Accounting for future redesign to balance performance and development costs

Villanueva, Diane; Haftka, Raphael T.; Sankar, Bhavani V.

doi:10.1016/j.ress.2013.11.013

Cited by 17 publications

(11 citation statements)

References 17 publications

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“…Previous work has illustrated this tradeoff when there is only a fixed constant model bias. [1][2][3] This study builds on previous work by considering spatial correlation in the epistemic model uncertainty. A Kriging surrogate is used to provide a flexible representation of the epistemic model uncertainty that allows the method to be applicable to a wide range of engineering problems.…”

Section: Introductionmentioning

confidence: 99%

“…1 Villanueva et al also studied the tradeoff between future redesign and performance for an integrated thermal protection system. 2 Price et al compared starting with a more conservative design and possibly redesigning to improve performance to starting with a less conservative design and possibly redesigning to improve safety. 3 These studies have demonstrated that integrated optimization of design, testing, and redesign can be used to manage redesign risk and tradeoff between the probability of future redesign and design performance.…”

Section: Introductionmentioning

confidence: 99%

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Simulating Future Test and Redesign Considering Epistemic Model Uncertainty

Price

Balesdent²,

Defoort³

et al. 2016

18th AIAA Non-Deterministic Approaches Conference

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International audienceAt the initial design stage engineers often rely on low-fidelity models that have high epistemic uncertainty. Traditional safety-margin-based deterministic design resorts to testing to reduce epistemic uncertainty and achieve targeted levels of safety. Testing is used to calibrate models and prescribe redesign when tests are not passed. After calibration, reduced epistemic model uncertainty can be leveraged through redesign to restore safety or improve design performance; however, redesign may be associated with substantial costs or delays. In this paper, a methodology is described for optimizing the safety-margin-based design, testing, and redesign process to allow the designer to tradeoff between the risk of future redesign and the possible performance and reliability benefits. The proposed methodology represents the epistemic model uncertainty with a Kriging surrogate and is applicable in a wide range of design problems. The method is illustrated on a cantilever beam design problem where there is mixed epistemic model error and aleatory parameter uncertainty

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simulating Future Test and Redesign Considering Epistemic Model Uncertainty

Price

Balesdent²,

Defoort³

et al. 2016

18th AIAA Non-Deterministic Approaches Conference

View full text Add to dashboard Cite

show abstract

“…Bjerga et al (Bjerga et al 2014) discussed an approach to consider model uncertainty in quantitative risk analysis with the focus on the uncertainty assessment. Villanueva et al (Villanueva et al 2014) studied future test and redesign to balance development cost versus performance by assuming that the epistemic model uncertainty would be reduced in the future.…”

Section: Introductionmentioning

confidence: 99%

Model uncertainty approximation using a copula-based approach for reliability based design optimization

Peng

Yang

2016

Struct Multidisc Optim

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Reliability-based design optimization (RBDO) has been widely used to design engineering products with minimum cost function while meeting reliability constraints. Although uncertainties, such as aleatory uncertainty and epistemic uncertainty, have been well considered in RBDO, they are mainly considered for model input parameters. Model uncertainty, i.e., the uncertainty of model bias indicating the inherent model inadequacy for representing the real physical system, is typically overlooked in RBDO. This paper addresses model uncertainty approximation in a product design space and further integrates the model uncertainty into RBDO. In particular, a copula-based bias modeling approach is proposed and results are demonstrated by two vehicle design problems.

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“…Matsumura et al compared reliability-based design optimization (RBDO) considering future redesign to traditional RBDO [14]. Villanueva et al demonstrated the tradeoff between expected design performance and probability of redesign for the ITPS example [15]. Price et al compared designer versus company perspectives on starting with a higher margin and possibly redesigning to improve performance to starting with a lower margin and possibly redesigning to improve safety [16].…”

Section: Introductionmentioning

confidence: 99%

“…Price et al compared designer versus company perspectives on starting with a higher margin and possibly redesigning to improve performance to starting with a lower margin and possibly redesigning to improve safety [16]. This study develops a generalized formulation of the previously application-specific formulations [13,15,16] and explores how the degree of conservativeness in the initial design relates to the expected design performance after possible redesign. In related work, Price et al introduced a Kriging surrogate to represent epistemic model uncertainty in order to consider spatial variations in model uncertainty in the context of simulating the effects of future tests and redesign [17].…”

Section: Introductionmentioning

confidence: 99%

Deciding Degree of Conservativeness in Initial Design Considering Risk of Future Redesign

Price

Kim

Haftka

et al. 2016

Journal of Mechanical Design

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International audienceEarly in the design process, there is often mixed epistemic model uncertainty and aleatory parameter uncertainty. Later in the design process, the results of high-fidelity simulations or experiments will reduce epistemic model uncertainty and may trigger a redesign process. Redesign is undesirable because it is associated with costs and delays; however, it is also an opportunity to correct a dangerous design or possibly improve design performance. In this study, we propose a margin-based design/redesign method where the design is optimized deterministically, but the margins are selected probabilistically. The final design is an epistemic random variable (i.e., it is unknown at the initial design stage) and the margins are optimized to control the epistemic uncertainty in the final design, design performance, and probability of failure. The method allows for the tradeoff between expected final design performance and probability of redesign while ensuring reliability with respect to mixed uncertainties. The method is demonstrated on a simple bar problem and then on an engine design problem. The examples are used to investigate the dilemma of whether to start with a higher margin and redesign if the test later in the design process reveals the design to be too conservative, or to start with a lower margin and redesign if the test reveals the design to be unsafe. In the examples in this study, it is found that this decision is related to the variance of the uncertainty in the high-fidelity model relative to the variance of the uncertainty in the low-fidelity model

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Accounting for future redesign to balance performance and development costs

Cited by 17 publications

References 17 publications

Simulating Future Test and Redesign Considering Epistemic Model Uncertainty

Simulating Future Test and Redesign Considering Epistemic Model Uncertainty

Model uncertainty approximation using a copula-based approach for reliability based design optimization

Deciding Degree of Conservativeness in Initial Design Considering Risk of Future Redesign

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