Application of an automated aircraft architecture generation and analysis tool to unmanned aerial vehicle subsystem design

Judt, David M.; Lawson, Craig

doi:10.1177/0954410014558691

Cited by 8 publications

(7 citation statements)

References 22 publications

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“…This basis is used by Yuan 48 to set up an automated functional decomposition framework. Functional decomposition is starting to be used in aircraft conceptual design, 46,49,50 and it is expected to gain popularity. Several different approaches to functional decomposition exist (well documented in multiple review papers 51,52 ), including Structure-Behaviour-Function (SBF) decomposition 53 and SAPPhIRE.…”

Section: Flexibility and Scalability Of Disciplinary Analysis Tools Imentioning

confidence: 99%

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Roelofs

Vos

2018

2018 AIAA Aerospace Sciences Meeting

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Evaluation and assessment of novel technologies for aerospace applications is essential for business strategy and decision making regarding development efforts. Since technology is evaluated in the conceptual design phase and little is known about the technology, large uncertainty is present. This uncertainty needs to be accurately assessed and managed. To investigate the research efforts that have been performed to perform technology evaluation under uncertainty, a literature review was conducted, focusing on methods and modeling approaches to assign and quantify these uncertainties. It is found that probability theory is still the most popular theory for representing uncertainty. Polynomial Chaos Expansions and Stochastic Collocation methods are gaining popularity for propagating uncertainty through a modeling environment, but Monte Carlo Simulations are still widely used. Commonly, surrogate models are used to reduce computational effort. Other efforts focus on the use of multifidelity approaches to reduce computational effort when high-fidelity methods are required. Four issues that may need to be addressed in future research were identified.

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Section: Flexibility and Scalability Of Disciplinary Analysis Tools Imentioning

confidence: 99%

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Roelofs

Vos

2018

2018 AIAA Aerospace Sciences Meeting

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“…This allows automatic selection of components to fulfill certain requirements. Additionally, representing a system in terms of its behavior instead of a certain fixed parameterization gains popularity in model-based systems engineering [6,[11][12][13][14], which may be attributed to the formalization of behavioral descriptions. While these allow for a structured approach to describe systems and enable automated design synthesis, they all rely on human specified functions, which effectively are only labels.…”

Section: Fig 1 Technology Evaluation and Selection Process [4]mentioning

confidence: 99%

Formalizing Technology Descriptions for Selection During Conceptual Design

Roelofs

Vos

2019

AIAA Scitech 2019 Forum

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actuator instead of hydraulic), to behaviors (e.g. a structural element doubling as electric conductor), and to entire systems (e.g. a fixed-wing aircraft as opposed to a helicopter). Previously, a review on technology selection [1] identified the following challenges with technology evaluation and selection: 1) The application of design tools is limited to a specific vehicle type, because the sizing method is fixed [2] 2) Flexibility and scalability of disciplinary analysis tools is lacking [2] 3) Parameterization of geometry proves challenging in a generalized sense (i.e. problem specific parameterizations are possible, but geometry parameterization that holds for any problem is challenging, at the least) 4) Modeling of technologies is usually avoided and replaced by impact factors 5) Extensive use of expert judgment raises challenges due to subjectivity, conservatism, overconfidence and lack of experts Additionally, from discussions with practitioners at Saab, the following challenges with technology selection in the conceptual design phase were identified: (i) non-performance metrics (e.g. the effect of improved human-machine interaction) need to be included, although prove difficult to define and quantify, (ii) technology descriptions are not yet meaningful, and cannot traverse from detailed to high-level descriptions, nor capture quantifiable effects and enabling fair comparison between technologies, (iii) finding the best technology portfolio without enumerating all possible combinations cannot yet be done objectively, (iv) the assessment of dependencies between technologies is too subjective, and (v) when uncertainty is quantified, decision making is impaired in case of wide uncertainty bands. Several conclusions can be drawn from these challenges. Metrics that are not quantifiable cannot be addressed, and hence, technologies affecting those have no meaning. Alternatively, additional analysis methods should be developed. Uncertainty should be associated with technology (and system) readiness level (both on impact and development time). Additionally, the amount of uncertainty should be limited or decision-making in the event of large uncertainty should be supported. Insight is more important at this stage than arriving at a most optimal result, hence preference is given to evaluating current possibilities, rather than performing optimization. Therefore, a structured and traceable solution to technology definition, evaluation and selection is sought. Conventionally, technology selection is performed by collecting technology TRL and IRL levels, describing their effects in an impact matrix and their compatibility in a Technology Compatibility Matrix (TCM). Such an approach was taken in the works by Amadori et al. [3, 4], who depict the process as in Figure 1. The data collection phase is essentially carried out completely using expert judgment, with limited traceability and objectivity.

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“…Important features for enabling systematic exploration is the explicit identification and tracking of architecting decisions, and enabling automated quantitative analysis of architecture candidates. Judt et al [9] and Frank et al [10] have applied evolutionary optimization algorithms to explore architecture design spaces. Recent developments are moving towards also taking connections between architecture components into account in the design space exploration [11,12].…”

Section: Introductionmentioning

confidence: 99%

System Architecture Design Space Exploration: An Approach to Modeling and Optimization

Bussemaker

Ciampa

Nagel

2020

Aiaa Aviation 2020 Forum

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Systematic modeling of architecture design spaces is needed when architecting complex systems, to support experts in making less biased decisions, and to formulate the optimization problem needed to explore the large combinatorial design space. Existing methods do not offer enough compatibility with the Model-Based Systems Engineering (MBSE) approaches, cannot model all needed design scenarios, or are not flexible enough when it comes to architecture evaluation. A new method is presented that provides a semantic representation of the architecture design space, modeled as the Architecture Design Space Graph (ADSG). The ADSG represents three types of architectural decisions: function-component mapping, component characterization, and component connection. The ADSG is constructed from a design space definition, and discrete architectural decisions are automatically inserted according to specified rules. Once decisions and metrics have been defined, the hierarchical, mixed-integer, multi-objective optimization problem can be formulated: decisions are mapped to design variables, and performance metrics are mapped to objectives or constraints. An application of the method to the Apollo mission architecting problem is presented. Nomenclature

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Application of an automated aircraft architecture generation and analysis tool to unmanned aerial vehicle subsystem design

Cited by 8 publications

References 22 publications

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Uncertainty-Based Design Optimization and Technology Evaluation: A Review

Formalizing Technology Descriptions for Selection During Conceptual Design

System Architecture Design Space Exploration: An Approach to Modeling and Optimization

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