enhancing the exploration of aircraft changeability … · changeability investigation. section 4...

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Abstract

Changeability is a characteristic of a system thatmay allow it to be changed in an affordablemanner to maintain value, despite changingexternal conditions. In this paper, a framework ispresented that employs concepts from Multi-Attribute Tradespace Exploration and Epoch-Era Analysis, together with interactivecommonality identification, to enable aircraftdesigners to explore the changeability of aircraftthat have different top-level configurations andto interactively identify where and how muchchangeability should be introduced in theirdesigns during conceptual design. The frame-work is demonstrated by illustrating how thechangeability of a short-range, environmentallyfriendly passenger aircraft can be explored.

1 Introduction

An important way for an aircraft to remainrelevant in a changing world is by being‘changeable’ itself. In other words, it should bedesigned such that it may be changed swiftly andaffordably, should the need arise. The word‘changeability’ is an umbrella term for many‘change-type ilities’, such as ‘flexibility’,‘adaptability’ and others [1]. For this paper,changeability will be considered to besynonymous with ‘evolvability’ – the extent towhich the design of a system can be “inheritedand changed across generations (over time)” [1].During conceptual design, it is essential that thedesign space be explored thoroughly todetermine how different designs will cope withchanging socio-economic conditions, competitoractivity, and technology development. Previouswork in changeability exploration in aircraftdesign usually only considers exploring pre-

determined top-level configurations (i.e. majorcomponents layouts) and pre-specified transitionpaths (e.g. upgrading the engines) – see forexample [2].The aim of the work was therefore to develop aframework that consists of procedures andenabling tools that could ensure: 1) that multipledifferent configurations (architectures) could beexplored simultaneously and 2) that interactiveidentification of transition paths could take place,rather than specifying these in advance.The paper is organized as follows: Section 2contains a brief overview of changeability andrelated concepts and Section 3 shows how thecombination of two well-known design spaceexploration techniques, namely ‘Multi-AttributeTradespace Exploration’ (MATE) and ‘Epoch-Era Analysis’ (EEA) could be applied to aircraftchangeability investigation. Section 4 is adescription of the proposed framework; and, inSection 5, the framework is demonstrated withthe changeability exploration of anenvironmentally friendly, short-range passengeraircraft. Finally, conclusions are drawn andfuture work is outlined in Section 6.

2 Overview of changeability concepts

2.1 What is Changeability?

Changeability is a particular way of dealing withuncertainty [3]. Essentially, it provides two-foldvalue to the stakeholders of the system in thepresence of uncertainty [4]: 1) value is provided,since the system maintains (or increases) itsperformance in changing circumstances, and 2)value arises because the changeable systemprovides the stakeholders with options.Unfortunately, this value comes at a cost, which

ENHANCING THE EXPLORATION OF AIRCRAFTCHANGEABILITY DURING CONCEPTUAL DESIGNAlbert S.J. van Heerden, Marin D. Guenov, Arturo Molina-Cristóbal, and Atif Riaz

Cranfield University, Cranfield, MK43 0AL, United Kingdom

Keywords: aircraft conceptual design, design for changeability, design space exploration

ALBERT S.J. VAN HEERDEN, MARIN D. GUENOV, ARTURO MOLINA-CRISTÓBAL and ATIF RIAZ

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often manifests as an extended and moreexpensive development period of the firstgeneration of the system. The changeable systemalso usually does not perform optimally in anysingle set of circumstances, as compared withnon-changeable systems.To be able to change, the system must have a‘change mechanism’. A change mechanismrefers simply to how the system is changed [3].For example, to decrease fuel-burn, an existingairframe could be upgraded with a new, moreefficient engine. The process of upgrading theaircraft is the change mechanism. Designing forchangeability includes 1) finding which changemechanisms are required and 2) to make thesemore affordable. Several ‘changeabilityprinciples’ can be employed for this purpose,including ‘modularity’, ‘simplicity’, scalability,and so forth. A comprehensive description ofthese is provided in [5]. Finally, note that achange mechanism also has a cost associatedwith it. This is the time, effort, and monetaryexpense needed to perform the change.

