Embed Size (px)
Citation preview
Chinese Journal of Aeronautics, (2015), 28(3): 617–635
Chinese Society of Aeronautics and Astronautics& Beihang University
Chinese Journal of Aeronautics
REVIEW ARTICLE (INVITED)
System of systems oriented flight vehicle
conceptual design: Perspectives and progresses
* Corresponding author. Tel.: +86 10 82339801.E-mail addresses: [email protected] (H. Liu), tianyongliang@ase.
buaa.edu.cn (Y. Tian).
Peer review under responsibility of Editorial Committee of CJA.
Production and hosting by Elsevier
http://dx.doi.org/10.1016/j.cja.2015.04.0171000-9361 ª 2015 The Authors. Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Liu Hu a,*, Tian Yongliang a, Gao Yuan a, Bai Jinpeng b, Zheng Jiangan c
a School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, Chinab Shenyang Aircraft Design Institute, Shenyang 110000, Chinac Aviation Industry Development Research Center of China, Beijing 100029, China
Received 29 December 2014; revised 9 February 2015; accepted 2 March 2015Available online 20 April 2015
KEYWORDS
Conceptual design;
Effectiveness;
Flight vehicle;
Modeling and simulation;
System of systems
Abstract In order to obtain optimized flight vehicle concepts which meet system of systems (SoS)
operation requirements, designers have to pay high attention to the impact of SoS at conceptual
design stage since operation environment goes increasingly complex. Based on this tendency, per-
spectives and progresses of SoS oriented flight vehicle conceptual design, which is abbreviate as
SoSed design, are reviewed in this paper. Such basic concepts of SoS as definition, characteristics,
differences between systems engineering and SoS engineering, as well as SoSed design process are
introduced, then SoS engineering process model for research and development of flight vehicles
and SoSed design wheel model for conceptual design are proposed. Related literature is classified
and analyzed in accordance with four major elements including requirements, design concept,
design analysis, and trade studies and optimization: typical SoS architectures, description and quan-
tization of indexes are introduced; Application of inverse design in designing concept is analyzed;
Modeling and simulation (M&S)-based methods and their applications in SoSed effectiveness eval-
uation are highlighted; According to SoSed trade studies and optimization related research, the
importance of such points as decision-making and using multidisciplinary design optimization
for reference are emphasized. Finally, the value of SoSed design is concluded, and five directions
which are worthy of attention in this field are presented.ª 2015 The Authors. Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA. This is an
open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
The development of modern high-performance flight vehicles,including military aircraft, civil transport aircraft, helicoptersand etc., is an extremely complex, lengthy and costly process.
It makes flight vehicle conceptual designers face many chal-lenges, since conceptual design always plays a vital role indevelopment. In fact, conceptual designers’ tasks have far
beyond the definition of ‘‘design’’ proposed in Raymer’s clas-sical conceptual design book,1 i.e., ‘‘creating the geometric
618 H. Liu et al.
description of a thing to be built’’. One of the arising chal-lenges which conceptual designers have to handle is ensuringtheir design concepts meet more and more requirements in
the context of system of systems (or System-of-systems,SoS),2,3 such as a net-centric operation scenario4 to militaryaircraft and a network described in next generation air trans-
portation system (NextGen)5 to airliners. This kind of designactivity is denoted as ‘‘system of systems oriented flight vehicleconceptual design’’ by the authors, and ‘‘SoSed design’’ is used
as abbreviation.A distinct trait between SoSed design and traditional con-
ceptual design lies in the transition from emphasizing measureof performance (MoP) to measure of effectiveness (MoE) and
capability.6 MoP is used to evaluate how well a system per-forms a task, but it is not sufficient when operational contextis taken into account. For example, flight 1000 km at
10000 m is a statement of a fighter aircraft’s performance,but the same aircraft has different effectiveness at completinga mission if the 1000 km distance is over water or over a hostile
area.7 As defined in Ref.8, MoE is ‘‘a criterion used to assesschanges in system behavior, capability, or operational environ-ment that is tied to measure the attainment of an end state,
achievement of an objective, or creation of an effect’’.Although the research and applications of MoE related topicshave a quite long history, just as the creation of multidisci-plinary design optimization (MDO) results from increasing
number and coupling of disciplines, the increasing complexityof operation environment makes designers inevitably use SoSprospective to extend conceptual design’s scope and use the
related new theories and methods to solve problems that didnot exist before. In short, the duty of conceptual designerskeeps the same but the world has changed.
Nowadays, SoS related research has covered both funda-mental theories and applications in many fields, such aspolicy-making, defense, air transport, medical and health man-
agement,9 etc. and research focus of different fields varies sig-nificantly. In aerospace, SoS has covered almost every categoryof flight vehicle, including commercial aircraft,10 manned mil-itary aircraft,11 unmanned aero vehicle,12 missile,13 space-
craft14 and launch vehicles,15 etc. Fig. 1 gives two typicalSoS examples in aerospace community, i.e., U.S. coast guardintegrated deepwater system16 and air transportation net-
work.17 The arising SoS cases make researchers working oneach branch of flight vehicles design not neglect potentialimpact of SoSed design.
Although there has been some overview on SoS,18–20 it israre to make an summary on up to date flight vehicles’SoSed design. Since military missions and civil transportationstend to become more and more complex, the authors believe
that SoSed design would be one of the promising hot pointsto aerospace community after MDO, while the main purposeof this paper is to show a general picture of perspectives and
progresses of SoSed design, discuss its impact and give system-atic reference, as well as inspire further exploration in this area.
2. Basic concepts of SoS
2.1. Brief history of SoS
The initial mention of SoS in public journal can be traced to1956, when Boulding21 imagined SoS as a ‘‘gestalt’’ in
theoretical construction creating a ‘‘spectrum of theories’’greater than the sum of its parts. In 1971, Ackoff22 consideredSoS as a ‘‘unified or integrated set’’ of systems concepts. He
proposed that ‘‘the systems approach to problems focuses onsystems taken as a whole, not on their parts taken separately’’.In 1984, Jackson and Keys23 suggested using the ‘‘SoS
methodologies’’ as interrelationship between differentsystems-based problem-solving methodologies in the field ofoperation research. It was not until 1989, with the Strategic
Defense Initiative, that we find the first use of the term‘‘system-of-systems’’ to describe an engineered technologysystem.24
As shown in Fig. 2, Ref.18 gives a summary of typical devel-
opment of SoS from 1990s to 2008, which shows that academicresearch in SoS was much earlier than industry and govern-ment applications. SoS was firstly introduced to military
research by Admiral W.A. Owens in 1995, and it has beenwidely investigated since then. Maier25 was considered to beone of the most influential contributors to the study of the
SoS field and proposed for the first time to use the character-ization approach to distinguish ‘‘monolithic’’ systems fromSoS. In 2008, the first two books dedicated to SoS were intro-
duced by Jamshidi9,26 and it can be expected that more workwill be available in future.
Nowadays there are some specialized research organiza-tions on SoS, such as the Office of the Secretary of Defense
(OSD),27 Group of Global Earth Observation,28,29 and theInternational Council on Systems Engineering (INCOSE).30
There are also some typical research groups in universities,
such as the System-of-Systems Signature Area Group ofPurdue University,3 Aerospace Systems Design Laboratoryof Virginia Tech,31 National Centers for System of Systems
Engineering in Old Dominion University,32 Centre forAutonomous Systems of Royal Institute of Technology,33
and Command Control Communication Computer
Intelligence Surveillance Reconnaissance (C4ISR)Laboratory of National University of Defense Technology.34
Some specialized international conferences like the IEEE inter-national conference on system of systems engineering35 have
also been launched, and some SoS related sessions are avail-able in traditional aerospace conferences like aerospacesciences meeting,36 AIAA/ISSMO multidisciplinary analysis
and optimization conference,37 and AIAA modeling and sim-ulation technologies conference.38
2.2. Definitions of SoS
There was no universally accepted definition of SoS, differentresearchers have given different descriptions, and the followslist some typical ones:
Definition 1. SoS is a collection of task-oriented or dedi-cated systems that pool their resources and capabilities
together to create a new, more complex system which offersmore functionality and performance than simply the sum ofthe constituent systems.39
Definition 2. In relation to joint war-fighting, SoS is con-cerned with interoperability and synergism of command,control, computers, communications, and information
(C4I) and intelligence, surveillance, and reconnaissance(ISR) systems.40
Fig. 1 Typical system of system (SoS) examples in aerospace community.
System of systems oriented flight vehicle conceptual design: Perspectives and progresses 619
Definition 3. SoS integration is a method to pursuedevelopment, integration, interoperability and optimizationof systems to enhance performance in future battlefield
scenarios.41
Definition 4. SoS is the large-scale concurrent and dis-tributed systems that are comprised of complex systems.42
Definition 5. SoS is a set or arrangement of systems thatresults from independent systems integrated into a largersystem that delivers unique capabilities.43
Definition 6. SoS are the large-scale integrated systemswhich are heterogeneous and independently operable ontheir own, but are networked together for a common goal.
