Category AIRCRAF DESIGN

Midrange Aircraft (Airbus 320 class)

All computations carried out herein follow the book instructions. The results are not from the Airbus industry. Airbus is not responsible for the figures given here. They are used only to substantiate the book methodology with industry values to gain confidence. The industry drag data are not available but, at the end, it will be checked if the payload-range matches the published data.

Given: LRC Speed and Altitude: Mach 0.75 at 36,089 ft.

Dimensions (to scale the drawing for detailed dimensions)

Fuselage length = 123.16 ft (scaled measurement differs slightly from the drawings) Fuselage width = 13.1 ft, Fuselage depth = 13 ft.

Wing reference area (trapezoidal part only) = 1,202.5 ft2; add yehudi area =

118.8 ft2

Span = 11.85 ft; MACwmg = 11.64 ft; AR = 9.37; Д1/ = 25deg; Cr = 16.5 ft, X = 0.3

H-tail reference area = 330.5 ft2; MACH-tail = 8.63 ft V-tail reference area = 235.6 ft2; MACV-tail = 13.02 ft Nacelle length = 17.28 ft; Maximum diameter = 6.95 ft Pylon = measure from the drawing Reynolds number per ft is given by:

Reperfoot = (Vp)/n = (aMp)/n = [(0.75 x 968.08)(0.00071)]/

(0.7950 x 373.718 x 10-9)

= 1.734 x 106 per foot

Drag Computation Fuselage

Table D1 gives the basic average 2D flat plate for the fuselage, CFfbasic = 0.00186. Table D2 summarizes the 3D and other shape-effect corrections, ДCFf, needed to estimate the total fuselage CFf.

Figure D1. Airbus 320 three-view with major dimensions (Courtesy of Airbus)

Table D1. Reynolds number and 2D basic skin friction CFbasic Parameter Reference area (ft2) Wetted area (ft2) Characteristic length (ft) Reynolds number 2D CFbasic Fuselage n/a 4,333 123.16 2.136 x 108 0.00186 Wing 1,202.5 2,130.94 11.64 (MACw) 2.02 x 107 0.00255 V-tail 235 477.05 13.02 (mACvt) 2.26 x 107 0.00251 H-tail 330.5 510.34 8.63 (MACht) 1.5 x 107 0.00269 2 x nacelle n/a 2 x 300 17.28 3x 107 0.00238 2 x pylon n/a 2 x 58.18 12 (MACp) 2.08 x 107 0.00254

Table D2. Fuselage ACFf correction (3D and other shape effects) Item ACFf % Of CFfbasic Wrapping 0.00000922 0.496 Supervelocity 0.0001 5.36 Pressure 0.0000168 0.9 Fuselage-upsweep of 6 deg 0.000127 6.8 Fuselage-closure angle of 9 deg 0 0 Nose-fineness ratio 0.000163 8.7 Fuselage nonoptimum shape 0.0000465 2.5 Cabin pressurization/leakage 0.000093 5 Passenger windows/doors 0.0001116 6 Belly fairing 0.000039 2.1 Environmental Control System Exhaust -0.0000186 -1 Total ACFf 0.0006875 36.9

Therefore, the total fuselage CFf = CFfbasic + ACFf = 0.00186 + 0.0006875 = 0.002547.

Flat-plate equivalent ff (see Equation 9.8) = CFf x Awf=0.002547×4333 =

11.3 ft2.

Add the canopy drag fc = 0.3 ft2.

Therefore, the total fuselage parasite drag in terms of ff+c = 11.33 ft2.

Wing

Table D1 gives the basic the average 2D flat plate for the wing, CFwbasic = 0.00257, based on the MACw.

The important geometric parameters include the wing reference area (trape­zoidal planform) = 1,202.5 ft2 and the gross wing planform area (including Yehudi) =

1,320.8 ft2. Table D3 summarizes the 3D and other shape-effect corrections needed to estimate the total wing CFw.

Table D3. Wing ACFw correction (3D and other shape effects) Item A CFw % of CFwbasic Supervelocity 0.000493 19.2 Pressure 0.000032 1.25 Interference (wing-body) 0.000104 4.08 Excrescence (flaps and slats) 0.000257 10 Total ACFw 0.000887 34.53

Therefore, the total wing: CFw = CFwbasic + ACFw = 0.00257 + 0.000889 = 0.00345.

Flat-plate equivalent: fw(Equation 9.8) = CFw x Aww = 0.00345 x 2,130.94 = 7.35ft2.

Vertical Tail

Table D1 gives the basic average 2D flat plate for the V-tail:

CFVTbasic = 0.00251 based on the MACVT; V-tail reference area = 235 ft2

Table D4 summarizes the 3D and other shape-effect corrections (ACFVT) needed to estimate the V-tail CFVT.

Table D4. V-tail ACFVT correction (3D and other shape effects) Item ACfvt % Of CFVTbasic Supervelocity 0.000377 15 Pressure 0.000015 0.6 Interference (V-tail – body) 0.0002 8 Excrescence (rudder gap) 0.0001255 5 Total ACfvt 0.000718 28.6

Therefore, the V-tail: Cfvt = CFVTbasic +ACfvt = 0.00251 + 0.000718 = 0.003228 Flat-plate equivalent fVT (see Equation 9.8) = CFVT x AwVT = 0.003228 x 477.05 = 1.54ft2.

