Sent: Thursday, May 19, 2011 8:27:35 PM

To: Llenza, Charles

Cc: CCE

‐ 2010

‐ BT

‐ CE

‐ 0014; Armstrong, Ashley

Subject: Example of a Practical Test Method for a CRE Manufacturer, dockets# EERE

‐ 2011

‐ BT

‐ TP

‐ 0024

RIN1904

‐ AC46 EERE

‐ 2010

‐ BT

‐ CE

‐ 0014 RIN1904

‐ AC23

Auto forwarded by a Rule

Hi, Charles

– I am sending this email a second time to your newer email address and an alternate docket

email address. No need to forward to Ashley, Laura or Brenda. Thanks.

– Carl Roberts

Hello Ashley,

At the recent CRE public meeting, you requested an example of how a CRE manufacturer might test all

the thousands of permutations of our products in an accurate and practical manner. Please find the

example below. The attached Word document is identical to the text in the body of this email. I hope

this helps. Please let me know if you have any questions, and thank you for giving us the opportunity to

participate in this process.

Best regards,

Carl Roberts

Engineering Lab Manager

Zero Zone, Inc.

110 N Oakridge Drive

North Prairie, WI 53153

Phone: 262

‐ 392

‐ 6400

Fax: 262

‐ 392

‐ 6450

Toll Free: 800

‐ 247

‐ 4496

Direct Ext: 262

‐ 392

‐ 1349

carl.roberts@zero

‐ zone.com

www.zero

‐ zone.com

Example of a Practical Method for a Commercial Refrigeration Equipment Manufacturer to Determine

the Efficiency of Their Equipment

Carl Roberts, Zero Zone, Inc., 5/19/11

Purpose of this example:

The purpose of this discussion and example is to help DOE understand the dilemma being faced by our

industry, and to seek feedback from DOE. The requirement for testing

– as currently envisioned

– is so

impractical and so burdensome it would put us out of business. The example below describes a practical

solution to this dilemma.Scope of this example:

The scope of this discussion and example is CRE equipment class VCT.RC.L, but the methodology

described is also applicable to VCT.RC.M and may be applicable to other equipment classes.

How manufacturers typically determine efficiency:

Manufacturers typically use a combination of physical testing and analytical methods to determine the

efficiency of customized CRE products. Manufacturers generally follow the guidelines in ARI Standard

1200 when developing these efficiency ratings. Each manufacturer develops their own unique offsets for

component change outs listed in ARI Standard 1200. Manufacturers also develop their own rating

offsets for other features like taller cases, longer or shorter cases or additional features like windowed

ends. Over time manufacturers develop tables with the energy use of various components from tests

run on different pieces of equipment. Typically the manufacturer tests one sample piece of equipment

to generate a starting point. The sample tested may be the highest energy user for the product line or

could be the product with the highest sales volume. Once the base line is established the rating offsets

are developed and published in product specification sheets. To provide guidance to the industry the

CRM section plans to update ARI Standard 1200 to include rating offsets for features and case sizes.

Why we must use this method of determining efficiency:

We offer multiple options for lighting systems, fan motors, anti

‐ sweat systems, case height and number

of doors. All of these choices affect efficiency. There are also add

‐ on options that affect efficiency, such

as windowed ends and cases joined back

‐ to

‐ back. We offer our customers any combination of these

options, so the number of possible combinations ranges into the tens or hundreds of thousands. It is

impractical to physically test every combination we sell. To resolve this dilemma, we use a factorial

statistical tool

– an AEDM supported by physical testing

– to accurately determine the efficiency of the

thousands of possible combinations we cannot physically test immediately. Certainly, we cannot test all

the actual combinations within the next 18 months, but we can test dozens or hundreds; which assures

there is always some physical testing that validates each component of the factorial statistical model

that works for all combinations.

Example:

This example starts with simple one

‐ factor comparisons of physical tests, and becomes more analytical

with each step. In each step, an independent factor, such as fan motor choice, affects the dependant ARI

Standard 1200 factor of CEC, and usually one other ARI1200 factor, such as FEC. In this example, we

determine the efficiency of 128 possible combinations by using about 14 pairs of actual tests selected

from a pool of about 24 individual, one

‐ sample tests. Each one

‐ sample test consisted of several

consecutive 24

‐ hour runs where standard deviation was calculated for each test to assure repeatability

for that specimen. Each reported measurement below is the average for the series of 24

‐ hour runs. A list

of individual abbreviations is below this example. The abbreviation used below for each one

