A hybrid approach for the automation of functional decomposition in conceptual design

Yuan, Lin; Liu, Yusheng; Sun, Zhongfei; Cao, Yanan; Qamar, Ahsan

doi:10.1080/09544828.2016.1146237

Cited by 32 publications

(15 citation statements)

References 29 publications

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“…Avoiding ambiguity is the aim of such a basis, while maintaining representational flexibility. 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.…”

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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“…Evidently, other efforts have been made towards similar goals. Mostly, systems are described by functions/behaviors, leading to the topic of functional decomposition [5][6][7][8][9][10], which is mainly used in design synthesis. This allows automatic selection of components to fulfill certain requirements.…”

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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“…In this way, the cost and production system design decisions become more effective (Cochran et al, 2017). Yuan et al (2016) completed the functional requirement decomposition with a proposed hybrid approach implemented with Systems Modeling Language (SysML). Nagel et al (2009) established a functional modeling tool called FunctionCAD for the interactive functional requirement decomposition.…”

Section: Literature Reviewmentioning

confidence: 99%

A computer-aided approach improving the Axiomatic Design theory with the distributed design resource environment

Chen

et al. 2019

AIEDAM

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In the traditional Axiomatic Design (AD) theory, the mapping from the functional domain to the physical domain is based on the designers’ own knowledge and experience, and there is no systematical approach including the design resources provided outside the designers themselves’ access. Thus, the raw materials for the design process are largely limited, which means they can hardly support the designers’ increasingly creative and innovative conceptions. To help AD theory better support the design process, this paper proposes a computer-aided approach for the mapping from the functional domain to the physical domain within a distributed design resource environment, which consists of numerous design resources offered on the Internet by the providers widely distributed in different locations, institutes, and disciplines. To prove the feasibility of this proposed approach, a software prototype is established, and a natural leisure hotel is designed as an implementation case.

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A hybrid approach for the automation of functional decomposition in conceptual design

Cited by 32 publications

References 29 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

A computer-aided approach improving the Axiomatic Design theory with the distributed design resource environment

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