The goal may be cost, performance, robustness, etc.44
In fact, it is common that some new concept appeared with-out universal definition, but with universally accepted charac-
teristics, just as the concept of agent45 in artificial intelligent.
So the key point for now is not to make a definition for SoS,
but to distinguish from its characteristics. The most widelyaccepted characteristics were proposed by Maier25, whichinclude:
(1) Operational independence of the individual systems.(2) Managerial independence of the individual systems.
(3) Geographical distribution of the individual systems.(4) Emergent behavior, where the system performs func-
tions not possible by any of the individual stand-alonesystems.
(5) Evolutionary development caused by continuous inter-operability relationships between constituent systems.
Maier also highlighted that the first and second ones areprincipal characteristics for distinguishing SoS from othercomplex systems. There are other researchers who proposed
Fig. 2 Historical timeline of SoS.18
620 H. Liu et al.
other characteristics in addition to Maier’s five criteria, e.g.,Sage and Biemer46 added self-organization and adaptation.
Moreover, DeLanrentis proposed inter-disciplinary study,heterogeneity of systems and networks of systems39, which isa typical example that researchers in flight vehicle design field
making contribution to fundamental theory of SoS.
2.3. Differences between SoS engineering and systemengineering
According to Defense acquisition guidebook,47 system of sys-tems engineering (SoSE) deals with planning, analyzing, orga-
nizing, and integrating the capabilities of a mix of existing andnew systems into an SoS capability greater than the sum of thecapabilities of the constituent parts. This description implies
Table 1 Differences between traditional systems engineering (SE) a
Feature System engineering perspective
Scope Project/product
Autonomous/well-bounded
Objective Enable fulfillment of requirements
Structured project process
Time frame System lifecycle
Discrete beginning and end
Organization Unified and authoritative
Development Design follows requirements
Verification System in network context
One time, final event
that SoSE can be regarded as the process and SoS is the goal.In most cases, there is no strict distinction between SoS and
SoSE. However, there are much difference between SoSEand system engineering (SE).48 As outlined in Table 1, SoSEgoes beyond traditional SE in a number of ways.49 One per-
spective50 on an SoS is to view it as an emergent phenomenonborn from the complex interactions among independent sys-tems and stakeholder interests, rather than a centrally designed
product. This view is the underlying perspective of traditionalSE, i.e. SE methodology typically assumes that there existscentral control of its components regarding how they function
and interact with one another. Centralized control cannot beguaranteed for SoS constituents, because they are oftenautonomous and act based on their self or stakeholderinterests.
nd system of systems engineering (SoSE).49
System of systems engineering perspective
Enterprise/capability
Interdependent
Enable evolving capability
Guide integrated portfolio
Multiple, interacting system life cycles
Amorphous beginning important history and precursors
Collaborative network
Design is likely legacy-constrained
Ensemble as a whole
Continuous, iterative
System of systems oriented flight vehicle conceptual design: Perspectives and progresses 621
During the past decades, SE has played a vital role inmodern aerospace industry and has affected traditional flightvehicle development process. An SE process model proposed
by Price et al.51 is shown in Fig. 3. Inspired by this modeland characteristics of SoS, the authors proposed an SoSEprocess model for research and development of flight vehicles
(Fig. 4), which emphasizes all of the requirements explo-ration, feasibility synthesis, system synthesis and engineeringdevelopment phases should closely interact with the SoS envi-
ronment. The term ‘Evolutional’ is used because it alwaystakes a long period of time to develop a modern flight vehicleand the operational environment must evolve to meet arisingchanges.
3. Process of SoSed design
The classical design wheel model proposed by Raymer1 isshown in Fig. 5. This model shows interrelation of require-ments, design concept, design analysis, sizing & trade studiesand optimization in traditional flight vehicle conceptual
design. Fig. 5 shows that design concept and sizing & tradestudies have a direct connection with requirements in designwheel, but design analysis influences requirements through
design concept and sizing & trade studies. In SoSed design,effectiveness analysis is an important part of design analysis,while effectiveness analysis is closely linked to SoSed require-
ments. This means not only effectiveness analysis is carriedout according to requirements, but also requirements’
Fig. 3 Systems engineering (SE) process model.51
Fig. 4 System of systems engineering (SoSE) process model.
verification and improvement is influenced by the results ofeffectiveness analysis. Therefore, ‘‘SoSed design wheel’’, whichcan also be regarded as refining model in Fig. 4 according to
conceptual design stage, is proposed in this paper as shownin Fig. 6. In SoSed design wheel, requirements are moved fromthe edge to the center of design wheel. In this way, require-
ments can establish a direct connection with design analysisand other elements at the same time. This also shows require-ments, especially SoSed requirements is the central role in
SoSed design process.Figs. 5 and 6 reflect the process for conceptual design, but it
is difficult to find a description of a complete and detailedSoSed design process from prior literature. In spite of this,
based on some specific problems in design, a number ofresearches have been devoted to proposing processes forsolving specific problems. For example, Talley and Mavirs52
proposed a multi-level robust design process (Fig. 7), whichcontains many levels, such as operational environment andscenario (OES) level, intermediate level A SoS, intermediate
levels B, C, etc., SoS and base level SoS. A design cycle is
Fig. 5 Design wheel model.1
Fig. 6 SoS oriented flight vehicle conceptual design (SoSed
design) wheel.
Fig. 7 Overview of robust SoSed design process.52
622 H. Liu et al.
formed between each two adjacent levels and each individuallevel has its own design process (Fig. 8). The core of all levels’process is to identify, model and simulate uncertainty factors
of systems and mission circumstances in SoS. For differentSoS, the number of levels is different: there may only beOES level and base level for simple SoS, while more than four
levels may exist for some large complex SoS.Another valuable process proposed by Biltgen53 is
capability-based technology evaluation process for SoS(Fig. 9). The process shows how to gradually transform macro
capability requirements into effectiveness indexes which can bequantitatively evaluated. It applies artificial intelligence, surro-gate model, simulation, K factor54 (a scale parameter proposed
by Mavris et al. on discipline level metrics) to research theimpact of main design parameters and new technologies, etc.on large-scale SoS. Similar to technology evaluation process,
Fig. 8 Design process
Soban and Mavris55 proposed technology impact forecasting(TIF) environmental creation and technical scenarios evalua-tion process when he researched the quantitative assessment
of the technology impact on a given baseline system, includingthe sensitivities to aircraft goals and constraints.
In Refs.52,53 the process focused on missions of a single
design object in SoS, while Frommer56 proposed process(Fig. 10) that involves how to design new aircraft in fleetby considering the objectives and constraints on SoS level.
This process includes the project design and resource alloca-tion at the same time. It uses concept of operations(CONOPS) as a beginning for which the system will haveconstituents designed and allocated. The CONOPS is broken
down into parameters such as range and loiter time, whichdefine the mission space. These parameters are then usedto size constituents throughout the design space so that sur-
rogate models of metrics like weight or cost can be created.Probabilistic distributions of the design mission parametersdescribe the likelihood of any given mission in the
CONOPS. Both civil aircraft and military aircraft have fleetresource allocation problems, especially when more andmore attention has been paid to application of unmanned
aerial vehicle (UAV) fleet57, so this type of parallel processreally needs more investigation and application.
As a matter of fact, the various typical processes above arenot fully integrated with traditional conceptual design process,
but the research of a new complete process is beyond the scopeof this article. Therefore, in order to facilitate the review of theexisting SoSed design research, hereinafter the paper will be
organized in accordance with major elements contained in‘‘SoSed design wheel’’ model, including requirements, designconcept, design analysis, trade studies and optimization. It is
important to note that sizing is not listed separately, but it willbe mentioned in design concept, trade studies and optimizationrelated sessions.
at each SoS level.52
Fig. 9 Capability-based technology evaluation process for SoS.53
Fig. 10 A generic process for framework to set up a concurrent
design and allocation problem.56
System of systems oriented flight vehicle conceptual design: Perspectives and progresses 623
4. Defining requirements in SoSed design
4.1. Architecture of SoS
Defining requirements in SoSed design involves two questionsat least: one is SoS description, and the other is description andquantitation of indexes. SoS description is also known as SoS
architecture. As mentioned in Ref.58, SoS architecture includesthe relationship between its members, members and environ-ment, and principle of guiding members to design and evolve,
etc. Fig. 11 is a typical description of SoS architecture.There are many ways to architect SoS and the early exam-
ple is Zachman framework59 proposed in 1987, which is a kind
of architectures with multiple views. The US Department ofDefense60 proposed Department of Defense architectureframework (DoDAF), which is the most widely used SoSarchitecture so far. DoDAF uses a series of systems or SoS
architecture views (such as operational view, system viewand technical standards view) to define system or SoS develop-ing level. Now it has already progressed to Version 2.0. Based
on DoDAF, the United Kingdom Defense Ministry61
increased several views and proposed the Ministry ofDefense architecture framework (MoDAF). DoDAF and
MoDAF are mainly applied to analysis composition and con-figuration of SoS from comparatively macroscopical perspec-tive in military, and it is rare to see direct application to
aircraft conceptual design.Because of the complexity of SoS, expect for focusing on
mission itself, sometimes it is needed to comprehensively con-sider many other stakeholders of SoS. Ayyalasomayajula62
proposed an SoS architecture which is descripted from fouraspects: resources, operations, policy and economics, and thisSoS architecture uses such Greek letter as a, b, c, and d to
express different levels of various aspects (see Fig. 12). Perlet al.63 used this architecture to analyze SoS related Marsexplorer design. Rovekamp and DeLaurentis64 took advantageof this method to design Lunar surface system architecture;
624 H. Liu et al.
Phillis and Kouikoglou65 even adopted this method in hisresearch on biology system.