2.2 Changeability exploration

Changeability exploration refers to exploring thedesign space to find designs that could maintainvalue under changing circumstances by beingchanged. One method to do this is Multi-Attribute Tradespace Exploration (MATE)together with Epoch-Era Analysis (EEA) (see,for example [4]). A Multi-Attribute Tradespace(MAT) is a plot of utility and cost of the system[6]. Utility is a variable, usually ranging in valuefrom 0 to 1, which represents the weightednormalized sum of system attributes that are ofimportance to the stakeholders. The cost isdetermined by the design parameters, and couldinvolve, for example, the investment required todevelop the system, or the lifecycle cost. Duringconceptual design, the MAT is populated withmultiple potential design points, where eachpoint represents the utility and cost of a specificdesign.Epoch-Era Analysis is a way of exploring howcircumstances affecting the system can changeover time and how the different designs respondto these changes [4]. An ‘era’ represents thelifetime of the system and consists of several

‘epochs’ strung together. An epoch is a time-period within an era where stakeholderpreferences, technological maturity, andenvironmental conditions are assumed to remainconstant. Every epoch can subsequently berepresented by its own MAT. Changemechanisms form allowable ‘transition paths’between different design points across sequentialepochs. Therefore, if designs have transitionpaths to other designs in the subsequent epochavailable, they could be changed across the‘epoch shift’ (i.e. the progression of time with anassociated change in circumstances). However,in the case of designs with multiple transitionpaths, only one of the paths will actually beexploited. This means that a strategy is requiredto select the transition path during the epoch shift[4]. This will be referred to in this paper as a‘change implementation strategy’. Such astrategy could be, for example, to choose the paththat will provide the highest utility of the designin the subsequent epoch, regardless of the cost, orchoose the path that will produce the design withthe lowest cost, at an acceptable utility.Most literature on aircraft changeability,although excellent, usually only enable a singletop-level aircraft configuration to be explored(see for example [2]). To improve on this, a morecomprehensive exploration is needed wheredifferent configurations can be investigatedsimultaneously. MATE with EEA enablesdesigns with completely different architectures tobe explored simultaneously, and the combinationof these was therefore selected for the currentwork as the analysis method.Also, in most previous work on changeabilityexploration, transition paths are specified inadvance. Several notable exceptions exist, suchas [7], which usually employ ‘Design StructureMatrices’ (DSMs) that will highlight componentsthat are most likely to change, and therefore to bemade modular. Unfortunately, these are notalways readily applicable to the parametricdesign tools usually employed during conceptualaircraft design. What seems to be needed is amethod to enable the designer to interactivelyvisualize and identify possible transition paths(involving both changing components, as well asdesign parameters), and trade off commonalitywith the potential transition paths.

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ENHANCING THE EXPLORATION OF AIRCRAFT CHANGEABILITYDURING CONCEPTUAL DESIGN

2.3 Interactive commonality identification

A field that is strongly related to changeability isthat of product family and product platformdesign. A major theme in the product familyliterature is identifying commonality betweenproducts aimed at meeting the requirements ofdifferent customers. To relate this tochangeability, commonality would refer to theparameters and components that do not need tochange with time. Subsequently, by identifyingcommonality, the designer implicitly alsoidentifies where transition paths are required.Certain product family methods to identifycommonality can therefore be adapted for use inchangeability. Of particular interest here arethose methods that enable the designer to identifycommonality in a visual and interactive manner,such as in [8] and [9]. In [9] it is shown how thedesigner can interactively select designparameters/modules to be made commonamongst the product variants, whilesimultaneously observing the effect in theobjective space.

