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APPENDIX C: TECHNICAL DESCRIPTION OF THE REVERSE ENGINEERING

COST ESTIMATION METHODOLOGY

C.1 INTRODUCTION

The manufacturing cost estimation methodology, or "reverse engineering", is a detailed,

component focused, activity based technique for rigorously estimating the manufacturing cost of a

product (direct materials, direct labor, and plant overhead costs). Arthur D. Little (ADL), a

Department of Energy contractor, has applied its technology based cost assessment successfully to

a broad range of products in various stages of development from early R&D to production. This

Appendix describes the technical aspects of the approach as applied to residential central air

conditioners and heat pumps (CACs). Refer to Chapter 4 in the body of the Technical Support

Document (TSD) for more information on assumptions and context.

C.2 TEAR-DOWNS

Our first step was to perform "tear-downs" on equipment samples that are typical of today's

minimum efficiency air conditioners. A tear-down is a thorough disassembly of the equipment

followed by a detailed inspection of the parts and subassemblies. We performed the minimum

number of tear-downs required to assess all four CAC product classes. Since split and packaged

equipment have different configurations, we selected one baseline (10 SEER) model from each class-

-both from the same manufacturer. Since heat pumps (HPs) and cooling-only air conditioners (ACs)

have similar configurations differing only by additional HP components, we selected only HPs rather

than a combination of HPs and ACs.

C.2.1 Split Heat Pump Tear-down

Initially, a representative 10 SEER split HP outdoor unit with a indoor fan-coil unit served

as the basis for the bill-of-materials (BOM) used by both split system products (AC and HP). We

disassembled the outdoor unit first and then the indoor fancoil unit. We made every attempt to

perform the disassembly in reverse of the actual assembly process, and the BOM reflects the order

of these operations. We assumed that major sub-assemblies arrive pre-assembled at the final

assembly line. For example, most outdoor units feature fans and wiring that are integrated into the

top cover subassembly. The model assumes that a assembly worker on the final assembly line

receives the top cover assembly as a single piece, places it atop the rest of the completed outdoor

unit, and attaches the wiring leads and screws. Sub-assemblies can also feed into other

sub-assemblies before being integrated on the final assembly line. This mimics cellular

manufacturing techniques often found in today's state-of-the-art plants.

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C.2.2 Packaged Heat Pump Tear-Down

We used the same processes and cost models to establish costs for 3 ton packaged systems

as we had for split systems. Since packaged systems have a significantly different configuration,

however, we tore down a representative 10 SEER packaged HP (PHP) model. As we had done for

split systems, we derived the packaged AC cost model directly from the PHP cost model by

removing HP-specific parts.

C.2.3 Confirming the Tear-Down Results

We confirmed our cost and weight predictions using a number of methods. Initially, we

compared shipping weight predictions with published shipping weights. Since cost and weight tend

to be highly correlated in manufactured goods, the ability to accurately predict weight is usually an

important indication of the accuracy of the cost model. However, we discovered that at least one

equipment manufacturer had published erroneous weight data. For example, the discrepancies at 16

and 17 SEER for evaporators are caused by inaccurate OEM submissions. Since we could not verify

all the published weights, we could not place as much confidence in the weight verification as we

would like.

Table C.1 below shows the degree to which the equipment weights we predict differ from

the weight listed by the manufacturer. Positive values denote where the model calculates weight in

excess of reported shipping weights.

Table C.1 Predicted versus Listed Equipment Weight

Split AC Split HP Packaged Packaged

AC HP

Efficiency Outdoor Indoor Outdoor Indoor

Level Unit Unit Unit Unit

(SEER)

10 3% 7% 0%/O -2% 1% 2%

11 -6% 8% XX X

12 6% 8% 6% -7% -2% 3%

13 -3% 18% 1% -4% 2%

14 -9% -1% -7% 4%

15 -14% -14% -22% -11%

16 -8% 1% -22% -16%

17 -8% -3%

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We attributed under-predictions primarily to the weight of non-efficiency items such as sound

blankets, cosmetic grilles, etc. found on today's high efficiency equipment that we assume would

be omitted from those units once they became commodity units. Section 4.2.5 in the TSD describes

this concept more fully.

