20 that were a source of competitive advantage for

21 the companies. And putting a model like this,

22 making it public was inviting new entrants,
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1 foreign new entrants. You were putting other

2 people in business potentially.

3 And the collective knowledge of people

4 participating in a rulemaking making a publicly5 available model and all your IP available online,

6 I don't know -- they suddenly all realized what

7 they were doing and they said, "Uh-uh, we don't

8 want to do that."

9 So, it is something to think about.

10 MR. SCHAEFER: Stephen Schaefer,

11 Hoshizaki America.

12 On No. 4 where it says, increased by

13 51 percent, the chassis growth, and you're

14 showing component change of $80, $80-some, I

15 think, over there, is that just material cost or

16 are you looking at everything that goes into it?

17 Because if you are increasing the

18 chassis size, we have got to increase the

19 refrigerant that goes in there. We might have to

20 do tooling changes. So, is this just component

21 parts?

22 MR. BURGOS: No, our model is designed
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1 to account for increases, also, in all the

2 tooling and all the conditioning, all the

3 incidentals, labor, depreciation.

4 MR. SCHAEFER: So, this is stacked

5 over a certain amount of models over 30 years?

6 Is that what it would be?

7 MR. RIVEST: That is production cost.

8 So, it wouldn't be a sales price.

9 MR. SCHAEFER: Okay.

10 MS. McCARTHY: Is there a way we could

11 get a list of like what your model includes as

12 far as that goes?

13 MR. BURGOS: I think some of it is

14 already in the NOPR.

15 MS. McCARTHY: Okay, okay.

16 MR. BURGOS: I think so, yes.

17 MR. SCHAEFER: So, just sales numbers?

18 Just sales increases is what you're looking at,

19 not actual material cost.

20 MR. BURGOS: Correct.

21 MS. ARMSTRONG: So, if you go with a

22 heat exchanger, right, what do you claim?
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1 MR. SCHAEFER: Well, if it is saying

2 50 percent, maybe if you have two plates, you're

3 adding a third plate, and you are growing the

4 chassis. So, you have got the cost for the

5 evaporator plate, and then, you have got the cost

6 for extra refrigerant. And you have got the cost

7 for growing the materials and changing the

8 tooling for it.

9 So, it is more than just $80. That is

10 what I am looking at.

11 MR. RIVEST: It would all be in there.

12 MS. ARMSTRONG: It would all be --

13 MR. RIVEST: The tooling cost would be

14 only the depreciation part of it, not that

15 initial investment lump sum.

16 MR. SCHAEFER: Because we are trying

17 to see what information we need to give you guys.

18 We are trying to help those cost figures.

19 MR. BURGOS: Yes, definitely comments

20 on these incremental costs.

21 MS. ARMSTRONG: But what you are

22 seeing there is the other components or the other
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1 types of things that would have to go with it are

2 included in that cost.

3 MR. BURGOS: Yes, we break that down

4 into what the cost of the evaporator growth was

5 compared to what the cost of growing the box was,

6 and all the other ones.

7 MS. McCARTHY: So, would it be helpful

8 if the manufacturers with that data included sort

9 of the more breakdown? If they say like, is it

10 represented in your number? Like if 80 is a low

11 number and maybe you are not taking into account

12 everything that they would have to do, would it

13 be helpful if they broke down exactly what they

14 feel like they have to do and whether or not it

15 --

16 MR. WESTPHALEN: Or maybe if there is

17 any example that is this big and one that is that

18 big, you know, what is the difference in price?

19 MR. ERBS: So, when I started to

20 digging into that, what I found is it is not that

21 simple, and I haven't given up. But, for

22 example, we may have two models that have
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1 different size evaporators and cabinets, but in

2 some cases I found the smaller size had a sheet

3 metal part that was more expensive than the

4 bigger size, right?

5 That doesn't make sense. So, I

6 started digging into it. "Aw, we run the smaller

7 sheet metal part on the old turret press because

8 our line's low."

9 So, our standard costing I can't give

10 you, right, because it doesn't make sense in

11 terms of this exercise. So now, you have got to

12 go, actually, through kind of more of a

13 theoretical costing exercise. Because if we

14 change our designs to meet these new standards,

15 we would change all of the machine tools and

16 things, right?

17 So, I am worried about giving you data

18 that will just confuse you.

19 MR. BURGOS: Ultimately, the point of

20 the model --

21 MR. ERBS: I haven't given up on that.

22 MR. SCHAEFER: There might be some
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1 models where they use the same compressors but

2 different evaporator plates. So, we can see how

3 those correlations --

4 MR. ERBS: Right. Oh, I know. Again,

5 that's my excuse for not giving the numbers so

6 far.

7 (Laughter.)

8 MR. WESTPHALEN: Yes. I mean, it is

9 helpful to know that there are these

10 complications. Ideally, the way our

11 manufacturing cost model worked, it is sort of

12 like a greenfield basis, as if you are designing

13 the plant now, you know.

14 MR. ERBS: Right. I just don't know,

15 I am not sure I am comfortable with all the

16 assumptions; that's all. But we are looking at

17 it.

18 MR. BURGOS: Yes, we will point out,

19 I guess, that one of the whole purposes of the

20 model is to try to tease out kind of the cost

21 really associated with increased efficiency and

22 almost try to bypass a lot of these differences
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1 that may or may not be due to -- that may or may

2 not have an effect on efficiency.

3 But, like we have said before, you

4 know, you end up with a bit more theoretical

5 unit.

6 MS. McCARTHY: This is Annie McCarthy

7 with NAAFEM.

8 I think this is my last question on

9 this point. But why do you use the depreciation

10 instead of the lump sum? Because, from our

11 manufacturer's we have heard that, because there

12 is a lump-sum investment, it affects the price

13 not in a way, that the depreciation that you use

14 doesn't --

15 MR. WESTPHALEN: Yes, but the

16 engineering analysis focuses on what it should it

17 cost to make that unit. And then, the

18 manufacturer impact analysis would, then,

19 evaluate, okay, well, what if one manufacturer

20 didn't have automated manufacturing operations

21 and wasn't flexible and had more investment to

22 make -- to try to capture the differences and,
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1 also, the upfront costs in that part of the

2 analysis.

3 MS. McCARTHY: Okay. So, the

4 manufacturing analysis does include the lump sum

5 and that would increase price not in a way that

6 the depreciation one does?

7 MR. RIVEST: There is just standard

8 practice about what you consider a cost in

9 management accounting. Depreciation and

10 amortization is you take a lump sum and you break

11 it down over the useful life of the equipment.

12 MS. McCARTHY: No, right. Yes, I

13 understand. We have just tried to -- that is not

14 in real life what ends up happening with their

15 costs because they have to raise it because of

16 the lump sum more than --

17 MR. CYMBALSKY: But if you factored

18 the whole lump sum into that, they couldn't sell

19 their product. You would see a million dollars

20 versus Daryl's $80, right?

21 MS. McCARTHY: Yes. I just wanted to

22 know if that was factored in at all.
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1 MR. CYMBALSKY: Oh, definitely.

2 MR. ERBS: I think we have already

3 provided estimates of what the total investment

4 would be to get to different levels of efficiency

5 gain. We had to make a lot of assumptions, but,

6 then, we came back and said, "Oh, it would be $5

7 million of capital investment" --

8 MS. McCARTHY: Right.

9 MR. ERBS: -- "to retool a complete

10 platform of modular ice machines," right? And

11 that is in the analysis, right, that capital

12 cost? Right? That is why our economic value

13 goes down so much.

14 MS. McCARTHY: Okay.

15 MR. ERBS: Right. I mean, so we are

16 not happy about it, but that is factored in.

17 MS. McCARTHY: Okay. That's how it is

18 factored? Okay.

19 MR. ERBS: Yes.

20 MS. McCARTHY: Thank you.

21 MR. WESTPHALEN: So, I guess back to

22 you.
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1 MR. BURGOS: All right. So, it is 12

2 o'clock. I think I was planning on at least

3 going through one more run, but I think I can

4 walk through one of the continuous curves pretty

5 fast or even walk through one of the other cost6 efficiency curves, if you are more interested in

7 seeing that.

8 MR. ERBS: I did want to poke at one

9 a little bit.

10 MR. BURGOS: Okay.

11 MR. ERBS: And then, you can continue

12 it.

13 But this one, 51 percent evaporator

14 area led to, I don't know, 13 percent improvement

15 efficiency. I kind of agree with you on that

16 one. That is rare.

17 There was another one a little further

18 down. I think it was a water-cooled one. I am

19 in the TSD, but --

20 MR. BURGOS: Right. Well, it is all

21 the same data.

22 MR. ERBS: I hope so.
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1 MR. BURGOS: Yes, there was another --

2 MR. ERBS: There was a water-cooled

3 one where you increased the area by --

4 MR. BURGOS: Forty-seven percent, yes.

5 MR. ERBS: Yes, and look what happened

6 to the efficiency. It went up three points. How

7 is that possible? It just doesn't make sense to

8 me. I just think you ought to go back and look

9 at that one, what's going on.

10 MR. BURGOS: Fair point.

11 MR. ERBS: We would never do that,

12 right? I mean, if we only got three points out

13 of a 40-percent bigger evaporator, it is a

14 terrible idea.

15 MR. BURGOS: Yes. Well, I mean, I

16 will say I guess --

17 MR. ERBS: I think we can get more

18 than what you predicted, but --

19 MR. BURGOS: Like to your point,

20 right, it is probably a mix of the different

21 compressors that are being used. You know, as

22 compressor capacity goes up versus, you know, we
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1 also balance that against the condenser increase

2 as well.

3 MR. ERBS: But, then, that suggests

4 that there could be interactions. Have you

5 really understood that? Before we set efficiency

6 levels, I think you ought to understand why big

7 increases in evaporator area don't always help.

8 MR. RIVEST: It depends what you do.

9 It is a sequencing issue, right? If you just

10 inverted the evaporator with the condenser, you

11 may see a big boost with the evaporator.

12 MR. WESTPHALEN: Yes. I mean, there

13 is also the idiosyncrasies in the compressor

14 performance where the EER is not the full story.

15 And then, one thing we ought to look

16 at for this is, is the evaporating temperature

17 already fairly high? And then, you get much less

18 benefit because you are already squeezing the

19 evaporator side.

20 MR. ERBS: Yes, okay. I mean, I would

21 just ask you to do a little analysis and make

22 sure.
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1 MR. RIVEST: You mentioned earlier

2 that you thought maybe the compressor, the model

3 was a --

4 MR. ERBS: Yes. Yes.

5 MR. RIVEST: It comes up always --

6 MR. ERBS: It is 10 percent that you

7 get from increasing the compressor area, and I

8 don't think you will get that.

9 And we ran some testing on a water10 cooled condenser because it was easy to do that.

11 What we found is, as you lower the condensing

12 temperature, the energy efficiency is getting

13 better, and then, all of a sudden, it just

14 flattened right out.

15 And that is one of those areas where

16 I think maybe we will try to give you some

17 guidance around it. If you drive the condensing

18 pressure below "X", you are not going to get any

19 more gain. Because what we find, then, is the

20 further increases in the freeze cycle efficiency

21 are offset by lousy harvesting. And that is why

22 it flattens out.
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1 MR. RIVEST: Right, right.

2 MR. ERBS: And maybe this one you have

3 driven things beyond what would actually happen,

4 so that you are not seeing much effect. But the

5 evaporator always helps you because more surface

6 area means you have a smaller delta T because

7 your heat flux is less. And raising the suction

8 pressure on a compressor will always help the

9 efficiency.

10 MR. WESTPHALEN: Well, we'll look at

11 that one.

12 MR. ERBS: Okay. So, in this case

13 someone just -- I don't know if it was

14 arbitrary -- but someone made a selection of the

15 sequence of design options?

16 MR. WESTPHALEN: No, I think this was

17 done based on running the different steps of

18 combinations and looking at which --

19 MR. ERBS: Okay.

20 MR. WESTPHALEN: -- steps would be

21 most cost-effective to do.

22 MR. RIVEST: You have to cost every
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1 condenser size.

2 MR. ERBS: No, they have an equation

3 --

4 MR. BURGOS: Right, yes.

5 MR. ERBS: -- which I have a little

6 heartburn with, but --

7 MR. RIVEST: Well, our suppliers don't

8 price on equations. They price on tooling and

9 how many they run of a certain size, and things

10 like that. So, there are discontinuities in real

11 life that I am not sure of, but I don't know if

12 that is the biggest issue we have.

13 MR. BURGOS: Right. I mean,

14 ultimately, the design options, like here, kind

15 of the same way for the IMH, for the air-cooled

16 one, the evaporator area, and the higher17 efficiency unit reflected a 51 increase. You

18 know, it is the 47 percent increase is reflected

19 in the higher-efficiency unit that we looked at

20 for water-cooled and Small-B.

21 But, yes, I think to your point,

22 definitely looking at the impact of the
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1 evaporator compared to maybe some of the other

2 design options that were also present in the

3 high-efficiency unit is the thing to do.

4 All right. All right. So, I mean, we

5 can look at one of the continuous ones as well.

6 We are kind of looking at a baseline unit.

7 All right. And so, for this one, what

8 we actually found is, when we did an efficiency

9 evaluation of this unit, we purchased the

10 reverse-engineered model. That model was about

11 6.5 percent below the standard. And so, we

12 actually had to go back and basically push that

13 model a bit more towards being a standard sized

14 unit. So, basically, make it a little less

15 efficient, so it would be at the baseline.

16 And what we did in this case is we

17 basically shrunk the condenser width. So, we

18 raised the condensing temperature in order to get

19 to that baseline level. And that is reflected

20 here in this first design option, condenser

21 width.

22 The unit that we looked at is probably
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1 closer to the 7 percent. You know, it is about

2 6.5, and this is kind of the jump that we had to

3 make in order to basically take it further down

4 to baseline.

5 And so, that is reflected here. You

6 know, we start out with our condenser, and we

7 have fan power airflow rates. It has got certain

8 tube rows, and it has got a certain number; it

9 has got the bigger number of tube rows and it has

10 got the smaller width.

11 And then, going up to the larger one,

12 you know, you can basically see that there's

13 basically four, there's now four tube rows.

14 There is sort of a slight increase in the width

15 of the condenser as well.

16 And that is what we run. That is a

17 combination of design options in order to get

18 sort of our increase. If we were to run the

19 model, you would basically see the condensing

20 temperature go up and down.

21 Compressor EER, again, you know,

22 knowing from 5.08 to 6.3 is pretty much a
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1 straight swap in compressor usage. You know, in

2 the places where we have the map, we are able to

3 map that out. In the places where that 6.3 EER

4 compressor, the map isn't available to us, we

5 will scale down, again, based off of EER.

6 MR. ERBS: Why would there not be a

7 map available?

8 MR. BURGOS: We look at publicly9 available sources, generally what's available on

10 the websites. Copeland is a really good source,

11 and they publish most of their data. Bristol is

12 a very good source. They publish a lot of their

13 data.

14 But, for some of the others --

15 MR. ERBS: So, who makes the 6.3

16 compressor?

17 MR. BURGOS: I haven't looked at the

18 data since --

19 MR. ERBS: I mean, are all of these

20 real-live --

21 MR. BURGOS: Yes.

22 MR. ERBS: -- currently-available
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1 compressors?

2 MR. BURGOS: Yes, from that list that

3 we have requested feedback on.

4 MR. ERBS: So, how do you know it is

5 a 6.3? So, they say it is a 6.3, but they won't

6 give you any underlying data?

7 MR. BURGOS: Well, we are unable to

8 get the compression map, yes.

9 MR. WESTPHALEN: Well, yes, you get

10 the performance -- you know, you get the list of

11 compressors, you know, capacity, EER, and the

12 rated conditions.

13 MR. ERBS: Yes, but the problem with

14 the rated conditions is they are so far away from

15 the operating conditions for an ice machine that,

16 when you get -- you know, we typically do like 10

17 degrees saturated suction and 110 saturated

18 condensing. It is the best single point to

19 characterize the efficiency and capacity of a

20 compressor for a freeze cycle. And if you have

21 got 45/130 data for that compressor --

22 MR. WESTPHALEN: Well, these are ones
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1 for which we would have 20/120.

2 MR. ERBS: Okay.

3 MR. BURGOS: 120/20, yes.

4 MR. ERBS: So, that's closer.

5 MR. BURGOS: Yes. I mean, I agree.

6 I think we would love to have more compressor

7 data.

8 MR. ERBS: My biggest fear is we are

9 using in most cases -- again, a lot of our ice

10 machines aren't at the baseline level. I think

11 they are Energy Star level.

12 Where we are already using the most13 efficient compressor that is available, and we

14 know that the only way to get to a higher-level

15 compressor efficiency from where we are today

16 will be accompanied by a big step change in cost,

17 right -- they can put a DC motor in or they can

18 do some other very radical things.

19 But they have pretty much run out all

20 of the mechanical efficiency things they can do,

21 and they are pretty much running at about as good

22 as you can get out of a PSC motor and a
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1 compressor.

2 So, I just want to make sure we are

3 not looking at compressor efficiency. And again,

4 I would really caution you about scrolls because

5 of what we have found. So, if there are any

6 really high-efficiency scrolls at 21/20, that is

7 where I would say there's the most risk that the

8 delivered gains will not match what you would

9 guess from looking at the performance map; that

10 we don't end up relying on compressor EER

11 increases because that is the only way to get

12 there, because it may not really be achievable

13 without big cost increases.

14 MR. WESTPHALEN: Yes, we are using

15 recent compressors except for the largest16 capacity machines that already have scrolls.

17 MR. ERBS: Right. But when I look at

18 the large, air-cooled ice making batch, most of

19 what you are relying on is compressor EER, and

20 you probably go on to scroll.

21 MR. WESTPHALEN: I don't think so, no.

22 MR. BURGOS: Not for any of these,
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1 though. Over the lunch break I can take a look.

2 MR. ERBS: Okay.

3 MR. BURGOS: Maybe I can sort of get

4 back to you guys on that.

5 MR. ERBS: Yes.

6 MR. BURGOS: But yes. I mean, I

7 agree.

8 MR. ERBS: So, this one, right?

9 MR. BURGOS: Yes.

10 MR. ERBS: That one there, right?

11 MR. BURGOS: Right.

12 MR. ERBS: So, 18, there is 10

13 percent; 10 points of the gain is compressor.

14 And we already have a PSC motor and batch water

15 fill.

16 I mean, I don't know; maybe our large

17 machine is already at the proposed new levels.

18 Anything is possible.

19 MR. WESTPHALEN: So, I guess we were

20 stepping through a continuous. Did we get all

21 the way through that or --

22 MR. BURGOS: Yes, we can finish it
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1 off.

2 MR. BINGHAM: This is Scott Bingham

3 with Follett.

4 MR. BURGOS: Sure.

5 MR. BINGHAM: And I don't need to pick

6 through each of those.

7 MR. BURGOS: That's fine.

8 MR. BINGHAM: But my point is that it

9 kind of echoes what others have said about

10 discretely plugging in one component for another.

11 You know, we had an experience where

12 we swapped out a compressor, and it simply would

13 not fit into the space available. It matched up

14 just perfectly from an efficiency and an output.

15 And then, also, there is not an

16 infinite world of compressors out there with the

17 improved efficiency and with the output you are

18 looking for. So, you swap it out and, then, you

19 get more outputs. Then, you get a higher

20 production rate of your ice maker, which there

21 are discrete production levels out in the

22 marketplace. And so, the customer won't give you
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1 credit. You know, if you have got a 1,000-pound

2 machine, and all of a sudden it is a 1100-pound

3 machine, you don't necessarily get credit for

4 that because the next size up is what they need.

5 And so, it is more capacity, but you don't buy

6 anything. You don't get any credit for that in

7 the marketplace.

8 And then, in addition, in a

9 continuous-style ice maker you increase the

10 production capacity. And now, the augur motor

11 doesn't have the torque to harvest that, and

12 especially at these lower temperatures.

13 And so, you, then, size up your augur

14 motor to harvest your greater production rate.

15 And then, you are using more energy there with

16 your larger augur motor.

17 So, there's a lot of tradeoffs that

18 theoretically it sounds nice, but in real life,

19 in practice you can't put it into practice.

20 MR. WESTPHALEN: Yes, I mean, in terms

21 of how to take that and work that back into the

22 model, I mean, we are looking at compressor swaps
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1 that are within 5 percent capacity of what -- and

2 trying to make it that limited, I think that's

3 the right number.

4 MR. BURGOS: Yes.

5 MR. WESTPHALEN: So, we wouldn't go 10

6 percent more.

7 In terms of knowing what classes have

8 issues where, if you went in efficiency for a

9 compressor, you wouldn't be able to fit it in the

10 package, I mean, I am just wondering, how can you

11 feed that information to us?

12 I mean, when DOE asks us for what is

13 the representative curve, we can't just say,

14 "Well, we don't know." And so, how can we feed

15 that information in, in order to say, okay, well,

16 if I wanted to put that compressor in, it

17 wouldn't work in this class unit.

18 MR. BINGHAM: And I don't know because

19 there are a number of different machines out

20 there. We have dimensionally reviewed the guts

21 of every machine, which you bought 41 machines

22 and not 141. I would challenge the same question
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1 there.

2 You know, the overarching concern is

3 in some cases we are making assumptions and

4 extrapolating, and it is an impossible task to

5 get all of these nuances and understand all these

6 nuances. In the end, a rule can be made that

7 penalizes us more than we want it to or isn't

8 practical.

9 And I don't know; you ask a great

10 question, and I don't know how to answer it.

11 MS. PETRILLO-GROH: This is Laura

12 Petrillo-Groh, AHRI.

13 I think the fact is that this is even

14 more compounded with having a model that hasn't

15 been released. You know, whereas, if this were

16 publicly available, the manufacturers might be

17 able to take it back to their lab and see what

18 could be made.

19 But discussing what we are discussing

20 now at this meeting, I am not sure it should have

21 been as shaded, such a black box as it has been,

22 knowing what parameters were changed upon each
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1 design option increase.

2 MS. ARMSTRONG: That has always been

3 publicly available ever since day one. We

4 started in the framework document with

5 identifying design options.

6 MS. PETRILLO-GROH: No, I understand

7 that the design options are publicly available,

8 but the specific changes to, you know, did you

9 increase the number of ice cubes when you

10 increased the evaporator EER or the evaporator

11 area, or how are all these other parameters taken

12 into effect when you are making these changes.

13 It is all within the model, but no one can use

14 it, use the public process.

15 MR. STARR: So, this is Louis Starr

16 with NEEA.