‐ sample test

is: TestNumber/#ofDoors/OpTemp/CaseHeigh t/DoorEff/Lighting/FanMotor/FanBlade. The units for the

ARI1200 parameters are kWh/day, with sample standard deviation (n

‐ 1 method) in parenthesis.1. We compared two fan motor options, expecting to prove the direct application of ARI1200

Appendix E without any adjustment works for alternate fan motors. Surprisingly, we discovered

that the adjustment factor is greater than 100%. Notably SSC fan motors move more air.

a. We analyzed three pairs of actual tests where the only difference was the fan motor,

namely Morrill brand SSC (ECM) model B12BVHUBA1 versus EM&S brand PSC model

S4BEB9E141. All six tested configurations were VCT.RC.L, standard height cases having

Optimax2 model 5

‐ foot LED lighting, ELM model doors with model 101 frame, and one

fan motor per door, each driving a AirDrive brand 30°/8” fan blade. Two samples were

2

‐ door cases and four samples were 5

‐ door cases. Some had Optimax2 5

‐ foot LED

lighting and some were T

‐ 8.

i. 01a/5/LT/StdHt/ELM/O2LED5’/SSC /30 CEC=15.834 (0.184), FEC=2.050 (0.038)

ii. 01d/5/LT/StdHt/ELM/O2LED5’/PSC /30 CEC=16.490 (0.071), FEC=3.384 (0.004)

iii. 01b/5/LT/StdHt/ELM/ T

‐ 8 /SSC /30 CEC=17.156 (0.114), FEC=2.047 (0.040)

iv. 01c/5/LT/StdHt/ELM/ T

‐ 8 /PSC /30 CEC=17.789 (0.119), FEC=3.392 (0.024)

v. 04a/ 2 /LT/StdHt/ELM/O2LED5’/SSC /30: CEC=6.827 (0.013), FEC=0.774 (0.004)

vi. 04b/ 2 /LT/StdHt/ELM/O2LED5’/PSC /30 CEC=7.115 (0.045), FEC=1.331 (0.003)

CEC Test 01d

versus

Test 01a Test 01c

versus

Test 01b Test 04b

versus

Test04a

PSC 16.490 17.789 7.115 kWh/day

SSC 15.834 17.156 6.827 kWh/day

# of doors 5 5 2

difference 0.131 0.127 0.144 average std dev

0.134 0.009

FEC 01dv01a 01cv01b 04bv04a

PSC 3.384 3.392 1.331 kWh/day

SSC 2.050 2.047 0.774 kWh/day

# of doors 5 5 2

difference 0.267 0.269 0.279 average std dev

0.271 0.006

Comparison to theoretical CEC

‐ to

‐ FEC ratio at

‐ 11.97 adjusted dew point and 2.261 COP

0.1201 Change in FEC / COP

0.1339 Change in CEC

111.6% Percentage of the difference in fan energy that shows up in the case CEC

(greater than 100% due to some interaction between case and fans chosen)

2. We compared ELM to ELM2 to prove the application of ARI1200 Appendix E with a percentage

adjustment works for alternate doors and frames.

a. We analyzed two pairs of actual tests where the only difference was ELM/101 versus

ELM2NHG/LEF. All four configurations were 2

‐ door LT, one 8” fan per door and

Optimax2 LED lighting. One pair was VZ with 30° fan blades and 5

‐ foot LEDs. The otherpair was VZT2 with 36° fan blades and 6

‐ foot LEDs, but we show the percentage

adjustment applied to ARI1200 Appendix E is essentially the same for both pair.

i. 04a/2/LT/StdHt/ ELM /O2LED5’/SSC /30: CEC=6.827(0.013), AEC=5.664(0.007)

ii. 04f/2/LT/StdHt/ ELM2 /O2LED5’/SSC /30: CEC=6.454(0.008), AEC=4.805(0.002)

iii. 05b/2/LT/Tall/ ELM /O2LED6’/SSC /36 CEC=7.851(0.050), AEC=5.538(0.006)

iv. 05c/2/LT/Tall/ ELM2 /O2LED6’/PSC /36 CEC=7.474(0.059), AEC=4.533(0.006)

b. The average per

‐ door differences were: dCEC=0.202 (0.023) and dAEC=0.466 (0.052)

CEC 04av04f 05bv05c

ELM 6.827 7.851 kWh/day

ELM2 6.456 7.474 kWh/day

# of doors 2 2

difference 0.186 0.189 average st dev

0.187 0.002

AEC 04av04f 05bv05c

ELM 5.664 5.538 kWh/day

ELM2 4.805 4.533 kWh/day

# of doors 2 2

difference 0.430 0.503 average st dev

0.466 0.052

Comparison to theoretical CEC

‐ to

‐ FEC ratio at

‐ 11.75 adjusted dew point and 2.268 COP

0.2055 Change in FEC/COP

0.1870 Change in CEC

91.0% Percentage of the difference in AEC that shows up in the case CEC

3. We applied a similar treatment for T

‐ 8 lighting versus Optimax2 LED lighting, to determine the

impact of this choice on CEC and LEC. In this step we analyze pairs that include some of the tests

used in the previous steps.

