| 1. |
- Etman, L.F.P., et al.
(författare)
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Coordination specification for distributed optimal system design using the chi language
- 2002
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Ingår i: A collection of technical papers. - Reston, Va. : American Institute of Aeronautics and Astronautics, AIAA. - 1563475502
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Konferensbidrag (refereegranskat)abstract
- Coordination plays a key role in solving decomposed optimal system design problems. Several coordination strategies have been proposed in the multidisciplinary optimization (MDO) literature. They are usually presented as a sequence of statements: the parallel nature of the multidisciplinary subproblems is often either not addressed or only briefly mentioned. However, a more formal description of the concurrency in the coordination is essential, in particular for large and non-hierarchic coordination architectures. This paper proposes to use concepts from communicating sequential processes (CSPs) developed in concurrency theory. CSPs allow the description of the MDO coordination as a number of parallel processes that operate independently and communicate with each other synchronously over pre-defined channels. For this purpose, we introduce elements of the language chi, a CSP-based language that contains data types such as reals, arrays, lists and tuples. The accompanying software tool set that enables the execution of a chi specification has been extended with a Python interface so that function calls to external software can be carried out. Through this interface, chi has been coupled with Matlab to run coordination specifications of distributed optimal system design problems on single or multiple parallel computers. An optimal design example is used to illustrate this. It is concluded that the use of a CSP-based language such as chi for coordinating the solution of MDO problems is quite promising
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| 2. |
- Etman, L.F.P., et al.
(författare)
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Coordination specification in distributed optimal design of multilevel systems using the χ language
- 2005
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Ingår i: Structural and multidisciplinary optimization (Print). - : Springer Science and Business Media LLC. - 1615-147X .- 1615-1488. ; 29:3, s. 198-212
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Tidskriftsartikel (refereegranskat)abstract
- Coordination plays a key role in solving decomposed optimal design problems. Several coordination strategies have been proposed in the multidisciplinary optimization (MDO) literature. They are usually presented as a sequence of statements. However, a precise description of the concurrency in the coordination is needed for large multilevel or non-hierarchic coordination architectures. This article proposes the use of communicating sequential processes (CSP) concepts from concurrency theory for specifying and implementing coordination strategies in distributed multilevel optimization rigorously. CSP enables the description of the coordination as a number of parallel processes that operate independently and communicate synchronously. For this purpose, we introduce elements of the language χ, a CSP-based language that contains advanced data modeling constructs. The associated software toolkit allows execution of the specified coordination. Coordination specification using χ is demonstrated for analytical target cascading (ATC), a methodology for design optimization of hierarchically decomposed multilevel systems. It is shown that the ATC coordination can be compactly specified for various coordination schemes. This illustrates the advantage of using a high-level concurrent language, such as χ, for specifying the coordination of distributed optimal design problems. Moreover, the χ software toolkit is useful in implementing alternative schemes rapidly, thus enabling the comparison of different MDO methods.
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| 3. |
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| 4. |
- Fellini, R., et al.
(författare)
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A sensitivity-based commonality strategy for family products of mild variation, with application to automotive body structures
- 2004
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Ingår i: Structural and multidisciplinary optimization (Print). - : Springer Science and Business Media LLC. - 1615-147X .- 1615-1488. ; 27:1-2, s. 89-96
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Tidskriftsartikel (refereegranskat)abstract
- Identification of the product platform is a key step in designing a family of products. This article presents a methodology for selecting the product platform by using information obtained from the individual optimization of the product variants. Under the assumption that the product variety requires only mild design changes, a performance deviation vector is derived by taking into consideration individual optimal designs and sensitivities of functional requirements. Commonality decisions are based on values of the performance deviation vector, and the product family is designed optimally with respect to the chosen platform. The proposed methodology is applied to the design of a family of automotive body structures. Variants are defined by changing the functional requirements they need to satisfy and/or the geometry of the associated finite element models.
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| 5. |
- 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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| 6. |
- Kokkolaras, Michael, et al.
(författare)
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Design optimization of hierarchically decomposed multilevel systems under uncertainty
- 2004
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Ingår i: Proceedings of the ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference - 2004. - New York : American Society of Mechanical Engineers. - 0791846946 ; , s. 613-625
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Konferensbidrag (refereegranskat)abstract
- This paper presents a methodology for design optimization of decomposed systems in the presence of uncertainties. We extend the analytical target cascading (ATC) formulation to probabilistic design by treating stochastic quantities as random variables and parameters and posing reliability-based design constraints. We model the propagation of uncertainty throughout the multilevel hierarchy of elements that comprise the decomposed system by using the advanced mean value (AMV) method to generate the required probability distributions of nonlinear responses. We utilize appropriate metamodeling techniques for simulation-based design problems. A simple yet illustrative hierarchical bi-level engine design problem is used to demonstrate the proposed methodology.
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| 7. |
- 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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| 8. |
- Kokkolaras, Michael, et al.
(författare)
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Impact of uncertainty quantification on design : an engine optimisation case study
- 2006
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Ingår i: International Journal of Reliability and Safety. - 1479-389X .- 1479-3903. ; 1:1-2, s. 225-237
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Tidskriftsartikel (refereegranskat)abstract
- The method for solving design optimisation problems when some or all design variables and/or parameters are not deterministic depends on how we quantify uncertainty. Probabilistic design methods can be employed when sufficient information is available. In reality, however, we often do not have enough knowledge and/or data to conduct statistical inference. The amount of available information about the uncertain quantities may be limited to ranges of values. Possibility theory may then be employed to reformulate and solve the optimal design problem. In this paper, we use both probability and possibility theories to determine optimal values of engine characteristics for a hydraulic-hybrid powertrain of a medium-sized truck while accounting for the most significant modelling uncertainties. A worst-case optimisation using interval analysis is considered as a special case of possibilistic design. We contrast the two sets of results, draw some conclusions and discuss features of the two approaches.
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| 9. |
- 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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| 10. |
- Li, Jing, et al.
(författare)
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Validating Designs Through Sequential Simulation-Based Optimization
- 2010
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Ingår i: Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference - 2010. - New York : American Society of Mechanical Engineers. ; , s. 1023-1031
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Computational simulation models support a rapid design process. Given model approximation and operating conditions uncertainty, designers must have confidence that the designs obtained using simulations will perform as expected. This paper presents a methodology for validating designs as they are generated during a simulation-based optimization process. Current practice focuses on validation of simulation models throughout the entire design space. In contrast, the proposed methodology requires validation only at design points generated during optimization. The goal of such validation is confidence in the resulting design rather than in the underlying simulation model. The proposed methodology is illustrated on a simple cantilever beam design subject to vibration
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