| 1. |
- Gunawan, Subroto, et al.
(författare)
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Design Optimization and Reliability Estimation with Incomplete Uncertainty
- 2006
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Ingår i: 2006 SAE world congress. - Warrendale, Pa. : Society of Automotive Engineers, Incorporated.
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Konferensbidrag (refereegranskat)abstract
- Existing methods for design optimization under uncertainty assume that a high level of information is available, typically in the form of data. In reality, however, insufficient data prevents correct inference of probability distributions, membership functions, or interval ranges. In this article we use an engine design example to show that optimal design decisions and reliability estimations depend strongly on uncertainty characterization. We contrast the reliability-based optimal designs to the ones obtained using worst-case optimization, and ask the question of how to obtain non-conservative designs with incomplete uncertainty information. We propose an answer to this question through the use of Bayesian statistics. We estimate the truck's engine reliability based only on available samples, and demonstrate that the accuracy of our estimates increases as more samples become available. Finally, we use this information-based reliability assessment to optimize the engine while maximizing the confidence that the design will meet or exceed a pre-specified reliability target.
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| 2. |
- Kokkolaras, Michael, et al.
(författare)
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Design optimization of hierarchically decomposed multilevel systems under uncertainty
- 2006
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Ingår i: Journal of mechanical design (1990). - : ASME International. - 1050-0472 .- 1528-9001. ; 128:2, s. 503-508
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a methodology for design optimization of hierarchically decomposed systems under uncertainty. We propose an extended, probabilistic version of the deterministic analytical target cascading (ATC) formulation by treating uncertain quantities as random variables and posing probabilistic design constraints. A bottom-to-top coordination strategy is used for the ATC process. Given that first-order approximations may introduce unacceptably large errors, we use a technique based on the advanced mean value method to estimate uncertainty, propagation through the multilevel hierarchy of elements that comprise the decomposed system. A simple yet illustrative hierarchical bilevel engine design problem is used to demonstrate the proposed methodology. The results confirm the applicability of the proposed probabilistic ATC formulation and the accuracy of the uncertainty propagation technique
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| 3. |
- Kokkolaras, Michael, et al.
(författare)
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Impact of uncertainty quantification on design decisions for a hydraulic-hybrid powertrain engine
- 2006
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Ingår i: 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. - Reston, Va. : American Institute of Aeronautics and Astronautics, AIAA. - 1563477963 ; , s. 4954-4963
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Konferensbidrag (refereegranskat)abstract
- The method for solving an optimal design problem under uncertainty depends on how the latter is quantified. When sufficient information is available the popular probabilistic approach can (and should) be adopted. In reality however, we often do not have sufficient data to infer appropriate probability distributions for the uncertain quantities modeled as random variables. The amount of available information about the uncertain quantities may be limited to ranges of values (intervals). In this case, the interval analysis approach can be employed to reformulate and solve the optimal design problem. In this study, we use both approaches to solve an engine design optimization problem that considers fuel economy and acceleration performance of a medium-sized truck with a hydraulic-hybrid powertrain. We then contrast the obtained results and comment on the characteristics and features of the two approaches. We also demonstrate an extension of the interval analysis approach to multilevel systems using a simple yet illustrative engine-related example
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