17 You know, with the design options

18 there's a number of other design options. So, if

19 you are making an ice machine and you can't

20 increase your case because of a new compressor,

21 then you would choose one of the other design

22 options.
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1 So, I don't think necessarily you are

2 expecting to use absolutely every design option

3 that is up there. That is why they are providing

4 a number of ways to get there. So, they are

5 trying to capture that in the model, I believe.

6 Is that not, more or less, the case or

7 is it --

8 MS. PETRILLO-GROH: No, it is actually

9 not. A lot of the levels that were chosen were

10 above what is max, what is currently max

11 available in the market. If we have doubts about

12 the -- if our manufacturers aren't seeing how you

13 can possibly get to these calculated max

14 available numbers, you have set a level that

15 eliminates all the -- or the max tech numbers and

16 you have eliminated all the product in the

17 marketplace.

18 We end up with a really severe impact

19 and a quandary.

20 MR. STARR: So, on the max tech, they

21 are using all the technology possible?

22 MS. PETRILLO-GROH: And it is well
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1 above, it is calculated well above what is

2 available in the marketplace now. I mean, these

3 are all competitors. They all really want to

4 sell their product. And if they can sell product

5 that would make the same amount of ice for saving

6 a lot of energy, you know, they would be doing

7 this.

8 I am not saying that there aren't

9 improvements that could be made, but I think we

10 have seriously overestimated the cost,

11 overestimated the available energy savings, and

12 we can't tell how it was done because the model

13 hasn't been released.

14 And the subsequent steps to what comes

15 out of the model --

16 MS. ARMSTRONG: That is the whole

17 purpose of this meeting.

18 MR. BURGOS: This is what we are

19 doing.

20 MS. ARMSTRONG: What can't you see

21 here?

22 MR. ERBS: Let me just take a stab at
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1 it. So, yes, we can use -- I think they have

2 identified the right design options, and the

3 discussion is around if the benefits-to-costs are

4 not correct for some of those design options,

5 right, then we might pick the wrong level of

6 efficiency standard, right? We might not really

7 be optimizing.

8 I mean, obviously, as manufacturers,

9 you know, we would rather keep more of the money

10 in our pile instead of giving it to the society,

11 but, you know, I understand the process.

12 But we certainly don't want to drive

13 things to a level where it really adds a lot more

14 cost than anyone anticipated, because that will

15 end up out in the marketplace, right? It will

16 have a negative impact on the whole industry and

17 to the end-users.

18 So, I think all we are asking is that

19 where we, based on our experience, feel that

20 there might be some inaccuracies in how this

21 model is taking something like -- again, you

22 know, the one that I am most concerned about is
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1 compressor efficiency, that we try to get that

2 right, and that there is some flexibility in

3 terms of the end efficiency level.

4 We don't disagree with the need to set

5 a new bar that is higher up, but we want to get

6 it right, so that we don't create too much of an

7 adverse impact on the whole ice machine industry.

8 And I don't think that is good for anyone.

9 MR. CYMBALSKY: So, the statute lays

10 out how we do this, right?

11 MR. ERBS: Right.

12 MR. CYMBALSKY: And we are going to

13 follow all the rules. And, you know, there is a

14 statutory requirement to set the standard.

15 Whether or not you philosophically believe in

16 that, that is off the table, right? Congress

17 told us to do it. We are doing it.

18 So, what we are doing here today --

19 and I thought we had been doing a pretty good job

20 -- is opening up what some have called a black

21 box, and we have demonstrated how our test data

22 has calibrated this model very precisely, in our
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1 minds.

2 What we are trying to do here is

3 figure out with your test data, and run some

4 simulations, if, in fact, this model performs as

5 we think it does. And so, that is what we are

6 doing here today.

7 We are going to take all your data,

8 and we are going to go back and sharpen the

9 pencils and adjust the numbers, if we need to

10 adjust them. That is what we are doing here.

11 MS. McCARTHY: So, we have quite a few

12 members who weren't able -- this is Annie with

13 NAAFEM -- who weren't able to make this meeting.

14 Is there a way that they will be able to have

15 this information, like these slides and, also,

16 that?

17 MS. ARMSTRONG: They are already

18 available.

19 MS. McCARTHY: Yes, I guess something

20 more the spreadsheet situation.

21 MS. ARMSTRONG: Some of those are

22 already in the docket as well.
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1 MS. McCARTHY: But, I mean, will it be

2 fully available --

3 MS. ARMSTRONG: It is already publicly

4 available.

5 MR. CYMBALSKY: Right. There won't be

6 another sit-down with these guys and look at

7 their computer model. I mean, that is what today

8 is.

9 But all the results that are

10 generated, to the extent we can put them in the

11 docket, we will put them in the docket.

12 MR. ERBS: They are very willing to

13 talk to anybody under NDA, right? So, I would

14 just pass that message back.

15 It doesn't have to be for things that

16 are under the NDA, sharing data and really having

17 discussions on it. We can do that, probably need

18 to do it quickly to have a best opportunity of

19 influencing the outcome.

20 So, I think we have an opportunity to

21 look at what we need to look at and provide

22 information. The challenge from my perspective
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1 is just sometimes the difficulty of doing that.

2 MS. McCARTHY: So, just to confirm,

3 you all would be available for people who weren't

4 able to physically be here and see this, to run

5 through this with them?

6 MS. ARMSTRONG: So, I don't think that

7 they are going to put on another full-day

8 presentation, like we just said, for people that

9 weren't available today.

10 With that being said, I think that you

11 can take back to your members that (a) we are

12 going to put the available information from today

13 in the docket. (b) There will be a transcript

14 from today and all the discussion in the docket.

15 (c) They are more than welcome to engage in

16 providing feedback on our models, on the

17 publicly-available data, additional discussions

18 or questions they may have with our consultants.

19 But I don't think we are going to have them take

20 a full day to go through this again.

21 MS. McCARTHY: Yes, but just if they

22 have specific questions --
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1 MS. ARMSTRONG: Sure, sure.

2 MS. McCARTHY: -- you all feel

3 comfortable with me telling them that they can

4 get in touch with you --

5 MS. ARMSTRONG: Yes, yes, yes, and

6 they have been available the whole time.

7 MS. McCARTHY: -- about the models?

8 Yes, yes.

9 MS. ARMSTRONG: Yes, absolutely.

10 MS. McCARTHY: Okay.

11 MR. CYMBALSKY: And the initial tables

12 came out on this rulemaking in January of 2012.

13 So, there's been a lot of numbers around for over

14 two years now.

15 MR. ERBS: I said they were wrong

16 then. They were wrong --

17 (Laughter.)

18 MS. ARMSTRONG: You're consistent.

19 (Laughter.)

20 MR. CYMBALSKY: At least one person

21 did.

22 MR. ERBS: They have responded. I
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1 think some areas are difficult to figure out how

2 to make it more correct. It is not easy.

3 MR. CYMBALSKY: Okay, I am going to

4 suggest a lunch break at this point. So, come

5 back at 1:30.

6 (Whereupon, the above-entitled matter

7 went off the record for lunch at 12:22 p.m. and

8 resumed at 1:37 p.m.)

9 MR. CYMBALSKY: Okay, welcome back,

10 everybody. We have a comment from Howard

11 Ricketts at Ice-O-Matic. And his comment reads,

12 "Extending the compressor EER impact on the

13 energy savings across all models without

14 validating any models is irresponsible." And he

15 said, "We are willing to refute with actual

16 data." And so -- that's great. So we're here to

17 provide data and then run our models and such.

18 So please provide your data that refutes anything

19 or supports anything that we're doing today.

20 MR. SOBAN: How long do we have for

21 that?

22 MR. CYMBALSKY: The comment period for
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1 the NOPR closed. So obviously data with the NDAs

2 with Navigant, or the labs, just keep putting it

3 in.

4 MR. SOBAN: But we don't have a

5 deadline right now?

6 MR. CYMBALSKY: You know, the comment

7 period is not open. But, you know, we continue

8 to take any data that we can.

9 MR. SOBAN: Okay.

10 MR. SCHAEFER: Should we send the data

11 to Navigant?

12 MR. CYMBALSKY: Yes.

13 MR. SCHAEFER: Okay.

14 MR. CYMBALSKY: Yes.

15 MR. SCHAEFER: Will do.

16 MR. CYMBALSKY: And, you know, we're

17 going to, based on what we learn here today, and

18 again, and what's provided, well, if we need to

19 open a comment period up for some reason, you

20 know, we'll examine the next steps based on what

21 we learn here today.

22 MR. ERBS: I don't completely
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1 understand maybe why we can't, given the model,

2 maybe there's some question of -- making that

3 available for our purview. Is there a way that

4 we could work with you to, like maybe, run the

5 model and show us the results so they can help

6 get some more insights into how well it, or how

7 well it, you know, it's actually working against

8 some of the cases?

9 MR. WESTPHALEN: We could.

10 MR. ERBS: Or just as an alternate way

11 to, you know, help that process.

12 MR. WESTPHALEN: And the intent was to

13 start some of that this afternoon.

14 MR. ERBS: Okay.

15 MR. WESTPHALEN: And, I mean, I think

16 what we have, you know, remaining on the agenda

17 today is, I mean, there's a couple of slides here

18 that are due additional work. And we've touched

19 on a lot of these items that, you know, this

20 ongoing information exchange.

21 Page 31 kind of highlights, you know,

22 some of those areas, direct components, on
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1 results that specifically would address, you

2 know, the question of whether the model properly

3 handles a replacement of a particular component

4 with something larger, or something with a better

5 EER. And, you know, we'll make sure that we're

6 in touch, you know, on a continual basis as we

7 move forward, trying to finalize.

8 MR. CYMBALSKY: But at this point, I

9 don't know if we need to do Slides 27, 26 and 27.

10 I think we've pretty much, I know we didn't flip

11 through them, you know, one by one, but I think

12 we've covered all the information. But unless

13 someone sees something there that needs more

14 highlighting, I think it was part of the active

15 discussion that we had. And so we did the

16 demonstration. I think we are at the additional

17 work slide. So I think, at this point, if you

18 want to walk through that, what we're thinking,

19 and then go --

20 MR. WESTPHALEN: Well, I think, you

21 know, what I've basically explained, I mean,

22 we've touched on the fact that there's
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1 information exchange going, on and that we will

2 consider that information, and modify the model

3 and the way we use its output.

4 We have also been in touch with the

5 manufacturers to collect more detailed

6 information on a range of products that we would

7 hope to feed into a supplementary analysis that

8 would basically, you know, be compared with the

9 energy model cost efficiency curve, based on the

10 existing products and their design features, that

11 would essentially show, okay, the existing market

12 shows this kind of a trend if you put that data

13 into a regression, and show that that is

14 consistent with what the model result would show.

15 And then as I said, you know, on Page

16 31 we have some of the detailed component swap

17 items for which we would like to see data. We

18 have some data on compressor replacements. We

19 don't have very much data at all. We have maybe

20 one or two of the models that we had purchased

21 and tested show impact to some extent on

22 evaporator size.
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1 It would be helpful to have more of

2 that data, more of the data on condenser size

3 swap, data showing the impact of moving to a

4 batch type of control for the water flow, for

5 batch ice makers, and then more data on

6 efficiencies, different fan motors.

7 And so that's sort of all we had

8 prepared here to put on the table. And so, you

9 know, at this point I think we could try to move

10 towards some real time modeling, you know, if you

11 want us to do some modeling now.

12 MR. CYMBALSKY: Yes. I think that's

13 good -- anyone else have, I mean, you can also,

14 before we do that, flip to the Slide 35, which is

15 the last slide. We'll sort of -- in my mind, you

16 know, in terms of process, close it out by just

17 showing you the timeline that we're under.

18 Again, we're working towards the final rule at

19 the end of the year.

20 You know, based on all the data we

21 receive and how it changes the analysis that was

22 from the NOPR, if there's a need to put out a
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1 NODA or something like that, we will consider

2 that. But otherwise, this is what we're working

3 towards. And of course, all the data that you

4 provide will factor into the next phase of the

5 rule making.

6 MR. SCHAEFER: I'm sorry. What's a

7 NODA?

8 MR. CYMBALSKY: Notice of data

9 availability.

10 MR. SCHAEFER: Okay, got you.

11 MR. CYMBALSKY: So it's one strategy

12 we can use if, in fact, the numbers change enough

13 where we think it's important to get another

14 public viewing of what happened before the final

15 rule. We've done that in transformers. We did

16 it in that rule making where some issues came up

17 about poles versus ground transformers and how

18 there could be differences. And we did a little

19 more analysis on that.

20 And we put out a NODA between the NOPR

21 and the final rule. And so it's an approach that

22 can be taken if we think it's something that
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1 should be done. But again, it's going to all

2 depend on the data you provide and how the

3 analysis may or may not change, basically.

4 MR. RIVEST: It's like a limited

5 opening of comments for specific issues.

6 MR. CYMBALSKY: Right, right, exactly.

7 There won't be a whole full blown NOPR again.

8 It'll just be here's a limited set of information

9 to get public comment on.

10 MS. MCCARTHY: And what's the, like,

11 general period for that?

12 MR. CYMBALSKY: Generally, it would be

13 a 30 day comment period.

14 MS. PETRILLO-GROH: And would you go

15 over some of the comments that you received in

16 the NOPR phase and see --

17 MR. CYMBALSKY: No. That would be in

18 the final rule. This would just be data. It

19 wouldn't be any, procedurally there can't be any

20 proposals or anything --

21 MS. PETRILLO-GROH: No, no, but, I

22 mean, I was saying how maybe the model changes,
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1 or any of the analysis changes based on comments

2 received during this period, during that period

3 and today.

4

5 MR. CYMBALSKY: I don't think they

6 would do that in the NODA. I think that would be

7 in the final rule.

8 MR. RIVEST: What's the question?

9 MS. PETRILLO-GROH: If there would be

10 any discussion of how the analysis changed, or

11 how the model changed based on comments received

12 during the number of days, during an open period,

13 and during today's meeting, and in response to

14 any additional data that's received from, you

15 know, after this meeting today. So what happens,

16 what --

17 MR. RIVEST: I don't know if there

18 would be a lot of narratives. It would probably

19 just be analysis results.

20 MR. CYMBALSKY: Yes.

21 MS. MCCARTHY: But it would show --

22 MR. CYMBALSKY: It would show a bunch
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1 of numbers based on all the comments and the data

2 we received. Then the model --

3 MS. MCCARTHY: And then we could note

4 the differences.

5 MR. CYMBALSKY: Right.

6 MR. RIVEST: You would see the

7 spreadsheets, probably?

8 MR. CYMBALSKY: Yes. That spreadsheet

9 would be percentage improvements, design options.

10 It probably would be the --

11 MR. RIVEST: Yes.

12 MR. CYMBALSKY: -- the output.

13 MR. RIVEST: Right.

14 MS. MCCARTHY: And so maybe that's

15 what they were talking about?

16 MR. CYMBALSKY: Right.

17 MS. MCCARTHY: Okay.

18 MR. CYMBALSKY: I think, procedurally,

19 and legally it can't be more than a bunch of

20 data.

21 MR. BINGHAM: So kind of a related

22 question, there's been a lot of data that's been
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1 submitted already over the public comment period

2 and since then, some of it public, some of it not

3 public. Based on that data that you've seen,

4 have your drawn any preliminary conclusions or

5 have you not analyzed it enough to have any

6 comments on how that affects the --

7 MR. CYMBALSKY: Well, I think that's

8 what we're having here today, right. So I don't

9 think --

10 MR. BINGHAM: No, just a question.

11 Prior to today, how --

12 MR. CYMBALSKY: I don't think we're

13 going to comment on what, you know, that's been

14 another page in the rules. Yes.

15 MS. PETRILLO-GROH: The next phase is

16 the final work, right?

17 MR. CYMBALSKY: Or a NODA, right? So

18 depending on what you give us.

19 PARTICIPANT: Creating an incentive

20 here.

21 MR. CYMBALSKY: Been pleading all

22 along. It's just more clear.
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1 MS. MCCARTHY: Is there a threshold

2 that makes you determine --

3 MR. CYMBALSKY: You know, I think it's

4 that if the numbers are such that there's some

5 changes that we think the public needs to see,

6 yes. I mean it's --

7 MS. MCCARTHY: It's not a percentage

8 of it?

9 MR. CYMBALSKY: Oh, no, no, no. It's

10 --

11 MS. MCCARTHY: Okay.

12 MR. CYMBALSKY: No. It's just a

13 judgement call on what we think is something

14 significant or not.

15 MS. MCCARTHY: Okay.

16 MR. ERBS: Can I ask one more data

17 question?

18 MR. CYMBALSKY: Like when are you

19 going to provide your data? Is that --

20 MR. ERBS: No.

21 (Laughter)

22 MR. ERBS: Do you need additional data
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1 to take some particular action on this 22 inch

2 category, or you're already convinced that you

3 need to do something special for that, given that

4 --

5 MR. CYMBALSKY: So I can't comment on

6 our direction there.

7 MR. ERBS: Yes.

8 MR. CYMBALSKY: I can say that we

9 heard the public comment at the NOPR phase. And

10 obviously considering what we heard, I can't tell

11 you any more than that. But obviously, any more

12 data to support the assertions that were made at

13 the meeting were obviously helpful in our

14 decision making.

15 MR. RIVEST: If I can ask for some

16 more specific things that come to mind, market

17 share information and --

18 MR. ERBS: I think we provided that

19 already. I think that's in the summary. We did

20 a model of the market share.

21 MS. PETRILLO-GROH: Yes. We provided

22 market share by efficiency level for all the
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1 batch units, and for TSL392.

2 MR. RIVEST: I mean the 22 inch --

3 (Simultaneous speaking)

4 MR. ERBS: Within there, you can

5 identify within that overall data circuit, so --

6 (Simultaneous speaking)

7 MR. ERBS: Yes. I'm just trying to

8 get some sense of what would help you understand

9 the difficulty, right, to try and achieve some of

10 those TSL, those technical options within that

11 category.

12 MR. WESTPHALEN: Right. I think the

13 request --

14 MR. ERBS: You know, the 50 percent

15 evaporators, the one that keeps coming to mind.

16 MR. WESTPHALEN: I think the requests

17 for detail design data to those units have been

18 sent to some of the manufacturers.

19 MR. ERBS: Yes. That's in the

20 spreadsheet you sent us.

21 MR. WESTPHALEN: So that's the key

22 part of that.
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1 MR. CYMBALSKY: So, I mean, we

2 understand the issue that you've raised.

3 MS. PETRILLO-GROH: Right, 48 percent

4 of all the IMH-W in the smalls, or 22 inch on the

5 side, 22 inch wide --

6 So that's why we questioned whether

7 some of the design options that increased chassis

8 size would be appropriate for those size, you

9 know, those classes with high percentages or size

10 constraints you had brought up. So data? Want

11 to run some models?

12 MR. CYMBALSKY: Okay. For now, yes,

13 this is the fun part. Thanks everyone for

14 coming. Now, this is your opportunity to see the

15 models. I know you're interested in that. And to

16 those people who couldn't make it, to the extent

17 that you can communicate what you learned today,

18 maybe it convinced them that providing data would

19 be helpful for the rule making effort. I'd

20 appreciate that as well. Okay. This is a little

21 free form, stuff we don't normally do. So I

22 don't know how this works.
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1 MR. WESTPHALEN: All right. Well, I

2 guess it's coming from Laura or --

3 MS. PETRILLO-GROH: I have 14 units

4 I'd like to run, if possible.

5 MR. RIVEST: Okay. So I don't know if

6 you can --

7 MS. PETRILLO-GROH: Well, as --

8 (Simultaneous speaking)

9 MS. PETRILLO-GROH: I have, well, I'm

10 not going to have the -- I sent over a list of

11 those --

12 (Coughing)

13 --and you said you had most of them,

14 do you know which ones that you don't have? That

15 helps me narrow down --

16 MR. BURGOS: Well, we have all the

17 data. You're just going to have to -- you have

18 all the data for them. You're just going to sit

19 through the two minutes of me typing in the data

20 format. So as I run you on, we can --

21 MR. RIVEST: Do you have an

22 overarching like, sort of, thought process as to
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1 what is it that you're aiming to achieve with the

2 modeling effort, like where we --

3 MS. PETRILLO-GROH: Sure.

4 MR. RIVEST: And maybe, to prioritize

5 it, if you have fourteen to do --

6 MS. PETRILLO-GROH: Yes. I will be

7 prioritizing on, we have some, you know, units

8 that we've tested as far as the certification

9 program and have, I have a direct -- we have all

10 the test data that it goes into them, sure, but

11 having the model to predict what the energy use

12 is. So for the first two I'd like to run, it

13 would be a direct compressor swap so we can look

14 at the data and what the model returns back

15 versus what the actual test data says it should.

16 MR. RIVEST: In all these you have, I

17 don't know. Only if you've actually got data for

18 --

19 MR. ERBS: I think we do.

20 MR. RIVEST: -- two compressors.

21 (Simultaneous speaking)

22 MR. ERBS: I think that's what she
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1 said.

2 MR. RIVEST: Oh, okay.

3 MR. SCHAEFER: We just wanted to see

4 what happened --

5 MS. PETRILLO-GROH: It was a little

6 difficult with the time constraints and --

7 MR. RIVEST: No. I was saying before

8 --

9 (Simultaneous speaking)

10 MS. PETRILLO-GROH: -- variable, you

11 know, publicly or --

12 MR. RIVEST: Okay, we heard that.

13 MS. PETRILLO-GROH: Yes.

14 MR. RIVEST: We heard that.

15 MS. PETRILLO-GROH: Yes. And not

16 knowing how the output's there that they were

17 seeing, how they were fed into the analysis.

18 MR. RIVEST: I think that is the most

19 valuable use of this time, is if we can keep as

20 many variables constant as possible. And then

21 you do, once, your challenge. That is actually

22 the best use of the time, if you have --
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1 MS. PETRILLO-GROH: You prioritize.

2 MR. WESTPHALEN: Well, maybe you ought

3 to come over and sit where I'm sitting and feed

4 the information that Tom --

5 (Simultaneous speaking)

6 MR. WESTPHALEN: Oh, absolutely.

7 MR. ERBS: Well, and this is my, you

8 know, single biggest concern is if the model's

9 not right, and we move forward, and we make

10 decisions, then it's going to be a bad decision -

11 -

12 MS. PETRILLO-GROH: Hopefully we'll

13 have all the information. We thought we can --

14 MR. BURGOS: We can play with it.

15 MS. PETRILLO-GROH: Do what you guys

16 need to --

17 MR. BURGOS: You can identify where the

18 model has deficiencies and figure out how to

19 correct them. And we're still trying to adjust

20 the --

21 MS. PETRILLO-GROH: That's why we're

22 here.
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1 MR. BURGOS: I know.