Horizotal Tail

Table D1 gives the basic average 2D flat plate for the H-tail:

Cmnask = 0.00269, based on the MACHT; the H-tail reference area SHT =

330.5 ft2

Table D5 summarizes the 3D and other shape-effect corrections (ACFHT) needed to estimate the H-tail CFHT.

Table D5. H-tail ACFHT correction (3D and other shape effects) Item ACfht % of CFHTbasic Supervelocity 0.0004035 15 Pressure 0.0000101 0.3 Interference (H-tail – body) 0.0000567 2.1 Excrescence (elevator gap) 0.0001345 5 Total ACfht 0.000605 22.4

Therefore, the H-tail: Cfht = CFHTbasic + ACfht = 0.00269 + 0.000605 = 0.003295

Flat-plate equivalent fHT (see Equation 9.8) = CFHTxAwHT = 0.003295×510.34 = 1.68 ft2.

Nacelle, CFn

Because the nacelle is a fuselage-like axisymmetric body, the procedure follows the method used for fuselage evaluation but needs special attention due to the throttle – dependent considerations.

Important geometric parameters include:

Nacelle length = 17.28 ft Maximum nacelle diameter = 6.95 ft

Average diameter = 5.5 ft Nozzle exit-plane diameter = 3.6 ft Maximum frontal area = 37.92 ft2 Wetted area per nacelle Awn = 300 ft2

Table D1 gives the basic average 2D flat plate for the nacelle:

CFnbasic = 0.00238, based on the nacelle length

Table D6 summarizes the 3D and other shape-effect corrections, ACFn, needed to estimate the total nacelle CFn for one nacelle.

For nacelles, a separate supervelocity effect is not considered because it is accounted for in the throttle-dependent intake drag; pressure drag also is accounted for in the throttle-dependent base drag.

Table D6. Nacelle ACFn correction (3D and other shape effects) Item ACFn % Of CFnbasic Wrapping (3D effect) 0.0000073 0.31 Excrescence (nonmanufacture) 0.0005 20.7 Boat tail (aft end) 0.00027 11.7 Base drag (aft end) 0 0 Intake drag 0.001 41.9 Total ACPn 0.001777 74.11

Thrust Reverser Drag

The excrescence drag of the thrust reverser is included in Table D6 because it does not result from manufacturing tolerances. The nacelle is placed well ahead of the wing; hence, the nacelle-wing interference drag is minimized and assumed to be zero.

Therefore the nacelle: CFn = CFnbasic + ACFn = 0.00238 + 0.001777 = 0.00416 Flat plate equivalent fn (Equation 9.8) = CFnt x Awn = 0.00416 x 300 = 1.25 ft2 per nacelle.

Pylon

The pylon is a wing-like lifting surface and the procedure is identical to the wing para­site-drag estimation. Table D1 gives the basic average 2D flat plate for the pylon; CFpbasic = 0.0025 based on the MACp.

The pylon reference area = 28.8 ft2 per pylon. Table D7 summarizes the 3D and other shape-effect corrections (ACFp) needed to estimate CFp (one pylon).

Table D7. Pylon ACFp correction (3D and other shape effects)

Item	ACpp	% of CFpbasic
Supervelocity	0.000274	10.78
Pressure	0.00001	0.395
Interference (pylon-wing)	0.0003	12
Excrescence	0	0
Total ACFp	0.000584	23

Therefore, the pylon CFp = CFpbasic + ACFp = 0.0025 + 0.00058 = 0.00312 Flat-plate equivalent: fp (see Equation 9.8) = CFp x Awp = 0.182 ft2 per pylon.

Roughness Effect

The current production standard tolerance allocation provides some excrescence drag. The industry standard uses 3% of the total component parasite drag, which includes the effect of surface degradation in use. The value is froughness = 0.744 ft2, given in Table D8.

Trim Drag

Conventional aircraft produce trim drag during cruise and it varies slightly with fuel consumption. For a well-designed aircraft of this class, the trim drag of ftrim = 0.1 ft2 may be used.

Aerial and Other Protrusions

For this class of aircraft, faerial = 0.005 ft2.

Air-Conditioning

This is accounted for in the fuselage drag as ECS exhaust. It could provide a small amount of thrust.

Aircraft Parasite Drag Buildup Summary and CDpmin

Table D8 provides the aircraft parasite drag buildup summary in tabular form.

Table D8. Aircraft parasite drag buildup summary and CDpmin estimation Wetted area Aw ft2 Basic CF ACf Total CF f (ft2) CDpmin Fuselage + undercarriage 4,333 0.00186 0.00069 0.00255 11.03 0.00918 fairing Canopy 0.3 0.00025 Wing 2,130.94 0.00255 0.00089 0.00346 7.35 0.00615 V-tail 477.05 0.00251 0.00072 0.00323 1.54 0.00128 H-tail 510.34 0.00269 0.00061 0.0033 1.68 0.0014 2 x Nacelle 2 x 300 0.00238 0.00178 0.00415 2.5 0.00208 2 x Pylon 2 x 58.18 0.00254 0.000584 0.00312 0.362 0.0003 Rough (3%) 0.744 0.00062 Aerial 0.005 0.000004 Trim drag 0.1 0.00008 TOTAL 25.611 0.0213 Notes: CDpmin = °.°213. Wing reference area Sw =1,202 ft2; CDpmin = f/Sw ISA day;36,089-ft altitude;and Mach 0.75.

ACDp Estimation

The ACDp is constructed, corresponding to the CL values, as given in Table D9.