4. We applied a similar treatment for Tall versus Standard Height cases, except we determine the

impact of case height on all the ARI1200 parameters (CEC, FEC, LEC, AEC & DEC). Again, we re

‐

use some previous tests.

5. We compare pairs of equivalent 2

‐ door and 5

‐ door cases and create linear functions to

determine the efficiency of 3

‐ doors and 4

‐ doors for each family of cases. We are confident in

this analytical method

– given the linearity of the input wattages and the physical dimensions.

Then we validate this analytical method by physically testing a few 3

‐ and 4

‐ doors, and

comparing these tests to corresponding 2

‐ and 5

‐ door tests already run.

6. In the final step of this example, we use foam data, lab test data and thermodynamic theory to

create a sensible analytical method to predict the efficiency of back

‐ to

‐ back cases. We are

confident in this mostly

‐ analytical method

– especially given the thermal symmetry and absence

of electrical loads affecting the back of the case. Then, we validate this method by physically

testing one or two back

‐ to

‐ backs.• The statistical confidence of this methodology comes from building a factorial model using dozens

of pairs drawn from a large pool of one

‐ sample tests. In each step of the analysis, we compare the

outcome to the expected outcome predicted by theoretical engineering principles, and to the

corresponding outcomes from other sets of tests. Flawed tests become obvious standouts,

because they don’t fit in with the trends of related results. We also check the outcomes against

our previous years of testing experiences. Thus, we are confident that this methodology will

adequately determine the efficiency of our products.

• Moreover, this methodology removes the impractical burden of testing the tens of thousands of

combinations we may sell. The above example allows us to determine the efficiency of 128

products, from about 24 one

‐ sample tests, each consisting of 3 to 6 consecutive 24

‐ hour test runs.

The exponential power of this methodology

– applied across the much larger array of actual

options

– allows us to accurately determine the efficiency of hundreds of thousands of models

(combinations of options) using only hundreds of tests

.

Abbreviations and Definitions

101 A model of standard

‐ efficiency (door) frame made by Anthony International

AEC Anti

‐ sweat Energy Consumption in kWh per day, with standard deviation in parenthesis

AEDM Alternative Energy Determination Method

AHRI Air

‐ Conditioning, Heating & Refrigeration Institute (a.k.a.: ARI until recently)

ARI 1200 The recognized method

‐ of

‐ rating standard for display refrigerators (a.k.a.: AHRI 1200)

Back

‐ to

‐ Back Essentially two display cases joined at the backs, except they actually share a single back

wall for efficiency

CEC Compressor Energy Consumption in kWh per day, with standard deviation in parenthesis

COP Coefficient Of Performance in watts of refrigeration per watt of input energy

CRE Commercial Refrigeration Equipment

dAEC Change in AEC

dCEC Change in CEC

dFEC Change in FEC

DOE United States Department of Energy

ECM Electronically Commutated Motor

ELM A model of high

‐ efficiency glass door made by Anthony International, usually with non

‐

heated glass

ELM2NHG Another non

‐ heated glass version of high

‐ efficiency glass door, designed to be used with

a LEF (low energy frame)

FEC Fan Energy Consumption in kWh per day, with standard deviation in parenthesis

LEC Lighting Energy Consumption in kWh per day, with standard deviation in parenthesis

LED Light Emitting Diode, a type of lighting system

O2 Optimax2

Optimax2 A model of LED lighting system made by Anthony International

PSC Permanent Split Capacitor, a type of mid

‐ efficiency fan motor

SSC Solid State Commutated motor (synonymous with ECM), a type of high

‐ efficiency fan

motorStdHt Standard Height, 83” high case

T

‐ 8 Fluorescent lighting with a vertical T

‐ 8 lamp

Tall Tall height, 88” tall case

VCT.RC.L Vertical display merchandiser with transparent (glass) doors, operating at LT (<32°F)

VCT.RC.M Vertical display merchandiser with transparent (glass) doors, operating at MT (>32°F)