Design System for Managing Complexity in Aerospace Systems

Becz, Sandor; Se, Lenze; Ma, Uxbridge; Pinto, Alessandro; Zeidner, Lawrence; Khire, Ritesh; Banaszuk, Andrzej; Reeve, Hayden

doi:10.2514/6.2010-9223

Cited by 35 publications

(18 citation statements)

References 6 publications

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“…With regard to the complex military information systems, Domerçant [16] proposes a structural complexity assessment method based on cyclomatic complexity. In order to manage the complexity of aerospace systems, Becz et al [18] have developed an abstract complexity measure approach. Furthermore, Ref.…”

Section: Introductionmentioning

confidence: 99%

A Structural Complexity Metric Method for Complex Information Systems

Luo¹,

Zhang²,

Mao³

et al. 2019

JSW

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A system structure is deemed as a trade-off between requirements and complexity. An effective measurement of structural complexity is the basis of choosing a reasonable system structure. In this paper, a structural complexity evaluation model of information systems is established through three factors: subsystem complexity, interactional complexity, and topological complexity. In addition, we introduce quantitative calculation methods related to the three factors. Finally, the proposed model is verified by the case of an air defense suppression system.

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Section: Introductionmentioning

confidence: 99%

A Structural Complexity Metric Method for Complex Information Systems

Luo¹,

Zhang²,

Mao³

et al. 2019

JSW

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“…The rise of complexity in engineering systems over the years has been a well‐documented trend . The challenges associated with complexity in system design include ensuring the accuracy of computational models to simulate and predict system behavior, committing considerable time and resources to conduct experimentation and testing, and managing large, globally distributed design teams.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis methods for mitigating uncertainty in engineering system design

Curran

Allaire

Willcox

2018

Systems Engineering

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For many engineering systems, current design methodologies do not adequately quantify and manage uncertainty as it arises during the design process, which can lead to unacceptable risks, increases in programmatic cost, and schedule overruns. This paper develops new sensitivity analysis methods that can be used to better understand and mitigate the effects of uncertainty in system design. In particular, a new entropy-based sensitivity analysis methodology is introduced, which apportions output uncertainty into contributions due to not only the variance of input factors and their interactions, but also to features of the underlying probability distributions that are related to distribution shape and extent. Local sensitivity analysis techniques are also presented, which provide computationally inexpensive estimates of the change in output uncertainty resulting from design modifications. The proposed methods are demonstrated on an engineering example to show how they can be used in the design context to systematically manage uncertainty budgets-which specify the allowable level of uncertainty for a system-by helping to identify design alternatives, evaluate trade-offs between available options, and guide decisions regarding the allocation of resources. K E Y W O R D S decision-making under uncertainty, SEE11 decision analysis/management, uncertainty quantification

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“…There is no question that systems have become more complex and therefore increasingly more difficult to design and manage [7,8]. Government agencies such as NSF and DARPA are among those taking up the challenge to address these problems, and are launching new research initiatives in complex systems design 3 .…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary Design Optimization for Complex Engineered Systems: Report From a National Science Foundation Workshop

Simpson

Martins

2011

Journal of Mechanical Design

129

103

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Complex engineered systems are typically designed using a systems engineering framework that is showing its limitations. Multidisciplinary design optimization (MDO), which has evolved remarkably since its inception 25 years ago, offers alternatives to complement and enhance the systems engineering approach to help address the challenges inherent in the design of complex engineered systems. To gain insight into these challenges, a one-day workshop was organized that gathered 48 people from industry, academia, and government agencies. The goal was to examine MDO’s current and future role in designing complex engineered systems. This paper summarizes the views of five distinguished speakers on the “state of the research” and discussions from an industry panel comprised of representatives from Boeing, Caterpillar, Ford, NASA Glenn Research Center, and United Technologies Research Center on the “state of the practice.” Future research topics to advance MDO are also identified in five key areas: (1) modeling and the design space, (2) metrics, objectives, and requirements, (3) coupling in complex engineered systems, (4) dealing with uncertainty, and (5) people and workflow. Finally, five overarching themes are offered to advance MDO practice. First, MDO researchers need to engage disciplines outside of engineering and target opportunities outside of their traditional application areas. Second, MDO problem formulations must evolve to encompass a wider range of design criteria. Third, effective strategies are needed to put designers “back in the loop” during MDO. Fourth, the MDO community needs to do a better job of publicizing its successes to improve the “buy in” that is needed to advance MDO in academia, industry, and government agencies. Fifth, students and practitioners need to be better educated on systems design, optimization, and MDO methods and tools along with their benefits and drawbacks.

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Design System for Managing Complexity in Aerospace Systems

Cited by 35 publications

References 6 publications

A Structural Complexity Metric Method for Complex Information Systems

A Structural Complexity Metric Method for Complex Information Systems

Sensitivity analysis methods for mitigating uncertainty in engineering system design

Multidisciplinary Design Optimization for Complex Engineered Systems: Report From a National Science Foundation Workshop

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