Table D9. ACDp estimation Cl 0.2 0.3 0.4 0.5 0.6 ACDp 0.00044 0 0.0004 0.0011 0.0019

Induced Drag, CDi The wing aspect ratio:

AR

induced drag, CD=0-034CL

Table D10 gives the CDi corresponding to each CL.

Cl	0.2	0.3	0.4	0.5	0.6	0.7	0.8
CDi	0.00136	0.00306	0.00544	0.0085	0.01224	0.0167	0.0218

Table D10. Induced drag

Total Aircraft Drag Aircraft drag is given as:

CD = CDpmin + &-CDp + CDi + [CDw = 0]

The total aircraft drag is obtained by adding all the drag components in Table D11. Note that the low and high values of CL are beyond the flight envelope.

Table D11. Total aircraft drag coefficient, CD

Cl	0.2	0.3 0.4	0.5	0.6
CDpmin		0.0213 from Table 7.9
&Cdp	0.00038	0 0.0004	0.0011	0.0019
CDi	0.00136	0.00306 0.00544	0.0085	0.01224
Total aircraft CD	0.0231	0.02436 0.02714	0.0309	0.03544

Table D11 is drawn in Figure D2 to show that the PIANO software aircraft drag checks out well with what is manually estimated in this book; hence, the PIANO value is unchanged.

Figure D2. Aircraft drag polar at LRC

Engine Rating

Uninstalled sea-level static thrust = 25,000 lb per engine. Installed sea-level static thrust = 23,500 lb per engine.

Weight Breakdown (with variations)

Design cruise speed, VC = 350 KEAS Design dive speed, VD = 403 KEAS Design dive Mach number, MD = 0.88

Limit load factor = 2.6

Ultimate load factor = 3.9

Cabin differential pressure limit = 7.88 psi

Component Weight (lb) Wing 14,120 Flaps + slats 2,435 Spoilers 380 Aileron 170 Winglet 265		Percentage of MTOW
Wing group total	17,370	(above subcomponent weights from [10])
Fuselage group	17,600	(Torenbeek’s method)
H-tail group	1,845
V-tail	1,010
Undercarriage group	6,425
Total structure weight	44,250
Power plant group (two)	15,220
Control systems group	2,280
Fuel systems group	630
Hydraulics group	1,215
Electrical systems group	1,945
Avionics systems group	1,250
APU	945
ECS group	1,450
Furnishing	10,650
Miscellaneous	4,055
MEW	83,890
Crew	1,520
Operational items	5,660
OEW	91,070
Payload (150 x 200)	30,000
Fuel (see range calculation)	41,240
MTOW This gives:	162,310

Wing-loading = 162,310/1,202.5 = 135 lb/ft2 Thrust-loading = 50, 000/162310 = 0.308 The aircraft is sized to this with better high-lift devices.

Payload Range (150 Passengers)

MTOM -162,000 lb

Onboard fuel mass: 40,900 lb

Payload – 200 x 150 = 30,000 lb

LRC: Mach 0.75, 36,086 feet (constant condition)

Initial cruise thrust per engine: 4,500 lb

Final cruise thrust per engine: 3,800 lb

Average specific range: 0.09 nm/lb fuel

Climb at 250 KEAS reaching to Mach 0.7

Summary of the Mission Sector Sector Fuel consumed (lb) Distance covered (nm) Time elapsed (min) Taxi out 200 0 8 Takeoff 300 0 1 Climb 4,355 177 30 Cruise 28,400 2,560 357 Descent 370 105 20 Approach/land 380 0 3 Taxi in 135 0 5 Total 34,140 2,842 424

Diversion-fuel calculation:

diversion distance = 2,000 nm, cruising at Mach 0.675 and at 30,000-ft altitude Diversion fuel = 2,800 lb; contingency fuel (5% of mission fuel) = 1,700 lb

Holding-fuel calculation:

Holding time = 30 min at Mach 0.35 and at a 5,000-ft altitude Holding fuel = 2,600 lb

Total reserve fuel carried = 2,800 + 1,700 + 2,600 = 7,100 lb.

Total onboard fuel carried = 7,100 + 34,140 = 41,240 lb.

Cost Calculations (U. S.$ – Year 2000)

Number of passengers 150

Yearly utilization 497 trips per year

Mission (trip) block time 7.05 hrs

Mission (trip) block distance 2,842 nm

Mission (trip) block fuel 34,140 lb (6.68 lbs/U. S. gallons)

Fuel cost = 0.6 U. S.$ per U. S. gallon

Airframe price = $38 million Two engines price = $9 million Aircraft price = $47 million

Operating costs per trip – AEA 89 ground rules for medium jet-transport aircraft:

Depreciation Interest Insurance Flight crew Cabin crew Navigation Landing fees Ground handling

$6,923 $5,370 $473 $3,482 $2,854 $3,194 $573 $1,365

Airframe maintenance $2,848 Engine maintenance Fuel cost Total DOC DOC/block hour DOC/seat DOC/seat/nm $1,208 $3,066 (5,110.8 U. S. gallons) $31,356 $4,449 $209 0.0735 U. S.$/seat/nm

[1] 3 15

Maximum camber Maximum thickness of The last two digits are

position in % chord maximum camber in 1/10 maximum t/c ratio in %

of chord of chord

[4] Civil aircraft design: For the foreseeable future, aircraft will remain subsonic and operating below 60,000 ft (large subsonic jets <45,000 ft). However, aircraft size could grow even larger if the ground infrastructure can handle the volume

[5] Type 1: Unprepared Surface. A grass field or a gravel field, for example, is des­

ignated as a Type 1 surface. These are soft runways that are prone to depres­

sions under a heavy load. Low-pressure tires with a maximum 45 to 60 lb per

square inch (psi) and a total ESWL load less than 10,000 lb are the limits of operation on a soft runway. The ground friction is the highest and these airfields are not necessarily long. This type of runway is the least expensive to prepare and they serve remote areas, as an additional airfield close to a

business center, or as a private airfield. Small utility aircraft can operate from Type 1 airfields.