From the perspective of flight vehicle design, the tri-
hierarchical model proposed by U.S. Defense AcquisitionUniversity (see Fig. 1353) may be probably one of the clearestdescriptions of SoS architecture. The three levels are system
of systems level, system level and subsystem level, corre-sponding to effectiveness, performance and technical param-eters. Systems engineering decomposition techniques define
the relevant MoPs for one or more MoEs. A similar decom-position can be performed to identify the relevant technicalperformance parameters (TPPs). This description of architec-ture is clear and easy to use, but it is limited that MoEs only
correspond to the SoS level. Because before SoS concept waswidely applied in military field, concept of system effective-ness had obtained a lot of attention and research.66,67
Based on this architecture, Biltgen53 further refined andformed another description of hierarchical architecture (seeFig. 14), which can establish more direct relationship from
the technical parameters, subsystem, system to SoS effective-ness indexes.
4.2. Description and quantization of indexes
The nature of indexes is to answer ‘‘how can I know if we get asatisfactory solution’’.6 Therefore, reasonable choice of effec-tiveness indexes is the basis for accurate definition of require-
ments. In SoSed design, it usually proceeds from definition ofcapability requirements to determination of the correspondingeffectiveness indexes concerning specific mission scenario
Fig. 11 A typical descripti
Fig. 12 Categories and hierarchy: an u
analysis. For example, in description of typical effectivenessindexes of long-range strategic bombers, Biltgen53 proposeda number of effectiveness indexes for long-range strike capabil-
ity requirements: duration of conflict, number of strikes flown,munitions fired, cost of munitions fired, number of red theaterballistics missiles (TBMs) fired, number of blue units lost,
number of blue aircraft lost, number of blue long range strike(LRS) aircraft lost, percentage of red time critical targets(TCTs) killed, etc. This case shows diversity of indexes
description.It is also necessary to give full consideration of uncertainty
(or random) characteristic in indexes description of SoSeddesign. Frommer56 proposed a metric method on integrated
design issues of design concept and fleet of aircraft concerninguncertainty. This method uses volume under a probability den-sity function (PDF) bounded by capability curves to describe
the probability of accomplishment (POA). In an effectivenessindexes issue of strike UVA design, Talley and Mavris52 pro-posed some indexes to describe demand when considering
uncertainty brought by time of coverage, domain of coverageand search interval time. These indexes are average attack timeof each task, number of aircraft required for each task, fleet
operation cost of specific tasks, purchasing cost, etc.Although these two examples are aimed at military aircraft,it is still applicable for requirements definition of civilian air-craft. Mane68 proposed indexes of direct operating costs and
the overall transportation network effectiveness on civilaircraft effectiveness indexes design concerning uncertainty.He also considered uncertainty due to market demand and
resource allocation.
on of SoS architecture.58
nfolded hierarchy unifying lexicon.62
Fig. 13 Relationship between SoS hierarchy and effectiveness indexes.53
System of systems oriented flight vehicle conceptual design: Perspectives and progresses 625
It can be seen that effectiveness indexes contain two levels:
indexes of flight vehicle itself and effectiveness indexes of thewhole SoS from the above studies. The former one is the mainsubject of SoSed design, but the latter one is getting more andmore importance, because it is necessary to connect the evalu-
ation of aircraft and its impact on SoS closely. For example, inJoint Test and Evaluation Methodology (JTEM) Plan,69 theU.S. Department of Defense proposed socialism, organization,
training, and some other attribute fields of SoS, specially
Fig. 14 Example of a hierarchical M&S environmen
aimed at the research of capability, system effectiveness, and
SoS capability evaluation. It is important to note that descrip-tion of system is ‘‘effectiveness’’, while description of SoS is‘‘capability’’ in JTEM research. Compared with descriptionin Fig. 13, it shows that there is still no unified and standard-
ized lexicon in this field.Quality function deployment (QFD) is a common method
for indexes quantification. It is a systematic mathematical pro-
cess used to translate the ‘‘voice of the customer’’ into the
t for aircraft design and effectiveness evaluation.53
626 H. Liu et al.
‘‘voice of the engineer’’, and ‘‘deployment’’ in QFD refers tothe process which converts matrix of demand results to indexesof lower levels.70 QFD has been widely applied to the develop-
ment of products for the aerospace, automotive, electronicsand other industries.71–74 An example of QFD is shown inFig. 1572, which is made of one or more hierarchical house of
quality (HOQ) tools, while the HOQ has found a lot of appli-cations in traditional design and tradeoff research.71,72 Theapplication of QFD is also worthy of paying attention to SoS
research. For example, Fiebrandt and Wilson69 applied QFDto convert demand indexes into joint mission effectiveness inJTEM plan. Biltgen53 also used QFD to decompose indexesfor the top-level strategic goals. It must be mentioned that
QFD is helpful to establish direct contact and quantization ofrequirements, design features and other aspects, but in orderto further determine a reasonable and optimized indexes, the
process of design wheel as design concept, effectiveness analy-sis, and trade study needs to go through. Certain design cyclesare also needed through mathematical analysis, M&S.
5. Designing concept in SoSed design
From such aspects as overall configuration, component/system
design, and layout design the authors think that comparedwith traditional ways, designing concepts in SoSed design basi-cally stay unchanged. New challenges mainly come from
uncertainty and requirements of seriation design when facingsizing problem, in other words, how to combine different mis-sion sections and scenarios with traditional sizing method. Forthis problem, there is no special literature containing related
research and elaboration, which deserves attention ofresearchers.
Inverse design is another method worthy attention for
design parameters’ sizing, which is proposed by Biltgen in hisresearch of capability-based technology evaluation for SoS.53
Fig. 15 Example of quality fu
He thinks that forward design is an ‘‘exploratory forecasting’’method, while inverse design is a ‘‘normative forecasting’’method. Forward design approach is used to assess the capa-
bility gaps of one or more systems and close the loop betweentechnology discovery and technology evaluation, while inversedesign approach is used to decompose capabilities into sys-
tems. Inverse design process is shown in Fig. 1653; central toinverse design technique is the use of Monte Carlo simulationsto populate the design space with a large number of points and
a dynamic graphical visualization of the results. Using inversedesign technique, it is possible to highlight a desired capabilityin relation to system of systems level metrics and identifypotential designs at the system and subsystem levels.
Obviously, it is very attractive to get the optimized designautomatically from capacity and effectiveness requirements,but it is very hard to implement. Therefore, the authors believe
that inverse design is a kind of parameter sizing method basedon a large number of samples and constraints, and designerscan make better decisions in constraint space with this method.
Exploratory design offers a way to perform rapid parametrictrades using a brute force approach or one-variable-at-a-timeoptimization, while inverse design uses probabilistic techniques
to partially automate the search for an elegant solution to thesame problem.