2 MR. CYMBALSKY: We just want to get it

3 right, that's all. We just want to say, you

4 know, for the record, this is an unprecedented

5 amount of, you know, interaction.

6 MR. ERBS: It gives us hope.

7 (Laughter)

8 MR. ERBS: We have hope. We have

9 hope, but we're not convinced yet.

10 MR. CYMBALSKY: Well, I know. We're

11 get on hopefully, like I said, get to the bottom

12 of the concerns.

13 MS. SHEPHERD: While they're doing

14 that, I have a question about sharing the love --

15 (Laughter)

16 MS. SHEPHERD: Is there a reason, like

17 what's the underlying sort of premise for the

18 model not just being available so that --

19 MR. CYMBALSKY: I think you missed

20 that earlier.

21 MS. SHEPHERD: Yes, I'm sorry.

22 MR. CYMBALSKY: Yes.
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1 MS. SHEPHERD: It's what?

2 MR. WESTPHALEN: Well, I don't think

3 it's, I mean --

4 MS. SHEPHERD: Oh, can you speak into

5 the -- so the folks on the phone can hear you?

6 MR. WESTPHALEN: I mean, the model was

7 initially developed and then -- Ron, who has, you

8 know, has worked with us and has allowed us to

9 use the model, but, you know, there's no clear

10 understanding that it's available for public

11 distribution.

12 MS. SHEPHERD: So I guess my question

13 is about, I guess, just that general approach, in

14 terms of duly noticing and comment rule making to

15 use a model that can't be publicly available. I

16 mean that, to me, seems very problematic. What

17 was the just the rationale behind that particular

18 approach?

19 MR. WESTPHALEN: Well, that was a

20 model that was available.

21 MS. SHEPHERD: But not publicly. I

22 mean, is there like an active restriction? I
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1 mean --

2 MR. WESTPHALEN: There is no publicly

3 available --

4 MS. SHEPHERD: So we just don't know

5 why this --

6 MR. CYMBALSKY: We'll get back to you

7 on that.

8 MS. SHEPHERD: Okay, that's fine.

9 MR. CYMBALSKY: Yes. So the other

10 question from the Webinar, Chris Salatino. "Was

11 there a correction factor used to match the

12 FREEZE model output to actual data?" And I think

13 we went through this, in pretty gory detail.

14 MR. ERBS: Well, it's not just the

15 0.7, but there're curve fits to the evaporator,

16 there are, I don't know what, so in my view,

17 everything's got at least some fudge factor, or

18 whatever you want, a correction factor, sounds

19 more scientific. But the question is -- is how

20 consistent are, I mean, that's my question. Show

21 me the scatter plot on the 0.7. I'm not seeing

22 that. You've said, well, 0.7 looks like
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1 something we can use for everything. But it'd be

2 better if we really had an uncertainty analysis

3 around that.

4 MR. WESTPHALEN: Well, let's go with

5 Javier, and we'll take a look at it.

6 MR. ERBS: And then the evaporator,

7 again, I understand the basis for your model.

8 And I don't think it's unreasonable. But again,

9 it would really be helpful if you had used the

10 data. And to do that curve fit, then how much

11 variability there on the curve fit. Because I

12 think then that tells us what confidence you had

13 in taking the model and using it for something

14 you haven't tested yet.

15 So we've talked about it, but I don't

16 think we have the data, and I give this any

17 measure of uncertainty.

18 MR. CYMBALSKY: So, Chris, I don't

19 know if that answered your question. But you're

20 un-muted if you wanted to speak.

21 MR. SALATINO: Thanks, Jon. It kind

22 of did, and it just parallels everybody's point
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1 regarding -- so the correction factor, how does

2 it apply across the portfolio of all the

3 products? Is it the same correction factor?

4 Because certainly there are differences amongst

5 the different ice makers, for sure. I don't know

6 if that came through.

7 MR. CYMBALSKY: Yes. Yes, it did. We

8 can hear you. Well, we're in --

9 PARTICIPANT: Hey, Chris.

10 MR. SALATINO: Hi, guys. Sorry I

11 couldn't make it. I had a wonderful day and

12 evening spent at the O'Hare Airport yesterday.

13 MR. ERBS: Yes. Tom was telling us

14 about it. Sorry about that.

15 MR. SALATINO: It's just to endorse

16 the points made earlier. And then, sorry, one

17 last point, since I'm un-muted right now.

18 (Laughter)

19 I've been working with the FREEZE

20 program. You know, it does a great job, I think,

21 of capturing all the technical aspects of the

22 variables that impact performance.
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1 But there are certainly -- I think,

2 Javier had mentioned wanting to work with us to

3 identify some of the other points that we

4 consider that would help make the FREEZE model a

5 bit more accurate. And my concern is that is

6 there enough time, truly, to do that? I mean,

7 we'd certainly be available and happy to do that.

8 It's just, I think, it comes back to the root

9 question of how much accessibility do we have

10 with the FREEZE program.

11 Because we've provided data, but it's

12 hard pressed to understand how that data actually

13 correlates to the model output. And then I'm

14 done with commentary. Thank you.

15 MR. LASECKI: This is Tom Lasecki

16 here, just kind of a side thought. I know

17 there're no comments being made about the FREEZE

18 program and the ability to distribute it. But

19 maybe just as a side thought, if you could, if

20 it's any more possible to set up where we might

21 get remote access to just even run it.

22 I don't know if that would get rid of
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1 any kind of issues with distributing the program

2 if we could just remotely see how it would

3 interact if we tried, you know, to run it with

4 any kind of data.

5 MR. ERBS: That was my idea. If

6 nothing else, we'd just --

7 MR. LASECKI: Yes.

8 MR. ERBS: -- give Javier it, say run

9 it, give us the results back.

10 MR. LASECKI: Of course any issue you

11 have with that was, you know, you have to rely on

12 his time which I, well, his time, which I assume

13 is hard to get a hold of, right. So if we could

14 remote access into it, just to run it, we don't

15 have to have any kind of real access to the

16 program itself. But we can still see the output,

17 just a thought.

18 MR. RAWALD: Yes. We'll have to

19 research that and get an answer on that. But

20 that's a complicated proprietary software, legal

21 issue. And, I mean, just even for people within

22 DOE to use it beyond that one user who has the
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1 authorization would be complicated to get it. So

2 if you like, we'll get back.

3 It'd be a lot easier to give data to

4 Javier and have him run it and get back to you, I

5 would say, just from a pure ease of permissions

6 idea. This is Doug Rawald, for the record. So,

7 but we'll have to look at that and get back to

8 you.

9 MR. ERBS: I think it's just

10 important. We might have a better idea how to

11 test boundaries than Navigant, just based on some

12 of our knowledge of, you know, how we design it.

13 So that's part of it, I guess.

14 MR. RAWALD: Well, tonight then we'll

15 take it back and think about it, think about

16 that.

17 MR. ERBS: Okay.

18 MR. CYMBALSKY: I think Javier's up

19 for a promotion to. So then maybe it's an

20 opportunity for him to --

21 (Simultaneous speaking)

22 MR. CYMBALSKY: We're just going to
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1 do model simulations now.

2 The contents are over there.

3 For my own -- I don't think we need to

4 do it for the record now. We can take this

5 offline.

6 (Whereupon, the above-entitled matter

7 went off the record at 2:04 p.m.)

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22
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A

$5 148:6

$80 140:14 142:9

147:20

$80-some 140:14

a.m 1:19 4:2 95:16,17

ability 107:9 136:5,10

199:18

able 33:6 42:18 49:2

51:13 74:6 98:20,22

102:6 103:12,14,18

104:11 108:21 110:1

110:8 112:10,13

113:2 120:21 122:11

128:5 130:15 157:2

164:9 165:17 171:12

171:13,14 173:4

above-entitled 95:15

175:6 202:6

absolutely 90:10 167:2

174:9 193:6

accept 13:1

acceptable 103:3

access 199:21 200:14

200:15

accessibility 199:9

accompanied 159:16

account 17:9 30:20

48:1 58:8 59:14 141:1

143:11

accounting 147:9

accurate 29:22 45:11

53:22 54:3,4 109:17

135:22 138:8 199:5

accurately 109:10

118:11

achievable 160:12

achieve 106:19 113:3

188:9 191:1

acquired 102:2

action 187:1

active 178:14 195:22

actual 42:13,15 59:19

97:7 104:13,15

105:16 106:3 109:17

110:17 141:19 175:15

191:15 196:12

Adam 2:16 5:10

add 29:19 63:9 67:13

67:18 83:2 86:5

adding 84:1 142:3

addition 31:20 163:8

additional 24:18 25:22

41:12 98:21 173:17

177:18 178:16 183:14

186:22

address 45:21 178:1

addresses 24:8 31:7

adds 17:7 169:13

Adjourn 3:22

adjust 29:3,17 57:21

63:22 98:12 118:14

123:7 171:9,10

193:19

adjusted 81:3 104:14

123:1 131:20

adjusting 51:6

adjustment 31:9,21

57:4 102:15 103:19

106:11

adjustments 31:7,16

32:5 109:11

adjusts 22:6

ado 43:9

advantage 28:8 127:2

139:20

adverse 170:7

AEDM 13:1,9

AEDMs 13:4

affect 38:4 107:9

afternoon 177:13

agencies 92:6

Agency 2:6 5:8

agenda 7:6,9 87:12

177:16

aggregate 135:17

aggregated 134:12

135:8 136:13

ago 92:11,14,15

agree 39:1,12 40:1

149:15 159:5 161:7

agreement 31:17

131:13

ahead 20:17 74:15

79:13 80:5

AHRI 2:14 6:9,10 12:4

35:10 40:3 46:19

105:9 109:8 134:8

165:12

aiming 191:1

ain't 36:1

air 24:1,14 29:4,10 62:4

62:9

air-conditioning 2:11

38:17

air-cooled 15:8 24:10

24:14 29:2 38:1,2

154:15 160:18

airflow 127:1 156:7

Airport 198:12

algorithms 30:17

Alliance 2:18

allow 6:18

allowed 195:8

alluded 61:18 63:11

alterations 77:3

alternate 177:10

alternative 98:21

aluminum 63:5

America 2:14 5:17 14:6

26:22 39:1 60:17

109:14 140:11

American 2:9 5:14

amortization 147:10

amount 17:17 23:14

44:7,10 82:7 141:5

168:5 194:5

Amy 2:14 5:13 6:10

134:7

analyses 11:7 29:9

analysis 3:14 4:14 7:18

9:3,21 11:16 14:19

16:14 25:19 27:16

28:6,10,17,18 38:16

40:13,17 41:10,13,16

45:6 48:20 54:10 61:2

82:10,13 87:13 89:3

95:6 96:4,6 103:5

118:3 128:22 139:8

146:16,18 147:2,4

148:11 151:21 179:7

180:21 181:19 182:3

183:1,10,19 192:17

197:2

analytical 8:22 11:13

analyze 57:9

analyzed 185:5

Annie 2:9 53:18 55:8

87:18 127:5 146:6

171:12

announcing 7:4

answer 20:8 40:1 48:16

121:2 165:10 200:19

answered 197:19

Anthony 14:17

anticipated 169:14

anybody 32:12 172:13

anymore 107:10 108:18

anyway 34:10

apparent 12:10

Appendix 24:12

apples 112:4,5,6

apples-to-apples 96:21

Appliance 2:8 4:7

applied 31:15 101:20

104:8,19

apply 103:13,14 112:6,6

113:11 126:16,17,17

198:2

appreciate 77:7 107:1

189:20

approach 181:21

195:13,18

appropriate 28:12

113:10 189:8

appropriately 29:17

approximate 59:2

approximation 51:21

April 8:17

arbitrary 153:14

area 23:16 29:12,13

49:8,18,21 50:3,9

71:20 76:5 100:20

106:16 118:21 121:9

121:13 149:14 150:3

151:7 152:7 153:6

154:16 166:11

areas 49:10 152:15

175:1 177:22

Armstrong 1:19 2:2

13:17 14:1,4 36:8,14

40:9 41:4,8,15,22

42:5 43:11,18 55:22

56:5,10,16 88:16,20

89:10,12,17,19 90:4,7

90:10 91:5,9,14,20

92:10,13,15 95:12

110:12 111:5,7,13

134:15 135:15 137:6

138:2,7 141:21

142:12,21 166:2

168:16,20 171:17,21

172:3 173:6 174:1,5,9

174:18

Artisan 85:17

Ashley 1:19 2:2

aside 7:16

asked 33:11 34:12,16

35:22 76:12

asking 35:19 88:16

93:3 169:18

asks 164:12

aspects 15:6 44:22

198:21

assertions 187:12

associated 16:14 19:11

25:13,14 28:8 119:6

130:1 145:21

Association 2:9 5:14

assume 200:12

assumes 86:6

assuming 49:13 84:20

120:13

assumption 19:11 30:6

53:4 129:13 137:18

assumptions 50:14

56:22 79:10,14 135:3

135:21 145:16 148:5

165:3

asymptote 72:13

attribute 91:16

attributes 90:8

audible 67:7

auger 16:6 58:8 80:8

86:1 97:21
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augers 80:22

augur 163:10,13,16

authorization 201:1

automated 146:20

AUTOMATIC 1:3

auxiliary 44:21 70:16

80:11 86:1

availability 181:9

available 39:11 96:12

98:4,11 99:3,14 100:3

102:21,22 103:12,17

104:18 140:5,5 157:4

157:7,9,9 159:13

162:13 165:16 166:3

166:7 167:11,14

168:2,11 171:18

172:2,4 173:3,9,12

174:6 177:3 194:18

195:10,15,20 196:3

199:7

Avenue 1:18

average 70:22

avoid 136:9

Aw 144:6

Awareness 2:8

awhile 64:21

awkward 113:2

B

b 40:11 72:19 173:13

back 10:8 13:20 25:5,15

25:16,18 30:19 31:22

34:19 35:10 37:3 40:4

43:12 64:21 72:4

76:21 87:2,9 91:3

95:11,14,22 98:12

110:13 118:16 120:17

125:21 128:6 129:6

131:17 148:6,21

150:8 155:12 161:4

163:21 165:17 171:8

172:14 173:11 175:5

175:9 191:14 196:6

199:8 200:9 201:2,4,7

201:15

back-and-forth 129:9

background 3:11 8:9

backwards 51:4

bad 193:10

bags 85:17

balance 18:1 22:15

70:11 151:1

bananas 112:5,7

bang 121:18

bar 170:5

base 96:10

based 4:8 22:4,15 24:3

24:15 27:6 30:8 31:11

41:2 42:22 43:1,2

44:14 47:21 48:6 59:3

61:21 64:1 67:10,21

73:7 77:8 80:20 82:2

85:4 89:16 96:15

97:15 101:9,21 106:3

118:2 121:5 132:3

153:17 157:5 169:19

176:17,20 179:9

180:20 183:1,11

184:1 185:3 201:11

baseline 27:8 35:13,17

79:8 93:18 94:7,14

97:1,5,14 98:8,13,19

99:2 101:6,14 102:11

102:13 103:9,21

104:15 105:5,20

114:5,9,13 115:15

123:8 126:12 130:19

133:8 155:6,15,19

156:4 159:10

basic 115:10

basically 9:6 15:11,22

21:3 23:14 24:6 26:4

27:10 30:7 42:12,18

42:20 43:22 44:9 45:8

45:20,21 46:9,13,14

47:1,3,7,20,22 48:4,4

55:6 59:14 61:4,8

63:4,9,10,17 67:9,20

68:9,17,22 69:6,12

70:6 71:17 75:22

80:12 82:16,17 84:1

84:17,19 85:5,7 86:4

87:7 94:9,11 96:2

98:10,16 99:1,12

100:4 101:14 102:14

103:6,8,13,14 104:10

105:5 114:11 120:8

120:13 121:6 122:7,8

122:11 127:11 129:16

129:17 135:9 155:12

155:14,17 156:3,12

156:13,19 178:21

179:8 182:3

basics 93:3

basis 19:22 60:3 82:1

137:17 145:12 178:6

197:7

batch 17:11 20:21

22:22 24:5 25:1 28:17

30:11 31:11 32:16

33:1 44:1,6,9,17 51:6

51:13 58:13 84:18

92:1 96:14 98:15

100:10 101:9 104:8

119:13 120:17 160:18

161:14 180:4,5 188:1

batch-type 20:10

begins 45:6

behavior 62:19

believe 106:17 117:3

119:17 167:5 170:15

bend 125:19

bending 129:4

bends 82:21

benefit 151:18

benefits-to-costs 169:3

best 4:10,15 7:7 39:5,9

66:4 115:12 139:9

158:18 172:18 192:22

better 30:15 36:22

62:12 64:3 68:11,12

102:1 111:19 118:13

123:1 129:11 152:13

178:4 197:2 201:10

beyond 112:2,12 153:3

200:22

big 63:14,14 66:15 98:1

108:19 138:12 143:17

143:18 151:6,11

159:16 160:13

bigger 26:18 51:11,12

104:16 116:19,19

120:19 126:9,10

127:1,2 144:4 150:13

156:9

biggest 154:12 159:8

193:8

billion 91:1

bin 52:14

Bingham 2:3 6:4,4

10:13,14,18 11:2 20:6

20:6,16 30:18,18 31:3

58:3,3,6,15,17 81:14

81:14,20 86:6,14

162:2,2,5,8 164:18

184:21 185:10

bit 9:19 12:4 19:11

20:14 22:21 27:19

31:13,19 46:8 48:22

53:16 61:18 71:21

72:16 76:17 78:17,22

79:21 89:16 93:14

96:2 118:19 146:4

149:9 155:13 199:5

black 165:21 170:20

blades 39:4

blanks 59:5

block 107:17

blocks 28:1 85:15

blown 182:7

board 41:17 65:21

boost 151:11

bottom 76:13 194:11

bought 164:21

boundaries 201:11

boundary 57:1

bounds 115:7

box 120:10 143:5

165:21 170:21

brand 85:8

braze 25:14

brazing 129:4

breadth 90:18

break 52:13 70:13

95:10 133:1 143:3

147:10 161:1 175:4

breakdown 143:9

breakdowns 131:15

breaking 52:17

bridge 25:22 50:18 52:8

bring 117:20 121:15

Bristol 157:11

broke 143:13

broken 130:21 131:3

brought 99:18 189:10

buck 121:19

budget 89:9

build 37:13 72:9

building 77:8

built 41:19 112:13

136:20

bunch 183:22 184:19

bundle 115:21

burden 13:15

Burgos 2:3 3:18 5:20

5:20 29:21 30:2,2

32:19 33:14,20 42:11

42:12 43:17,20 46:17

46:21 47:12,15,18

48:18 49:6,11,19 50:4

50:7,10,13,16,22 51:5

51:17 54:4,8,14,17

55:10,17,20 57:5,10

58:2,5,11,19 60:20,22

64:7,11 65:15,20 66:2

66:12 67:5,8 68:4

69:22 72:5,7,15,20

73:5,11 74:4,6,20

75:1,5,7,13,16 76:16

77:10,14,17,21 78:1,9

78:21 80:5 82:14 83:5

83:14,16 85:2,4,18

86:9,13,15 92:14

93:13 95:21 105:13

106:13 109:19 110:10

114:1 115:13,19

116:2,7,18 117:2,6,8

118:18 122:19 123:2

123:16 124:2,6,10,14

124:17,22 127:7,10

127:15 128:7,11,16

129:15 130:20 131:1

131:9,18 132:7

140:22 141:13,16,20

142:19 143:3 144:19

145:18 149:1,10,20
(202) 234-4433 Washington DC www.nealrgross.com

Neal R. Gross and Co., Inc.