[10] The main-wheel load is computed at the aftmost CG, which gives lREAR = 9.4­

1.7 = 7.7 m (25.26 ft).

• Equation 7.2 gives Rmain = (IrEAr x MTOW)/lBASE = (7.7 x 11,000)/8.7 = 9,736 kg (21,463 lb).

• The load per strut is 4,868 kg (10,732 lb). It is better to keep the wheel load below 10,000 lb in order to have a smaller wheel and tire.

• Then, make the twin-wheel arrangement. For this arrangement, Equation 7.5 gives the ESWL = 4,868/1.5 = 3,245 kg (7,155 lb).

[11] Small variant aircraft MTOM = 7,000 kg (15,400 lb) (refined in Chapter 8)

• Fuselage length = 13.56 m (44.5 ft)

(i) Raised or bubble-type canopy or its variants. These canopies are mostly associated with military aircraft and smaller aircraft. The canopy drag

[13] Other effects on the fuselage (increments are given in a percentage of 2D CFf) are listed herein. The industry has more accurate values of these incre­

mental ACFf. Readers in the industry should not use the values given here – they are intended only for coursework using estimates extracted from industrial data. (See Section 3.21 for an explanation of the terminology used in this sec­tion.)

(a) Canopy drag. There are two types of canopy (Figure 9.4), as follows:

[16] Manufacturing origin. This includes aerodynamic mismatches as discreet rough­ness resulting from tolerance allocation. Aerodynamicists must specify surface – smoothness requirements to minimize excrescence drag resulting from the dis­crete roughness, within the manufacturing-tolerance allocation.

[17] Front fuselage length, Lf = 3.5 m with a uniformly varying cross-section

[18] Mid-fuselage length LFm = 5.95 m with an average constant cross-section diam­eter = 1.75 m

[19] Aft-fuselage length LFa = 5.79 m, with a uniformly varying cross-section

• Wetted area

• front fuselage, AwFf (no cutout) = 110 ft2

• Mid-fuselage, AwFm (with two sides of wing cutouts) = 352 – 2 x 6 = 340 ft2

• Aft fuselage, Aw Fs (with empennage cutouts) = 180 – 10 = 170 ft2

• Include additional wetted area for the wing-body fairing housing the under­carriage « 50 ft2

[20] Pressure

/ 6 0.125

ACfw = CFw x 60 x (aerofoil t/c ratio)4 x ar j

= (0.003 x 60) x (0.1)4 x (6/7.5)0125 = 0.18 x 0.0001 x 0.973 = 0.0000175(0.58% ofbasic Cfw)

[21] V-tail

• wetted area, AwVT = 81 ft2

• basic CFH-tail = 0.003

It is a T-tail configuration with interference from the T-tail (add 1.2%).

• fVT = 1.262 x 0.003 x 81 = 0.307 ft2

• H-tail

• wetted area, AwHT = 132.2 ft2

• basic CF_V-tail = 0.0032

• fHT = 1.25 x 0.0032 x 132.2 = 0.529 ft2

• surface roughness (to be added later): 3%

[22] each pylon exposed reference area = 14 ft2

• length = 2.28 m (7.5 ft)

• t/c = 10%

• two-pylon wetted area Awp = 56.7 ft2

• pylon Re = 7.5 x 1.2415272 x 106 = 9.3 x 106

• basic CFpylon = 0.00295

• for two pylons (shown in wetted area):

fpy = 1.26 x 0.00295 x 56.7 = 0.21 ft2 • surface roughness (to be added later): 3%

[23] 3D effects (Equations 9.9, 9.10, and 9.11):

• Wrapping:

ACFf = CFf x 0.025 x (length/diameter) x Re 0 2 = 0.025 x 0.0021 x (9.66) x (9.53 x 107)-02 = 0.000507 x 0.0254 = 0.0000129 (0.6% of basic CFf)

• Supervelocity:

ACFf = CFf x (diameter/length)15 = 0.0021 x (1/9.66)15 = 0.0021 x 0.033 = 0.0000693 (3.3% of basic CFf)

• Pressure:

ACFf = CFf x 7 x (diameter/length)3 = 0.0021 x 7 x (0.1035)3 = 0.0147 x 0.00111 = 0.0000163 (0.8% of basic CFf)

• Other effects on the fuselage (intake included – see Section 9.18):

Reference [3] suggests applying a factor of 1.284 to include most other effects except intake. Therefore, unlike the civil aircraft example, it is simplified to only the following:

• Intake (little spillage – remainder taken in 3D effects): 2%

[24] turn performance g-load

• maneuver g-load

• roll rate g-load

[25] Wing Dihedral Г (see Figure 12.5). Sideslip angle в increases the angle of attack, a, on the windward wing, Aa = (Vsin r)/u generating ALift. For small dihe­drals and perturbations, в = v/u, which approximates Aa = в Г. The restoring moment is the result of ALift generated by Aa. It is quite powerful – for a

[26] At zero thrust, Equation 13.29 becomes:

[27] Experiments cannot represent the real flight envelope (e. g., Re and temper­ature) and are limited by flow nonuniformity, wall effects, and transient – dependent separation.