6. SoSed effectiveness evaluation
Traditional aircraft effectiveness evaluation methods includeanalytic hierarchy process (AHP),75,76 index method77,78 andsome other methods. Most of them are mathematical models
based on experience or analysis. In SoSed design, effectivenessevaluation needs to take into account not only more complexrelationship between systems, fleet resources allocation prob-
lems, but also effectiveness of both flight vehicle itself andthe overall SoS. In these cases, some mathematical methods
nction deployment (QFD).72
System of systems oriented flight vehicle conceptual design: Perspectives and progresses 627
which are usually used in operations’ research field and othersubjects have also been applied. For example, Mane et al.79
proposed a mixed-integer and nonlinear method to solve trade-
off problem of fleet allocation and new aircraft parameterdesign. This method realized the target of minimum directoperation cost in the whole fleet SoS. However, when the fleet
size increases to a certain extent, the traditional mixed-integer,nonlinear programming approaches cannot obtain a solution,Mane et al. further proposed MDO decomposition strategies
to find solutions for these larger problems.Most of SoSed effectiveness evaluation uses M&S-based
method. As Bociaga and Crossley57 said, simulation evalua-tions have gone beyond just answering ‘‘what if’’ problem
statements, but can now be used to answer ‘‘how to’’ as well.Similarly, according to research of Oden et al.80, simulation‘‘can be used to explore new theories and to design new exper-
iments to test these theories’’ and ‘‘also provides a powerfulalternative to the techniques of experimental science andobservation when phenomena are not observable or when
measurements are impractical or too expensive’’. In fact,M&S has been developed for many years as an independentfield. Some methods have been introduced to SoSed design
and have played an important role in effectiveness evaluation.The followings are several typical ones:
(1) Discrete event methodDiscrete event systems refer to
dynamic event-driven systems in which system statechanges by leaps and bounds, and system state onlychanges at discrete time points, as well as the points
are generally uncertain.81 Discrete event systems mainlydescribe time-driven dynamic systems in production,manufacture, services, and some other fields, and they
may change at discrete time point and get value incountable state space.82 Since the systems within SoSare operationally independent, interactions between sys-
tems are generally asynchronous in nature. A simple yet
Fig. 16 Inverse d
robust solution to handle such asynchronous interac-
tions (specifically, receiving messages) is to throw anevent at the receiving end to capture the messages fromsingle or multiple systems. Such system interactions can
be represented effectively as discrete event models.83,84
Flight vehicles’ SoSed operation has typical characteris-tics of discrete event. A typical related research wasdeveloped by Mittal and Martı́n Jr.,85 who proposed a
set of discrete event systems specification (DEVS) simu-lation framework based on discrete event method, andapplied it to the modeling of closed air support system
and UAV joint mission system.(2) Artificial intelligence methodIn the research of design for
operation, Liu et al.86 proposed a virtual world descrip-
tion model. Agent is emphasized as one of the basic ele-ments with objects, people and environment in thismodel, for the reason that complexity of real problemscannot get all the elements to be controlled by human.
Similarly, in SoS modeling, agent and its derivativemulti-agent method also play an important role. Agenthas the characteristics of autonomy, social and reactiv-
ity,45 which is able to well reflect the emergence and evo-lution of SoS. Ranque et al.12 defined each UAV as anagent when investigating forest fire monitoring and sal-
vage at sea with UAV fleet in SoS environment. It isimportant to note that the agent is not the only methodof artificial intelligence used in SoSed design. In
Biltgen’s research53, a cognitive model was developed todescribe behavior characteristics of FCSAMController(Air defense controller that detects airborne targets,calculates track information on the targets and conveys
the track information to a fighter controller.),FCAIRInterdiction (Air-to-ground attack aircraft thatcan be vectored toward a geographic feature, a coordi-
nate, or a unit.) and some other elements, which alsobelongs to the application of artificial intelligence.
esign process.53
Fig. 17 Dimension coordinates in SoS problems.91
628 H. Liu et al.
(3) Uncertainty modeling methodUncertainty is one of the
most distinct differences between SoSed design and tra-ditional design pattern as mentioned in the evaluationindexes of SoS. For example, Talley and Mavris52 ana-
lyzed uncertainty factors from area, time, etc. in SoSoperation of UAV; Frommer56 focused on search scope,distance and some other factors as uncertainty in hisresearch of maritime search and rescue. The larger an
SoS operation is, the more random factors there willbe. So uncertainty should be well concerned in M&Sfor effectiveness evaluation. Monte Carlo method,
stochastic probability function and probability distribu-tion function are commonly used in uncertainty model-ing, and they have been applied in some research.7,56,68
This also shows the value of introducing multi-disciplinary theories and methods for SoSed design.
(4) System dynamics methodSystem dynamics is a highlyvisual method for modeling complex SoS.87 Its charac-
teristic is transferring traditional formulation data intoshowing the model structure and how system elementsrelate to each other. The method is suitable for forecast-
ing a long time influence of the whole SoS brought bychanges of a variable.88 Csala and Sgouridis89 used sys-tem dynamics to model air transport SoS. They studied
the main factors influencing global air transport SoS andeffects of technology innovation for aviation industrychain. Although direct applications of system dynamics
are rare in SoSed design, this method is good at estab-lishing dynamic association between system elementsof SoS and the whole SoS, so the authors believe thatits potential application is worthy of attention.
(5) Complex network methodComplex network is a self-organization, self-similar, attractor and small world;some or all of its properties are in scale-free network.
Its structure is complex, node number is huge, and theconnections among its nodes will be generated or disap-pear along with time past. The network will also
evolve.90 For network is also regarded as one of themajor characteristics of SoS39, it is a natural choice touse complex network method in SoSed design.DeLaurentis et al.91 used this method to handle the con-
nectivity problems of air transportation agent, makingthe whole SoS have stronger robustness and scalability.
The five kinds of typical method above, along withexperience-based and analytic methods, provide many choicesfor researchers of SoSed design. In order to provide some guid-
ance for selecting methods in different situations, DeLaurentiset al.91 used three dimensions as systems type, control andconnectivity to build connection of critical dimension and
application field for SoS problems (Fig. 17), then he classifieddifferent problems to the resulting space to correspond withdifferent methods. Fig. 17 offers a representation of the taxon-omy that illuminates variations in system type, control, and
connectivity within an SoS. A1, A2 respectively representtwo different coordinates on the axis of Control. C1, C2respectively represent two different coordinates on the axis
of Connectivity. S1, S2 respectively represent two differentcoordinates on the axis of System type. So A1C1 is the inter-section of A1 and C1.
On the other hand, because characteristics of various M&Smethods are different, and SoS complexity often makes a
single method insufficient to completely describe an SoS oper-ation, an important trend of M&S is to composite several dif-ferent methods. For example, DeLaurentis et al.92 composited
complex network with multi-agent model to solve air networkconnectivity problem. Borshchev and Filippov93 comparedthree major paradigms in simulation modeling: system dynam-
ics, discrete event and agent-based modeling with respect tohow they approach such systems that contain large numbersof active objects and studied composition of the three model-
ing methods.In order to support M&S more effectively in SoSed design,
some special software systems have been developed and thesesoftware are significantly different in size and function.
There are some relatively large M&S systems as joint syntheticbattlespace (JSB)94 and joint warfare system (JWARS),95 somecommercial systems as FLAMS,96 SEAS,97 STK,98 DWK,99
Eagles,100 and some ‘‘in-house’’ systems as ATMAS (AircraftTime Critical Target Mission Analysis Simulation)52 andNetlogo.12 These software are not only helpful to effectiveness
analysis and evaluation, but also useful to trade study andoptimization. As every software has different functions andgoals and SoSed analysis method is in continuous development
and improvement, the authors do not think there is a profes-sional M&S software which has central-standard position inSoSed design (just like the status of CATIA in aviation indus-try for computer aided design). Integrated application of dif-
ferent software should be a more reasonable way in SoSeddesign according to available resources.
7. SoSed trade studies and optimization
7.1. Trade study
Trade study in conceptual design has always been a key step,and some basic trade methods as sizing matrix and carepet
plot1,101 have been widely applied. As mentioned in Ref.101,‘‘It will be necessary to conduct several parametric trade-offstudies to fully understand the various competing aspects of
the design’’. Trade study is also very important in the relatedstudies of SoSed design. Biltgen53 expounded the concept oftechnology evaluation as ‘‘the assessment of the relative benefitof a proposed technology with respect to one or more
System of systems oriented flight vehicle conceptual design: Perspectives and progresses 629
capability-level metrics.’’ It can be seen that trade is still thebasis of technology evaluation. Because only under the condi-tion of massive parameter combinations and technology level
changing, can we evaluate the effect of a technology on SoSeffectiveness comprehensively and reasonably.
There are several kinds of typical technology evaluation
methods including technology development approach(TDA),102 technology identification evaluation and selection(TIES)103 and quantitative technology assessment (QTA),104
etc. Exploratory analysis105,106 method which is proposed byRand Corporation is another trade method in dealing withuncertainty problems. This method tries to understand andfind the relationship between data variables beyond the com-
plex phenomenon to explore all possible results through study-ing the result of various options in a large number of uncertainconditions. In order to obtain quantitative trade results, M&S
method has been attached to attention and has found a lot ofapplication. Typical cases include Biltgen’s research7 of tech-nology evaluation on long-range bombers and the research
of Bociaga and Crossley57 on studying the influence of fleeteffectiveness by changing UAV quantities and performanceparameters.