3 Applying MATE and EEA to aircraftchangeability studies

In this section it will be shown how Multi-Attribute Tradespaces could be set up for aircraft,which will enable Multi-Attribute TradespaceAnalysis and Epoch Analysis to be performed toanalyze and enhance aircraft changeability. Theterm ‘scenario’ will be used here instead of‘epoch’. A scenario will therefore refer to aperiod where external conditions are assumed toremain constant. Also, because the changeabilityunder discussion is evolvability, the word

‘generation’ will be employed to refer to designsin successive time periods. Each time period willtherefore refer to a generation.Fig. 1 shows two MATs – the one on the leftrepresents a possible first generation scenario,whereas the one on the right represents a possiblesecond generation scenario. The designrepresented by the orange dot on the left is a newdesign for the first generation. The utility of thisdesign represents how well it is expected to meetthe particular combination of requirements of thescenario associated with the first generation. Thecost (to the manufacturer) of one aircraft of thisdesign comprises the Research, Development,Test, and Evaluation (RDT&E) cost per aircraft,plus the first-unit manufacturing cost. There arethree options an original equipment manufacturer(OEM) has for the subsequent generationdesigns: completely re-use the first-generationdesign (orange dot on the right in Fig. 1), designa completely new aircraft (blue dot), or upgradethe first-generation design (green dot). The firstoption would only be possible if the utility of thefirst-generation design is still above zero in thesecond generation timeframe. The cost in thiscase will only comprise the unit manufacturingcost, which will be less than the first-unitmanufacturing cost (when adjusting forinflation). Cost will therefore be low, but theutility probably also. If, on the other hand, theOEM opts for the second option, the full RDT&Ecost for a new aircraft, plus first-unitmanufacturing cost, will be incurred. The costwill be high, but requirements may possibly beexceedingly well met.A more likely course of action would be the thirdoption, which is a compromise. In this case,changes are made to the design that enable it to

Fig. 1: Multi-attribute tradespaces for aircraft evolution. The arrows indicate options and not necessarily transition paths.


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better meet the new requirements at reduced cost.This entails a combination of re-usingcomponents that do not need to change anddesigning new ones where necessary. Thisprovides savings on both RDT&E andmanufacturing costs, since only selectedcomponents would need to be re-developed. Acommon example of this is to refit an existingairframe design with new propulsion. However,this strategy usually delivers designs with lessperformance than what is actually possible withnew technologies that have since becomeavailable. This is represented by the lower utilityof the green dot in Fig. 1 (right).To enable further savings in the development andmanufacturing cost of future generations, theOEM could invest in designing the firstgeneration to be more changeable (i.e. bestow itwith planned changeability). Such a course ofaction would likely entail a higher first-generation RDT&E cost per aircraft and first-unitmanufacturing cost and lower utility, ascompared with designs with no plannedchangeability, but may help to increase second-generation utility. This is illustrated with the redand black dots in Fig. 1.The above constitute fundamental underlyingconcepts that are employed throughout theframework, which is discussed next.

4 Description of the framework

The framework consists of a step-by-stepprocess, along with enabling techniques that canbe employed for each step. A diagram illustratingthe steps is shown in Fig. 2.The basic input to the framework is, inter alia,information on socio-economic and technologytrends, competitor activity, and so forth. Theoutput is a set of designs that are most promisingin terms of meeting both the current requirementsand able to change, at a reasonable cost, to meetfuture requirements (i.e. the most changeabledesigns).

Step 1: Scenario identification and development

The objective of this step (Step 1 in Fig. 2) is toidentify current and future requirements and usethese to create MATs with associated utilitydefinitions for the different possible scenarios.

Fig. 2: Steps of the aircraft changeability explorationframework.