After the PHP tear-down and the quotation process, we also tore down a 12 SEER split HP

outdoor unit to verify some of our assumptions regarding higher efficiency equipment Even though

different OEMs produced our 10 and 12 SEER equipment samples the physical similarities between

the two models were striking. This tear-down was not meant to establish a BOM since we based the

BOM solely on the 10 SEER baseline. Rather, we wanted to confirm the components used in the

design and that the BOM model was using the correct component data. Our coil model predicted the

weight of the 12 SEER coil within /2 pound of its actual weight. The difference was due to the larger

end-plates of the coil which, being made of galvanized cold rolled steel, do not contribute

significantly to equipment cost. Furthermore, the 10 and 12 SEER defrost controllers were quite

similar despite being manufactured by different companies.

C.3 SELECTING ADDITIONAL EQUIPMENT SAMPLES

Although we had detailed information on the three tear-down samples, we needed many more

samples to span a broad range of efficiency levels in each of the four product classes. That approach

would provide us discrete cost points to compare against the cost curves that ARI would provide.

We asked OEMs to provide us with a list of equipment models, one model at each efficiency level,

that they felt best represent baseline equipment. We then asked for detailed information on the

components and physical parameters that we identified as being related to the efficiency of the

product.

Four OEMs responded to our request and provided data to us directly for equipment they

considered to most closely represent baseline models at each efficiency level. That yielded

information on 62 samples. We supplemented that data with nine more selections from ARI's

Unitary Directory and associated physical data from ARI's Product Attribute database and OEM

literature. Where possible, we obtained exact specifications for purchased parts.

Our subsequent cross-checking exposed a few errors in the data we had collected. We

corrected all aberrations either by replacing them with correct values or by omitting the unit

altogether if we determined that the entire configuration did not represent a prospective baseline

system. For example, one 11-SEER SAC unit that we selected was comprised of a 10 SEER outdoor

unit and a variable speed fan-coil unit. Because such combinations would be significantly more

expensive than a more efficient outdoor unit coupled with a less sophisticated fancoil unit, that

sample was an obvious outlier. We therefore omitted it on the presumption that the mass market will

not sustain large price differences at the baseline efficiency level.

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C.4 CREATING THE BILL-OF-MATERIALS

We used the tear-down process to create a complete and structured bill-of-materials (BOM)

for the baseline equipment. We built four separate BOMs from our two tear-downs--one for each of

the four product classes. While we completely dismantled each piece of equipment, we characterized

every part according to weight, dimensions, material, quantity, and, the manufacturing processes

used to fabricate and assemble it.

As a simplification, we assumed that the structure of the BOMs we developed for the teardown samples also applied to the samples that we did not tear down. This allowed the physical

parameters and parts to vary across samples, but fixed the assembly process (and the associated plant

equipment) for each sample. Since a typical OEM manufacturers a wide range of products in the

same plant and assembly line, we consider this a valid simplification for isolating efficiency-related

changes within a product platform and within a single manufacturer. Our simplification cannot

reproduce cost variability across OEMs due to differences in assembly processes or product

platforms.

The BOMs incorporate all materials, components, and fasteners with estimates of raw

materials and purchased parts and sub-assemblies. We based our assumptions on the sourcing of

parts and in-house production on our previous industry experience, recent information in trade

publications, and discussions with high and low-volume original equipment manufacturers (OEMs).

To reinforce our understanding of the industry's current manufacturing practices, we also visited

several manufacturing plants. These visits focused on observing and characterizing current

manufacturing practices.

Figure C.1 illustrates a small section from a structured bill of materials. It shows:

1. Part number: Assigned during disassembly

2. Description: A description of the part. The step-like ladder approach identifies logical

groupings of parts to denote which go together where in the assembly process. A reverse

indentation denotes parts that are sub-assembled onto a part prior to final assembly on

the manufacturing line.

3. Category: Primary part material for raw material costing and sorting purposes

4. V: This entry denotes whether a part is a purchased component or fabricated in house.

We assumed that all plastic components were outsourced.

5. #: How many parts are assembled in a given assembly step.

6. OD, Length, Depth, Thickness: Physical parameters that describe the finished part.

7. Painted surface: Describes how many square inches of paint are required for each part.

We assumed that any "green field" plant would rely exclusively on pre-painted steel and

priced the paint coatings accordingly.

8. Weight: Final weight of part in pounds.

9. Material cost: Final material cost of the part (calculated), accounting for scrap losses but

excluding required assembly, painting, fabrication, or joining costs.

