Simulating Future Test and Redesign Considering Epistemic Model Uncertainty

Price, Nathaniel B.; Balesdent, Mathieu; Defoort, Sébastien; Riche, Rodolphe Le; Kim, Nam Ho; Haftka, Raphael T.

doi:10.2514/6.2016-0950

Cited by 2 publications

(3 citation statements)

References 23 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the model error is not constant, then it may incentivize starting with a lower margin in order to have a high-fidelity evaluation close to the limit surface gðx; uÞ ¼ 0. In related work, a Kriging surrogate is introduced to model epistemic uncertainty in order to account for spatial correlations in model uncertainty [17].…”

Section: Limitations and Future Workmentioning

confidence: 99%

“…The proposed method may be computationally expensive because it involves a Monte Carlo simulation (MCS) of a design/ redesign process nested inside a global optimization problem. To reduce the computational cost, surrogate models can be fit to the mean probability of failure and mean design cost as a function of the margins as described in Appendix [17]. Surrogate models were not used in the examples in this study because the design models were not computationally expensive.…”

Section: Limitations and Future Workmentioning

confidence: 99%

“…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%

See 2 more Smart Citations

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

Price

Kim

Haftka

et al. 2016

Journal of Mechanical Design

Self Cite

View full text Add to dashboard Cite

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

show abstract

Section: Limitations and Future Workmentioning

confidence: 99%

Section: Limitations and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Price

Kim

Haftka

et al. 2016

Journal of Mechanical Design

Self Cite

View full text Add to dashboard Cite

show abstract

Advanced Space Vehicle Design Taking into Account Multidisciplinary Couplings and Mixed Epistemic/Aleatory Uncertainties

Balesdent

Brevault

Price

et al. 2016

Springer Optimization and Its Applications

View full text Add to dashboard Cite

International audienceSpace vehicle design is a complex process involving numerous disciplines such as aerodynamics, structure, propulsion and trajectory. These disciplines are tightly coupled and may involve antagonistic objectives that require the use of specific methodologies in order to assess trade-offs between the disciplines and to obtain the global optimal configuration. Generally, there are two ways to handle the system design. On the one hand, the design may be considered from a disciplinary point of view (a.k.a. Disciplinary Design Optimization): the designer of each discipline has to design its subsystem (e.g. engine) taking the interactions between its discipline and the others (interdisciplinary couplings) into account. On the other hand, the design may also be considered as a whole: the design team addresses the global architecture of the space vehicle, taking all the disciplinary design variables and constraints into account at the same time. This methodology is known as Multidisciplinary Design Optimization (MDO) and requires specific mathematical tools to handle the interdisciplinary coupling consistency. In the first part of this chapter, we present the main classical techniques to efficiently tackle the interdisciplinary coupling satisfaction problem. In particular, an MDO decomposition strategy based on the “Stage-Wise decomposition for Optimal Rocket Design” formulation is described. This method allows the design process to be decentralized according to the different subsystems (e.g. launch vehicle stages) and reduces the computational cost compared to classical MDO methods. In the first part of this chapter, we present the main classical techniques to efficiently tackle the interdisciplinary coupling satisfaction problem. In particular, an MDO decomposition strategy based on the "Stage-Wise decomposition for Optimal Rocket Design" formulation is described. This method allows the design process to be decentralized according to the different subsystems (e.g. launch vehicle stages) and reduces the computational cost compared to classical MDO methods

show abstract

Simulating Future Test and Redesign Considering Epistemic Model Uncertainty

Cited by 2 publications

References 23 publications

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

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

Advanced Space Vehicle Design Taking into Account Multidisciplinary Couplings and Mixed Epistemic/Aleatory Uncertainties

Contact Info

Product

Resources

About