In order to explore a feasible design in a wider range ofdesign space, generating a large number of samples based onM&S technology becomes an effective way. But this will causetwo problems: one is tradeoff of large sample experiment and
computing resources and the other is decision-making withlarge amounts of data. To solve the former problem, surrogatemodel and design of experiment (DOE) are effective
approaches,107 which use analytical method or other modelsto replace bottom simulation through the way of data fitting.With DOE to compress program space and scenario space,
they can also achieve the goal of reducing simulation runningtimes and saving time. Common methods used in surrogatemodel are polynomial response surface108 and neural
Fig. 18 Main effects w
network,109 etc. while in DOE are Latin hypercube, screeningor characterization experiments,53 etc.
The ultimate goal of tradeoff in SoSed design is to better
support designer’s decision-making, and this involves how todemonstrate connections among the data more intuitively,especially in the situation of ‘‘big data110,111 which is produced
by a large number of tradeoff. Aimed at this problem, Bociagaand Crossley57 normalized four goals: operating cost, averagerevisit time, maximum revisit time, acquisition cost in his
research on UAV design and tradeoff. They gave the corre-sponding weight coefficients to the four goals and finally gotthe global objective function. The main effects with equalweights generated from DOE are shown in Fig. 1857. This
can reflect the effects of design parameters directly on globalgoal and help to find out the optimal design point.
A similar approach is in Biltgen’s research53 on capability-
based technology evaluation. He used Pareto chart to describethe multivariable coordinate diagram of aircraft technicalparameters, as shown in Fig. 19. The multivariate plot
shows the correlation between the corridor choices andaircraft/engine design variables by color-coding different corri-dors selected. This intuitive data correlation chart enables
decision-makers to find the most important variable factorthat has an influence on the global state.
7.2. Optimization
The impact of various design parameters on effectiveness andcapability can be obtained through trade studies, and this is akind of optimization in itself. Of course, traditional optimiza-
tion refers to a kind of automation process, but the complexityof SoS operation brings challenges to automotive optimiza-tion. However, it is actually not impossible. Purdue
University’s work3,17,79,91,92,112,113 on civil aircraft and airtransport network is a typical attempt. Researchers as
ith equal weights.57
Fig. 19 Multivariate plot.53
630 H. Liu et al.
DeLaurentis and Crossley et al. carried out a series of studieson SoS analysis, concept design, allocation of fleet resources
and multi-objective/multidisciplinary optimization. In opti-mization of SoSed design, the authors think the followingthree aspects to be noted:
(1) Difference between SoS optimization in traditionalnational defense area and optimization in SoSeddesign. Typical traditional objects include SoS’s capa-
bility structure, composed structure, and structurescale.114 For example, Holmes et al.115 established 54kinds of SoS architecture program and 3 kinds of typ-
ical operation scenarios to evaluate and optimize SoS
architecture program on the purchasing issues of US
maritime dominance in the littorals. Though SoSeddesign focuses on the impact of different elementson an SoS, the key point of the optimization is still
the flight vehicle or its fleet.(2) Challenges brought by the uncertainties and application
of seriation design/fleet. As mentioned by Talley and
Mavris,52 although it is possible to optimize a fleet ina very specific mission, any changes will affect the wholecapability in SoS operation. His view is that in
uncertainty environment, the optimization goal is todetermine the most robust fleet, rather than searchingfor the optimal fleet. The change of optimization goal
System of systems oriented flight vehicle conceptual design: Perspectives and progresses 631
will have an impact on the selection of optimal variables
and constraints, application of optimization algorithmand strategy.
(3) Relationship between SoSed design optimization and
MDO. These two directions have some similarities atthe starting point: SoSed design optimization tends tofocus on the optimization of the overall effectiveness,rather than one system or serval systems in SoS; MDO
focuses on the overall optimization of system, ratherthan optimization of a single discipline. With regard tothe optimization method and strategy, some methods
as surrogate model, DOE and genetic algorithm arewidely used in MDO related research.116 These methodsare also used in analysis and optimization of SoSed
design. With the development of MDO, some optimiza-tion strategies such as collaborative optimization(CO),117 concurrent subspace optimization (CSSO),118
and bi-level integrated system synthesis (BLISS)119 are
proposed and they are also considered in the optimiza-tion of SoSed design. For example, Kim andHidalgo120 adopted multi-hierarchy and multi-stage
methods to solve SoS optimization problems. Thismethod connects system design with system configura-tion closely considering hierarchy characteristics and
evolutionary characteristics of SoS. These similaritiesmean that references from MDO methods will help tofurther develop optimization in SoSed design.
8. Conclusions and prospective directions
Through the review of SoS concepts, SoSed design process andthe related main elements studies, it can be seen that SoSeddesign is definitely not going to take place of the existingdesign ways, but it indeed brings a more comprehensive per-
spective and a new design mode for flight vehicle conceptualdesign. SoSed design helps designers to have more close andcomprehensive understanding about demands in design pro-
cess, and it also provides a support for development of moreoptimized flight vehicles which are applicable for the actualoperation environment.
SoSed design is still a very ‘‘young’’ field and there are alarge number of complex issues, so it provides a broad spaceto research and exploration of new philosophy, concept, the-
ory, method and technology. In addition to the former studiesmentioned above, the following five directions are worthy ofattention in this field:
(1) Proactive investigation on concept ofoperationsCompared with the traditional effectivenessevaluation which focuses on individual mission, SoSed
design pays more attention to flying vehicles’ effective-ness in complex operation environment and its contribu-tion to SoS effectiveness. Therefore, designer should
change passive design mode which means waiting forusers to propose design requirements and should bemore proactive in investigating possible concept of oper-ations and interacting with users. By this means, not
only more reasonable requirements could be obtained,but also some new design ideas could be motivated.
(2) Formulation of SoSed design processAlthough this
paper introduces some processes that are proposedaccording to some specific problems in design, they arenot systematic and lack discussion on how to combine
SoSed design with traditional process. Therefore, a moresystematic, standard SoSed design process is needed tobe formed, so that it can play a guiding role in theresearch and development of flight vehicle SoSed design
for academia and industry.(3) Innovation of M&S methodsM&S has become a crucial
approach in SoSed design, and the complexity of SoS
means that innovative M&S still needs to be exploredconstantly, then different characteristics of SoS can bewell studied in a more comprehensive and profound
way. On the one hand, new methods and strategies areneeded to be developed through the cross of differentdisciplines and fields from a broader vision. On the otherhand, researchers are also required to explore and dis-
cover the nature of SoS, derive new modeling idea andtheory.
(4) Tradeoff and decision-making support based on big
dataSoSed design involves complexity of operation envi-ronment, large numbers of systems and uncertain fac-tors, so it will produce large amounts of data in whole
process of ‘‘design wheel’’. If lacking effective methods,these data cannot bring valuable guidance. On the con-trary, they may even make designers get lost. Therefore,
it is necessary to draw lessons from big data theorywhich is currently fast developed in information andmanagement field, and develop tradeoff and decision-making support methods which is suitable for SoSed
design. This will help designers to explore rules andmechanisms from big data, and then better understandand develop design concepts which are suitable for an
SoS.(5) Development of integration platform for SoSed
designThe method of SoSed design must be realized
in the process of flight vehicle research and develop-ment, which requires efficient and reliable softwaretool to convert them into a part of daily work ofdesigners. Nowadays there are a lot of computer aided
flight vehicle design systems and M&S tools, so SoSeddesign software platform must integrate with themaccording to the process and different levels. This
SoSed design integration platform will not be unique,but the characteristics of integration and openness arewhat a valuable platform should have. Because theo-
ries and methods of SoSed design is in constantdevelopment, only with these characteristics can aplatform help to validate new theories and methods,
as well as transfer them to engineering practice moreefficiently.
Acknowledgements
The study was supported by the Fundamental Research Funds
for the Central Universities of China. And the authors aregrateful to Tian Yifeng and Wang Long for their contributionsin collecting and analyzing references.
632 H. Liu et al.
References
1. Raymer DP. Aircraft design: a conceptual approach. Reston
(VA): American Institute of Aeronautics and Astronautics, Inc.;
2006, p. 1–10.
2. Systems and Software Engineering. Systems engineering guide for
systems of systems.Washington (DC):Office of theUnder Secretary
of Defense for Acquisition, Technology and Logistics; 2008.
3. Crossley WA. System of systems: an introduction of Purdue
University Schools of Engineering’s Signature Area. In
Engineering Systems Symposium, 2004 Mar 31. Cambridge
(MA): MIT Engineering Systems Division; 2004.
4. Office of the Secretary of Defense. Network centric warfare.
Washington (DC): Department of Defense; 2005.