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10. Labor: The manual labor (in seconds) required to handle all parts or assemble them into

the unit. Some parts such as fasteners also require additional tool time which is

accounted for in the later section of the BOM spreadsheet.

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Figure Sample IC.1 I of m I I .aA I I .ls

Tabl1.02e lis.s these pre l _ CB sses. 4 4 4 1.S I Collarfing 1 ntegl fast B s -MOu.01. W..tl~rap _l__ I I KM____O_ Y I 335 38.5 7 00 M _3 135 1.0 15

I Fir. Pni E Deburring faste - Other 2 1.07 - Fif- p ..I . P- 5-, 1 T 287 1F r"- - - 1m

.0The tear-don procs -d 2 2 o t s 5d 5prove s th 06tart s ,.0 M. A-~P-1 TI -3i WAS ~ _ 25 30.75 0 .a0 ON o &sM 1.0 a

p .1a 0 and material pric, F Y 2 amor014 0p44 0.785 squ 0 t .

,.-2- lE»WFlPfSbO8 M Y. 3 2.5 _I _ _ . 8

Li3 IEw 1DFan A ens Panl GC 3 - _ __ , 0.5 0.04 7.57s81 7T38 1% 11.74

hapter 4) descibs or assu a daa tions nd sources. Ti_ s seio 0.briy s . e proc 4 e 

To ov o _ ens --- l 'Y '-- ' =- .BC-5 1.1 ~ W o Sos. o s| | 2 _ _ITO 0 .75 0 | .la 0.0 = 

,. 2 -_ _M~c» ,, r nr k I Gd _ 3725 T 31 I 0G 0o.08 02 no T 0.82 a

Figure C.1 Sample structured bill of materials

The BOMs also capture the major manufacturing processes required to make selected parts.

Table C.2 lists these processes.

Table C.2 Manufacturing Processes

Captured in the Bills-of-Material

Fabrication Assembly/Joining

Fixturing Adhesive bonding

Stamping Spot welding

Brake forming Brazing

Cutting/shearing Press fitting

Collaring Integral fasteners

Deburring Other fasteners

C.5 ADDITIONAL PRODUCTION COST DATA

The tear-down process and the development of the structured BOMs provide the starting

points for estimating production costs, but we still needed information on manufacturing operations,

part and material prices, wages, plant equipment amortization, and plant overhead. The TSD

(Chapter 4) describes our assumptions and data sources. This section briefly describes the processes

we used to gather the data and how we used them.

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C.5.1 Labor and Factory Overhead

Information on equipment and tooling costs, typical process cycle times, and materials used

for fabrication were obtained from the ADL manufacturing databases. Plant equipment suppliers

provided us with details concerning equipment capabilities and processing parameters (cycle times,

scrap rates, etc.). Fabrication cycle rates are directly entered into the model and depend on part

complexity and the processes used.

C.5.2 Depreciation

Depreciation, or amortization, is the accounting process by which capital costs are allocated

to production volume. Amortization occurs over a period of time so that at the end of that time, all

capital costs are accounted for in the full cost of producing the product over that time. For example,

if a manufacturer produces 1 million air conditioners over ten years and amortizes a $10 million

investment over the same ten years, each air conditioner produced during that time would include

$10 in amortization charges. The methodology we used to allocate depreciation depended on whether

we assumed the plant machinery to be dedicated or non-dedicated to the production of the sample

product.

Dedicated machinery is tied solely to the production of the sample product. During times

when a piece of dedicated machinery is not needed for that product, it sits idle. The entire capital cost

of a piece of dedicated machinery is amortized across the annual volume of the sample product.

Conversely, non-dedicated machinery may be used to produce another product when it is not needed

for the sample product. Only a fraction of the capital cost of non-dedicated machinery is allocated

to the sample product based on the time the machinery was used to produce the sample product. For

example, a non-dedicated press that was used 55 percent of the time to produce the sample product

would allocate 55 percent of its depreciation charges to the sample product and 45 percent to the

other products to which it is associated. A dedicated press, on the other hand, would allocate 100

percent of its depreciation to the sample product, even if its utilization was 55 percent, since the

press is not used for any other production.