The activities in this step will usually fall underthe responsibility of the business or technicalstrategy departments of the OEM. To initiate theprocess, information is collected on relevantsocio-economic trends, tendencies in technologydevelopment, future environmental sustainabilitytargets, competitor activity, and so forth. Usingthis information, utility definitions can beformulated for different possible scenarios foreach generation of designs. This means that a setof MATs can be constructed that coincide witheach planned entry-into-service (EIS) date foreach successive generation. The different MATsfor each generation therefore represent thedifferent possible combinations of requirementsand conditions that might be prevalent at the timeof service entry and during (most of) the lifetimefor that generation. This is illustrated in Fig. 3.

Fig. 3: Aircraft evolution: generations and scenarios.

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Consequently, each possible ‘string’ of scenarios(epochs), indicated by the arrows in Fig. 3 formsa possible era for the aircraft.Finally, change implementation strategies for thedifferent scenarios, which will be dictated by theconditions prevalent to each scenario, should alsobe formulated in this step.

Step 2: Concept generation

In this step, different concepts, includingdifferent top-level configurations and systemsarchitectures, are generated that could potentiallymeet the requirements of both current and futurescenarios. Seemingly ‘inferior’ concepts shouldnot be discarded in this step.

Step 3: Population of scenario tradespaces

Once a set of promising concepts have beenidentified, modelling of the concepts cancommence. After the models are constructed,simulation can proceed in order to populate theMATs with potential solutions. This can beperformed with the use of an appropriateenumeration technique, such as optimization or adesign-of-experiments (DoE) study. The authorsrecommend a DoE for changeability/evolvability, however, since the uncertaintyinvolved may be significant and it may be moreappropriate to cover more of the design space bymaking use of a DoE.

Step 4: Identification of potential transition paths

With the MATs populated, they can be used toidentify where transition paths can/should beintroduced. This can be done with the followingprocedure, which is adapted from [8] and [9] (seeSection 2.3): Based on the change implementation

strategies identified for the differentscenarios, select a ‘region of interest’ for eachdifferent top-level configuration and eachsystems architecture in each scenario. Thiswill require the MATs of aircraft withdifferent top-level configurations to beviewed separately. The region of interest is theset of designs that will most likely be selectedbased on the economic conditions for aspecific scenario. The selection can beautomated, by specifying a minimum utilityand maximum cost, or the designer couldinteractively select the area on the MAT. The

advantage of using this (for evolvability) isthat it already acts as a filter that couldpotentially reduce the number of designs thathave to be compared.

Compare the designs on the region of interest,selected across all scenarios and allgenerations, and find parameters ortechnologies that are shared. This can be doneby making use of multi-dimensionalvisualization techniques. If only a fewscenarios and few parameters/technologiesare considered, parallel coordinates may beappropriate.

The result of this process is therefore a reducedset of designs that have some parameters/components shared and some not. Forcombinations of parameters/components thatcannot be made common (i.e. irreducible sets),two courses of action are possible: The first is to look for designs that may have

increased commonality, but could havedecreased predicted performance. One way ofdoing this is by going back to the tradespaceand increasing the regions of interest.Alternatively, the designer could select theparameter values/components that he/she maywant to be shared across scenarios (i.e. toenforce commonality), which will reverse theprocess and indicate how large the region ofinterest must be. In this manner, the designercan interactively explore the design space togain a better understanding of what thetradeoff is between the commonality andchangeability of the designs and to find a setof solutions that he/she may deem acceptable.

The second course of action can be invoked ifthe increase in the region of interest is notacceptable. In this case, transition paths willhave to be formulated to enable transitionbetween the parameters that cannot be madecommon. This is discussed in Step 5.

Note that this method will usually providemultiple combinations of parameters/components that could be changed (i.e. multipletransition paths that can achieve the same effect).The designer could choose to investigate all ofthem, or choose only selected ones, based ondomain knowledge. Also, the potential transitionpaths identified will depend on the sequence inwhich parameters/components were selected to


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be made common. Again, the designer maychoose to explore different sequences, or selectan appropriate one, based on experience.