5. FAA.gov. NextGen implementation plan [Internet]. Washington
(DC): U.S. Department of Transportation, Federal Aviation
Administration; 2014. Available from: <http://www.faa.gov/
nextgen/> [updated 2014 Nov 17; cited 2014 Dec 16].
6. Sproles N. Formulating measures of effectiveness. Syst Eng
2002;5(4):253–63.
7. Biltgen PT. Technology evaluation for system of systems. In:
Jamshidi M, editor. System of systems engineering: principles and
applications. New York: CRC Press; 2009, p. 133–58.
8. U.S. Department of Defense. Joint Publication 1-02, Department
of Defense Dictionary of Military and Associated Terms
[Internet]. Available from: <http://www.dtic.mil/doctrine/
new_pubs/jp1_02.pdf> [updated 2014 Nov 15; cited 2014 Dec
16].
9. Jamshidi M. Systems of systems engineering: innovations for the
21st century. Hoboken (NJ): John Wiley & Sons, Inc.; 2008, p.
1–13.
10. Wilber GF. Boeing’s SoSE approach to e-enabling commercial
airlines. In: Jamshidi M, editor. Systems of systems engineering:
innovations for the 21st century. Hoboken (NJ): John Wiley &
Sons, Inc.; 2008, p. 232–56.
11. Office of the Under Secretary of Defense. Systems of systems:
systems engineering guide, Version 0.9. Washington (DC): Office
of the Under Secretary of Defense for Acquisition, Technology
and Logistics; 2006.
12. Ranque P, Freeman D, Kernstine K, Lim D, Garcia E, Mavris
D. Stochastic agent-based analysis of UAV mission effectiveness.
In 11th AIAA aviation technology, integration, and operations
(ATIO) conference; 2011 Sep 20–22; Virginia Beach (VA).
Reston (VA): American Institute of Aeronautics and
Astronautics, Inc.; 2011.
13. Ender TR, Mcclure EK, Mavris DN. A probabilistic approach to
the conceptual design of a ship-launched high speed standoff
missile. The AIAA 2002 missile sciences conference; 2002 Nov 5–7;
Monterey (CA). Reston (VA): American Institute of Aeronautics
and Astronautics, Inc.; 2002.
14. Bhasin KB, Hayden JL. Communication and navigation network
in space system of systems. In: Jamshidi M, editor. Systems of
systems engineering: innovations for the 21st century. Hoboken
(NJ): John Wiley & Sons, Inc.; 2008, p. 348–84.
15. Jolly SD, Muirhead BK. System of systems engineering in space
exploration. In: Jamshidi M, editor. Systems of systems engineer-
ing: innovations for the 21st century. Hoboken (NJ): John Wiley
& Sons, Inc.; 2008, p. 317–47.
16. Office of Inspector General. Improvements needed in the U.S.
coast guard’s acquisition and implementation of deepwater
information technology systems. Washington (DC): Office of
Inspector General, U.S. Department of Homeland Security;
2006.
17. Delaurentis DA. Understanding transportation as a system of
systems problem. In: Jamshidi M, editor. Systems of systems
engineering: innovations for the 21st century. Hoboken
(NJ): John Wiley & Sons, Inc.; 2008, p. 520–41.
18. Gorod A, Sauser B, Boardman J. System-of-systems engineering
management: a review of modern history and a path forward.
IEEE Syst J 2008;2(4):484–99.
19. Minor RR, Dagli C. An overview of system-of-systems interface
management on the future combat systems program. In
Proceedings of IIE annual conference and expo 2007-industrial
engineering’s critical role in a flat world; 2007 May 19–23,
Nashville (TN). 2007. p. 445–50.
20. Nilsson M, Sollenborn M. Advancements and trends in medical
case-based reasoning: an overview of systems and system
development. Proceedings of the seventeenth International
Florida Artificial Intelligence Research Society; 2004 May 17–
19. Miami Beach (FL): American Association for Artificial
Intelligence; 2004. p. 178–83.
21. Boulding KE. General systems theory – the skeleton of science.
Manage Sci 1956;2(3):197.
22. Ackoff RL. Towards a system of systems concepts. Manage Sci
1971;17(11):661–71.
23. Jackson MC, Keys P. Towards a system of systems concepts. J
Oper Res Soc 1984;35(6):473–86.
24. U.S. Congress, Senate Committee on Armed Services and House
Committee onArmed Services.Restructuring of the strategic defense
initiative (SDI) program. Washington (DC): U.S. Congress, Senate
Committee on Armed Services and House Committee on Armed
Services; 1989. Report No.: H.A.S.C. No. 100-96.
25. Maier MW. Architecting principles for systems-of-systems. Syst
Eng 1998;1(4):267–84.
26. Jamshidi M. Systems of systems engineering: principles and
applications. 1st ed. Boca Raton (FL): CRC Press; 2009, p. 1–36.
27. Office of the Under Secretary of Defense for Acquisition,
Technology and Logistics. Systems engineering guide for systems
of systems, V1.0. Washington (DC): Pentagon, Office of the
Under Secretary of Defense for Acquisition, Technology and
Logistics; 2008. Report No.:ODUSD(A&T)SSE.
28. Pearlman J. GEOSS-global earth observation system of systems.
2006 IEEE international SoS conference; 2006 Apr 24; Los
Angeles (CA). Piscataway (NJ): IEEE Press; 2006.
29. Uhlir PF, Chen RS, Gabrynowicz JI, Janssen K. Toward
implementation of the global Earth observation system of
systems data sharing principles. J Space Law 2009;35(1):201–90.
30. Incose.org. International Council on Systems Engineering
Overview [Internet]; 1996–2014. Available from: <http://www.
incose.org/about/index.aspx> [updated 2012 Jan 3; cited 2014
Dec 5].
31. Aerospace Systems Design Laboratory of Virginia Tech.
Advanced systems engineering [Internet]; 2014. Available from:
<http://www.asdl.gatech.edu/Advanced_Systems_Engineering.
html> [updated 2014 Nov 28; cited 2014 Nov 28].
32. National Centers for System of Systems Engineering. National
Centers for System of Systems Engineering in Old Dominion
University [Internet]; 2014. Available from: <http://www.odu.
edu/ncsose> [updated 2013 July 24; cited 2014 Nov 28].
33. CAS. The centre for autonomous systems [Internet]; 2014.
Available from: <http://www.kth.se/en/csc/forskning/cas> [up-
dated 2013 Feb 6; cited 2014 Dec 5].
34. C4ISR Laboratory of National University of Defense
Technology. Specific research areas (2) [Internet]; 1999–2014.
Available from: <http://www.nudt.edu.cn/ArticleShow.
asp?ID=76> [cited 2014 Nov 28].
35. IEEE.org. IEEE 9th international system of systems engineering
conference [Internet]; 2014. Available from: <http://sosengineer-
ing.org/2014/> [cited 2014 Dec 16].
36. American Institute of Aeronautics and Astronautics. 53rd AIAA
aerospace sciences meeting [Internet]; 2014. Available from:
<http://www.aiaa-scitech.org/ASM/> [updated 2013 Nov 22;
cited 2014 Nov 28].
37. American Institute of Aeronautics and Astronautics. 16th AIAA/
ISSMO multidisciplinary analysis and optimization conference
System of systems oriented flight vehicle conceptual design: Perspectives and progresses 633
[Internet]; 2014. Available from: <http://www.aiaa-aviation.org/
MAO/> [cited 2014 Nov 28].
38. American Institute of Aeronautics and Astronautics. AIAA
modeling and simulation technologies conference [Internet];
2014. Available from: <http://www.aiaa-aviation.org/MST/>
[cited 2014 Nov 28].
39. Wikipedia. System_of_systems [Internet]; 2014. Available from:
<http://en.wikipedia.org/wiki/System_of_systems> [updated
2014 Aug 14; cited 2014 Nov 28].
40. Manthorpe WH. The emerging joint system of systems: a systems
engineering challenge and opportunity for APL. John Hopkins
APL Technical Digest 1996;17(3):305–10.
41. Pei RS. Systems of systems integration (SoSI) – a smart way of
acquiring Army C4I2WS systems. In Proceedings of the Summer
Computer Simulation Conference, 2000 Jul 16–20; Vancouver,
BC, Canada. San Diego (CA): Society for Computer Simulation
International; 2000. p. 134–39.
42. Carlock PG, Fenton RE. System of systems (SoS) enterprise
systems for information-intensive organizations. Syst Eng
2001;4(4):242–61.
43. Defense Acquisition University. Defense acquisition guidebook
[Internet]; 2011. Available from: <https://dag.dau.mil> [up-
dated 2011 July 28; cited 2015 Mar 19].
44. Jamshidi M. Introduction to system of systems. In: Jamshidi M,
editor. System of systems engineering: principles and
applications. New York: CRC Press; 2009, p. 1–6.