We assumed that all coil fabrication machinery is non-dedicated. Given the substantial

equipment and space investment costs associated with coil lines, OEMs install universal, high

volume machinery to achieve high production efficiencies and low costs. The total coil volume and

the many configurations manufactured by OEMs ensure that no one coil type dominates the coil

production centers. Thus, every type of product will only be a fraction of the total output, and

amortization charges are based on that fraction.

We assumed that machinery other than coil assembly machinery is dedicated. Unlike old

plant designs, new facilities usually feature several production lines under one roof that handle

almost every part of fabrication and assembly. Instead of belonging to a functional department, all

employees and equipment are dedicated to individual product lines. The industry is moving to this

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manufacturing philosophy. Our model reflects the possibility that a piece of production machinery

can be used to produce different parts for the same product. That is, each product, not each part, has

a set of dedicated machinery. All equipment and process costs are spread across the entire production

volume, unlike the coil model where costs are assigned on the basis of utilization.

We also allocated labor to operate a piece of machinery based on whether the machinery is

dedicated or non-dedicated.

As equipment utilization rates approach 100 percent, the costs associated with dedicated vs.

non-dedicated equipment costs become equal. However, few dedicated pieces of equipment ever

achieve 100 percent utilization due to lack of demand, capacity mismatches between process steps,

scheduled downtime, etc. Thus, non-dedicated equipment results in lower overall costs per part, as

depreciation, maintenance, and other costs are only assessed on the basis of how much time each part

uses a piece of equipment.

As equipment samples vary, so do the manufacturing equipment and labor requirements.

Depreciation charges therefore also vary across equipment samples. Figure C.2 shows how the model

allocates depreciation and labor to a sample product.

f_ iB gPa4.00 E 1300 5.00 14. 00 16.00 11 . 00 200 1 000 1400 . I 0.00 3700

Figure C.2 Sample Fabrication and Assembly Summary Table

C.5.3 Parts and Materials

Cost estimates for raw materials and purchased components were drawn from ADL's

manufcturing databases and supplemented with information obtained from manu.facturer and

supplier sources. We adjusted our cost estimates as appropriate to include price discounts typically

seen in the industry as the result of high-volume purchases.

As purchased components make up the bulk of the unit costs, special consideration was given

to establishing accurate OEM-level price data. Through manufacturer submissions, industry

literature, and active research, we were able to ascertain the exact specifications for the majority of

components used in the AC and HP units under investigation. For the relatively few purchased

components we could not identify, we substituted parts from comparable equipment

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For example, a manufacturer's technical data sheet may convey that a sample condenser uses

a certain type of compressor supplied by a particular company, but may not state the precise size or

part number. In the cases when distributors could not positively identify the part, our industry

experts would compare the known attributes of similar condenser units (such as coil size, compressor

specs, capacities, etc.) and those of the equipment under question. We would then select a specific

compressor size based on an interpolation of the available data.

We then consulted local distributors, wholesalers, parts suppliers, and OEMs to determine

high-volume pricing. We applied a discount to the prices we received from each of those sources

based on their place in the distribution chain.

These discounts were based on markup data and our previous experience in the industry. The

many different data sources and the large purchased parts list also allowed for some cross-checking

of price data and discounts. We selected those that, in our best judgement, most likely reflected

actual prices to OEMs. The discount on each component were a function of the total dollar volume

of a typical OEM's account with a typical supplier. Since we are modeling high volume OEMs who

deal with one supplier for each component, this results in substantial discounts relative to retail or

wholesale prices.

C.5.4 Coil Fabrication Costs

While purchased components make up the largest part of costs in AC and HP units, coils,

generally fabricated by the OEM, also are important. To model the fabrication cost of both the indoor

and outdoor coils, we constructed a second cost model for each of the four product classes. These

costs were then fed back into the main cost models.

C.6 STRUCTURE OF THE COST MODELS

Once we had collected all of the information required to estimate production costs for each sample,

we used spreadsheet models to perform the required calculations. As stated earlier, the costs for each

CAC unit are calculated with the help of two cost models: the main model and the coil model. Figure

C.3 illustrates the structure and relationship of the spreadsheets that comprise the two models.

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Global dta d

Output and othi Cer oo working p ramafr* c rOntI i-ta

Page Outputut ndothr o- P |_______ working para mters

Coil model

Request for Deprecitlon

Information Labor costs

Indirnct materia.

dCo Mcoilol, Equipmnt dedication

Component e_ Crot & d

decriptions PadecrptP Purchsd compomnnt

costs and weights

Figure C3 Overall Model Structure

C.6.1 Main Cost Model

The main model serves holds data and performs the calculations that determine the

production cost of the final assembled equipment. It contains a number of worksheets that perform

different functions.