205

150:1,4,10,15,19

154:4,13 157:8,17,21

158:2,7 159:3,5

160:22 161:3,6,9,11

161:22 162:4,7 164:4

168:18 190:16 193:14

193:17 194:1

business 111:8 136:2

140:2

Butler 2:4 5:6,6

buy 88:8,13,17 116:9

128:9 163:5

bypass 145:22

C

c 173:15

C-O-N-T-E-N-T-S 3:7

cabinets 144:1

calcs 83:4

calculate 16:22 35:11

35:12 43:7 44:18 46:2

47:7 58:20 61:11

62:21 70:6,19 82:16

83:1 119:6 127:12,17

calculated 21:3 30:16

48:5 70:3 72:3 101:8

101:15 167:13 168:1

calculates 16:1,2 70:7

127:13

calculating 16:10 83:22

119:8

calculation 17:11 18:6

18:8 21:8 24:7 47:3

47:17 81:19 86:19

calculations 15:20 21:9

43:3 54:20 59:13

60:15 83:4,11 84:5

85:21

calibrate 27:9 79:2 94:4

95:2 99:6 104:14

calibrated 30:7 41:2

170:22

calibrating 82:3 97:15

calibration 41:1,12

76:18 98:19 132:5

call 25:2 35:2 50:1

186:13

called 14:16 170:20

capacity 23:3,4 31:9

35:17 63:12,18,21

64:4,11 65:4,13 67:18

68:5 72:12 90:21

150:22 158:11,19

160:16 163:5,10

164:1

capital 148:7,11

capture 33:6 84:18

146:22 167:5

capturing 84:19 109:5

198:21

care 17:8 108:17,18

136:6,7

carefully 36:18

Carnot 70:19 86:2

case 11:5 51:5,14 63:22

64:4 69:8 70:17

104:17 106:13 115:9

120:19,21 121:10

126:11 153:12 155:16

166:20 167:6

cases 97:4 98:9,16,22

99:11 103:11,16,18

107:21 144:2 159:9

165:3 177:8

category 187:2 188:11

caution 160:4

cavities 25:9,21

cells 51:20

certain 90:7 96:11

117:21 130:1 134:10

137:22 138:1 141:5

154:9 156:7,8

certainly 57:20 96:17

169:12 198:4 199:1,7

certification 191:8

certify 37:14

cfm 62:10

challenge 53:5 164:22

172:22 192:21

challenging 53:13

chance 4:13

change 23:19 24:2 29:2

52:7 53:15 57:3 71:10

98:14,14 102:17

104:3 107:4 119:18

122:1,1 125:10,11

140:14 144:14,15

159:16 181:12 182:3

changed 51:10 84:12

93:2 108:4 165:22

183:10,11

changes 69:4 83:17

84:9,20 100:16

102:20 105:10 117:13

118:10 127:9 133:9

140:20 166:8,12

180:21 182:22 183:1

186:5

changing 23:22 142:7

changing-out 110:16

characteristics 22:18

characterize 158:19

chart 8:22 9:9 17:21

charts 17:15

chassis 140:13,18

142:4 189:7

check 83:11 94:16

checks 69:6

Chicago 85:15

choose 54:1 89:15,15

89:20 166:21

chosen 167:9

Chris 2:13 196:10

197:18 198:9

Christopher 2:6 5:7

circle 13:20

circled 9:1

circuit 22:16 33:16

188:5

circuits 62:17 125:8

claim 141:22

clarify 39:14

class 115:12 164:17

classes 20:1 35:19

164:7 189:9

clear 11:21 12:1 14:1

185:22 195:9

clearly 22:10 30:13

close 52:16 73:9 94:7

96:22 109:17 180:16

closed 176:1

closer 81:4 156:1 159:4

Closing 3:20

cocktail 85:10

coefficient 29:12 49:15

coefficients 23:16

46:12,13 49:1,15 69:5

103:20 119:10

cognizant 92:5

cold 60:22 71:7

colder 18:8

collect 123:6 179:5

collective 140:3

columns 34:14,21

combination 115:1

121:11 156:17

combinations 153:18

combined 81:10

come 10:8,22 34:19

39:9 79:15 81:1 91:3

91:5,10 95:14 118:15

129:5 131:11 134:13

139:11,12 175:4

187:16 193:3

comes 11:14 22:2 38:5

102:19 119:2,13

152:5 168:14 199:8

comfortable 7:18,20

38:12 145:15 174:3

coming 4:5 16:15 59:21

60:5 61:9 62:11 81:8

188:15 189:14 190:2

comment 6:22 10:19

23:5 39:19 40:4,16

91:15 109:7,8 113:22

117:9 122:21 175:10

175:11,22 176:6,19

182:9,13 185:1,13

187:5,9 195:14

commentary 199:14

comments 5:3 8:18

10:15 42:1,3 107:22

132:4 142:19 182:5

182:15 183:1,11

184:1 185:6 199:17

Commercial 1:3 11:8

common 106:21

communicate 189:17

companies 2:15 134:18

139:21

company 85:14 136:7

137:13,15

comparable 37:4

comparative 29:15

compare 35:12 55:6

74:13 87:3 93:5 99:3

116:16

compared 105:1 123:4

123:5 143:5 155:1

179:8

comparing 105:20

114:13

comparison 66:10 86:2

99:5 116:19

comparisons 36:18

compartment 60:22

compensate 29:3

competitive 136:8

139:20

competitors 168:3

complete 66:16 148:9

completely 176:22

complex 48:20

complicated 16:9

200:20 201:1

complicates 109:4

complications 145:10

component 15:9,10

38:9,15 94:22 107:7

140:14,20 162:10

178:3 179:16

components 16:2,4

21:5,13 22:1 58:9

110:16 114:3,7

142:22 177:22

compounded 165:14

compression 158:8

compressor 16:8,8,13

21:7,13 22:1,4,16

23:2 38:6 63:8,9 64:1

64:12,20 65:8,11,12

66:7,17 67:9,10,19

68:20 69:7,8 70:14

72:12,12 74:9 76:6

84:9 85:22 98:14,17
(202) 234-4433 Washington DC www.nealrgross.com

Neal R. Gross and Co., Inc.

206

100:15,18 102:18,20

103:13,14,17,20,21

104:1,6,9,10,12,17,18

104:19 105:2,11

106:11 107:5,7

108:12,20 109:20,21

109:21 111:17 114:19

116:20 117:3,12,18

118:2 121:14 127:16

127:19,22 131:4

137:9,10,14 150:22

151:13 152:2,7 153:8

156:21 157:1,4,16

158:20,21 159:6,13

159:15 160:1,3,10,19

161:13 162:12 163:22

164:9,16 166:20

170:1 175:12 179:18

191:13

compressors 26:18

39:3 65:6 68:11 74:10

99:9 102:22 103:2,6

105:15 106:16,19

107:1 108:6,13 110:7

112:4 113:18 114:13

128:3,9 138:17 145:1

150:21 158:1,11

160:15 162:16 191:20

compromise 136:10

computer 172:7

concern 112:9 165:2

193:8 199:5

concerned 169:22

concerns 4:8 194:12

concluded 28:7

conclusions 185:4

condenser 15:8 16:5

21:5 22:16 23:14 24:1

24:10,14 29:2,5 31:8

31:10,14,15,19 38:1,2

48:21 49:3,4 61:14

62:20 70:15 71:19

74:8 83:16 86:1 87:1

87:6 100:16,19 101:1

114:11 116:20 121:14

122:2,14 125:4,7

126:9,10 127:2

128:17 129:3 151:1

151:10 152:10 154:1

155:17,20 156:6,15

180:2

condensers 23:20 30:5

37:22 61:16 62:21

125:3

condensing 22:5,14

30:5 55:2 63:13 68:6

71:14 84:2 98:5 102:8

152:11,17 155:18

156:19 158:18

condition 18:10 72:11

conditioning 141:2

conditions 22:6,14 57:1

67:12 97:19 98:3

158:12,14,15

conductance 26:8

conducted 14:15 17:12

conduction 25:12,16

25:18 27:18 47:16

49:16

conductivity 63:4

confidence 197:12

confidential 136:12

configurations 61:19

76:11

confirm 173:2

confuse 144:18

confusion 135:7

Congress 170:16

consensus 13:12 95:9

consequence 118:9

Conservation 1:3 2:4

conservative 117:10

consider 12:22 28:5,18

147:8 179:2 181:1

199:4

consideration 31:2

considered 22:19 106:7

considering 187:10

consist 98:13

consistent 23:15 79:10

79:11 99:13 104:3,22

106:1,6 120:6 174:18

179:14 196:20

consists 98:16

constant 16:4 27:5 30:5

49:17 50:19 66:1

192:20

constitutes 125:18

constraints 189:10

192:6

consultants 173:18

Consulting 2:3,13,19

consuming 88:3

consumption 70:2,10

86:20 105:5 106:2

126:18

content 8:7 95:11

contents 202:2

continual 178:6

continue 18:12 149:11

176:7

continuing 112:20

continuous 20:21

23:20 24:2 27:3 30:19

32:17,22 44:2,4,6

58:6 59:22 74:19

79:20,21 80:1,7,21

81:22 98:15 100:11

149:4 155:5 161:20

continuous-style 163:9

continuous-type 20:12

27:2

contrast 49:3

control 180:4

controlling 81:7

controls 16:6 21:7,15

44:20,21 97:21

convection 49:14

convex 72:14

convinced 187:2

189:18 194:9

cool 16:13 17:14 24:1

108:14

cooled 17:19 61:17

83:21 152:10

cooler 107:16

cooling 16:15 17:22

22:6 26:2 67:13

COP 74:10

Copeland 22:3 157:10

copper 25:18

COPs 105:1

core 29:5

Corporation 2:3 6:5

10:14

correct 29:8 41:3 81:20

116:4 132:1 141:20

169:4 175:2 193:19

correction 196:11,18

198:1,3

correctly 79:7,9 94:19

94:21

correlated 51:19

correlates 199:13

correlation 128:14

correlations 24:16,20

61:22 62:18 145:3

cost 9:7 10:1,3 11:7,18

11:20 51:8 54:10 69:1

75:19 76:1 88:10,11

94:16 100:6,7 117:20

117:20,21 118:3

121:15 127:18,22

128:3,20 129:1,6,8,15

130:9 131:6 132:5,11

133:5,10,13 135:3,12

135:21 136:18,20,22

137:4,9,15,18 140:15

141:7,19 142:4,5,6,13

142:18 143:2,4,5

145:11,20 146:17

147:8 148:12 149:5

153:22 159:16 160:13

168:10 169:14 179:9

cost-effective 153:21

cost-efficiency 97:8

99:20 100:2

cost-efficient 121:18

costing 144:9,13

costs 97:9 118:6

129:18,19 130:4,16

130:18,19 131:12,15

131:21 132:12,12

135:12,16 137:2

142:20 147:1,15

Coughing 190:12

Council 2:5,11 5:19

count 118:1

counting 21:4

couple 34:13 43:22

61:19 63:20 69:6

91:17,17 97:12 99:18

99:20 177:17

course 22:12 71:10,11

79:18 107:14 124:5

181:3 200:10

court 5:1

covered 61:13 99:16

134:10 135:4 178:12

crazy 88:6

CRE 11:8

create 170:6

Creating 185:19

credit 163:1,3,6

criteria 112:2

critical 53:7

cube 30:21 53:9 85:12

120:14

cubes 52:9,16 53:10

85:16 120:6,7 166:9

cubic 25:8

curious 138:9

current 73:17

currently 13:16 87:4

88:20 103:9 123:3

167:10

currently-available

157:22

curve 9:7 11:7,19,20

46:2,14 48:5 52:1

67:21 69:1 76:1,13

94:17 97:8 100:8

101:14 102:13 112:18

112:20 114:4 115:6

132:17 164:13 179:9

196:15 197:10,11

curves 51:8,9 68:5

75:20 99:20 100:2

135:13 149:4,6

customer 162:22

cycle 16:3 17:11 21:2,2

21:6,18 22:12 30:11

31:9 51:2 66:11,16

69:15 70:1,6 71:8,8,9

71:13 72:21 84:18,20

86:2,3,4,5 98:1,1
(202) 234-4433 Washington DC www.nealrgross.com

Neal R. Gross and Co., Inc.

207

108:7 152:20 158:20

cycles 50:21 70:20

cylinder 107:6

Cymbalsky 1:19 2:2

3:12 4:3,6 6:11,14,17

6:20 20:17 34:3,9,11

34:16 35:21 36:6 37:9

37:17 39:14,22 40:5

56:18 77:19 80:3 85:7

85:10 88:7,10,15 89:5

89:8,11 90:12,15 91:7

91:10 92:3 95:9,13,18

133:20 134:2,14,17

134:20 137:12,19

138:4,11 139:2,7

147:17 148:1 170:9

170:12 172:5 174:11

174:20 175:3,9,22

176:6,12,14,16 178:8

180:12 181:8,11

182:6,12,17 183:5,20

183:22 184:5,8,12,16

184:18 185:7,12,17

185:21 186:3,9,12,18

187:5,8 189:1,12

194:2,10,19,22 196:6

196:9 197:18 198:7

201:18,22

D

D.C 1:18

da 69:20

Danfoss 68:21

Daryl 2:5 6:2 12:20

18:19 20:4 23:6 28:21

33:4 39:2 51:19 76:22

77:4 106:8 120:12

Daryl's 121:2 137:13

147:20

data 4:17 7:22 10:16

19:16 27:6 30:14

31:16,18 33:21 34:1,4

34:13 36:9 37:4,21

39:8,17 40:12,15 41:3

41:16,20 43:1 44:14

45:16,20 46:19 51:11

54:21 55:7,12,13,16

55:18 56:6 57:5,8,20

60:18,20 62:13 63:2

63:15 64:2,3,13,14

65:18 68:9,11 70:20

71:9 74:11,14 75:14

76:7,10,17 79:3,18

80:20 81:4 82:3,18

85:5 86:3 89:6 93:16

94:15,18,20 95:1

96:11 97:18 98:3 99:5

101:15,16 102:2,4,7

102:20 103:12 104:11

104:15 108:1,1,11

109:7,9 110:22

112:16 113:6,12

114:16 115:15 118:16

119:14 120:18 121:5

123:1,7,11 127:21

128:4,20 129:10

130:5 132:9 136:18

137:22 138:5,8 139:5

143:8 144:17 149:21

157:11,13,18 158:6

158:21 159:7 170:21

171:3,7 172:16

173:17 175:16,17,18

176:1,8,10 179:12,17

179:18,19 180:2,2,3,5

180:20 181:3,8 182:2

182:18 183:14 184:1

184:20,22 185:3

186:16,19,22 187:12

188:5,17 189:10,18

190:17,18,19 191:10

191:14,15,17 196:12

197:10,16 199:11,12

200:4 201:3

date 8:13 42:3

day 13:20 26:14,16

37:14 40:8 106:6

166:3 173:20 182:13

198:11

days 183:12

DC 159:17

de-evaporating 98:4

de-identified 135:9

deadline 10:19 176:5

dealer 135:19

deals 21:22

deceptively 48:19

decided 4:10 28:5

decision 40:15 139:17

187:14 193:10

decisions 193:10

deck 7:2

decompose 37:7

decrease 122:12

decreases 72:13

deepest 54:12

Defense 2:10 5:19

deficiencies 193:18

define 45:4

definitely 43:12 54:19

76:17 78:22 136:7

142:19 148:1 154:22

defrost 75:4

degrees 60:7 73:15

80:15 111:21,21

158:17

delivered 107:12 160:8

delivers 108:20

delta 61:12 153:6

demonstrate 22:22

demonstrated 170:21

demonstration 3:17

10:10 42:9 178:16

demonstrations 12:12

Department 1:1 2:2,2,4

2:7,12 7:15 8:2

depend 13:3 182:2

depending 65:8 69:16

100:11 123:10 185:18

depends 38:9 40:10

89:22 151:8

depreciation 130:1

141:3 142:14 146:9

146:13 147:6,9

derate 23:2

derating 23:7 31:9

derive 11:6,18

derived 11:17

describe 43:21

described 130:13

description 9:18 10:7

12:11 14:14 32:11

42:8

design 11:10,11,14,15

28:6,9 37:1 76:2

77:10 79:15 82:13

86:22 94:7,8,10,13,19

94:21 95:3 96:5 97:2

98:21 99:2,3 100:4,6

100:17 105:13,21

110:5 111:2 115:21

116:16,22 117:16,19

118:22 121:16 122:6

153:15 154:14 155:2

155:20 156:17 166:1

166:5,7,17,18,21

167:2 169:2,4 179:10

184:9 188:17 189:7

201:12

designed 44:6 45:15

140:22

designing 145:12

designs 11:18 28:15

73:17 76:9 82:9,11

96:11,18 98:21 111:3

144:14

desist 70:18

detail 20:20 75:22

133:7 188:17 196:13

detailed 34:12 70:20

179:5,16

details 4:12 24:12 27:7

30:4 71:13 76:15

131:8

determine 60:17 63:18

186:2

determines 24:6

Detlef 2:19 3:15 5:21

9:14 35:3 42:16 44:11

45:14 46:22 61:17

63:11 83:22 89:22

90:18 91:5

develop 27:5 90:16

98:7 130:3

developed 14:17,19

15:4,11 31:16 42:17

47:6 61:22 139:19

195:7

developing 37:9 97:14

development 14:18

36:19 37:20

device 77:2

diagnostic 71:17,21

difference 108:5,12

143:18

differences 20:11 66:15

90:21 145:22 146:22

181:18 184:4 198:4

different 12:7 14:10

27:17 41:9 42:20

45:10,11 47:5,8,20

48:1 56:6 61:15,19

63:12,19 65:5 66:6

67:14,18 69:17 73:18

76:6,9 79:4 80:21

81:16 82:9 83:21

91:20 97:12 108:3

121:16 122:18 128:19

129:17 130:17 132:16

144:1 145:2 148:4

150:20 153:17 164:19

180:6 198:5

difficult 46:11 139:1

175:1 192:6

difficulty 173:1 188:9

digging 143:20 144:6

digits 73:17

dimensionally 164:20

direct 99:5 106:22

177:22 191:9,13

direction 187:6

directly 16:22 17:1 22:2

47:11

disagree 170:4

discharge 102:8 108:11

disclose 135:6 137:8

disclosed 134:11

disclosing 135:10

discontinuities 154:10

discreet 47:9

discrepancy 132:19

discrete 162:21

discretely 162:10

discuss 20:14 44:8

64:19

discussed 97:13
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discussing 118:19