• There are very high energy costs associated with large wind tunnels.

• CFD is faster and less costly than experiments for obtaining valuable insight at an initial stage.

[28] brittleness: when a sudden rupture occurs under stress application (e. g., glass)

• ductility: the opposite of brittleness (e. g., aluminum)

[29] Fuel charges

[30] parts list and tool list

• BOM definition

• bill of resources

• routings

• process sheets/work instructions

During the evolution of a product design and manufacturing process planning, the same PPR dataset can be used to validate the maintainability of a product, as well as to develop technical-publication documents containing text, images, and movies derived directly from the 3D-based process plans. The core PPR technology sup­ports any number of views of process-planning data related to product and resource, thereby providing a way to associatively develop maintenance plans concurrent with manufacturing planning. This ability to concurrently validate the design as well as the maintenance operations for a product is one more example of the significant leverage provided by the PPR data model. This allows the idea of leveraging the results of 3D process planning and analysis directly into Web-based technical publi­cations for maintenance operations (e. g., analysis of 3D process plans for producibil – ity and Web-based technical publications for maintenance). 3D enables a new busi­ness paradigm, as follows:

• 3D is now leveraged not only for design but also for manufacturing planning, simulation-based validation, work-instruction authoring, and delivery to the shop-floor workforce, enabling a true paperless manufacturing process. This is easily extended for 3D maintenance and repair instructions.

• Operational and maintenance scenarios can be simulated using ergonomic anal­ysis early in the design cycle to provide efficiencies later in the life cycle of a product. With a systematic methodology, a true design for customer business process can be supported.

• The virtual production mock-up eliminates the requirement for prototype parts to prove out mock-ups of production tooling and fixtures, reducing cost and time.

• Tooling orders can be placed much later in the development plan with the latest design revisions incorporated because they will work the first time, eliminating costly change orders to tools and parts designs.

• Designs can be modified early in the design cycle to accommodate manual assembly and maintenance tasks; therefore, the requirement for special tools can be eliminated.

In the near future, the conceptual design stage of a new aircraft project must assess in detail and then incorporate the benefits that can be derived from the digital man­ufacturing process management.

A key area where this integrated environment provides value is in the ability to define, evaluate, and document various manufacturing alternatives, such as alter­nate routings and resource utilization, based on evolving conditions in operations. The PPR-hub-based manufacturing database can be used to drive discrete event simulations of the alternatives to determine the impact on material flow, through­put, and utilization under various scheduling and product-mix conditions. The pro­cess plans, resource allocations, and precedence requirements in the PPR hub can be further analyzed to balance the work across the manufacturing facility and to provide proper utilization of workers. It provides an interface to feed the shop floor directly from the PPR hub database, ensuring optimal reuse of data created in the CAD/CAM and manufacturing and planning environments. One of the most signif­icant ways to leverage a PPR-based database is to reuse the data directly as the basis for 3D work instructions on the shop floor. The immediate benefits of this approach are as follows:

• Eliminate the possibility for a mismatch between shop-floor instructions and engineering data because the instructions are derived directly from the PPR hub database, including the related CAD geometry and attributes.

• CAD-based work instructions provide a means to eliminate paper drawings on the shop floor because all required data, tolerance, notes, and related specifi­cations can be embedded within the 3D dataset that also provides all required manufacturing information.

• Intuitive 3D (i. e., CAD-based) work instructions, combined with authoritative engineering data and attributes, empower machinists to perform their job faster and with fewer mistakes. In such cases, the reduction in overall manufacturing flow time and cost can be dramatic.

• Provide a data-feedback loop from the shop floor to the manufacturing planning environment to provide visibility on shop-floor-based changes representing as – built product buildup and processing.

• Leverage this data-feedback loop and related PPR-hub infrastructure to rec­oncile and evaluate the differences among the as-designed, as-planned, and as – built datasets.

Finally, with respect to the in-service phase of the products’ life cycle, this architec­ture provides a means to also capture and manage the evolving (i. e., complex) con­figuration of the related BOMs and collection of processes performed during ongo­ing maintenance operations. As appropriate, PPR-hub data defined in one phase of the life cycle can be reused for other phases, thus providing potentially significant savings.

One of the major areas for improvement within an aerospace enterprise is in the integration among engineering, manufacturing, and operations organizations. The drivers for such integration are to allow the maximum reuse and appropriate con­solidation of data and business systems throughout the program life cycle. The degree to which this is achieved typically has a major influence on overall costs, both recurring and nonrecurring. Traditionally, the engineering and manufactur­ing (i. e., CAD and CAM) domains operate in a self-contained operating unit. As a result, it is difficult – if not impossible – to actually leverage engineering data down­stream in the shop-floor operations area where typically bills of material (BOMs) are defined, along with related procurement data and shop-floor work instructions. One major problem with this traditional scenario is that effectively managing engi­neering changes and reconciling the parallel worlds of CAD and CAM and shop – floor operations is extremely difficult and expensive to do accurately. Digital man­ufacturing provides a means to integrate these two domains (i. e., CAD/CAM and operations), as well as the in-service domain, through several core technologies and application layers.