C.6.1.1 Global Controls Sheet

This worksheet sets parameters such as production volume and wages while also displaying

the cost results by sub-assembly. The basic parameters (e.g. days available per year) of the Global

Controls page are linked to the coil model. A sample section of those controls is shown below

(Figure C.4). Shaded fields are also varied in the Monte-Carlo analysis (refer to Section 4.2.9 in the

TSD).

The Global Controls page also shows costs broken down by sub-assembly and cost category.

The results were shown in two tables. One table (Figure C.5) featured costs by major sub-assembly

and cost type by efficiency level. Another more detailed table formed the basis for the CAC Cost

spreadsheets (see Appendix A in the TSD). Cost breakdowns to this fine level allowed us to zero on

the differences between equipment across efficiency levels and facilitated the calibration and

industry review processes.

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A_:,11_

:400 'A '. i',' :?es~i 7 i I

Figure C.4 Sample of Global Controls Sheet

Figure C.5 Sample table showing major sub-assembly and plant

overhead costs

C.6.1.2 Manufacturer Data Sheet

The data tables in this worksheet define most equipment-specific attributes of the CAC

samples. For example, coil parameters are stored here for use by the coil model and the quantity,

weight, and cost results from the coil model are returned and stored for use by the BOM and Global

Controls worksheets.

The headings in Figure C.6 are taken from our questionnaire. Further data tables capture

individual parameters such as cost per hairpin tube, volume of enclosure, and other physical

parameters. These parameters form the basis for several calculations. For example, in the Global

Controls page the total enclosed volume of a CAC unit drives the size of the manufacturing facility

and the assembly line. This reflects the assumption that, all else equal, a plant dedicated to producing

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larger equipment requires more storage and assembly space than a plant dedicated to producing

smaller equipment

Figure C.6 Sample Data Fields from Manufacturing Data

Sheet

C.6.13 Purchased Parts Sheet

Three types of data are found on the Purchased Parts page: major purchased components

unique to each model; minor, common purchased components used by every model; and, raw

material costs for parts that are fabricated in-house.

Q A

Major Purchase Components d 

Every major purchased part has its own data table, and every sample draws its information

from a line item in its table. The exact model numbers for major purchased parts are entered here

along with multiple price quotations and part weights. The quotations come from multiple sources

and are discounted as appropriate. These tables determine at least 45 percent of total cost The weight

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and minimum cost for each line item is passed on the BOM page, which queries the results by unit

number.

Minor, Common Purchased Components

These include items such as connectors, wire, fasteners, board transformers, and other smaller

parts that OEMs are likely to purchase from outside suppliers. We gathered price quotations from

multiple sources (suppliers, distributors, prior experience) in quantities that are typical for OEMs.

We then passed the lowest price on to the BOM entry which queries the table unless we had reason

to believe that a higher price was more credible.

Raw Material Costs

When parts are made in-house from materials such as pre-painted sheet metal, the main

model estimates the cost of the part from the cost of its raw material. We obtained raw material

prices from common suppliers in volumes typical for OEM requirements. The BOM scales the

material price for each fabricated part based on the calculated weight of the part and its price per unit

of weight

We assume that OEMs fabricate most of these parts themselves. One general exception is

plastic parts, which require a different set of skills and facilities than typical OEMs possess. The

price of a plastic part is a function of the underlying value of the resin, and an assumed cost to

manufacture the part (including the tool) with an applied gross margin. A purchased part premium

applies to any fabricated part, including a plastic part, that we assume is manufactured elsewhere.

The purchased parts premium is set at 150 percent over the underlying material cost. Given that few

parts meet this description, this simplification has only a slight impact on the overall cost estimate

for the equipment

*'-- I xtal -renr.,-: .,;, a'.. < sI.

t4w~$b £. :,-_.:: e g e : ..