165:19,19

discussion 13:18 28:3

77:8 86:21 96:5

131:13 169:3 173:14

178:15 183:10

discussions 16:20

93:21 172:17 173:17

dissatisfied 52:15

distinction 11:4

distribute 199:18

distributed 89:13

distributing 200:1

distribution 53:14

195:11

distributor 135:19

dive 95:7

divide 21:21

docket 171:22 172:11

172:11 173:13,14

document 166:4

documents 18:22

DOE 3:14 5:6 13:1

20:20 31:12 55:17

56:2 80:13 81:12

86:10,12 88:4,20

97:18 100:2 102:5,21

164:12 200:22

DOE's 55:22

doing 18:19 19:3 20:1

36:19 37:17,19,20

54:19 62:7 66:10 69:7

81:11 84:5 92:5 107:4

111:3 140:7 168:6,19

170:17,18,19 171:6

171:10 173:1 175:19

194:13

dollars 91:1 147:19

dot 61:12

doubts 167:11

Doug 2:12 201:6

downward 72:14 97:6

102:16

downwards 98:12

Dr 35:2

drain 16:15 101:3,7,10

draw 11:4 67:1

drawing 48:14 62:8

drawn 52:1 185:4

drew 48:5

drive 152:17 169:12

driven 153:3

drop 44:5 68:16 81:6

drops 72:17

due 45:17 146:1 177:18

duly 195:14

duplicate 92:4

durations 71:18

dwell 32:9

dynamic 58:22 64:19

84:14

dynamics 108:3

dynamo 124:15

dynamometer 124:13

E

earlier 61:18 87:19

152:1 194:20 198:16

ease 201:5

easier 74:13 201:3

easily 47:11 52:13

66:22

easy 152:10 175:2

echoes 162:9

ECKMAN 2:4

ECM 87:4 99:11 101:1,2

115:3 122:1 124:11

126:21

ECMs 122:7 123:2

124:5

economic 148:12

economically 39:11

EER 100:15,18 102:18

102:20 103:16,22

105:1 107:19 108:5

114:18 116:20 117:3

117:18 121:14 127:16

127:19 128:1 151:14

156:21 157:3,5

158:11 160:10,19

166:10 175:12 178:5

EERs 106:19 128:13

effect 112:15 146:2

153:4 166:12

effectiveness 68:15

efficiencies 87:3,8

110:20 113:3 123:14

123:18 180:6

efficiency 2:17 9:7,22

10:3 11:7,11,15,19,20

13:13 27:9 28:8 35:14

38:4 51:8,9 52:4,12

54:10 65:11,11 68:8

68:10 69:1 75:19 76:1

76:2 80:9 90:22 94:17

97:1 99:13 100:5,8,21

103:9,10 105:19

107:15 112:2 115:1,4

117:21 118:3 121:19

123:4,8 126:13

130:17 132:17 133:4

135:13 145:21 146:2

148:4 149:6,15 150:6

151:5 152:12,20

153:9 154:17 155:8

158:19 159:15,20

160:3 162:14,17

164:8 169:6 170:1,3

179:9 187:22

efficient 39:7 104:18

106:10 155:15 159:13

effort 189:19 191:2

effusion 24:4

eight 101:4,5

either 10:15 18:8 31:2

37:17 76:4 91:1 98:14

eliminated 167:16

eliminates 167:15

Elizabeth 2:10 5:18

EMILY 2:8

encompasses 13:19

end-all-be-all 43:4

end-customer 135:20

end-to-end 125:20

end-users 169:17

endorse 198:15

ends 147:14

Energetics 2:8

energies 70:14

energized 21:16

energy 1:1,3 2:2,2,4,7

2:12,17 3:11,14,17

5:8,11 8:20 9:16,17

9:21 14:15 15:20 16:1

16:7,19,21 21:4,8,9

21:20 29:3 30:4 31:18

35:11 42:8 44:12

55:15 59:15 70:2,10

82:4 85:22 87:13 93:7

95:5 96:7,9,9 98:2

101:6,8,18 105:5,22

106:2,12 107:12

108:19 121:1,3,4

126:18 127:12,13

152:12 159:11 163:15

168:6,11 175:13

179:9 191:11

energy-efficient 39:6

engage 173:15

engineered 32:8 82:18

engineering 4:12 8:20

9:3,21,22 37:11 47:21

48:9 61:3 62:7 87:13

95:6 97:17 119:3

146:16

entails 114:17

enter 84:4

entering 61:2

enthalpy 23:19 24:2,3

60:5

entrants 139:22 140:1

Environmental 2:6 5:8

EPA 5:11 14:18 93:7

equal 23:17

equals 23:21

equation 121:15 154:2

equations 15:13 61:12

154:8

equipment 2:10 5:14

35:19 130:2 137:5

147:11

ERA 62:1

Erbs 2:5 6:2,2 12:20,20

13:7,21 14:3 18:16,18

18:19 19:6,9,15,20

20:5 23:5,6,10 28:21

28:21 29:18 33:4,4,17

33:19,22 34:6,10,14

34:19 35:5 36:1,5,11

36:17 37:16,22 47:16

49:5,7,13,20 50:6,8

50:11,14,17 51:3,16

51:22 52:3,20 53:5

56:14,17,21 57:8,14

58:1 64:6,10,22 65:19

65:22 66:9,14 67:4

72:4,6,8,18 73:2,6,12

74:5 75:8,11,14 78:3

78:13,20 83:10,15

84:22 85:3,14 93:4

106:8,8,14 109:12

111:4,6,9,15 113:1,14

113:21 117:9 118:7

118:15 123:12,20

124:4,8,11,15 135:17

136:20 137:2 138:15

138:22 139:6 143:19

144:21 145:4,14

148:2,9,15,19 149:8

149:11,22 150:2,5,11

150:17 151:3,20

152:4,6 153:2,12,19

154:2,5 157:6,15,19

157:22 158:4,13

159:2,4,8 160:17

161:2,5,8,10,12

168:22 170:11 172:12

174:15,22 176:22

177:10,14 186:16,20

186:22 187:7,18

188:4,7,14,19 191:19

191:22 193:7 194:6,8

196:14 197:6 198:13

200:5,8 201:9,17

error 106:17

errors 71:20

especially 43:13 58:22

59:8 77:3 114:4

163:12

essence 45:15

essentially 47:17 49:11

51:10 54:22 179:11

estimate 66:5 81:2

129:5

estimated 100:6 101:19

130:18
(202) 234-4433 Washington DC www.nealrgross.com

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estimates 131:11 148:3

estimating 123:3

evaluate 146:19

evaluation 155:9

evaporating 22:5,14

63:13 71:14 77:1 82:4

82:7 151:16

evaporator 16:12 17:5

17:8 18:2,7 22:17

23:13 24:3,12 25:1

26:10,12 28:4 33:7,16

33:17 45:2,6,9,19

46:3,6,10 47:4 48:10

49:7,9 51:12,20 52:4

55:1,2,3,3 57:3,16

58:20,22 59:2,8,9

60:12 61:8,14 62:20

69:17 70:22 71:3,15

71:19 72:2,3,10 76:5

80:16,18 81:15,17

82:2,9,12 87:8 100:20

101:19,21 102:7

114:10 116:19 117:11

118:1,21 119:2,18

120:19 121:7,7,9,13

125:6 128:17,19

142:5 143:4 145:2

149:13 150:13 151:7

151:10,11,19 153:5

154:16 155:1 166:10

166:10 179:22 196:15

197:6

evaporators 23:21 25:3

25:3 47:21 53:13

80:22 102:1 144:1

188:15

evening 53:12 198:12

eventually 34:19

117:20

everybody 4:22 175:10

everybody's 95:7

197:22

everyone's 99:22

everything's 196:17

exact 137:9

exactly 29:15 35:8

50:13 83:13 111:5

143:13 182:6

examine 176:20

example 9:8 19:17

24:14 26:10,22 116:1

116:4,5 143:17,22

Excel 74:12

exchange 177:20 179:1

exchanger 68:14 101:3

101:7,10 141:22

exchangers 22:18

23:13,19 99:10

excuse 81:15 145:5

exercise 144:11,13

exist 114:21

existing 109:22 110:1

110:11 179:10,11

exit 81:6

expect 57:15 108:21

expected 38:3

expecting 167:2

expensive 144:3

experience 162:11

169:19

experimental 19:15,21

38:9,14 39:2

explain 58:7

explained 178:21

explicitly 29:1

Extending 175:12

extent 37:20 41:2

172:10 179:21 189:16

exterior 81:11

external 81:6

extra 142:6

extrapolate 122:8,12

125:12

extrapolating 165:4

extrapolation 112:10

extreme 107:21

F

face 76:21

faced 29:13

fact 64:18 165:13 171:4

178:22 181:12

factor 81:11 181:4

196:11,17,18 198:1,3

factored 147:17,22

148:16,18

factors 23:8 37:5

fair 28:10 54:8 150:10

fairly 10:12 24:21 66:22

151:17

fall 35:12 111:21

falling 49:15

falls 52:14

familiar 9:5 34:1

fan 16:5,10 21:5 29:3

29:17 39:4 62:3,4,8

70:15 86:1 87:1,3,6

97:21 100:21 101:1

121:22 122:3,13,14

124:22 126:6,18

156:7 180:6

fancy 85:10

fans 39:3 110:8,9,11

113:19

far 6:16 64:13 78:7

99:16 129:12 136:1

141:12 145:6 158:14

191:8

fast 149:5

faster 50:20

fear 159:8

feasible 39:11

features 179:10

fed 192:17

feed 57:21 96:5 131:16

164:11,14 179:7

193:3

feedback 40:11 41:11

42:1 103:1 127:20

131:16 158:3 173:16

feel 7:20 43:13,19

143:14 169:19 174:2

Fifteen 116:7

figure 25:19 61:9

128:14 171:3 175:1

193:18

figured 38:7 64:22

figures 116:15 142:18

figuring 16:11

files 69:1 101:12

fill 34:8 161:15

film 26:1 47:2 49:15

fin 25:19 32:15 61:16

61:19,20,20,21 63:2,2

63:3 84:6,8 126:4

final 4:18 7:21 8:1

14:21 24:13 82:3

180:18 181:14,21

182:18 183:7 185:16

finalize 178:7

finally 78:9 84:3 98:18

101:3

find 46:14 66:15 68:17

105:14,15 123:18

152:19

fine 162:7 196:8

finish 8:10 58:19 119:9

161:22

finned 63:7

fins 63:3

first 14:6,17 35:21

41:20 44:7 46:5 58:14

77:20 79:1,11 86:16

87:16 92:17 97:12

101:4,5,5 102:17

127:4 155:20 191:12

first-level 82:22

fit 162:13 164:9 197:10

197:11

fits 196:15

five 92:9 117:4

five-pound 85:16

fix 64:17

fixed 120:13

flatten 112:18

flattened 152:14

flattens 152:22

flexibility 170:2

flexible 146:21

flip 178:10 180:14

float 60:1

flow 8:22 23:18,22 29:4

29:10 30:9 31:21

45:13,18 53:7,12,14

62:4,9 69:10 101:21

180:4

flowing 71:2

flows 74:8

fluid 23:22

flushing 30:20

flux 153:7

fly 4:16

focus 9:2

focused 25:2

focuses 9:21 10:2

146:16

folks 195:5

Follett 2:3 6:4 10:14

20:7 30:19 58:4 162:3

follow 7:7 170:13

Food 2:10 5:14

Foodservice 2:5 6:3

force 53:11

foreign 140:1

forever 112:20

forget 116:14

forgive 88:2

form 25:8 71:5 189:21

format 190:20

formation 17:15,20

formed 17:17,18 18:10

forming 25:20 26:3

forms 24:6 25:8 26:5

34:5

formula 127:17

forth 65:9

Forty-seven 150:4

forward 28:14 43:14,19

71:4 178:7 193:9

found 7:22 23:1 65:16

104:7 114:10 121:6

143:20 144:2 152:11

155:8 160:5

four 61:15 119:17

125:11 156:13,13

fourteen 191:5

fraction 63:6 125:22

126:1

frame 54:3 93:7

frames 47:6

framework 9:1 166:4

frankly 131:5

free 43:13,19 189:21

freeze 14:16 15:5,14,19

17:11 21:2,17 22:12

24:10 30:11 42:13,16
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46:3,4 50:20 66:10

71:9 75:3,5,6,7 108:7

152:20 158:20 196:12

198:19 199:4,10,17

freezed 46:7

freezing 59:16 73:14

frozen 16:17

fudge 196:17

full 16:3 21:2 88:13

97:8 151:14 173:20

182:7

full-cube 96:18

full-day 173:7

fully 172:2

fun 189:13

funded 31:12

further 41:21 43:9

149:17 152:20 156:3

future 124:21

G

gain 68:15 100:5 148:5

152:19 161:13

gains 105:20 126:18

160:8

gamut 88:13

gas 107:5,10,13

general 135:22 182:11

195:13

generally 24:20 60:7,10

61:11 80:14 81:13

94:12 126:1,7 128:1

128:18 129:9 131:11

157:9 182:12

generated 172:10

geometries 82:20

geometry 47:4 48:9

81:1

getting 42:3 52:9 71:7

86:21 90:15 110:16

127:1 128:21 137:14

152:12

GH-019 1:17

gin 85:13

give 4:12 17:16 23:7

48:4 67:20 69:4 70:10

70:20 71:13,22

118:16 128:8 138:5,8

142:17 144:9 152:16

158:6 162:22 185:18

197:16 200:8,9 201:3

given 27:6 38:19 67:22

139:17 143:21 144:21

177:1 187:3

gives 8:21 44:17 47:20

47:22 65:16,17 71:11

120:11 194:6

giving 7:5 8:8 144:17

145:5 169:10

glasses 43:13

go 5:1 10:11 12:10 16:7

18:21 20:18 23:10

27:21 35:10 37:3 39:2

39:3,3 42:19 43:6

49:22 50:9 53:12

59:11 65:10 67:10

72:4 73:20 74:7 75:21

76:21 79:13,22 85:8

87:2,6,9 94:4,13

95:18 96:2 98:12,15

100:12 102:15 103:22

119:1 120:5,7,17

121:5,12,17,21 122:3

123:10 125:5,6,7,21

126:4 128:22 129:2,6

130:4 133:8 141:21

143:1 144:12 150:8

155:12 156:20 160:20

164:5 171:8 173:20

178:19 182:14 197:4

goal 7:19 36:20

goes 59:6 62:18 63:2

65:7 72:12 76:18

101:11 120:14 140:16

140:19 141:12 148:13

150:22 191:10

going 4:21 6:12,17 7:11

8:19 9:2,10,13,17

10:5,6 12:14 15:22

16:16,17 18:15 20:19

21:22 23:5,6,10 28:5

30:19 39:17 40:3,5,16

44:8 45:4,8 46:10,15

47:8 48:11,12 59:10

59:15 63:18 70:5,6,12

70:12 71:10,16 73:2,4

80:15 83:6 87:12

88:11 95:7,20,22 98:9

98:17 100:9 105:17

105:18 112:18 118:15

120:10 123:5 125:10

125:14 126:8,10

138:19 149:3 150:9

152:18 156:11 170:12

171:7,8 173:7,12,19

175:3 176:17 179:1

182:1 185:13 186:19

190:10,17,18 193:10

201:22

good 4:20 8:14 13:10

13:12 23:1 29:6 31:17

34:3,5 38:1 43:5,5

53:4,14 65:7,17 67:5

71:11 73:7 83:9 91:12

93:22 94:16 95:11

99:18 102:3 111:10

128:3 157:10,12

159:21 170:8,19

180:13

gory 196:13

gotten 42:2 103:1

127:20 136:8

government 88:7 92:6

133:21

granted 66:5

graph 74:13

great 6:11 7:8 20:19

33:19 165:9 175:16

198:20

greater 26:7 163:14

greenfield 145:12

grid 52:9 120:9

grid-style 52:6

Groh 6:9 105:9

gross 119:22

ground 90:4 181:17

group 41:9,11

grow 119:18

growing 142:3,7 143:5

grows 57:18 119:8

growth 44:9 114:12

120:1,6 140:13 143:4

growths 125:8

guess 9:16 25:7 29:3

41:1 58:19 74:7 83:18

89:14 95:19 96:2

101:5 114:1 126:1

127:10 129:14 131:20

132:22 145:19 148:21

150:16 160:9 161:19

171:19 190:2 195:12

195:13 201:13

guidance 152:17

guts 164:20

guy 68:17,18,20 73:11

116:21 137:14

guy's 74:16

guys 39:8,18 40:14 41:1

43:18 72:16 79:20

80:6 93:17 110:21

137:7 142:17 161:4

172:6 193:15 198:10

H

hairpin 125:19

half 97:13 137:15

half-cube 96:20

hand 6:22 7:11

handles 16:21 178:3

hands 6:18 103:18

happen 79:17 153:3

happened 150:5 181:14

192:4

happening 26:4 30:13

67:2 147:14

happens 31:14 75:18

111:16 183:15

happy 148:16 199:7

hard 43:11 53:6 199:12

200:13

harder 78:17 123:19

harvest 16:19,21 17:3,4

17:6 19:8,13 21:11,16

21:19 31:9 44:12,12

44:15,19 45:7 50:2

51:19 52:10 57:4,16

66:20,22 67:1,2 70:1

70:4,8,18 101:17

105:17 106:3,4,12

107:11,13 108:12,20

119:13 120:16 121:4

163:11,14

harvested 121:1,1

harvester 70:16

harvesting 75:9 152:21

he'll 91:3

hear 195:5 198:8

heard 8:18 9:4 146:11

187:9,10 192:12,14

heartburn 154:6

heat 15:13 17:5 18:6

22:17,18 23:13,14,15

23:17,18,21 24:4,5,15

25:10,19 27:17 28:1

29:11 38:3 46:11

57:18 58:21 59:3,4,21

60:13 61:2,9 68:14,15

70:11 81:8 82:5,7

85:21 101:3,7,10

107:9,17 119:4

141:22 153:7

Heating 2:12

heats 45:7 70:11 71:16

heavier 50:19

help 14:16 33:9 39:8

45:15,21 82:14 93:14

96:5 112:8 114:2

121:16 138:13 142:18

151:7 153:8 177:5,11

188:8 199:4

helpful 8:2 36:12 90:2

143:7,13 145:9 180:1

187:13 189:19 197:9

helps 95:2 153:5

190:15

hermetic 107:7

herringbone 61:20

Hey 198:9

Hi 198:10

high 26:8 27:9 29:12

35:17 114:22 115:3

126:12 151:17 189:9

high-efficiency 79:5

94:10,14 99:4,8 114:5

114:8,14,19 115:16

155:3 160:6
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higher 27:18 35:13 52:4

65:11 93:9 103:10

105:21 106:19 127:22

154:16 162:19 170:5

higher-efficiency 93:6

93:19 106:16 107:16

109:21 114:18 127:18

154:19

higher-level 159:14

highlight 118:20

highlighting 178:14

highlights 177:21

historically 38:13

history 139:3

hold 4:11 200:13

holistically 61:7

home 4:16

honed 129:21

honest 52:1

hook 62:7

hope 14:10 39:20 40:1

40:13 149:22 179:7

194:6,8,9

hopefully 4:14 50:20

193:12 194:11

horizontal 25:7

horizontally 27:21

horribly 69:8

Hoshizaki 2:14 5:17

14:6 26:22 27:2,14,16

28:4,19 39:1 60:17

109:14 140:11

hot 62:9 107:13

hotel 85:8

hour 45:22 97:14

hour-and-a-half 77:13

hours 21:20 121:3

Howard 175:10

huge 108:7,12

hundred 26:15

hurts 52:12

HVAC 136:19

hydraulic 119:4

hypotheses 64:16

hypothesis 64:8,18

I

ice 1:3 2:7,13 4:9 6:7

12:5,8 13:10 14:8,9

15:9 16:2,3,17 17:11

17:15,17,18,20 18:4,9

18:12,13,20 19:12

20:1,10,12,22 21:18

21:18,19,20 22:10,22

23:20,22 24:3,5,6

25:1,8,12,19,20 26:3

26:5 27:21 28:1,17

30:7,11,19,21 31:11

32:6 37:13 38:21

40:20 42:18 44:11

46:6,9,12,19 47:2,5

48:2,11,12 50:1 51:17

60:1 61:2 69:15 70:8

70:16,21 71:1,3,5

72:1,6,10 73:22 76:4

80:9 84:22 85:11,12

85:15,17,20 93:8,9

96:15,18 98:1 101:9

102:4 103:3 111:18

111:21 120:6,7,11,14

120:20 121:1 124:4

135:18 148:10 158:15

159:9 160:18 162:20

163:9 166:9,19 168:5

170:7 180:5 198:5

Ice-O 25:4

Ice-O-Matic 27:1

175:11

ice-side 46:8,15

ICF 2:16 5:10

ICUA 80:19

idea 23:7 128:3 133:21

150:14 200:5 201:6

201:10

ideal 128:13

Ideally 145:10

ideas 64:12

identified 169:2

identify 5:3 188:5

193:17 199:3

identifying 166:5

idiosyncrasies 151:13

illuminated 106:22

illustrate 33:21 82:15

93:14 114:2

illustrating 32:12

IMH 154:15

IMH-A 93:15

IMH-A-Large-C 100:10

IMH-A-Small 101:12

IMH-A-Small-B 100:10

100:15

IMH-W 189:4

impact 49:17 53:17

108:19 117:11,12

146:18 154:22 167:18

169:16 170:7 175:12

179:21 180:3 198:22

impacts 107:12

impatient 79:21

implement 133:9

implemented 30:9,14

93:7

important 10:11 57:2

133:2 181:13 201:10

impossible 165:4

improve 107:15

improved 162:17

improvement 11:11

107:19 108:8,9 109:4

149:14

improvements 168:9

184:9

in-house 130:7

inaccuracies 169:20

incentive 185:19

inch 63:3 187:1 188:2

189:4,5

inches 114:12

incidentals 141:3

include 75:4 119:18

147:4

included 143:2,8

includes 16:14 21:12

21:15 46:9 141:11

including 101:5

inclusion 41:16

incorporate 126:15

increase 49:8,21 50:2

51:15 57:11 59:8,10

76:4,5 99:10,12

100:18,19,20,21

101:1 102:18 105:11

114:10 117:18 118:1

118:21 120:13 121:9

121:13,14,20 123:10

125:12,15,18 126:2,4

126:15 140:18 147:5

151:1 154:17,18

156:14,18 163:9

166:1,9,20

increased 29:4 47:5

49:18 126:13 140:12

145:21 150:3 166:10

189:7

increases 72:11 125:1

141:1,18 151:7

152:20 160:11,13

increasing 47:2 140:17

152:7

incremental 130:16,18

135:12 142:20

increments 45:11 55:6

Independence 1:18

indicate 57:9

indicates 57:6

indirect 107:3

individual 37:1 38:15

52:16 53:9 94:5 114:6

114:20 120:9,14

132:11 134:17 135:16

industries 139:12

industry 28:11 38:17

134:3 136:8,14,19

137:6 139:15 169:16

170:7

industry-specific

136:18

infinite 162:16

influence 36:22

influencing 172:19

inform 40:15

information 31:14 33:6

131:7 136:4,9,13

142:17 164:11,15

171:15 172:22 173:12

177:20 178:12 179:1

179:2,6 182:8 187:17

193:4,13

initial 42:7 83:4 119:12

142:15 174:11

initially 17:18 26:2

195:7

inlet 80:12

input 10:21 11:13 16:1

16:5,8,10,11,22 18:12

19:1,5 20:3 21:6,12

23:3 42:21 45:20 49:2

50:4 59:6,19 61:10

70:8,9,17 75:11,13,15

80:8,10,11,19 85:1

87:1,1,5 113:12

122:10 124:12

inputs 9:11 10:22 22:8

22:13 31:10 32:4

39:17 42:19,22,22

43:6,8 44:3,7,13,13

46:14 48:19 62:11,14

62:15 63:1 67:21

68:19 74:11 79:9

82:17 83:21 85:19

101:15

inside 59:4,17 126:3

insides 25:21

insights 177:6

instance 15:7 25:13

66:7,8 74:17

instances 135:2

Institute 2:12

instrument 33:6

instrumentation 73:21

insulation 61:5 81:9

intangible 44:22

integrating 123:15

intent 10:5 177:12

interact 200:3

interaction 194:5

interactions 151:4

interchanger 84:11

interest 4:10

interested 61:1 149:6

189:15

interesting 33:22

interface 15:15

internal 108:3

International 2:16 5:11
(202) 234-4433 Washington DC www.nealrgross.com

Neal R. Gross and Co., Inc.

212

interrupt 18:16 49:5

intervals 33:13

introducing 4:22

Introductions 3:9

inverted 151:10

investment 142:15

146:12,21 148:3,7

inviting 139:22

involved 23:16

involves 16:11 23:2

61:5 106:20

IP 140:5

irresponsible 175:14

ISO-compliant 56:14

isolate 66:21

isolated 108:15

issue 36:17 133:17

134:13 135:9 137:13

151:9 154:12 189:2

200:10,21

issues 164:8 181:16

182:5 200:1

it'd 197:1 201:3

It'll 182:8

item 57:20

items 63:19 177:19

179:17

iteration 54:19

J

January 8:11 174:12

Javier 2:3 3:18 5:20

22:21 29:7,20 30:2

33:20 42:9,11 43:15

46:16 89:22 95:19

96:1 115:11 197:5

199:2 200:8 201:4

Javier's 201:18

JOANNA 2:8

job 13:10 111:10

170:19 198:20

John 1:19 2:2 3:12 4:5

9:16,20 32:8 35:19

76:11

Jon 197:21

judgement 186:13

jump 20:17 46:21 74:15

156:2

June 1:11 8:13,19

K

keep 29:10,13 50:18

96:20 132:10 169:9

176:2 192:19

keeps 134:2 188:15

Kent 2:6 5:7,7

key 21:14 188:21

KIDO 2:7

kilowatt 21:20

kind 8:21 19:1 40:18

42:14 45:4,7,14,21

46:7 47:22 48:13,18

48:20 49:3 55:4 60:1

61:17 63:10 67:21

69:15,19 70:3 71:2,6

72:17 73:15 74:7

76:18,21 77:2 78:22

79:16 81:7,10 82:16

82:21 86:8 87:7 90:8

91:2 93:14,16,19 94:2

94:6 95:2,2 96:3,3,4,7

96:8,12,16,18,22 97:6

97:15 99:5,9 101:13

101:20 102:9 104:5

105:19,20 108:6

113:6,11 115:1,5,6

118:19,21 119:1,4,7

119:10,15 120:5,8

121:20 122:8,12

123:7 125:5,7,8,15,19

126:5,7,16 127:17

128:1,7,21 129:1

130:3 144:12 145:20

149:15 154:14 155:6

156:2 162:9 177:21

179:12 184:21 197:21

199:16 200:1,4,15

kinds 61:15

knew 64:17

know 7:3 8:15 13:8 16:4

17:4 19:8 26:9 27:8

32:12,17 35:10 38:16

48:11 54:4 56:11

59:20 60:4 65:13

68:18 73:18 78:5

79:20 83:8 87:14 92:5

95:6 96:10 97:11 98:8

98:20 100:18 101:13

103:8 107:21 108:6

108:15 109:4,7 112:1

113:13 114:16,17

115:20 117:16 118:7

118:15,18 119:3,15

119:21 120:2,4,18

122:9 123:9,10,17,19

124:19 125:3,15

126:20 127:16,20,21

128:17 130:19 132:18

133:3,17 136:1,2,14

138:13 140:6 143:18

145:4,9,13,14 146:4

147:22 149:14 150:21

150:22 153:13 154:11

154:18 156:1,6,12,21

157:1 158:4,10,11,16

159:14 161:16 162:11

163:1 164:14,18

165:2,9,10,15 166:8

166:17 168:6 169:9

169:11,22 170:13

176:6,7,16,20 177:7

177:11,16,19,21

178:2,5,6,9,10,11,21

179:8,15 180:9,10,16

180:20 183:15,17

185:13 186:3 188:14

189:9,15,22 190:5,14

191:7,17 192:11

193:8 194:1,4,5,10

195:8,9 196:4,16

197:19 198:5,20

199:16,22 200:3,11

201:12

knowing 128:19 129:22

156:22 164:7 165:22

192:16

knowledge 140:3

201:12

known 128:2

L

lab 62:6 122:4 165:17

labor 141:3

laboratories 56:2,15

labs 36:5 56:8,10

124:20 176:2

large 160:18 161:16

largely 38:8,14

larger 29:5 31:19 49:21

49:22 52:3 53:12,13

57:16 121:7 132:19

136:19 137:4 156:11

163:16 178:4

largest 43:15 160:15

Lasecki 2:7 6:6,6

199:15,15 200:7,10

Laughter 13:6 34:2,15

34:18 36:4 69:21 74:1

77:18 78:8,19 85:9

87:22 90:14 91:8

111:11 118:17 145:7

174:17,19 186:21

194:7,15 198:18

Laura 2:11 6:8 12:3

35:9 40:3 46:18 105:8

165:11 190:2

Laura's 68:2

layer 25:11 26:1,5,6

51:17

layers 48:2

lays 170:9

Leads 1:20

learn 91:17 176:17,21

learned 189:17

led 149:14

left 9:8 58:9

Legacy 2:15

legal 200:20

legally 184:19

less-efficient 98:17

let's 4:3,21 5:4 7:2

13:17 40:6 43:9 68:2

68:17 95:18 110:12

111:14 113:19 116:14

121:2 125:6 197:4

level 9:22 37:19 38:16

40:11,11 53:17 60:2

94:11 95:1 97:1,5,7

106:6 117:22 118:4

129:13 133:9 137:22

155:19 159:10,11

167:14 169:5,13

170:3 187:22

leveling 26:9

levels 93:10 111:1

130:18 133:4 148:4

151:6 161:17 162:21

167:9

lies 60:11

life 147:11,14 154:11

163:18

limit 30:10 31:21 45:22

115:9

limitation 82:11

limitations 12:6 45:17

limited 31:12 109:22

164:2 182:4,8

limiter 101:21

limits 44:9 52:6

line 37:10 48:4,14 68:14

line's 144:8

linear 51:21 72:8

lines 102:9

liquid 67:13 102:9

107:11 108:17

list 60:4 103:6 128:8

141:11 158:2,10

190:10

listed 21:13

little 9:8,19 12:4 19:11

20:14 22:21 31:13

46:8 47:22 48:22

53:16 61:18 66:19

69:18 72:15 76:17

78:17,22 89:15 93:14

115:10 118:19 120:20

149:9,17 151:21

154:5 155:14 181:18

189:20 192:5

load 16:12 17:7 18:3

38:3

long 123:13 175:20

longer 51:2,3 57:15

69:18
(202) 234-4433 Washington DC www.nealrgross.com

Neal R. Gross and Co., Inc.