45. Shoham Y. Agent-oriented programming. Artif Intell
1993;60(1):51–92.
46. Sage AP, Biemer SM. Processes for system family architecting,
design, and integration. IEEE Syst J 2007;1(1):5–16.
47. United States Department of Defense. Defense acquisition guide-
book, Ch. 4.2.6., system of systems engineering. Washington (DC):
Pentagon, United States Department of Defense; 2004. Report
No.: DoDI5000.02.
48. Blanchard BS, Fabrycky WJ. Systems engineering and
analysis. Englewood Cliffs (NJ): Prentice Hall; 1990, p. 1–26.
49. Duffy M, Garrett B, Riley C, Sandor D. Future transportation
fuel system of systems. In: Jamshidi M, editor. Systems of systems
engineering: innovations for the 21st century. Hoboken
(NJ): John Wiley & Sons, Inc.; 2008. p. 409–42.
50. Kotegawa T. Analyzing the evolutionary mechanisms of the air
transportation system-of-systems using network theory and
machine learning algorithms [dissertation]. West Lafayette (IN):
Purdue University; 2012.
51. Price M, Raghunathan S, Curran R. An integrated systems
engineering approach to aircraft design. Prog Aerosp Sci
2006;42(4):331–76.
52. Talley DN, Mavris DN. Robust design method for a battlespace
system of systems. 49th AIAA/ASME/ASCE/AHS/ASC struc-
tures, structural dynamics, and materials conference; 2008 Apr 7–
10, Schaumburg (IL). Reston (VA): American Institute of
Aeronautics and Astronautics Inc.; 2008.
53. Biltgen PT. A methodology for capability-based technology eval-
uation for systems-of-systems [dissertation]. Atlanta (GA):
Georgia Institute of Technology; 2007.
54. Mavris DN, Mantis G, Kirby MR. Demonstration of a prob-
abilistic technique for the determination of economic viability. In
The 2nd world aviation congress and exposition, 1997 Oct 13–16,
Anaheim (CA). 1997.
55. Soban DS, Mavris DN. Assessing the impact of technology on
aircraft systems using technology impact forecasting. J Aircraft
2013;50(5):1380–93.
56. Frommer JB. System of systems design: evaluating aircraft in a
fleet context using reliability and non-deterministic approaches
[dissertation]. West Lafayette (IN): Purdue University; 2012.
57. Bociaga ML, Crossley WA. A UAV design/sizing study from a
system of systems perspective. 51st AIAA aerospace sciences
meeting including the new horizons forum and aerospace
exposition; 2013 Jan 07–10, Grapevine (TX). Reston (VA):
American Institute of Aeronautics and Astronautics, Inc.; 2013.
58. Cole R. SoS architecture. In: Jamshidi M, editor. Systems of
systems engineering: principles and applications. New
York: CRC Press; 2008, p. 37–70.
59. Zachman J. A framework for information systems architecture.
IBM Syst J 1987;26(3):276–92.
60. United States Department of Defense. DoD architecture frame-
work Version 2.0. Washington (DC): Pentagon, United States
Department of Defense; 2009. Report No.: 20301-6000.
61. U.K. Ministry of Defence. The MOD architecture framework
Version 1.0. London: U.K. Ministry of Defense; 2005. Report
No.: MODAF-M07-022.
62. Ayyalasomayajula S, Fry DN, Delaurentis DA. A framework for
formulating design problems in a system-of-systems context. 12th
AIAA/ISSMO multidisciplinary analysis and optimization confer-
ence, 2008 Sep 10–12, Victoria, British Columbia Canada. Reston
(VA): American Institute of Aeronautics and Astronautics, Inc.;
2008.
63. Perl SM, Delaurentis DA, Caldwell BS. A system-of-systems
approach to enhance information exchange for the Mars Science
Laboratory Mission. SpaceOps 2010 conference, 2010 Apr 25–30,
Huntsville. Reston (VA): American Institute of Aeronautics and
Astronautics, Inc.; 2010.
64. Rovekamp RN, Delaurentis DA. Multi-disciplinary design opti-
mization of Lunar surface systems in the context of a system-of-
systems. SpaceOps 2010 conference, 2010 Apr 25–30, Huntsville
(AL). Reston (VA): American Institute of Aeronautics and
Astronautics, Inc.; 2010.
65. Phillis YA, Kouikoglou VS. System-of-Systems hierarchy of
biodiversity conservation problems. Ecol Model 2012;235–
236:36–48.
66. Barber D. System effectiveness-summary. In Symposium on deep
submergence propulsion and marine systems, 1966 Feb 28–Mar 1,
Forest Park (IL). Macon (GA):U.S. Naval Ordnance Plant; 1966.
p. 37–8.
67. Dale J, Nash J. System effectiveness evaluation-Issues and trends.
Digital avionics systems conference, 1981 Nov 17–19, St. Louis
(MO). Reston (VA): American Institute of Aeronautics and
Astronautics, Inc.; 1981. p. 75–80.
68. Mane M. Concurrent aircraft design and trip assignment under
uncertainty as a system of systems problem [dissertation]. West
Lafayette (IN): Purdue University; 2008.
69. Fiebrandt MJ, Wilson JP. System of systems measurement
framework in evaluation of joint mission effectiveness. U.S. Air
Force T&E Days, 2008 Feb 5–7, Los Angeles (VA). Reston
(VA): American Institute of Aeronautics and Astronautics,
Inc.; 2008.
70. Revelle JB, Moran JW, Cox CA. The QFD handbook. New
York: Wiley Press; 1998, p. 3–12.
71. Brandt SA, Stiles RJ, Bertin JJ, Whitford R. Introduction to
aeronautics: a design perspective. Reston (VA): AIAA Press;
2004.
72. Mavris DN, Griendling K, Dickerson CE. Relational-oriented
systems engineering and technology tradeoff analysis framework.
J Aircraft 2013;50(5):1564–75.
73. Olhanger J, West BM. The house of flexibility: using the QFD
approach to deploy manufacturing flexibility. Int J Oper Prod
Manage 2002;22(1):50–79.
74. Mazur T, Yokohama GH. The application of quality function
deployment (QFD) to design a course in total quality manage-
ment (TQM) at the University of Michigan College of
Engineering. Proceedings of international conference on quality,
1996 Oct 15–18, Yokohama. Yokohama: Union of Japanese
Scientists and Engineers (JUSE); 1996. p. 1–7.
75. Deng H, Liu C, Li S. The effectiveness evaluation approach of
UAV based on fuzzy AHP. Aero Weaponry 2005;(4):40–2
[Chinese].
634 H. Liu et al.
76. Ji D, Song B, Yu T. The method of decision-making based on
FAHP and its application. Fire Control Command Control
2007;(11):38–41 [Chinese].
77. Liu X, Zhu H, Zhao H. Fighter combat effectiveness evaluation
with index method. Milit Oper Res Syst Eng 2004;(02):59–65
[Chinese].
78. Wang R, Zhang A, Shi Z. Effectiveness evaluation of advanced
fighter plane based on power series and fuzzy AHP. Fire Control
Command Control 2008;(11):73–6 [Chinese].
79. Mane M, Crossley WA, Nusawardhana A. System-of-systems
inspired aircraft sizing and airline resource allocation via
decomposition. J Aircraft 2007;44(4):1222–35.
80. Oden JT, Belytschko T, Fish J, Hughes TJR, Johnson C, Keyes
D, et al. Simulation-based engineering science: revolutionizing
engineering science through simulation. Arlington
(VA): National Science Foundation; 2006.
81. Hu F, Wen X, Sun G. Discrete event dynamic system theory and
application. Com Inf Technol 2001;(5):10–20 [Chinese].
82. Yu Y, Zhu X, Zhang L. Discrete event dynamic system simulation.
Beijing: Beihang University Press; 2003. p. 1–17 [Chinese].
83. Sahin F, Jamshidi M, Sridhar P. A discrete event XML based
simulation framework for system of systems architectures. In
Proceedings of the IEEE international conference on system of
systems, 2007 Apr 16–18, San Antonio (TX). Piscataway (NJ):
IEEE Press; 2007. p. 1–7.
84. Parisi C, Sahin F, Jamshidi M. A discrete event XML based
system of systems simulation for robust threat detection and
integration. IEEE International conference on system of systems,
2008 June, Monterey (CA). Piscataway (NJ): IEEE Press; 2008.
p. 1–8.
85. Mittal S, Martı́n Jr LR. Netcentric system of systems engineering
with DEVS unified process. Boca Raton (FL): CRC Press; 2013,
p. 499–548.
86. Liu H, Tian Y, Zhang C, Yin J, Sun Y. Evaluation model of
design for operation and architecture of hierarchical virtual
simulation for flight vehicle design. Chin J Aeronaut
2012;25(2):216–26.