Figure C.7 Sample Raw Material Data Tables and Part Price List from the

Purchased Parts Sheet

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C.6.1.4 Bill-of-Materials Sheet

The Bill-of-Material (as illustrated in Figure C.1 and discussed in Section C.4 of this

Appendix), serves as a structured assembly tree, summarizes fabrication and assembly tool data, and

calculates production costs based on the price of the part or material and the labor and machinery

required to fabricate or assemble it. The BOM sheet also adjusts many other costs in response to

changes in physical parameters. For example, the model adjusts baseline sheet metal sizes to

incorporate different enclosure sizes. The size of the fiberglass (FG) insulation is a function of the

sheet metal it has to cover and the efficiency level of the unit (insulation is thicker at higher

efficiencies). Many fastener quantities and labor costs are also a function of the sheet metal walls

they are to secure. The result is that every unit cost estimate is unique, using the initial BOM as a

starting point

Labor, parts, materials, and depreciation costs are aggregated by sub-assembly and linked

back to the Global Controls page.

C.6.1.5 Equipment Data Sheet

This page is a list of the installed costs for all the plant machinery involved in the production

of the CAC samples. An implicit assumption is that the plant equipment required to produce lower

efficiency samples is also able to produce higher efficiency samples without any modification. The

equipment data shows installed equipment costs, depreciation life, whether equipment is dedicated,

labor requirements per station, and consumables costs. The installed costs include price quotations

for the equipment plus markups to account for installation labor and auxiliary equipment

Eauiment Cost I$/Unitl

Dgonriatin 12 20 15 1

Straidt Deamciation oer piece of Eouipment / Yea r

Finance Cost per piece per year 4A5L

Dedicated Equipnment? N N N N

Pleone orMachi 1 1.f5 00

I,,.~,.,~,~ see B-elow e~ t~ I ~i~0 a& I .1I

Figure C.8 Sample from Equipment Data Sheet

C.6.2 Coil Model

The coil model converts each coil's physical descriptors into coil costs by calculating the

number of fins, hairpin bends, U-bends, take-offs, and coil ends. The model accounts for tube

diameters, spacing, material choices and thickness, rifling, and other physical characteristics that

affect coil cost. It relies on a process-based cost model that accounts for every fabrication and

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assembly step. We obtained fabrication equipment costs and processing times from equipment

vendors and raw material prices from vendors based on their pricing at the time.

Al Fin ,-,11- Lacing _ Bend Oven Tesi Tine

Copper |

CoilCl StoSok

EBnds Ring Machine

CSoil

Ends

End Stock

Figure C.9 Flow Diagram for Coil Model

Our coil flow diagram (Figure C.9) represents a state-of-the-art coil manufacturing facility

sized for high-volume production. While this coil line may not be representative of all manufacturing

facilities, we assume it to be representative in general.

The coil and main cost models work together. The equipment data sheets in the main model

supplied the coil parameters, and the coil cost and weight results were returned to the main model.

Any update in either model is automatically reflected in the other model.

Because our computational power was limited, we severed the links between the main model

and the coil model during a Monte Carlo analysis. However, the effects of the Monte Carlo analysis

on coil results were easily incorporated within the main model. For example, if when labor costs

varied in the main model, the labor costs components of a coil would simply vary in proportion.

Thus, a large number of Monte Carlo trials (2,500 to 3,000) could be accommodated over the span

of a few hours.

Figure C.10 is a section of a row header for a indoor coil unit showing fin and U-bend

parameters. Weights and quantities are sent to the Manufacturing Data Sheet in the main model.

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Final Mass of Single Fin (Ib)

# of Fins per Unit

Annual Fin Production (K)

Evap. Coil Fin Material

Evap. Fin Material Scrap (%)

Evap. Coil Fin Length (in)

Evap. Coil Fin Width (in)

Evap. Coil Fin Thckness (in)

Evap. Coil Density (fins/in)

# of Large U-Bend per Unit

Mass per U-Bend (lb)

# of U-Bends per Unit

Annual Production of U-Bends (K)

Figure C.10 Coil model

output fields

The manufacturing costs for coils are also captured in a table. Figure C.11 illustrates the

header of the table which is linked back to the Manufacturer Data Sheet. Some of these costs are

direct costs, such as material costs used by the BOM Sheet, while others are overhead costs

referenced by the Global Controls page.

Condenser Assy

1 2 3 4 5 6 7 8 9

Low Level Labor I Material Prices

Direct Indirect Coil Ends Tube Fin U-Bend

Figure C.11 Coil manufacturing costs calculated in the coil model

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