213

look 13:14 18:22 34:1

48:9,21 51:13 57:10

59:1,2 61:7 68:7,7

69:9 71:22 72:1 73:2

73:4,20 76:8,22,22

77:1 79:21 80:22,22

81:9 87:2,5 90:19

93:16,20 94:11,20

96:19 99:1,2,7 102:21

105:19 114:4,8,18

115:13 119:20,22

126:1 128:13 150:5,8

151:15 153:10 155:5

157:8 160:17 161:1

172:6,21,21 191:13

197:5 201:7

looked 29:7,9 75:14

84:7 115:8 126:12,12

154:19 155:22 157:17

looking 17:10 48:7 51:8

58:22 59:5 61:1,5,6

62:16 66:3 82:4 84:10

86:19 93:15,18,19

94:3 98:4 99:9 100:14

100:17 106:5,15

108:10 115:7 118:22

118:22 119:1 120:9

122:6 126:20,21

129:3,4 140:16

141:18 142:10 145:16

153:18 154:22 155:6

160:3,9 162:18

163:22

looks 34:1 63:10 66:4

66:17 128:15 196:22

loss 21:19 107:6

lot 10:6 16:6 24:17

46:10 54:19 59:13,14

66:20 74:13 82:12,19

84:13 85:12 131:6

139:18 145:22 148:5

157:12 159:9 163:17

167:9 168:6 169:13

174:13 177:19 183:18

184:22 201:3

lots 102:6

loudly 5:2

Louis 2:17 166:15

lousy 152:21

love 159:6 194:14

low 16:16 73:16 143:10

144:8

low-mass 96:17

lower 9:8 16:17 17:21

29:11 65:10 152:11

163:12

lower-efficiency 93:5

lowest 69:11

lowest-cost 121:19

lump 142:15 146:10

147:4,10,16,18

lump-sum 146:12

lunch 161:1 175:4,7

M

machine 12:5 13:11

27:7,10 37:13 84:18

110:17 135:18 144:15

158:15 161:17 163:2

163:3 164:21 166:19

170:7

machines 14:8 19:22

20:1 38:21 69:15 93:8

93:9 111:18 124:4

148:10 159:10 160:16

164:19,21

maintain 136:3,5,10

major 107:6

maker 15:9 17:11 20:10

23:20 24:3 25:1 28:17

30:7,19,21 42:18 61:3

76:4 162:20 163:9

makers 1:4 4:9 12:5,8

14:9 20:12,22 22:10

22:22 24:5 30:11

31:11 32:6 40:20

46:20 60:1 96:16

101:10 102:5 103:4

180:5 198:5

making 8:12 16:3 22:17

120:4 129:2 139:22

140:4 160:18 165:3

166:12,19 177:2

181:5,16 187:14

189:19 195:14

management 147:9

Manager 4:6

Manitowoc 2:5 6:2

12:21 18:19 23:6 25:4

27:1 28:22 33:5 38:14

106:9

manufacture 135:18

manufacturer 67:13

131:14 135:16 137:11

146:18,19

manufacturer's 146:11

manufacturers 2:10

5:15 13:14 55:14

64:13 93:22 103:2,11

106:18 110:14 111:3

119:14 128:2 129:7

138:1,15 143:8

165:16 167:12 169:8

179:5 188:18

manufacturing 131:6

132:15 145:11 146:20

147:4

map 22:3 35:14,16 55:1

63:12,14 67:17,19

69:8 84:10 103:13,14

103:17 104:1,6,9,18

157:2,3,4,7 158:8

160:9

mapped 119:9

mapping 118:13

maps 22:2 63:9

March 8:16

MARCHETTI 2:8 6:13

6:16,19

margin 136:11

margins 136:3

market 56:1 90:18,19

91:16 93:21 98:11

99:8,14 102:22 103:3

115:8,12,17 167:11

179:11 187:16,20,22

marketplace 162:22

163:7 167:17 168:2

169:15

markup 130:6

mass 23:18,22 31:20

45:13,18 50:1 55:3

57:18 59:1,7,8,9,16

59:17 60:13 70:4,8,21

72:1 74:8 77:1 101:21

119:2,19 120:1

match 23:1 26:12 38:2

45:15 62:12 64:3,14

65:17 73:9 79:4 81:4

97:22 102:1,10 160:8

196:11

matched 162:13

matching 37:12 71:19

material 10:2 24:20

129:18 140:15 141:19

materials 81:15,16

130:8 142:7

Matic 25:5

matrix 34:8

matter 95:15 123:15

175:6 202:6

MAUER 2:8

max 97:10 115:16

167:10,10,13,15,20

maximum 45:13

McCARTHY 2:9 5:13,13

53:18 54:6,13,16 55:8

55:11,19 56:4,7 87:15

87:18 88:1,9,12,19

89:4,7,14,18 90:3,6,9

90:11 91:19 92:2,7,12

92:19,22 93:3 127:5,8

127:14 128:4 129:12

130:10,14 131:19

132:2,6,13,18,22

133:6,11,14,19 134:1

134:6 141:10,15

143:7 146:6,6 147:3

147:12,21 148:8,14

148:17,20 171:11,19

172:1 173:2,21 174:2

174:7,10 182:10

183:21 184:3,14,17

186:1,7,11,15

mean 20:18 26:17

29:21 35:16 37:10,18

38:16 40:9 50:4 51:1

51:3 53:6 54:8 57:12

57:14 58:12 65:4 76:9

76:16 77:12 78:2

82:14 83:10 86:17

89:13 91:2 92:21

108:22 112:16 113:15

124:17,22 127:10

128:7 129:5 132:7

136:12 137:8,20

138:5 145:8 148:15

150:12,15 151:12,20

154:13 155:4 157:19

159:5 161:6,16

163:20,22 164:10,12

168:2 169:8 172:1,7

177:15,17 178:21

180:13 182:22 186:6

188:2 189:1 195:3,6

195:16,22 196:1,20

199:6 200:21

means 117:22 153:6

meant 124:8

measure 62:10 73:22

124:12 197:17

measured 47:11 77:5

measurement 30:8

122:11

measurements 19:21

29:15 43:2 67:19

97:16,20

measuring 66:22

mechanical 159:20

meet 93:9 144:14

meeting 1:6,17,20 4:9

4:11 5:2 7:14 8:16,17

8:20 9:4 10:5 40:4

165:20 168:17 171:13

183:13,15 187:13

meltage 19:12

melting 44:15,15

member's 132:11

members 128:6 132:11

132:19,20 171:12

173:11

memory 99:22

mention 17:3 22:20

34:4

mentioned 14:14 21:11
(202) 234-4433 Washington DC www.nealrgross.com

Neal R. Gross and Co., Inc.

214

31:8,20 32:8 42:17

44:12 45:6,14 53:20

63:20 68:10 76:9,22

83:22 98:6 102:5

152:1 199:2

Merriam 14:20 15:3

message 172:14

met 1:17

metal 46:7 59:3 61:6

63:5 81:10 144:3,7

metals 81:16

method 139:3,9

methods 68:9

MICHAEL 2:7,13

microchannel 61:16

83:19

mid 14:17 35:17

mid-tier 98:10

middle 76:14

midpoint 108:7

Mike 6:1 11:3

million 147:19 148:7

mind 13:7 132:10

180:15 187:16 188:15

minds 171:1

mine 68:3

minimum 60:2

minus 21:19 63:15,16

minutes 54:15,18 69:16

69:19,20 71:6 72:21

88:6 190:19

missed 91:15,16

194:19

mix 24:5 76:2 94:1

150:20

model 3:11,14,17 4:12

4:13 8:20 9:6,12,16

9:17,21 10:1,7,10,15

11:6,9,19 12:6,7,16

12:22 13:12,18 14:16

14:20,22 15:6,7,8,14

15:16,18,19 16:1,13

16:20,21 17:1,9 18:21

20:3,12 21:22 22:6,8

22:22 23:1 24:9,15

25:10,12 26:11 30:4

30:20 31:8 32:11

33:10 38:7 41:1,2,19

41:19 42:7,9,13,16

43:4,8 44:1,2,2,13,22

47:1,2,7,19 49:7

50:15 54:2,18 57:21

59:14,19 60:12,13

61:10,15 62:1 63:16

63:17 64:14 65:14,18

66:12,13 67:14,16,20

68:12,14,20 69:9 70:7

72:19 74:12 75:8,18

76:5,19 77:8,20 78:4

79:5,6,9,14 80:10

81:16 82:17 84:13,14

87:10,13 90:5,8 92:7

94:4,20 95:5,22 96:8

97:2,2,6,14 98:20

101:7 102:13 103:15

104:13 105:11,16,21

108:22 109:22 110:4

110:20 112:11 114:19

114:21 115:1 117:10

117:15 118:9 120:21

125:4 127:12 129:1

129:15,20 131:12,16

136:22 139:15,16,21

140:5,22 141:11

144:20 145:11,20

152:2 155:10,10,13

156:19 163:22 165:14

166:13 167:5 168:12

168:15 169:21 170:22

171:4 172:7 177:1,5

178:2 179:2,9,14

182:22 183:11 184:2

187:20 191:11,14

193:18 194:18 195:6

195:9,15,20 196:12

197:7,13 199:4,13

202:1

model's 193:8

modeled 14:7,8 49:1

65:2 94:18 100:17

101:6,8 102:18

103:20,22

modeling 12:11 13:10

14:15 18:20 19:3 20:1

20:9 24:11,11 25:2

27:15 48:15 54:9

56:22 59:13 60:9

69:13 80:14 93:17

105:16 112:9 117:14

180:10,11 191:2

models 4:17 28:14 37:5

41:10,12 43:5,7 48:3

57:2 65:13 67:15 79:4

79:8 80:22 82:18 83:9

84:7 86:8 87:20 88:4

88:21 89:21 92:21

93:6 96:22 97:4,15

98:7,9,10,19 105:14

110:2 112:13 117:17

122:7 126:8,8 127:11

129:1 131:6 136:21

139:13,19 141:5

143:22 145:1 173:16

174:7 175:13,14,17

179:20 189:11,15

modifications 11:1

32:1

modified 10:15 14:22

modify 111:18 179:2

modifying 110:14 111:3

modular 148:10

money 91:2 169:9

monitor 62:10

MOP 30:12

motor 16:6 58:8 80:8,9

86:1 99:14 100:21

101:1,2 107:15,18

108:16 115:3,4,10

121:22 122:1,10

123:18,19 124:13

126:10,19,19 127:2

137:4 159:17,22

161:14 163:10,14,16

motors 97:21 99:11,11

99:12 107:16 124:3

124:18 125:1 180:6

move 7:21 40:20 42:8

43:14,19 178:7 180:9

193:9

moving 21:1 23:12

24:22 28:14 31:6 46:1

71:4 180:3

multiple 11:17,18 37:5

118:9

multiplied 44:16

multiplier 63:21,22 64:2

64:4 65:13 68:22

104:7,13,20

multipliers 63:20 65:4

multiply 49:9

N

NAAFEM 53:19 87:19

127:6 146:7 171:13

nail 29:15

naïvete 88:2

name 5:4 7:4

narratives 183:18

narrow 190:15

natural 2:10 5:18 8:17

nature 38:20

Navigant 2:3,13,19 5:20

5:22 6:1 11:4 15:20

96:1 176:2,11 201:11

NDA 34:5,6 103:2

128:10 133:15 134:18

135:4 138:12 172:13

172:16

NDAs 134:11,14 176:1

necessarily 163:3

167:1

necessary 80:4

NEEA 166:16

need 20:8 32:9 40:13

43:19 63:22 90:20

91:16 94:13 97:8

112:7 114:15 142:17

162:5 163:4 170:4

171:9 172:17,21

176:18 178:9 180:22

186:22 187:3 193:16

202:3

needed 77:3 102:15

needs 17:8 130:6

178:13 186:5

negative 169:16

negotiated 39:9,15

negotiation 36:16

net 117:13

never 78:4 133:20

150:11

new 37:10 49:10 74:17

85:10 103:14 139:22

140:1 144:14 161:17

166:20 170:5

nice 84:6 163:18

night 69:20

NODA 181:1,7,20 183:6

185:17

Noll 2:10 5:18,18

non-disclosure 131:13

non-disclosure-agre... 131:7

non-standard 22:7

non-uniformity 53:3

NOPR 8:15 9:4 10:17

14:14 28:16 30:10

32:2 40:22 41:13,21

42:3 75:20 82:9 92:1

99:21 103:5 115:14

127:17 130:11 135:6

135:13 141:14 176:1

180:22 181:20 182:7

182:16 187:9

NOPR-based 132:4

normally 189:21

North 2:9 5:13

Northwest 2:4,17

note 184:3

notice 39:18 181:8

noticing 195:14

NSD 64:8

nuances 20:11 165:5,6

number 17:12 21:21

27:11 42:19 44:5

51:20 62:14,17 63:8

65:7,17 67:14 76:20

76:20 80:21 93:9

106:3 125:9 130:22

131:1 134:3 138:1

143:10,11 156:8,9

164:3,19 166:9,18

167:4 183:12

numbers 55:15 106:5

141:17 145:5 167:14

167:15 171:9 174:13
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Neal R. Gross and Co., Inc.

215

181:12 184:1 186:4

nutshell 86:17

O

o'clock 149:2

O'Hare 198:12

O-F 3:7

observe 68:9

obtaining 23:1

obvious 11:5

obviously 4:16 8:12

10:1 16:7,9,18 21:14

24:2 25:16 27:17

37:18 39:18 73:21

169:8 176:1 187:10

187:11,13

OD 62:16

official 100:2

offline 202:5

offset 152:21

Oh 19:20 23:9 58:11

78:20 111:22 116:11

118:12 145:4 148:1,6

186:9 192:2 193:6

195:4

okay 4:3 6:11,17,20 8:6

8:21 11:2 12:2,13

13:21 14:3 19:9,17

20:13 23:11 26:20

27:12 29:6,9,18 30:1

31:3 32:20 33:2,17

41:18 42:6 49:20

51:16 52:2 54:16 55:8

55:19 56:4,4,7 58:1

58:15,16 60:21 65:19

67:4 74:5 78:20 84:16

85:3 89:4,7,18 90:3,6

90:9 91:14,19 92:2,12

92:19 95:12,13,18

111:20 116:11,14

130:10,14 131:14,19

132:2,6 133:6,11,14

133:19 134:1,6,6

136:6 137:3 141:9,15

141:15 146:19 147:3

148:14,17,18 149:10

151:20 153:12,19

159:2 161:2 164:15

174:10 175:3,9 176:9

176:13 177:14 179:11

181:10 184:17 186:11

186:15 189:12,20

190:5 192:2,12 196:8

201:17

old 144:7

once 68:18 71:6 78:4

98:18 115:5 192:21

one-by-one 118:10

ones 32:17 99:18 103:7

143:6 155:5 158:22

190:14

ongoing 177:20

online 140:5

open 36:15 40:3 68:1

74:17 99:16 100:12

102:3 113:13 127:21

176:7,19 183:12

opening 8:4 170:20

182:5

operate 37:2

operated 38:20

operating 68:5 158:15

operation 21:12

operations 146:20

operator 96:17

opportunity 93:5 109:3

172:18,20 189:14

201:20

opposed 84:18 118:10

optimistic 117:11

optimization 52:5

optimize 90:22

optimizing 169:7

option 11:12 28:6 76:2

77:10 83:19 96:6

100:5,7 105:14

116:22 118:22 155:20

166:1 167:2

options 11:10,14,15

37:1 79:15 82:13

86:22 94:7,8,10,13,19

94:21 95:3 96:15 97:2

98:22 99:2,3 100:17

101:4 102:19 105:21

106:15 115:21 116:16

117:16,19 121:11,17

122:6 153:15 154:14

155:2 156:17 166:5,7

166:17,18,22 169:2,4

184:9 188:10 189:7

oranges 112:5,7

order 25:10 28:9 29:16

30:14 35:1 42:17 47:7

61:8 63:17 81:18 82:6

86:16 94:13 98:12

101:22 102:12 104:13

104:14 121:15,16

155:18 156:3,17

164:15

orifice 30:12

ought 124:21 150:8

151:6,15 193:2

outcome 172:19

outer 25:11

outgrowth 8:18

outline 133:15

output 33:12 74:12

122:9 162:14,17

179:3 184:12 196:12

199:13 200:16

output's 192:16

outputs 9:11 54:21

75:18,19 162:19

outside 81:2,8 115:7

126:3

overall 11:16 44:18

86:5 106:3 188:5

overarching 165:2

190:22

overestimated 168:10

168:11

overly 117:11

Overview 3:11

P

P-R-O-C-E-E-D-I-N-G-S

4:1

p.m 175:7,8 202:7

package 164:10

packages 99:10

page 15:22 17:10 18:15

21:1 27:4 30:3 31:6

177:21 179:15 185:14

Pages 32:6

pair 126:9

paired 36:18

pan 126:13

paper 66:17 108:5,7

parallel 14:18

parallels 197:22

parameter 18:13 19:5

53:20

parameters 19:14 22:8

22:11 29:16 45:1 74:9

165:22 166:11

part 9:2 11:16 12:17

17:5 21:14 28:3 36:17

40:10 60:14 64:22

66:10 75:3 77:11

91:15 130:6 142:14

144:3,7 147:1 178:14

188:22 189:13 201:13

PARTICIPANT 35:2

185:19 198:9

Participated 2:22

participating 140:4

participation 40:12

particular 76:2 79:7

131:12 178:3 187:1

195:17

particularly 33:7

parts 24:8 138:15

140:21

party 56:2 97:18

pass 49:16 172:14

passed 89:2

passing 23:18

pathway 97:6

pattern 32:15

pay 128:9 137:19

pays 88:7

penalizes 165:7

pencils 171:9

people 52:14 130:4

138:12,19 140:2,3

173:3,8 189:16

200:21

per-cycle 98:2

per-pound 60:3

percent 50:9,9 60:7

64:9 89:21 97:9 101:8

102:14 105:22 109:15

114:11,22,22 116:7

120:1 123:3,5,6 125:2

125:2,2 140:13 142:2

149:13,14 150:4

152:6 154:18 155:11

156:1 161:13 164:1,6

188:14 189:3

percentage 44:16

107:19 121:10 184:9

186:7

percentages 122:17

189:9

perfect 85:12

perfectly 162:14

perform 64:20

performance 18:2 22:2

22:2,3 64:1 88:18

102:11 151:14 158:10

160:9 198:22

performs 171:4

period 40:4,16 46:3,4

86:7 175:22 176:7,19

182:11,13 183:2,2,12

185:1

permission 15:20

permissions 201:5

person 174:20

perspective 172:22

Petrillo 6:8 12:4 40:3

46:18 105:8

Petrillo-Groh 2:11 6:8

11:22 12:3,13,19

19:18 20:4 26:11,15

26:20 28:13,20 32:14

32:21 33:2 35:9,10

36:15 37:8 40:2,19

41:6,14,18 42:4 46:16

46:18 48:17 56:8,13

68:3 74:21 75:2,6,10

75:12,17 76:3 77:6

105:8 136:17,22

165:11,12 166:6
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167:8,22 182:14,21