87. Forrester JW. Industrial dynamics. Cambridge (MA): Productivity
Press; 1961, p. 1–16.
88. Pfaender H, Jimenez H, Mavris D. A system dynamics analysis of
fleet technology and policy options for environmental impact
mitigation. 27th international congress of the aeronautical sciences,
2010 Sep 19–24. Nice, France: The International Council of the
Aeronautical Sciences; 2010. p. 1–12.
89. Csala D, Sgouridis S. Modeling dynamic transitions in the global
air transportation system: a system dynamics tool for competitive
industry assessment [dissertation]. Abu Dhabi: Masdar Institute
of Science and Technology; 2013.
90. Liu Y. Invulnerability optimization and evaluation techniques of
complex network [dissertation]. Beijing: Beijing University of
Posts and Telecommunications; 2011 [Chinese].
91. DeLaurentis DA, Crossley WA, Mane M. Taxonomy to guide
systems-of-systems decision-making in air transportation prob-
lems. J Aircraft 2011;48(3):760–70.
92. Joshi A, DeLaurentis DA, Peeta S, Agusdinata DB. Integrated
framework and assessment of on-demand air service in multi-
modal context. J Aircraft 2014;51(2):402–11.
93. Borshchev A, Filippov A. From system dynamics and discrete
event to practical agent based modeling: reasons, techniques,
tools. In: Kennedy M, Winch GW, Langer RS, Rowe JI, Yanni
JM, editors. System dynamics society. Proceedings of the 22nd
international conference of the System Dynamics Society, 2004
July 25–29. Oxford, England; Harvard (MA): Ventana Systems,
Inc; 2004. p. 45–6.
94. Mckeon BM. Systems of systems development in net centric
environments. AIAA international air and space symposium and
exposition: the next 100 years, 2003 July 14–17, Dayton (OH).
Reston (VA): American Institute of Aeronautics and
Astronautics, Inc.; 2003. p. 1–5.
95. Maxwell DT. An overview of the joint warfare system (JWARS).
McLean (VA): MITRE Corporation; 2000. p. 1–15.
96. FLAMES.com. FLAMES [Internet]. Available from: <http://
www.ternion.com/> [cited 2014 Nov 28].
97. SEAS.com. SEAS [Internet]. Available from: <http://www.
teamseas.com/> [cited 2014 Nov 28].
98. Civil Air Patrol.org [Internet]. Satellite tool kit. Available from:
<http://members.gocivilairpatrol.com/cadet_programs/library/
satellite-tool-kit/> [cited 2014 Dec 16].
99. APPSOFT Corporation. DWK distributed simulation develop-
ment platform [Internet]. Available from: <http://www.app-
soft.com.cn/2012/0530/DWK.html> [cited 2014 Dec 16]
[Chinese].
100. OSTW Corporation. OSTW DEESP V1.0 Release [Internet].
Available from: <http://www.ostw.com.cn/news_nr.aspx?-
catid=670&parentid=664&id=2091> [updated 2012 May 31;
cited 2014 December 16] [Chinese].
101. Jenkinson LR, Simpkin P, Rhodes D. Civil jet aircraft design.
London: Arnold, a member of the Hodder Headline Group;
1999. p. 316.
102. Department of Defense. Rotary wing vehicle technology develop-
ment approach (TDA) 4.0. Technology efforts and objectives
(TEO), Washington (DC): Department of Defense; 1994.
103. Preisner L, Delaurentis DA, Mavris D, Schrage D. Application
of a technology identification, evaluation, selection (TIES)
method for a conceptual VTOL UAV. In AIAA’s 1st technical
conference and workshop on unmanned aerospace vehicles, 2002
May 20–23, Portsmouth (VA). Reston (VA): American Institute
of Aeronautics and Astronautics, Inc.; 2002.
104. Brown DA. Quantitative technology assessment in AFRL. In
The Department of Defense M&S conference, 2006 May 4.
Washington (DC): the Department of Defense; 2006.
105. Perry WL, Button RW, Bracken J, Sullivan T, Mitchell J.
Measures of effectiveness for the information-age navy: the effects
of network centric operations on combat outcomes. Santa Monica
(CA): RAND; 2002. Report No.:MR-1449-NAVY.
106. Darilek R, Perry WL, Bracken J, Gordon J, Nichiporuk B.
Measures of effectiveness for the information-age army. Santa
Monica (CA): RAND; 2001. Report No.:MR-1155-A.
107. Peng L, Liu L, Long T. Optimization strategy using dynamic
radial basis function metamodel. J Mech Eng 2011;47(7):164–70
[Chinese].
108. Luo S, Luo W, Wang Z. Design of experiment and response
surface approximation-methods in multidisciplinary design opti-
mization for hypersonic cruise vehicle. Missiles Space Veh
2003;266(6):2–9 [Chinese].
109. Guo H, Lu Z. Structure design optimization based on BP-neural
networks and genetic algorithms. J Aerosp Power 2003;18(2):216–
20 [Chinese].
110. Howe D, Costanzo M, Fey P, Gojobori T, Hannick L, Hide W,
et al. Big data: the future of biocuration. Nature
2008;455(7209):47–50.
111. Zikopoulos P, Eaton C. Understanding big data: analytics for
enterprise class hadoop and streaming data. New
York: McGraw-Hill Osborne Media; 2011, p. 6–19.
112. Mane M, Agusdinata D, Crossley WA, DeLaurentis D. Fleet-
wide environmental impacts of future aircraft technologies. In
10th AIAA aviation technology, integration, and operations
(ATIO) conference, 2010 Sep 13–15, Fort Worth (TX). Reston
(VA): American Institute of Aeronautics and Astronautics, Inc.;
2010.
113. Crossley WA, Mane M, Nusawardhana A. Variable resource
allocation using multidisciplinary optimization: initial investiga-
tions for system of systems. In 10th AIAA/ISSMO multidisci-
plinary analysis and optimization conference, 2004 Aug 30–Sep 1.
System of systems oriented flight vehicle conceptual design: Perspectives and progresses 635
Albany (NY): American Institute of Aeronautics and
Astronautics, Inc.; 2004.
114. Li T, Yang X, Guo Q. Optimization method of information
weapon equipment system based exploratory computational
experiments. J Acad Equip Command Technol 2008;22(1):1–5.
115. Holmes M, Huynh T, Kline J. Conceptual system of systems
enabling maritime dominance in the littorals. Alexandria (VA):
Military Operations Research Society; 2004. Report No.:0704-
0188.
116. Kroo I, Altus S, Braun R, Gage P, Sobieski I. Multidisciplinary
optimization methods for aircraft preliminary design. In 5th
symposium on multidisciplinary analysis and optimization, 1994
Sep 7–9, Panama City (FL). Hampton (VA): NASA Langley
Research Center; 1994. p. 697–707.
117. Hulme K, Bloebaum C. A comparison of solution strategies for
simulation-based multidisciplinary design optimization. In
Proceedings of 7th AIAA/USAF/NASA/ISSMO symposium on
multidisciplinary analysis and optimization, 1998 Sep 2–4, St.
Louis (MO). Reston (VA): American Institute of Aeronautics
and Astronautics, Inc.; 1998. p. 2143–53.
118. Tedford N, Martins J. Comparison of MDO architectures within
a common python framework. In 47th AIAA/ASME/ASCE/
AHS/ASC structures, structural dynamics, and materials confer-
ence, 2006 May 1–4, Newport (RI). Reston (VA): American
Institute of Aeronautics and Astronautics, Inc.; 2006.
119. Perez R, Liu H, Behdinan K. Evaluation of multidisciplinary
optimization approaches for aircraft conceptual design. In 10th
AIAA/ISSMO multidisciplinary analysis and optimization confer-
ence, 2004 Aug 30–Sep 1, Albany (NY). Reston (VA): American
Institute of Aeronautics and Astronautics, Inc.; 2004.
120. Kim HM, Hidalgo IJ. System of systems optimization by pseudo-
hierarchical multistage model. In 11th AIAA/ISSMO multidisci-
plinary analysis and optimization conference, 2006 Sep 6–8,
Portsmouth (VA). Reston (VA): American Institute of
Aeronautics and Astronautics, Inc.; 2006.
Liu Hu is an associate professor in Beihang University, and is executive
deputy director of Department of Aircraft Design. His main research
interests are aircraft conceptual design, virtual simulation and system
of systems engineering. He has published nearly 50 academic papers
and one book on aircraft design. He won two national excellent aca-
demic paper awards and a third prize of the National Defense Science
and Technology Progress (ranking No. 1). He is also the main
instructor of Aircraft Conceptual Design, a National Excellent Course.
Tian Yongliang is a Ph.D. candidate in Beihang University. His main
research interests are aircraft conceptual design, virtual simulation and
system of systems engineering.