An additional benefit of this type of integration is the ability to reduce the num­ber of redundant business systems between the CAD/CAM and operations organi­zations. This reduces recurring and overhead costs. Specifically, the following classes of business systems can be consolidated into the PPR-hub-based solution suite: [30]

• reconciliation analysis: estimate versus actual (e. g., in preparing BOM)

• simulation-based validation

• cost estimating

• production-flow analysis

• human resources

This software suite provides engineers with the tools to bring the process planning solutions into the application-specific disciplines of manufacturing. It assists engi­neers in verifying process methodologies with actual product geometry and defin­ing processes within a 3D environment. The human module offers tools that allow users to manipulate accurate, standard, digital, human mannequins, referred to as “workers,” and to simulate task activities in the process-simulation environment. Thus, worker processes can be analyzed early in the manufacturing and assembly life cycle. The assembly process simulation module sets a new paradigm for devel­oping manufacturing and maintenance processes. It offers manufacturing engineers and assembly process planners an end-to-end solution by incorporating a single, uni­fied interface for preplanning, detailed planning, process verification, and shop-floor instructions.

Resource Modeling and Simulation

This software suite provides engineers with the tools to develop, create, and imple­ment resource, mechanical programming, and application routines that are integral to the process planning and the process detailing and validation solutions. Within this set of solutions, resources such as robots, tooling, fixtures, machinery, automa­tion, and ergonomics are defined for completing a manufacturing solution. Digital manufacturing also offers flexible, object-based, discrete event-simulation tools for efficient modeling, experimenting, and analyzing of a facility layout and process flow. Both 2D schematic and 3D physical models are quickly created (i. e., rapid prototyping) through pushbutton interfaces, dialog boxes, and extensive libraries. Real-time interaction enables modification of model variables and viewing parame­ters during runs.

Process Planning and Simulation

The process planning software suite provides an effective process and resource­planning support environment. It improves methodically structured planning, early recognition of process risks, reuse of proven processes, traceable changes and deci­sions, and the use of scattered-process knowledge. The module is often used during the conceptual design phase, with the process-design and alternative-manufacturing concepts maturing through all the stages to production. The treatment of the rela­tionships among product, process, and manufacturing resource data, including the plant layout, helps to avoid planning mistakes and to obtain a precise overview of the needed investment costs, production space, and manpower at an early stage.

17.10.1 Product, Process, and Resource Hub

Digital manufacturing allows manufacturing engineers and process planners to define, validate, manage, and deliver to the shop floor the content needed to manu­facture an aircraft. The combination of this PPR hub environment is uniquely able to provide savings to an enterprise in the following ways by reducing risk, time to market, and overall costs of manufacturing:

• Concurrent engineering design and manufacturing planning and process valida­tion occur before release is final, in the midst of ongoing change.

• Manufacturing producibility analysis directly influences design, thus providing a true DFM/A environment.

• The enterprise is able to formally capture and reuse manufacturing best prac­tices.

• Manufacturing plans are prevalidated in a 3D environment to avoid unexpected problems on the shop floor.

• Unbuildable conditions are found early in the design cycle when the cost of change is minimal.

• Quality targets are met sooner due to reduction (or elimination) of reworking and engineering change orders generated on the shop floor.

Through a combination of methodologies, significant reductions can be achieved in time to market, overall cost, and effective risk for an aircraft program. A key enabler for reducing the time to market is the ability to support concurrent engineering design and manufacturing planning before a release is final. When this is possible, manufacturing producibility analysis directly influences design, thus providing a true DFM/A environment. In this scenario, the total costs of the engineering changes that occur during the program are dramatically less because they are identified and resolved much earlier in the life cycle. The total effective cost of the changes is dramatically less because they are mostly identified and resolved before tooling is procured and production starts.

The key technology enabler for the digital manufacturing solution, prod – uct/process design, and validation is the PPR hub and business transformation. This database environment – supporting complex configuration and affectivity rules that are required in the aerospace world – provides the infrastructure to allow process and resource planning to occur in the context of the engineering data and contin­uously changing environment. In contrast to traditional systems, the PPR hub pro­vides the means to explicitly manage PPR objects and the relationships among them. Because such relationships are explicitly defined and managed within the database, it is possible to see directly the impact of changes of one object class on any other (e. g., “If a part is changed, which manufacturing plans are affected?”).

The digital manufacturing process management is a newer concept and is still evolving; it is software-driven. Although the industry has already deployed digital
manufacturing in some areas, the full scope of application is yet to stabilize. This section describes a model briefly studied at the QUB [7]. It outlines the nature of changes taking place. These types of studies are conducted in many places, propos­ing many different models. The core fact is that digital manufacture is here to stay, grow, and replace or merge with traditional manufacturing philosophies.

Today, the microprocessor-driven digital manufacturing process is rapidly over­taking older methods. In fact, all modern production plants are already using it to the extent that it can be advanced. The advantage of microprocessor-based tools is that they are digitally controlled and driven by software. These tools deliver the desired “quantum leap” in manufacturing assembly techniques for future aircraft. This section outlines the role of MPM and identifies the benefits of PLM through the reconciliation perspective (i. e., estimation versus actual) between design and manufacturing engineering disciplines. PLM is a business strategy and part of MPM as a management strategy. (Life cycle is used in a generic sense, meaning all aspects of a product, from concept to retirement.)

Digital-manufacturing solutions enable the continuous creation and validation of the manufacturing processes throughout a product’s life cycle. It allows manu­facturers to digitally plan, create, monitor, and control production and maintenance processes, providing complete coverage of the manufacturing processes. With the advent of new processes and techniques, there has been a greater use of software in the design and engineering of aircraft. CAD, CAM, CAE, and CAPP tools are now used to determine electronically how an aircraft system must be built. NC machines are linked with CAM.