183:9 185:15 187:21

189:3 190:3,7,9 191:3

191:6 192:5,10,13,15

193:1,12,15,21

Ph.D 74:2

phase 10:17 27:16

28:16 29:9 30:10 32:2

40:21,22 41:20,21

91:22 92:1 115:14

122:22 131:21 181:4

182:16 185:15 187:9

phases 41:5,9

PhD 109:1,1

philosophically 170:15

phone 195:5

physical 30:12 43:1

62:15,22 97:16

110:17

physically 173:4

physics 57:15

pick 68:2 75:15 91:17

94:9 117:16 162:5

169:5

picture 15:17 71:11

piece 25:18 60:10 86:17

pieces 25:13 83:1 130:1

pile 169:10

pin 76:13

pitch 62:17

place 4:20 34:7 91:10

112:19

placed 112:20

places 85:11 157:2,3

plain 61:20

plan 8:12 90:16

planning 118:5 149:2

plant 145:13

plastic 60:10

plate 25:2,5,15,16,17

27:22 28:11,17

108:17 142:3,5

plates 25:7 27:2,13,14

27:20 142:2 145:2

platform 148:10

play 193:14

pleading 185:21

please 5:2,3 19:19

175:18

pleased 7:15

plot 26:8 65:3 66:4 68:7

73:3 196:21

plots 71:22

plug 63:14,16 65:13

87:10

plugged 101:16 120:3

plugging 120:10 162:10

plus 71:3

PMs 123:9

point 14:1 20:10 22:13

32:13 39:15 40:9 45:8

48:7 70:21 88:2 91:21

97:9 112:3,4 113:22

114:2,15 115:21

117:4 118:12,20

129:14 132:16 138:6

139:10 144:19 145:18

146:9 150:10,19

154:21 158:18 162:8

175:4 178:8,17 180:9

197:22 198:17

pointed 120:12

points 45:5 47:9 48:1,4

48:8 73:8 99:6 114:20

115:15 119:16 128:5

128:20 129:10 130:15

150:6,12 161:13

198:16 199:3

poke 149:8

pole 100:22

poles 181:17

polynomial 69:5

popped 13:7

portfolio 198:2

position 113:2

possible 13:10 24:18

38:19 97:1 133:8

150:7 161:18 167:21

190:4 192:20 199:20

possibly 167:13

post 100:1

post-harvest 44:17

post-meeting 4:17

potable 59:18 98:15,15

potential 52:10 106:17

potentially 16:6 122:22

140:2

pounds 26:14,16 45:22

106:6

power 2:4 16:5,7,8,9,11

21:6 23:3 29:14,17

44:21 60:12 62:3,5,8

63:12,19,21 67:1,18

68:7 70:16,17 71:15

80:8,11 86:1,19 87:1

104:3,19,22 122:9,10

122:12,13 126:7

156:7

power-using 16:2

powers 97:21,21 122:3

practical 165:8

practice 147:8 163:19

163:19

pre 119:12

pre-batch 101:17

pre-cycle 66:19

pre-harvest 18:13,20

19:2,12 20:2 21:18

44:16 50:5 51:6,13

120:17

precise 54:11

precisely 94:5 170:22

precision 45:12

predict 79:14,17 96:9

125:9 191:11

predicted 150:18

predictive 79:14

prefer 111:13

preliminary 27:16 28:14

40:21 41:10,20 83:4

91:22 122:21 131:20

185:4

premise 194:17

prepared 10:2 180:8

present 2:1 15:9,10

68:15 155:2

presentation 47:1

173:8

presented 9:20

presiding 1:20

press 144:7

pressed 199:12

pressure 33:12 68:16

74:9 81:5 152:18

153:8

pretty 29:21 43:14,21

44:8 46:1,5 52:6

54:11 63:11 67:17

68:16 73:7,9 80:15

82:21 84:7,21 85:19

94:7 96:13,14 97:11

116:18 119:19 122:2

123:12 125:17,18

126:2 149:4 156:22

159:19,21 170:19

178:10 196:13

previous 9:20

price 127:9,12 141:8

143:18 146:12 147:5

154:8,8

principles 37:12

prints 54:22

prior 42:3 185:11

prioritize 191:4 193:1

prioritizing 35:1 191:7

probably 10:10 34:7

36:21 43:15 53:9

72:16 92:11 93:4

108:1 109:1 110:3

114:15 127:9 150:20

155:22 160:20 172:17

183:18 184:7,10

problem 27:18 52:11

158:13

problematic 195:16

problems 53:3

procedurally 182:19

184:18

procedure 56:3

process 6:21 36:10

44:1,2,4 58:7 75:3

79:12 80:2,7 96:3

120:16 130:9,12

166:14 169:11 177:11

180:16 190:22

processes 129:2

processing 129:18

product 37:10,20 88:17

89:12 90:20 137:2

147:19 167:16 168:4

168:4

production 16:3 21:18

70:1,2,4,5 98:1 141:7

162:20,21 163:10,14

products 56:1 90:16

110:14 122:5 132:15

179:6,10 198:3

profits 136:5

program 4:6 5:9,12

14:19 21:3 45:22

62:22 63:10 67:9 93:7

125:11 139:3 191:9

198:20 199:10,18

200:1,16

programs 38:18

progression 17:13,21

27:8 119:11

project 2:9 74:3

projecting 112:12

projects 109:2

promotion 201:19

properly 22:19 178:2

properties 30:16 59:1

property 63:5

proportionality 53:2

proposal 4:9

proposals 182:20

proposed 161:17

proprietary 28:15

139:18 200:20

protecting 138:19

Protection 2:6 5:8

provide 42:18 45:8 99:4

172:21 175:17,18

181:4 182:2 186:19

provided 8:1 10:16

109:8 139:4 148:3

176:18 187:18,21

199:11

providing 167:3 173:16

189:18

PS 121:22

PSC 87:4 99:12 100:21

100:22,22 115:4

123:5 159:22 161:14

PSCs 122:6 124:7,8
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Neal R. Gross and Co., Inc.

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psychics 93:1

public 1:6,17 4:9,11

8:16,17 10:19 134:4

136:1 139:16,22

166:14 181:14 182:9

185:1,2,3 186:5 187:9

195:10

publicly 140:4 157:8

165:16 166:3,7 172:3

192:11 195:15,21

196:2

publicly-available

173:17

publish 157:11,12

published 8:15 127:16

pull 89:21

pulled 41:9,11

pump 16:5,10 21:7,15

60:12 70:16 97:21

101:2 122:15

pumped 60:11

purchased 32:7 55:22

130:6 155:9 179:20

purchasing 124:3

pure 201:5

purge 59:20

purpose 7:14 9:6

168:17

purposes 13:13 54:9

88:22 89:1 145:19

pursuant 56:2

purview 177:3

push 155:12

put 10:22 17:5 26:18

32:10 41:10 52:3

64:14 68:19 82:1

106:10 111:14 113:2

120:17 122:20 125:11

159:17 163:19 164:16

172:10,11 173:7,12

179:12 180:8,22

181:20

putting 23:2 139:21

140:1 176:2

Q

QC 69:6

quality 80:10 84:22

85:20 90:16

quandary 167:19

question 10:13 20:8

29:7 32:1 34:12 40:20

48:13,16 51:18 53:19

55:12 56:21 65:1

72:18 74:22 87:15

110:13 113:17 116:2

121:2 134:9 138:10

146:8 164:22 165:10

177:2 178:2 183:8

184:22 185:10 186:17

194:14 195:12 196:10

196:19,20 197:19

199:9

questioned 189:6

questioning 53:1

questions 10:9 18:18

32:13 42:7 67:5 99:15

113:18 135:5 173:18

173:22

quick 95:10

quickly 10:12 79:22

172:18

quite 27:18,19 31:19

97:5 117:14 134:15

171:11

R

radical 159:18

raise 6:18,21 7:11 50:8

147:15

raised 155:18 189:2

raising 153:7

ran 54:11 96:15 152:9

range 53:22 65:5,12

110:20 179:6

ranges 35:18

rank 103:8

rare 149:16

rate 18:6 23:17,21 24:5

44:16,19 45:7,14 62:4

62:9 70:1,2,4,4,5

101:18 106:4,4

120:16 162:20 163:14

rated 116:6 122:3 126:6

158:12,14

rates 105:17 156:7

rating 11:15 37:15

44:19 64:9 67:11

112:3

ratings 62:4

ratio 49:9 50:3 74:9

87:7

rationale 195:17

ratios 122:18

Rawald 2:12 200:18

201:6,14

reach 13:12 133:4

138:13

reached 44:11

read 20:19 43:12

reads 175:11

real 48:13 74:13 96:12

137:13 147:14 154:10

163:18 180:10 200:15

real-live 157:20

realize 113:17

realized 139:18 140:6

really 7:16 11:9 13:9

28:22 36:15 37:6

38:10 39:12 52:17

53:6 64:16 66:9 78:11

79:2,6,8,11,17 80:16

82:22 96:7,20 97:22

108:8 126:3 128:21

136:15 139:1 145:21

151:5 157:10 160:4,6

160:12 167:18 168:3

169:6,13 172:16

197:2,9

reason 82:13 139:17

176:19 194:16

reasonable 75:15

reasons 132:14

recalculate 87:9 125:22

receive 180:21

received 182:15 183:2

183:11,14 184:2

recognize 7:1 82:10

recognized 7:12

record 5:3 95:16 175:7

194:4 201:6 202:4,7

redesign 106:18

redesigned 93:8

redoing 32:3

reduce 13:15

reducing 29:11

reduction 31:10 105:6

105:22 109:15 127:13

reductions 96:9

reference 24:16 88:1

referenced 87:20 132:8

referring 46:22

refers 90:13

reflect 93:12 109:10

115:2

reflected 114:10 122:2

154:17,18 155:19

156:5

reflects 115:1

REFPROP 30:16

refresh 99:22

refrigerant 23:18,19

25:11 30:9,15 31:20

33:8 53:14 58:21

70:13 73:3 83:7 85:20

98:3 140:19 142:6

refrigerant-side 24:11

refrigerants 67:15

refrigeration 2:12 11:8

18:1 22:15 33:16

85:21

refrigerator 14:19,21

15:1,6,7,10,12 24:9

24:13 62:1

refrigerators 14:7,9

refute 175:15

refutes 175:18

regarding 198:1

regression 179:13

regulatory 13:15

rejection 38:3

related 184:21

relations 128:2

relatively 49:17 50:19

52:16 65:22 93:22

108:15 126:5

released 165:15 168:13

releasing 136:9

reliable 108:18

rely 126:8 131:6 200:11

relying 117:17 160:10

160:19

remaining 177:16

remains 83:17

remarks 3:20 7:5 8:4

remember 78:3

remote 199:21 200:14

remotely 200:2

removed 17:6 18:4,7

removing 24:4

reopen 40:16

replacement 178:3

replacements 179:18

replicate 31:5

reported 130:17,19

reporter 5:1

represent 86:3

representation 17:16

30:15

representative 19:2,16

28:9 35:18 91:11

106:4,7 111:1 122:18

131:15 164:13

represented 143:10

representing 17:13

represents 17:17 18:3

18:13 110:4 121:10

request 122:21 139:15

188:13

requested 158:3

requests 128:6 188:16

requirement 8:10

170:14

requirements 13:4

research 90:20 200:19

resistance 25:14,17

26:1,6 29:4 72:10

Resources 2:10 5:19

responded 174:22

response 41:22 42:1

67:7 135:5 183:13

restricted 69:10

restriction 30:13

195:22

result 39:5 82:4 113:11
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Neal R. Gross and Co., Inc.

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179:14

resulting 41:16

results 31:17 91:12

100:7 111:19 172:9

177:5 178:1 183:19

200:9

resumed 95:16 175:8

retesting 110:15,18

retool 148:9

return 125:19

returns 191:14

reverse 32:7 47:21 48:8

61:3 62:7 82:18 97:17

119:3

reverse-engineered

99:1 122:5 155:10

reversed 139:16

reviewed 164:20

revise 40:13

revised 40:17

Richard 14:20

Ricketts 175:11

rid 199:22

right 8:6 9:12,15 14:13

19:6 20:2,8,16 22:4

27:11 34:7 37:16 40:1

41:9,14,18 42:11

43:20 46:3,4,6 47:4

47:12,15,18 48:18

49:14 50:13,16,20,22

50:22 53:10,14 54:5

56:17 57:14,19 58:2

59:11 61:13 66:2 67:4

67:8 68:13 73:3,5,20

74:4 75:13,16 77:2,14

77:17,21 78:1 83:5,14

83:16 85:18 86:9,14

86:15 88:9 89:9 90:11

92:2,2,13 93:13 94:12

95:8,21 99:21 106:14

110:13 111:22 112:5

113:12,14 114:1

117:5,6,14 124:2,14

125:16 127:14 130:5

131:18 132:13 134:19

135:14 137:12,20

138:16,22 139:6

141:22 144:4,10,16

145:4,14 147:12,20

148:8,10,11,12,15

149:1,20 150:12,20

151:9 152:14 153:1,1

154:4,13 155:4,4,7

159:17 160:17 161:8

161:10,11 164:3

169:2,5,6,15 170:2,6

170:10,11,16 172:5

172:13 176:5 182:6,6

184:5,13,16 185:8,16

185:17 188:9,12

189:3 190:1 193:9

194:3 198:17 200:13

risk 160:7

Rivest 2:13 6:1,1 11:3,3

12:2 78:11 115:11,18

115:20 116:8,11,14

116:22 117:4,7

132:10,14,21 133:2,7

133:12,18 137:8

138:20 139:10 141:7

142:11,13 147:7

151:8 152:1,5 153:1

153:22 154:7 182:4

183:8,17 184:6,11,13

187:15 188:2 190:5

190:21 191:4,16,20

192:2,7,12,14,18

road 4:4

roll 134:3

rolled-up 134:12 135:8

Ron 2:15 195:7

Room 1:17

root 199:8

rotary 66:7

rough 85:16

roughly 53:10 92:11

120:2

round 92:17,18 110:20

routines 15:13

routing 107:4

row 62:17 125:17

rows 62:18 125:10

132:8,8,8 156:8,9,13

rule 4:18 7:21 8:1 14:21

24:13 112:20 113:9

165:6 180:18 181:5

181:15,16,21 182:18

183:7 189:19 195:14

rulemaking 8:9,10 9:1

12:8 13:13 14:15,21

15:2,4 39:9,15,19

83:20 89:2,6 90:17

112:14 135:2 140:4

174:12

rulemakings 124:21

129:21

rules 112:6 170:13

185:14

run 4:17 36:18 49:21

54:18 68:12 69:10,19

71:18 78:4 84:14,17

86:12,16 89:6 101:13

105:3 107:16 110:1

111:20 121:8,8,12,12

127:8 136:2 144:6

149:3 154:9 156:16

156:18 159:19 171:3

173:4 175:17 177:4

189:11 190:4,20

191:12 199:21 200:3

200:8,14 201:4

running 32:3 68:19

74:16 82:5 108:14

153:17 159:21

runs 32:3 68:18 84:15

S

S.W 1:18

Salatino 2:13 196:10

197:21 198:10,15

sales 141:8,17,18

sample 91:11 138:11

138:21

sanity 83:11

Sarah 2:4 5:5,6

satisfy 112:2

saturated 72:11 73:8,12

158:17,17

saturation 33:8

save 69:3

saving 168:5

savings 168:11 175:13

saw 42:13 45:19 73:7

91:21,22 97:15 109:7

126:13

saws 85:15

saying 19:20 36:11 66:8

142:1 168:8 182:22

192:7

says 71:17 72:9 91:15

123:11 133:15 135:4

140:12 191:15

scale 87:7 157:5

scale-down 104:2

scaling 29:16

scatter 65:3 66:4

196:21

Schaefer 2:14 5:16,16

14:5,5 26:21,21 27:12

38:22 39:1,20 60:16

60:16,21 109:13,14

110:6 140:10,10

141:4,9,17 142:1,16

144:22 176:10,13,15

181:6,10 192:3

schedule 80:6

scientific 196:19

Scotsman 2:7,13 6:6

25:4 27:1

Scott 2:3 6:4 10:14 20:6

30:18 58:3 81:14

162:2

screaming 138:16

screen 43:14

screenshots 42:14

scroll 108:2,14,16,17

160:20

scrolls 160:4,6,16

second 33:12 91:22

92:18

secondary 53:16

seconds 54:22 84:16

88:5

section 9:17 87:11 95:4

95:20

see 4:13 9:8 14:6,7,10

15:17 17:1,18 20:20

22:9 25:4 26:8 34:1

37:3 39:4 40:7 43:11

44:4 58:7,9 62:8

68:21 71:4 73:10

74:17 79:6,8 82:12

94:6,12 104:5 107:19

109:16 113:16 114:18

123:7 125:6 130:5

133:20 134:5,20

135:14 137:13 142:17

145:2 147:19 151:11

156:12,19 165:17

168:20 173:4 179:17

182:16 184:6 186:5

189:14 192:3 200:2

200:16

seeing 8:6 39:10 45:16

45:17 101:22 142:22

149:7 153:4 167:12

192:17 196:21

seek 137:22

seen 41:13,15 42:2

185:3

sees 178:13

segregated 42:20

select 44:4 68:20 88:21

96:22 103:7

selected 89:1 102:12

selection 153:14

selections 93:12

sell 135:18,19,20

138:16 147:18 168:4

168:4

selling 138:18

sells 85:16

send 176:10

sense 10:9 72:9 73:19

137:9 144:5,10 150:7

188:8

sensitive 133:17

sensitivities 78:18

sensitivity 131:8

sent 188:18,20 190:10

separate 27:13 37:4

44:3 47:6,19 127:11

separately 14:8 41:6

61:11 121:12,13

sequence 153:15

sequencing 151:9
(202) 234-4433 Washington DC www.nealrgross.com

Neal R. Gross and Co., Inc.

219

series 11:14 21:10

42:21 43:7

serious 38:10

seriously 168:10

serve 46:14

served 46:13

serves 45:20 94:15

119:15

set 7:16 18:20 22:15

25:12 44:20 45:10,22

46:2 48:8,19 60:2

83:20 94:6,8,9 101:11

112:1 119:5 127:11

151:5 167:14 170:4

170:14 182:8 199:20

sets 18:10 24:6 56:6

95:3 99:1

setting 45:15

settings 54:11

seven 101:5

severe 167:18

shaded 100:22 165:21

shaft 80:9

shape 72:14

share 36:9 39:8 78:14

108:1 123:20,22

133:16,18 187:17,20

187:22

share/not 133:16

shared 107:22 133:22

sharing 40:12 172:16

194:14

sharpen 171:8

sheer 88:11

sheet 46:12 47:5 48:11

120:20 144:2,7

sheets 119:7,15

shell 107:5

Shepherd 2:14 6:10,10

134:7,7,19,22 137:3

137:17,21 138:9

194:13,16,21 195:1,4

195:12,21 196:4,8

shoot 35:20 76:11

shooting 87:21 90:13

short 86:7

shot 35:21

show 4:4 32:18 49:4

119:10 177:5 179:11

179:13,14,21 183:21

183:22 196:20

showed 26:22

showing 131:8 140:14

180:3,17

shows 7:13 11:10 17:21

100:4 113:13 179:12

shrink 78:11

shrunk 155:17

side 9:22 10:3,3 24:15

25:1 49:3,14 52:12

58:10,21 70:13 80:16

80:19 83:7 94:22

132:5 151:19 189:5

199:16,19

sides 25:20

sign 34:5 134:18

138:12

significant 186:14

similar 104:10 139:14

Similarly 21:6

simple 38:16 48:19

143:21

simpler 84:13

simplifies 80:16

simply 162:12

simulation 37:11 38:18

49:22

simulations 171:4

202:1

Simultaneous 13:5

36:3 55:21 56:20

58:18 73:1 77:22 85:6

188:3,6 190:8 191:21

192:9 193:5 201:21

single 73:16 158:18

193:8

sit 8:19 190:18 193:3

sit-down 172:6

site 22:4

sits 59:18

sitting 193:3

situation 64:19 139:14

171:20

six 46:12 54:18 69:18

69:20 88:6

size 26:11 29:2 30:12

57:3 59:9 65:9 82:1

99:9 105:12 126:3,19

138:21 140:18 144:1

144:2,4 154:1,9 163:4

163:13 179:22 180:2

189:8,8,9

sized 29:14 155:13

sizes 65:5 117:17

128:17,17,19

skip 7:3

slab 18:22 49:22 50:1

50:19 52:10

slide 7:2,3,10,13 8:8,21

9:20 15:18 23:13 24:8

24:22 178:17 180:14

180:15

slides 10:12 20:18

113:5 171:15 177:17

178:9

slight 156:14

slit 61:21

slots 25:6

slowly 17:20

small 17:12 71:17 108:8

136:14

Small-B 93:16 154:20

smaller 121:3,6 125:5

132:20 144:2,6 153:6

156:10

smalls 189:4

smooth 61:20

snapshots 84:19

SOBAN 2:15 175:20

176:4,9

society 169:10

software 42:14,15

86:18 200:20

solenoid 21:16

solid 64:16

solution 45:1

solve 15:13

somebody 66:6

soon 107:8

sooner 7:8

sophisticated 38:18

sorry 20:5 39:21 51:4

181:6 194:21 198:10

198:14,16

sort 14:18 17:15 44:8

44:10,17 47:1,3,5,22

48:5,15,22 51:11 55:2

59:22 60:10 62:5

63:19 68:9 69:6,11

71:5,8,11,15,16 74:12

78:5 80:11 81:1,11,12

84:4,14 87:9 93:18

94:11 97:11,13 98:7

98:12 101:18 102:5,7

102:13,14 103:7

104:12 114:2 115:3,7

119:2,5 120:2,11,21

121:2,10,16 122:3

123:3 125:12 126:16

127:11 143:8 145:11

156:14,18 161:3

180:7,15 190:22

194:17

sorts 99:17 102:7

Sound 95:11

sounds 163:18 196:18

source 139:20 157:10

157:12

sources 157:9

space 20:21 162:13

spaces 25:8

speak 5:2,4 19:18 195:4

197:20

speaking 13:5 36:3

38:13 55:21 56:20

58:18 73:1 77:22 85:6

188:3,6 190:8 191:21

192:9 193:5 201:21

spec 119:15

special 187:3

specialized 132:20

specific 59:3 104:11

110:11,22 111:2

113:8 128:4 135:2

137:1,7,10,10 166:8

173:22 182:5 187:16

specifically 133:16

178:1

specifications 82:19

specified 119:4

specify 62:2 81:5

spend 10:6 85:12 91:1

spent 198:12

sphere 79:16

spiraling 83:8

spit 76:19

spits 11:20 67:20

Spitz 2:16 5:10,10

SPM 123:4

SPMs 123:8

spot 7:20 8:14

spreadsheet 11:10 47:7

47:19 48:15 55:16

100:1 171:20 184:8

188:20

spreadsheets 82:15

184:7

squeezing 151:18

stab 168:22

stabilize 86:16

stacked 141:4

stagger 121:20

stakeholders 4:13 7:18

10:16

stamping 129:3

stand 99:21

standard 39:18 61:12

68:16 85:19 97:7

102:15 106:5 144:9

147:7 155:11,13

169:6 170:14

standards 1:3 2:8 4:7

144:14

stands 42:15

Star 5:8,11 55:15 93:7

159:11

Starr 2:17 166:15,15

167:20

start 4:21 5:4 7:2 27:19

30:10 43:9 44:22 45:2

45:3 48:7 64:17 68:19

73:14 86:21 94:17

95:20 108:10 112:12

119:9 126:20 138:19

156:6 177:13

start-up 86:7
(202) 234-4433 Washington DC www.nealrgross.com

Neal R. Gross and Co., Inc.