Step 5: Modelling and simulation of transitionpathsIn the previous step, combinations of parametersand/or components were identified that requiretransition paths. This essentially means that atransition is required from one aircraft design toanother. In this step, the ‘how’ of this transitionmust be addressed. The changeability principlesfrom [5] could prove useful in this step, butdomain knowledge is essential. An examplecould be a required change in span (from the firstgeneration to the second). In this case, twotransition paths are possible: design a new wing,or a pre-planned span extension. Both of thesewill add additional considerations during thedesign of the first generation aircraft, whichcould involve, for example, aero-elasticity andstability concerns, amongst others. However, forconceptual design purposes, at least the increasein mass and cost, and the effect on aerodynamicsof the transition paths must be reasonablycaptured. Once the transition paths are identified,they can be modelled, after which anotherdesign-of-experiments study should be run tofurther populate the MATs – now with designsthat have planned changeability incorporated.

Step 6: Final down-selectionThe goal of the final step is to analyze all thedesigns and to find those that are most likely tomeet both current and future requirements. Forthis purpose, the change implementationstrategies defined in Step 1, as well aschangeability metrics (see Section 2) can beapplied to find the most valuably changeabledesigns. Aircraft with dissimilar top-levelconfigurations are now considered together.

5 Framework demonstration: evolvablesingle-aisle passenger aircraft

In this section, the changeability frameworkproposed above is applied to find first-generationdesigns for a short-range, single-aisle passengeraircraft that could be changed ‘easily’ to second-generation aircraft that would excel in differentscenarios that may have diverse and conflicting

combinations of requirements regardingenvironmental friendliness. These includerequirements related to fuel-burn, Nitrogen-Oxide (NOx) emissions, noise, and field length.For the purposes of this example, EIS for the firstgeneration will be 2020, whereas the EIS for thesecond generation will be 2030. Therequirements that will not change are the numberof passengers (150) and range (2700nm).Note that the goal of this study was not to designaircraft that would meet future environmentaltargets, but to illustrate how the framework couldbe used – the parameter values used are notionalonly and do not reflect any actual aircraft ortechnologies.

Step 1: Scenario identification and developmentOne scenario for the first-generation (2020) andthree for the second-generation aircraft (2030)were considered. These were adapted from thescenarios presented by Northrop Grumman fortheir N+3 study [10]. The scenarios for 2030 arebriefly described as follows [10] and [11]: “Bright Bold Tomorrow (BBT)” 2030: Field-

length requirements are much stricter than fortoday’s aircraft. Also, the OEM would happilypay more to develop aircraft with higherutility.

“Not in My Back Yard (NiMBY)” 2030:Strict regulations regarding noise andemissions are in place and considered moreimportant than fuel-burn and field length. TheOEM would like to find a balance betweenhigh utility and high cost.

“King Carbon (KC)” 2030: Carbon emissionsis the prime consideration. Noise and field-length is not considered important. The OEMwould like to find designs that have low fuel-burn, but at the lowest cost possible.

The scenario for 2020 was called ‘BBT 2020’and had the same weightings as BBT 2030. Theutility values could be calculated as follows(adapted from [10]):� = � � � � + � � � � + � � � � + � � � � � � (1)

where � � , � � , � � , and � � � are weighting factorsfor fuel-burn, noise, emissions and field length,and:

� � =

⎩⎨

⎧0; if � > � �

� � − �

(1 − � � )� �; if � ≤ � � and � > � � � �

1; if � ≤ � � � �

(2)

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� � is the “System Effectiveness Ranking” (SER)[10] for fuel-burn. � is the block-fuel peravailable-seat-mile (ASM) of the design inquestion [lbm/nm]; � � is a reference block-fuelper ASM of 0.83 lbm/nm (a typical value for a1990’s technology 2700 nm, single-aisle 150 seatpassenger aircraft). � � represents a specifiedfraction of block-fuel per ASM, compared withthe reference value (for example, if � � = 0.3, itrepresents a fuel-burn reduction of 70%).Similarly,