The new frontier with software suites focuses on PLM, emphasizing the man­ufacturing processes. PLM is a business strategy that allows companies to share product data, apply common processes, and leverage corporate knowledge to develop products from conception to retirement across the extended enterprise. By including all participants in this process (i. e., company departments, business partners, suppliers, and operators and customers), PLM enables the entire net­work to operate as a single entity to conceptualize, design, build, and support products.

MPM is segmented into process detailing and validation, resource modeling, and process planning simulation. Within each segment are several modules, dis­cussed as follows.

The definition of design for customer relates to the merit of the design by establish­ing a value index. The suggested definition is as follows:

(DOC*/DOC) x (Unit Cost*/Unit Cost)
(t/10t * + 0.9)

Kn is inversely proportional to the DOC and aircraft unit cost; that is, a lower DOC gives a higher Kn; a value of more than 1 is better. The unit cost includes the engine and aircraft size and the DOC includes the design merits, passenger number, and range capability. Typically, an aircraft with more passengers has a lower DOC, driv­ing the value to more than 1, but it is evaluated with respect to price and delivery time.

17.9.1 Worked-Out Example

From the worked-out example of the Bizjet, the following values are obtained. Because derivative aircraft values are obtained through simplified assumptions, they must be worked out in better detail. The linear relationship is used to work out the following example to provide a general idea.

Standard Parameters of the Baseline Aircraft

Unit cost* in millions of U. S. dollars = $8 million; MTOM = 9,400 kg.

DOC* in U. S. dollars per seat/nm = $0.352 per nautical mile per passenger (ten passengers).

Delivery time t* in years (from the placement of order) = 1 year.

Baseline aircraft = $0.8 million/passenger and $0.000851/kg MTOW.

Parameters of the Extended Variant Aircraft

Unit cost in millions of U. S. dollars = $9 million; MTOM = 10,800 kg.

DOC in U. S. dollars per seat/nm = $0.2875 per nautical mile per passenger (fourteen passengers).

Delivery time t in years (from the placement of order) = 1 year.

Large variant aircraft = $0.6428 million /passenger and $0.000833/kg MTOW.

K (DOC* /DOC) (0.352/0.2875)

nJarger = (.UnitCost/UnitCost*) x (t/10t* + 0.9) = (9/8) x (1/10 + 0.9)

= 1.224/(1.125 x 1) = 1.088 (a better value)

Parameters of the Shortened Variant Aircraft

Unit cost in millions of U. S. dollars = $6 million; MTOM = 7,600 kg.

DOC in U. S. dollars per seat/nm = $0.482 per nautical mile per passenger (six passengers).

Delivery time t in years (from the placement of order) = 1 year.

_____________ (DOC* /DOC)_________________ (UnitCost/UnitCost*) x (t/10t* + 0.9)

Small variant aircraft = $1 million/passenger and $0.00079 kg MTOW.

= 0.7303/(0.75 x 1) = 0.974 (a lower value)

In general, a smaller derivative aircraft is penalized because it is heavier than it would be if it were a baseline design. The wing area is larger than what is required. The smaller variant is competitive aircraft, and there are aircraft in this class with similar DOC. Several new all-composite, four-passenger lighter jet aircraft have appeared recently and are selling at less than $5 million with better DOC. These new aircraft are yet to be proven in operational usage, and it will be some time before all-composite aircraft overtake conventional construction. The example of a six-passenger, smaller variant is a robust, all-metal aircraft with a larger cabin vol­ume and high-end amenities suited to corporate demand. In a mixed fleet of three sizes, the total package offers benefits that are difficult to match. When an airline operates a mixed fleet of variants along with its baseline aircraft, the spare-parts

600 a.

MTOM kg)

stock, training, and maintenance costs can be shared. Private ownership in this class is increasing and there is room for both types.

However, modifications to the smaller variant can improve the index Knsmaller. If re-engined with a smaller turbofan (i. e., the Williams type) and re-engineered with lighter and less expensive equipment, the aircraft price could be decreased to about $5 million, making it even more attractive; however, it loses some component com­monality and incurs additional development costs. If the wing tips can be shortened at practically no extra cost, then the weight can be decreased to less than 7,000 kg, and the index can reach a value of more than 1.

Design for customer can help manufacturers establish the aircraft price for a family of variants, giving each type a comparative value for the customer. Typically, the smaller variant should be priced lower with a smaller profit; other aircraft prices are adjusted, with the baseline aircraft price unchanged. That is, the price of the larger aircraft can be increased to compensate the family price structure. The goal of the baseline size is maximum sales. In the example, the price of the smaller variant is $6 million, resulting in a small profit. The baseline aircraft aims for the most sales to maximize profit.

In general, route traffic load continues to increase; consequently, sales of the larger variant also increase and also the associated profits for the manufacturer, making up for the relatively smaller profit from the smaller variants. When a new market emerges for a larger traffic load, manufacturers seize the opportunity for a new baseline design.

The aircraft price per passenger and unit mass are explained herein. There is a sharp increase in per-passenger price with a smaller payload, as shown in Fig­ure 17.2. In the figure, the dashed lines represent generic data; it is a magnified ver­sion of Figure 16.2. The solid lines represent the Bizjet family. The smaller aircraft price is slightly depressed to suit the market.

Nicolai [8] introduced a meaningful term, Design for Mankind, which should be the goal for all designs, not exclusive to the aerospace industry. This book focuses on specific issues of engineering design and operation by suggesting the Design for Customer, as explained in this section.