220

started 8:5 32:2 101:13

143:19 144:6 166:4

starting 45:5,8 73:8,13

129:13,14 132:16

starting-out 104:6

starts 17:20 26:8 46:4

69:12 71:7

state 38:19 64:21 86:8

states 73:3

static 84:15

status 28:4

statute 170:9

statutory 8:10 170:14

stay 49:16

stays 57:17

steady 38:19 64:21 86:8

steel 107:18

step 18:1,5,11 21:8

24:7 42:9 45:10 46:1

53:20 54:12 58:13

69:11 71:4 74:18 79:1

79:11 83:18 93:17

95:22 99:19 110:12

110:21 113:6,6,16,17

113:19 118:20 159:16

step-through 100:4

Stephen 2:14 5:16 14:5

38:22 60:16 109:13

140:10

stepped 96:4

stepping 43:10 54:20

104:16 161:20

steps 3:20 17:13 18:11

21:10 40:6 45:10

57:12 69:10 79:1

97:12 106:20 118:14

153:17,20 168:14

176:20

Steve 26:21

stick 7:8

stored 107:17

story 151:14

straight 84:2 116:19

119:3 157:1

straightforward 48:22

123:13 126:5

strategy 181:11

stream 107:10

strictly 44:14

strong 95:7

strongest 139:4

stuck 52:9

student 109:1

stuff 40:18 79:21 90:8

96:19 134:4 189:21

styles 122:18

sub 22:5 67:12

sub-cooling 22:7 62:2

submitted 55:14 131:22

185:1

subsequent 168:14

subsystem 38:15

suction 67:12 68:14

72:11 73:4,8 102:9

106:22 107:3,5,10

153:7 158:17

sudden 152:13 163:2

suddenly 140:6

sufficient 91:15

sufficiently 40:17

suggest 91:3,6 175:4

suggesting 66:8 138:7

suggestions 99:17

113:8

suggests 151:3

suite 94:12

sum 69:4 142:15

146:10 147:4,10,16

147:18

summary 70:1 71:8

187:19

summed 21:9

sump 17:14 18:9 45:2,9

59:18,21 60:9,14

super 119:4

superheat 59:5 67:12

81:6

superheating 22:5,7

supplementary 179:7

suppliers 111:17 154:7

supply 60:4

support 15:1,4,19

139:7 187:12

supporting 5:11

supports 175:19

sure 18:17 20:19 22:17

34:12 37:11 42:2

46:17 49:6 55:10 58:5

58:11,15,17 72:5,5

75:1 77:2,15 79:9

81:22 83:12 87:17

107:22 127:7,10

128:7 135:1,22

145:15 151:22 154:11

160:2 162:4 165:20

174:1,1 178:5 191:3

191:10 198:5

surface 23:16 26:6

27:22 49:8,18 50:3,8

153:5

surprised 66:16

swap 31:14,15 109:22

157:1 162:18 179:16

180:3 191:13

swapped 162:12

swaps 109:20 110:11

163:22

switch 101:2

system 17:6 18:2,3

59:12 65:2,3 66:18

81:8 87:2 95:1

Systems 2:7,13

T

T 61:12 153:6

T-A-B-L-E 3:7

ta 69:20

table 4:22 7:12 12:22

13:22 170:16 180:8

tables 174:11

tabs 42:21 44:5

take 4:16 17:8 30:20

39:17 51:1 57:10 69:9

69:18 70:7 76:3,20

80:3 88:5 95:10,22

108:16 109:20 110:12

111:9,17 114:4,7

117:15 127:2 128:6

129:16 147:10 156:3

161:1 163:21 165:17

168:22 171:7 173:11

173:19 176:8 187:1

197:5 201:15 202:4

taken 14:2 22:4 31:1

58:8 136:18 166:11

181:22

takes 17:9 54:18 64:21

69:18 84:16 85:15

talk 9:5,11,16 11:9 12:4

12:15 34:22 76:17

78:22 112:17 134:10

134:22 138:2 172:13

talked 29:1 127:3

197:15

talking 11:9 12:18

87:19 125:1 184:15

task 165:4

team 9:14 89:2 90:19

tear 80:21

tear-down 80:20

tear-downs 61:4

tease 145:20

tech 97:10 115:17

167:15,20

technical 9:13 18:21

106:15 188:10 198:21

technologically 39:10

technologies 94:1

technology 28:4,18,19

167:21

teleconference 2:22

tell 18:22 54:9,17 69:7

113:9 133:12,21

168:12 187:10

telling 174:3 198:13

tells 197:12

temperate 55:1

temperature 16:16,18

17:14 30:6 31:10 33:8

45:2,3 55:3 59:20

60:4 67:13 68:5,6

70:22 71:1,1,14,15

73:9 80:13 81:12 82:5

82:7 84:2 98:5,5

101:20 108:11 151:16

152:12 155:18 156:20

temperatures 17:22

45:19 55:1,2 63:13

67:12 72:2 74:8 77:1

101:22 102:8,8,9

163:12

tends 53:11 69:10

128:22

terms 7:4 12:7 20:2

31:18 40:12 45:18

51:7 54:20 60:9 70:11

70:14,20 71:18 72:6

79:15 82:19 86:19

90:7 93:15,17 94:16

99:8 100:14,16

102:10,11 103:8

116:15 126:18 129:9

129:22 131:4 144:11

163:20 164:7 170:3

180:16 195:14

terrible 150:14

test 23:1 27:6 30:14

31:2,5 33:21 36:9

41:3,16,20 42:22

44:14 45:16,20 51:11

55:7,12,13,16,18 56:6

56:9,10 57:5,8 60:18

60:20 62:12 64:1,3,14

65:18 66:15 74:13

76:7,10 79:3,4,18

81:13 85:5 86:10,12

88:8,13 89:6,10,15

90:16,20 91:18 93:16

94:18 95:1 96:11

97:18,18 99:5 101:15

101:16 102:1,4,7

104:15 109:19 110:22

112:13 114:6,16

115:15 116:12 119:14

120:18 121:5 124:20

170:21 171:3 191:10

191:15 201:11

tested 19:22 32:7 33:13

46:20 56:1 60:6 65:2

65:12 91:21,22 94:6

116:8 179:21 191:8

197:14

testing 13:16 19:8

20:20 31:12,12 33:5

37:18,19 40:22 42:18
(202) 234-4433 Washington DC www.nealrgross.com

Neal R. Gross and Co., Inc.

221

48:10 62:6 80:14 88:4

92:4,10 94:22 101:9

102:4 108:8 109:18

114:3 122:4 152:9

tests 110:1

Thank 12:13,19 28:20

36:14 37:8 42:4 77:6

90:11 132:6 148:20

199:14

Thanks 4:5 9:15 20:4

23:11 189:13 197:21

theoretical 144:13

146:4

theoretically 109:16

133:13 163:18

thermal 16:12,14 17:7

18:3 25:14 26:1,5,7

45:5 57:18 59:7,9

60:8,14 61:1 63:3

thermocouples 33:15

thermodynamics 82:22

they'd 8:4

thick 52:11

thickness 48:2 50:18

51:15 52:7 53:11

62:16 63:2

thicknesses 125:9

thin 25:17 52:8

thing 107:14 132:10

151:15 155:3

things 28:22 33:9 35:1

37:13 57:1 59:5 66:5

80:17 92:6 96:19

106:21,21 107:4

109:4 112:17 115:2

133:13 134:10 135:3

143:1 144:16 153:3

154:9 159:18,20

169:13 172:15 187:16

think 4:14,16,19 7:3

8:13 10:20,21 12:9

13:2,9,11,14 14:12

26:13 27:4 29:1 32:9

40:6,7,10 42:13 43:15

43:17 45:16 47:14

48:20 51:7,22 54:21

57:11 64:16 66:3,14

67:1 73:6 74:20 91:11

99:16,17 100:12

106:14 109:3,5

110:13,19 111:10

112:3 113:7 114:3,21

115:9 116:20 117:10

118:18 122:22 123:6

125:14 130:22 132:8

133:2 136:15,20

139:2,8 140:9,15

141:13,16 146:8

148:2 149:2,3,18

150:8,17 151:6 152:8

152:16 153:16 154:21

159:6,10 160:21

164:2 165:13 167:1

168:9 169:1,18 170:8

171:5 172:20 173:6

173:10,19 175:1

177:15 178:10,11,14

178:16,17,20 180:9

180:12 181:13,22

183:5,6 184:18 185:7

185:9,12 186:3,5,13

187:18,19 188:12,16

191:19,22 192:18

194:19 195:2 196:12

197:8,12,16 198:20

199:1,8 201:9,15,15

201:18 202:3

thinking 12:1 178:18

thinner 120:20

thinning 51:14

third 56:2 97:17 142:3

thought 78:17 152:2

170:19 190:22 193:13

199:16,19 200:17

three 26:15 51:7,9 71:5

117:12 128:19 150:6

150:12

three-and-a-half 71:6

threshold 186:1

throw 12:21

thrown 37:5

thumb 113:10

THURSDAY 1:10

time 4:18 5:4 7:1,16 8:3

10:7 17:13,22 18:5,11

18:11 21:6,8 33:7

34:4 35:1,22 37:2

44:13 45:9,10 47:6

50:21 53:20 54:3,12

57:4,16 69:11 70:1,6

72:1,19,19 73:14

74:10 80:4 85:12 88:3

91:12 93:6 98:1,6

100:12 101:18 102:3

111:16 123:15 129:22

174:6 180:10 192:6

192:19,22 199:6

200:12,12

timeline 180:17

times 11:17 17:12 21:6

23:19 24:1 55:13

69:15

timing 69:19

today 4:15 6:15 7:19,22

9:2,11 14:11 40:7

99:18 100:9 115:17

139:11 159:15 170:18

171:6 172:7 173:9,12

173:14 175:19 176:17

176:21 177:17 183:3

183:15 185:8,11

189:17

today's 183:13

told 133:8 170:17

Tom 2:4,7 6:6 193:4

198:13 199:15

tonic 85:13

tonight 201:14

tool 11:6,13 37:11

38:11 39:12 71:17,21

96:8

tooling 140:20 141:2

142:8,13 154:8

tools 15:11 38:1 144:15

top 17:16 68:6 76:13

93:20 94:3,19 97:3

115:6

torque 163:11

total 70:12 118:3 119:8

121:11 130:22 148:3

totally 14:9

touch 9:19 174:4 178:6

179:4

touched 177:18 178:22

track 20:7

tradeoffs 163:17

transcript 173:13

transducer 33:12

transfer 15:13 22:17

23:14,15,17,21 24:5

24:15 25:11 28:2

29:12 58:21 59:4 82:6

107:9

transferred 25:20 46:11

82:6

transformers 181:15,17

transparent 7:17

treat 28:15

trend 33:8 128:15

179:12

trials 56:3

trick 115:22 116:3

tried 66:6 81:22 96:22

103:6,8 147:13 200:3

true 53:9 132:21

truly 199:6

trust 56:17

try 4:3 7:6 10:11 43:21

62:10 76:12 79:8,13

81:1 99:7,19 110:19

112:7 131:16 136:9

145:20,22 146:22

152:16 170:1 180:9

188:9

trying 16:13 31:4 34:8

36:20,20 37:3 39:4,6

39:7 50:11 52:4,19,22

61:7 64:3 79:2,3,6,17

82:22 84:17 87:5

90:22 93:20 96:10,19

96:20 97:1,22 98:7

99:12 102:10 113:16

115:9 121:17 127:17

136:2 138:16 142:16

142:18 164:2 167:5

171:2 178:7 188:7

193:19

TSD 24:13 149:19

TSL 188:10

TSL392 188:1

tube 25:15 26:6 61:16

61:19 62:16,16,17,17

63:6 84:6,8 125:10,17

129:4 156:8,9,13

tube-fin 125:22

tune 112:11

turn 8:3 9:13

turns 82:20

turret 144:7

Twenty-three 115:18

two 44:13 48:6,14 79:1

84:16 88:5 92:14,15

99:1,6 102:19 106:20

106:21 108:6,13

115:15 121:4 125:8

128:18 142:2 143:22

174:14 179:20 190:19

191:12,20

TXV 30:10

TXVs 45:17

type 27:4 30:21 67:11

69:17 180:4

types 12:7 44:3 65:6

87:20 139:12 143:1

typical 23:15 96:16

typically 21:17 52:12

73:10,14,16 89:19

106:19 107:18 158:16

typing 190:19

U

U.S 1:1 2:6 5:7

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44:10 46:5,5,6,8,15

46:15 48:3 49:10

61:10 62:19,21 71:3

71:20 72:3,16 81:2,6

81:18,21,22 83:1 84:1

84:5,5 87:8 119:8,9

120:14

UAI 69:5

UAs 46:2 47:8 82:21

87:9 119:5 120:4,11

120:12 125:13

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(202) 234-4433 Washington DC www.nealrgross.com

Neal R. Gross and Co., Inc.

222

ultimately 59:6 68:12

100:7 105:4 144:19

154:14

un-muted 197:20

198:17

unable 158:7

uncertainty 65:10

197:2,17

unchanging 84:21

underlying 158:6

194:17

understand 20:11 35:5

36:6,7,21 38:4 77:20

113:5 118:13 134:16

147:13 151:6 165:5

166:6 169:11 177:1

188:8 189:2 197:7

199:12

understanding 31:1

94:1 118:11 195:10

understood 36:22

109:6,12 151:5

uniformity 53:7

unique 52:20

unit 26:12 51:11 60:3

79:7 93:18,19 94:5

99:4 100:11,11

102:11,12,14 104:20

106:7 114:5,5,8,9

116:5 119:20 121:6

126:11,13 146:5,17

154:17,19 155:3,6,9

155:14,22 164:17

units 30:21 35:12,13,14

60:6 61:4 88:8 91:3

98:7 99:2,3 104:8

105:16 128:18 188:1

188:17 190:3 191:7

unprecedented 194:4

unreasonable 197:8

up-sizing 82:12

update 24:19

updated 15:12

updates 12:15

upfront 147:1

upgraded 15:15 24:9

upwards 29:17

usage 59:18 60:18

157:1

use 3:14 13:9 15:21

21:20 24:19 27:4

28:13 32:12 38:2

49:14 53:21 54:1

56:18 63:17 66:5

67:15 84:6 85:22

86:18 87:12 89:5,6,8

92:8 95:5 96:9,9 98:2

101:8 103:3 117:16

117:22 127:13 129:20

130:3 145:1 146:9,13

166:13,14 167:2

169:1 179:3 181:12

191:11 192:19,22

195:9,15 197:1

200:22

useful 38:8 89:16

147:11

user 15:15 200:22

user-specified 61:17

uses 15:6,11 35:11

68:21

usually 63:5

V

valid 92:21

validate 79:18 112:11

119:16 120:22

validated 109:16

validating 118:9 175:14

validation 33:9 129:7

valuable 192:19

value 27:5,5,19 76:21

148:12

values 72:3 77:4

valve 21:16 59:22 60:1

valves 39:7

variability 82:11 197:11

variable 53:21 192:10

variables 68:16 192:20

198:22

variation 22:11

variety 132:14

Varone 14:18 15:3

vary 26:18 62:12

vein 40:2

velocity 29:11

vendors 123:19

verification 88:17,22

89:1,9

verify 88:18

version 15:5,16 24:10

78:6,10 100:1

versus 51:20 63:12

65:18 66:18 68:5,6

70:15,15 105:1 114:9

123:8 147:20 150:22

181:17 191:15

vertical 25:7

vertically 27:21

view 196:16

viewing 181:14

visual 17:16

W

waffle 25:2 27:14,22

28:11,17 52:21 119:7

waffle-plate 33:1

waffle-plated 96:16

waffle-type 32:15

waiting 10:21

walk 42:12,15 149:4,5

178:18

wall 62:16 125:8

Wang 24:15

want 6:22 7:17,17 11:4

11:5 12:21 13:14 20:7

29:10,13 53:10 56:11

69:3,10 74:18 84:4

89:21 90:17 92:3

103:22 111:4 112:19

118:8,8 128:5,5

135:22 136:9,15

137:16 138:12,20

139:10 140:8 149:8

160:2 165:7 168:3

169:12 170:5 178:18

180:11 189:10 194:2

194:3 196:18

wanted 9:5 13:21 35:16

76:10 147:21 164:16

192:3 197:20

wanting 199:2

wants 138:4

warm 16:15

warms 17:4

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wasn't 137:3 146:21

water 16:15,16 17:14

17:19 18:2,4,9 23:22

24:1 26:1,2 39:6,7

46:8 53:7,12 59:12,15

59:16,17,18,20,21

60:2,5,11,13,18 61:16

70:9 71:2,6 80:12

83:21 98:15,16 101:3

101:7,10 120:11

152:9 161:14 180:4

water-cooled 30:4

149:18 150:2 154:20

water-side 46:5,15

water/ice 24:4

watt 121:3

watts 126:14,14,21,22

wavy 61:20

way 13:12,15 21:22

25:22 27:22 37:2 38:7

63:15 86:11 97:9

111:14 112:8 128:22

141:10 145:10 146:13

147:5 154:15 159:14

160:11 161:21 171:14

177:3,10 179:3

ways 43:22 167:4

we'll 4:15,16 7:6,8 8:6

9:12 12:18 19:13 38:6

58:12 75:21 76:15

77:15 79:22 80:3 81:9

87:2,9 93:16 94:10

95:7,10 153:10

176:20 178:5 180:15

193:12 196:6 197:5

200:18 201:2,7,14

we're 4:19 6:17 8:13,13

9:2,10 10:21 11:9

20:19 26:2 27:10 31:4

34:8 36:12,15,19,19

37:3 38:20 39:4,5,15

39:17 40:5,7 43:5

44:7 49:2 59:4 60:22

62:6,15 79:3,5 80:5

82:5 83:22 84:1,5,20

87:3,5 90:15,22 92:5

94:3,7 96:8 98:7

101:22 124:2 175:16

175:19 176:16 178:5

178:18 180:17,18

181:2 185:8,12

193:19,21 194:9,10

198:8 201:22

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16:20 19:14 42:1

51:22 77:12,19 78:16

83:20 84:7 91:11,15

94:5 177:18 178:10

178:12,22 181:15

191:8 197:15 199:11

Web 22:4

Webinar 6:12,21 7:11

196:10

website 100:3

websites 157:10

weighed 119:19

weight 18:14,21 19:1,2

19:8 20:2 21:19,20

44:9,11,17 48:12 50:1

50:1 51:6,20 101:17

119:13 120:17 129:16

weights 51:14 129:17

welcome 3:9 173:15

175:9

well-known 30:17

went 35:19 69:14 85:21

85:22 93:8 95:16

108:2 150:6 164:8

175:7 196:13 202:7

weren't 104:11 118:5

171:12,13 173:3,9

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12:9,17 13:2 14:12

18:17 19:4,7,10 20:13

21:1 23:9,12 26:13,17

27:3,15 28:16 29:6,19

30:1,3,22 31:4 32:16

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Neal R. Gross and Co., Inc.

223

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76:8 77:12,15 78:16

81:18,21 83:3,6,13

86:11 87:11,17 92:17

92:20 93:1,11 95:4,19

109:6 110:3 112:22

113:4,15 115:22

116:3,9,13 118:5,12

122:16,20 123:17,22

124:16,19 128:8,12

130:12,16,21 131:3

131:10 132:1,3

135:11 143:16 145:8

146:15 148:21 151:12

153:10,16,20 158:9

158:22 160:14,21

161:19 163:20 164:5

177:9,12,15 178:20

188:12,16,21 190:1

193:2,6 195:2,6,19

196:2 197:4

wide 189:5

width 62:16 100:16,19

114:12 121:14 125:14

125:16,17 155:17,21

156:10,14

willing 172:12 175:15

Windows 15:4 78:6,10

78:15

Windows-based 15:16

winds 26:9

wire 62:9

wish 5:4

wonderful 198:11

wondering 164:10

word 39:16 54:6

work 14:9 19:11 24:17

24:19 36:9 40:14

52:17 53:6 163:21

164:17 177:4,18

178:17 185:16 199:2

worked 24:21 139:9

145:11 195:8

working 36:12 177:7

180:18 181:2 198:19

works 4:14 39:13 84:16

189:22

world 96:12 162:16

worried 144:17

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108:21 111:4 141:8

164:5,9,17 182:19

wrapped 78:12

wrong 29:8 69:8 111:9

116:4 130:5 169:5

174:15,16

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X 89:21 152:18

Y

year 4:19 8:11 89:21

92:8,11 180:19

years 92:7,9,14,15

139:8,19 141:5

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yesterday 198:12

Z

zero 69:14 97:9

0

0 69:15

0.010 54:14 69:11

0.7 64:5,6,7 65:5,7,8,16

65:20 66:4 67:3 68:22

196:15,21,22

1

1 52:11 125:2

1,000-pound 163:1

1.3 65:8

1.6 120:7

1:30 175:5

1:37 175:8

10 63:16 109:1 152:6

158:16 161:12,13

164:5

10:40 95:13

10:41 95:16

10:55 95:13

1000 1:17

11 6:16

11.9 121:4

11:00 95:17

110 158:17

1100-pound 163:2

12 149:1

12:22 175:7

120/20 159:3

126-cube 26:10,12

13 149:14

134a 67:16,16

14 190:3

141 164:22

15 21:1 33:12 69:15

72:21

15-minute 95:10

15-second 55:5

16 126:14,19

166 120:5

16th 10:18

17.5 116:15

17th 8:16

18 114:22 121:3 161:12

19 1:11 114:22

190 120:7

194 34:14

1995 12:16

19th 8:19

1st 8:11

2

2 7:3 102:14 114:12

2.3 125:2

2:04 202:7

20 69:16 73:15

20/120 159:1

201 3:20

2011 15:1 92:18

2012 92:18 174:12

2014 1:11 8:16

2015 8:12

202 3:22

20s 73:13

21/20 160:6

22 20:18 32:6 187:1

188:2 189:4,5

23 20:18 32:6 116:8

126:21

23.6 116:6

25 122:17 123:4

26 178:9

27 178:9,9

280 105:17

3

3 117:7 118:22 125:1

30 54:21 64:9 141:5

182:13

30-second 55:5

300 26:14 106:6

300-column 34:17

31 177:21 179:16

310 105:18

320 105:18

35 180:14

4

4 3:9 117:7,8 133:9

140:12

4.86 103:22 104:3 105:1

40 91:3 111:20,21

40-percent 150:13

41 40:20 56:5,6 164:21

42 3:18

45 122:17 123:6

45/130 158:21

47 154:18

48 189:3

5

5 63:16 164:1

5-B 24:13

5.08 156:22

5.1 117:3

5.25 104:1,4 105:1

50 50:9,9 114:11 126:14

142:2 188:14

50-percent 126:19

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6 3:12

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6.5 120:1 155:11 156:2

6.51 119:2

60 91:9

65 122:17 123:6

7

7 78:10,15 104:7 156:1

70 60:7

71 126:22

75 122:17 123:3

78 60:7 80:15

8

8.0 30:16

80 91:6 143:10

9

9 3:15 8:8

9.76 120:4

9.8 120:2

9:00 1:19

9:05 4:2

90 81:13 120:10

90/70 60:6 80:15 81:13

903 104:21

90s 14:17 24:18

939 120:5

97 14:22

975 104:21
224 

C E R T I F I C A T E 

This is to certify that the foregoing transcript 

In the matter of: Public Meeting 

Before: us DOE 

Date: 06-19-2014 

Place: Washington, D.C. 

was duly recorded and accurately transcribed under 

my direction; further, that said transcript is a 

true and accurate record of the proceedings. 

Court Reporter 

NEAL R. GROSS 

COURT REPORTERS~ TRANSCRIBERS 

1323 RHODE ISLAND AVE., N.W. 

WASHINGTON, D.C. 20005-3701