� � =

⎩⎨

⎧0; � � � � � − � < 0

� � � − �

� �

; � � � � � − � ≥ 0 � � � � � � − � < � �

1; � � � � � − � ≥ � �

(3)

Here, � � is the SER for the Effective PerceivedNoise Level (EPNL) of the design in question; �is the predicted noise in effective perceived noisedecibels (EPNdB) produced by the design inquestion; � � � is the Federal Aviation Regulation(FAR) Part 36, Section 103 Stage 4 limit on noise[12] for an aircraft with the same mass as that ofthe design in question [EpNdB]; and � � is targetmargin on noise with respect to the FAR Stage 4limit [EPNdB]. Furthermore,

� � =

⎩⎪⎨

⎪⎧ 0; if � > � � �

� � � − �

(1 − � � )� � �; if� ≤ � � � and� > � � � � �

1; if � ≤ � � � � �

(4)

Where � � is the SER for Nitrogen-Oxideemissions; � is the landing-takeoff cycle (LTO)NOx emission index in g/kg of the design inquestion [13]; � � � is a reference value, based onthe Committee on Aviation EnvironmentalProtection’s (CAEP) CAEP/6 standard [13]; and� � represents the fraction of NOx emissionscompared with the CAEP/6 standard.Finally, � � � = �

0; if � � � > � � � � � �1; if � � � ≤ � � � � � �

(5)

Where � � � is the SER for balanced field length;� � � is the balanced field-length required by thedesign in question (maximum of the takeoff orlanding balanced field lengths) in ft; and � � � � � �is the required BFL in a specific scenario [ft].The values for the weightings, and for � � , � � ,� � and � � � � � � selected are shown in Table 1.

Table 1: Weightings/reference values for the scenarios.BBT2020

KC2030

NiMBY2030

BBT2030

� � 0.25 0.65 0.1 0.25� � 0.5 0.4 0.4 0.4� � 0.2 0 0.4 0.2

� � [EPNdB] 42 71 71 71

� � 0.25 0.35 0.4 0.25� � 0.25 0.2 0.2 0.2� � � 0.3 0 0.1 0.3

� � � � � � [ft] 6500 6000 6000 5000

� � , � � , and � � correspond to NASA’s N+2 andN+3 targets, summarized in [14].

Step 2: Concept generationOnly two top-level configurations wereconsidered for this study: a ‘conventional’ layoutand a T-tail, with fuselage-mounted engines (seeFig. 4). All the concepts have airframes that aremade mostly of composites. The enginetechnology considered for the 2020 generationaircraft are similar to the newest enginesavailable on today’s short range aircraft, with abypass-ratio (BPR) of 11 and an overall-pressureratio (OPR) of 40. These will be referred to as‘TF 2020’. Engines considered for the 2030generation aircraft were future three-shaftturbofans (‘F3STF’), with BPR = 18 and OPR =50, and counter-rotating open-rotor (‘CROR’)engines with OPR≈36. Laminar flow (LF) wingswere also considered for the 2030 aircraft.Finally, two types of flaps were considered:single-slotted (SSF) and triple-slotted (TSF).

Fig. 4: Concepts generated for 2020 and 2030 EIS (images rendered in PACELAB [17]).


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Step 3: Population of scenario tradespacesThe concepts generated were subsequentlymodelled and analyzed to populate thetradespaces. NASA’s flight optimization system(FLOPS) [15] was employed as the main meansof modelling, whereas the cost model developedby [16] was used for the cost analysis, since itallows cost estimation on component level. Thecost values were converted to 2016 U.S. Dollars.FLOPS was inadequate to predict the noise forthe future engine technologies and it wasassumed that the noise levels would be about 20EPNdB lower than the Chapter 4 standard for theF3STF and only about 10 EPNdB for the open-rotor. To model the open-rotor fuel-flow, anengine-deck for a generic open-rotor, obtainedfrom PACELAB [17], was used. Models werecreated to estimate the LTO NOx emissionsbased on the LTO fuel flow and scaled notionalvalues for LTO NOx values presented in [10] and[18]. The area of laminar flow (LF) on the wingwas predicted using a model from [19]. Fuel flowfor the F3STF and CROR was estimated byscaling values presented in [10] and [18],respectively. Fractions of fuselage mass wasadded to the concepts employing the open-rotor,based on expected increases discussed in [18].[20] was used for the BFL estimates. Aftermodelling was complete, a full-factorial DoEwas performed with the parameters shown inTable 2 to populate the tradespaces. The resultingtradespaces can be seen in Fig. 5.