Using a holistic approach as a tool to address simultaneously the sixteen “Design for…” considerations in trade-off studies, the author suggests a Design for Customer index to measure the merits of a product [3]. This applies to pricing of the variants in a family of derivatives but also can include the pricing structure of competition aircraft. The expression of Design for Customer is not substantiated by a large database; it may require fine-tuning for better accuracy. However, it conveys the idea that there is a need for such an index to compare the merits of any aircraft within a class. It suggests a pricing policy to arrive at the most satisfying product line that offers the best value with the widest sales coverage.

Using an empirical formula, a set of standard parameters can be established for the baseline aircraft in the class (i. e., payload range). To remain within the linear range of variation, the family-variant parameters and competition aircraft should not differ by more than about ±15%. The baseline standard parameters of inter­est are denoted by an asterisk as in the DOC* in U. S. dollars per seat per nau­tical mile, the Unit Cost* in millions of U. S. dollars, and the delivery time t* in years (from the placement of an order). To evaluate variant designs, they must be compared to the baseline design. DOC levels out well before it reaches the design range.

A baseline aircraft is designed to have the best L/D ratio at midcruise weight (i. e., the LRC condition). Normally, the L/D characteristic is relatively flat and the family derivative designs have an L/D ratio close to the maximum design value of the baseline aircraft. The Breguet range equation indicates that the range is pro­portional to the square root of the W/S. A shortened variant with a lower payload and range has a lower W/S with a derated engine. This aircraft has more wing than what is required and has a better takeoff performance but a slightly degraded range performance. Conversely, the extended version has more payload; weight control may have to be traded with range capability. The takeoff mass invariably increases, requiring uprated engines, especially to make up the takeoff performance due to a higher W/S (i. e., undersized wing).

This formulation is inline for comparison, satisfying the customer’s operational requirements for the product usefulness in terms of unit cost, operational cost, and timing to meet the demand. From this definition, an increase in the product value is achieved through improved performance (better), lower cost (cheaper), and less time (faster delivery). The DFM/A methodology contributes directly to lower cost, improves quality, and reduces manufacturing cycle time, thereby increasing the product value.

The higher the value, the better it is for a customer to use a product family incorporating a wide range of design considerations to satisfy operational require­ments at the optimal ownership cost and purpose. In the absence of standard LCC data, the DOC is used. In this context, the following section introduces a design for customer index to compare the values of other aircraft in the class.

Design for Six Sigma: This is an integrated approach to design with the key issue of reducing the scope for mistakes and inefficiencies – that is, make a product right the first time to prevent the waste of company resources (see Section 17.5). A measure of its success is reflected in the final cost of a prod­uct; therefore, an estimation method indicates at what cost (i. e., at what effi­ciency) the Six Sigma approach is working.

Designfor Cost/Design to Cost: This is the classical question of Design to Cost (DTC) or Design for Cost (DFC) or a combination of both. The tendency of management to emphasize DTC through a “lean” organizational setup may be counterproductive if it is carried to the extreme application.

Design for Training and Evaluation: This is an area that currently is not under strong consideration at the conceptual design stage. Aircraft DOC estimation does not include the cost of T&E. Design considerations including common­ality and modular concepts could reduce T&E costs and, therefore, must be addressed in an early stage.

Design for Logistic Support: This is an operational aspect with second-order consideration for civil aircraft design. The existing support system addresses most of the logistic details without infringing on any major changes required in aircraft design, unless a special situation arises. Early input from operators for any design consideration helps control costs.

Design for Ground-Based Resources: This also may be deemed a lower – order consideration for civil aircraft design at the conceptual design stage, unless special-purpose equipment is required. In general, ground-based sup­port resources are becoming standardized and can be shared by a large fleet, thereby distributing the operation costs at a lower priority in the conceptual design stage.

Design for Special Equipment: This is more meaningful in military air­craft applications. If any special-purpose equipment must be introduced for ground-based serviceability, then a cost trade-off study at the conceptual design stage is beneficial.

Separate minimization of individual costs through the separate design considera­tions listed previously may prove counterproductive by preventing the overall min­imization of ownership cost. In a holistic overview, this chapter introduces the term Design for Customer to unify the individual considerations in the early stages of design evolution in order to offer the best value of the product by satisfying require­ments, specifications, and integrity to lower the LCC (or DOC). It is a front-loaded investment for eventual savings in LCC (or DOC).

Design for Manufacture: The trade-off study is concerned with the appro­priate process required for parts fabrication, including cost-versus-material selection, process selection, the use of NC machines, parts commonality, and modularity considerations to facilitate assembly. A key issue in the concep­tual design stage is a low parts count to reduce assembly time. The lowest parts count may not be the least expensive method – compromise may be necessary.

Design for Assembly: This is concerned with the fewest manhours required to assemble parts. Traditional practices in aircraft assembly include numerous components and a complex organizational structure in the engineering, logis­tics, and management disciplines. This results in an inefficient use of factory floorspace, and quality is compromised due to the unnecessary operations and fasteners required to join mating parts. DFA minimizes manufacturing costs by optimizing engineering methods using innovative best-practice techniques of jigs and tool design, whether a manual or computerized assembly method. Product configuration and the detailed design of parts are important in the assembly process.

Design for Quality: Adherence to the specification requirements is the essence of quality control. One example is meeting the aerodynamic surface – smoothness requirements through surface-tolerance specifications at the component final assembly. Currently, many quality issues are addressed in the post-conceptual design stage; they should be advanced to the conceptual design stage.