Table 2: Design-of-Experiments parameters.Conventional T-tail

Parameter/component

EIS2020

EIS 2030 EIS2020

EIS2030

Engine type 2015TF

F3STF 2015 TF F3STF,CROR

Flap type SSF,TSF

SSF, TSF SSF,TSF

SSF,TSF

Slat Yes Only non-LF

Yes Onlynon-LF

LF (N=None,P=Passive)

N N; P N N; P

Thrust [lbf] Min=20 000; Max=30 000; 6 LevelsSpan [ft] Min=105; Max=130; 6 Levels

Root chord[ft]

Min=17; Max=23; 7 Levels

Cruise Machnumber

0.78 0.78;0.73(LF)

0.78 0.78;0.73(LF)

Quarter-chord sweep

[°]

25° 25°,4°(LF)

25° 25°, 4°(LF)

Fig. 5: Tradespaces for the scenarios. The legend appliesto all the plots.

Step 4: Identification of potential transition paths

After the tradespaces were populated, the region ofinterest could be selected for each scenario. Fig. 6shows the region of interest for the T-tail in KC2030. As can be seen, affordability is important inthis scenario, but reasonable performance is stillexpected. Once the regions of interest for both theconfigurations in all the scenarios had beenidentified, the designs falling in these regions werecompared to find potential transition paths. Fig. 7shows a parallel coordinates plot of the T-taildesigns of interest for the different scenarios. Ascan be seen, large values of span and root chordare favored in BBT 2030, whereas the opposite istrue for KC 2030 and BBT 2020. Also, the onlyscenario in which the open-rotor is a desirableoption is in KC 2030. The designer can now usethis plot to find what parameters can be madecommon.

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Fig. 6: Region of interest for T-tail in KC 2030.

One possible solution (for the T-tailconfiguration) is shown in Fig. 8. As can be seen,transition paths are required to enable a spanincrease from 110ft to 125ft, as well as a rootchord increase from 18ft to 19ft for BBT 2020 toBBT 2030. There was no change required inthese parameters for BBT 2020 to NiMBY 2030and KC 2030. However, there was a changerequired from a non-laminar wing to a laminarwing for BBT 2020 to NiMBY 2030.Also, although span and root chord is the samefrom BBT 2020 to NiMBY 2030, the wing masswould likely differ between these two scenarios.In other words, although the wing geometry maybe the same, a transition path could still beformulated to change to a wing with a differentmass. Finally, provision needed to be made forthe engine changes as well.Note that Fig. 8 shows that, for this example, theparameters sets have been reduced to only onecombination per scenario. This need not be thecase and, indeed, sets should always beconsidered. The options for the transition paths

for the t-tail are now as follows: Retain airframe design, develop new systems

and re-engine (only possible for BBT 2020 toKC 2030)

Re-engine, develop completely new wing,new undercarriage, new systems, and retainfuselage and empennage where possible.

Re-engine, develop a partially new wing(retain root section) and new systems, retainundercarriage, and retain fuselage andempennage.

The designer could just as well have wished torather select designs that all have common winggeometry and only look at re-engining options.To do this, he/she may ‘drag’ the points of theparallel coordinates plot that relate to span androot chord to specify the geometry that is desired.As stated earlier, this could result in designs withdecreased performance.However, for the purposes of this discussion, it isassumed that the designer is happy with thetransition paths selected.

Fig. 8: Selected parameter ranges for T-tail designs.

Step 5: Modelling and simulation of transitionpaths

Next, the transition paths identified in theprevious step were modelled and analyzed. Forthe re-engine rule, it was only necessary to ensurethat the airframe and undercarriage will havesufficient structural strength for incorporating thenew engine. This was done by determiningwhether any of airframe components of theaircraft with the future engines are heavier than

Fig. 7: Parallel coordinates plot showing the T-tail designs of interest for the different scenarios.


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that of the aircraft with the older engine. The onlyfuture engine for which this will be the case wasthe CROR, which would require structuraltreatments to its fuselage to deal with fatigue andnoise. These treatments will, however, be addedwhen the new aircraft is developed. For the re-engine and complete new wing, the fuselage andempennage were kept common to the 2020airframe (their mass values fixed as inputs toFLOPS) and a new wing and undercarriage weredesigned. For the partial re-wing, the inner rootsection of the wing, up till just past where themain undercarriage is attached to the wing, waskept common (Fig. 9), whereas the outer wingwas re-designed. To be able to do this, it wasrequired to know the mass distribution withrespect to span of the wing and, for this purpose,a method to determine wing mass distribution,presented in [21], was employed.

For the BBT 2030 wing, the common section ofthe wing will also require a 1ft chord increase(visible in Fig. 9). The negative effects of havinga common wing section is more mass and (in thecase of laminar wings) there is reduced laminarflow area, which meant smaller reductions infuel-burn attainable than with a full laminarwing.

Fig. 9: Wings for the different scenarios. Sectionscommon to the 2020 wing is shown in grey.

Step 6: Final down-selection

When the cost and utilities of all the designs,including those with planned changeability, hadbeen determined, the final down-selection ofcandidates took place. For this purpose, themetrics in [4] could have been be used. However,because the number of designs, scenarios, and

generations was small, visual inspection of theMATs sufficed. For example, Fig. 10 shows theMATs for the first-generation designs (BBT2020), and one of the three scenarios (BBT 2030)for the second-generation. As can be seen, thechangeable designs (A and B) maintain highutility at much reduced cost in the secondgeneration, as compared with completely newdesigns. This utility is slightly smaller than whatcan be achieved with new designs, however.

Fig. 10: MATEs for BBT2020 and one of the futurescenarios (BBT2030) showing designs with planned (inBBT2020) and executed changeability (in BBT 2030).

6 Conclusions and future work

In this paper, a framework was proposed thatemploys concepts from Multi-AttributeTradespace Exploration and Epoch-EraAnalysis, along with interactive commonalityidentification, to enhance the exploration ofaircraft changeability during conceptual design.In particular, it was demonstrated how a designercould compare the changeability of aircraft with

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different top-level configurations and tointeractively identify where transition paths needto be inserted. A strong focus in this paper wasto search for commonality to identify transitionpaths. However, it is important to note thatenabling changeability is not simply aboutenforcing commonality, and other architecturalaspects (changeability principles), such assimplicity and independence need to beconsidered as well. Some of these are implicitlyincorporated in the proposed framework, asdifferent top-level configurations and systemsarchitectures are investigated together.However, more work is required to furtherinvestigate the implications of the otherchangeability principles within the context of theframework.The next step would be to implement theframework into a software called ‘AirCADiaExplorer’ [22]. This software employs object-oriented techniques to enable the synchronizationof different plots of the design and performancespaces, which will allow the interactivity of theframework to be exploited. Future work will alsoconcern the further development of the methodsto enable more scenarios and architectures to beexplored. Also, it is endeavored to investigatehow object-oriented techniques can be employedto automate selected steps of the framework.Finally, it is aimed to evaluate the frameworkwithin an industrial setting.

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Contact Author Email Address

Albert S.J. van HeerdenMailto: [email protected]

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