Design Potential Method for Robust System Parameter Design

Tu, Jian; Choi, Kyung K.; Park, Young H.

doi:10.2514/2.1360

Cited by 108 publications

(38 citation statements)

References 15 publications

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“…As a result, nested double-loop methods are computationally expensive for a complex engineering design [11,9,12]. Therefore, decoupled double-loop methods have been developed to address the computational challenges [9,12,13,14,15,16,17]. A reliability-based design optimization problem can be formulated as follows:…”

Section: Optimization Methods and Validationmentioning

confidence: 99%

Reliability-based aeroelastic optimization of a composite aircraft wing via fluid-structure interaction of high fidelity solvers

Nikbay

Fakkusoglu

KURU

2010

IOP Conf. Ser.: Mater. Sci. Eng.

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Reliability-based aeroelastic optimization of a composite aircraft wing via fluid-structure interaction of high fidelity solvers Abstract. We consider reliability based aeroelastic optimization of a AGARD 445.6 composite aircraft wing with stochastic parameters. Both commercial engineering software and an inhouse reliability analysis code are employed in this high-fidelity computational framework. Finite volume based flow solver Fluent is used to solve 3D Euler equations, while Gambit is the fluid domain mesh generator and Catia-V5-R16 is used as a parametric 3D solid modeler. Abaqus, a structural finite element solver, is used to compute the structural response of the aeroelastic system. Mesh based parallel code coupling interface MPCCI-3.0.6 is used to exchange the pressure and displacement information between Fluent and Abaqus to perform a loosely coupled fluid-structure interaction by employing a staggered algorithm. To compute the probability of failure for the probabilistic constraints, one of the well known MPP (Most Probable Point) based reliability analysis methods, FORM (First Order Reliability Method) is implemented in Matlab. This in-house developed Matlab code is embedded in the multidisciplinary optimization workflow which is driven by Modefrontier. Modefrontier 4.1, is used for its gradient based optimization algorithm called NBI-NLPQLP which is based on sequential quadratic programming method. A pareto optimal solution for the stochastic aeroelastic optimization is obtained for a specified reliability index and results are compared with the results of deterministic aeroelastic optimization. IntroductionThe challenging task of aircraft design requires a systematic approach which can couple several engineering disciplines in the design process. Today, this task can be more efficiently and accurately performed by employing high fidelity tools in collaboration with multidisciplinary optimization techniques. The improvement of multidisciplinary optimal design depends on the fidelity level of the individual analysis used inside each discipline, how effectively the coupling method handles interaction between the disciplines and also the computational efficiency and accuracy of the algorithm used to solve the optimization problem. As being one of the most important criteria of aircraft design, aeroelasticity requires high fidelity level solutions for the fluid-structure interaction phenomena for real complex geometries. Besides, the reliability level of the proposed design is an important criteria in decision making for production of an aircraft. Thus, recently, reliability based aeroelastic optimization of aircraft structures has

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Section: Optimization Methods and Validationmentioning

confidence: 99%

Reliability-based aeroelastic optimization of a composite aircraft wing via fluid-structure interaction of high fidelity solvers

Nikbay

Fakkusoglu

KURU

2010

IOP Conf. Ser.: Mater. Sci. Eng.

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“…The reason of modifying the limit state functions in current study is to find deterministic equations that are equivalent to the probabilistic equations. Tu et al (23) , Zou and Mahadevan (24) and Li et al (18) also decoupled an RBDO problem through revising the limit state functions. Tu et al (23) and Zou and Mahadevan (24) approximated the limit state functions by using Taylor expansion which is apparently different with the current study.…”

Section: The Description Of the Proposed Algorithmmentioning

confidence: 99%

“…Tu et al (23) , Zou and Mahadevan (24) and Li et al (18) also decoupled an RBDO problem through revising the limit state functions. Tu et al (23) and Zou and Mahadevan (24) approximated the limit state functions by using Taylor expansion which is apparently different with the current study. Li et al (18) modified the RHS value to find the equivalent-deterministic constraints.…”

Section: The Description Of the Proposed Algorithmmentioning

confidence: 99%

An Iterative Topology Optimization Algorithm That Considers Randomness in Design Parameters

Liao

2012

JAMDSM

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Topology optimization often incorporates a reliability analysis to take the randomness in the design parameters into account. This strategy is inherently a double-loop procedure due to the probabilistic constraints in optimization. To simplify the calculation procedure, an equivalent-deterministic constraint, which is constructed by adding a penalty on the right hand side (RHS) of a limit state function, is used to reformulate a reliability-based topology optimization (RBTO) problem into a deterministic topology optimization (DTO) problem. To obtain a converged solution, the DTO and the search of equivalent-deterministic constraints must be executed iteratively. The accuracy and efficiency of the proposed approach are investigated through several numerical examples. Results indicated that the proposed algorithm is able to deliver an optimal topology with predefined reliability. Furthermore, one can incorporate the proposed algorithm with existing software to facilitate the design process.

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“…Tu et al (2001) and Zou and Mahadevan (2006) decoupled an RBDO problem through revising the constraints. In their approaches, the constraints were approximated by using Taylor expansion, which was performed repeatedly to increase the accuracy of the reliability approximation.…”

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confidence: 99%

A single loop reliability-based design optimization using EPM and MPP-based PSO

Liao

Ivan

2014

Lat. Am. j. solids struct.

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A reliability-based design optimization (RBDO) incorporates a probabilistic analysis with an optimization technique to find a best design within a reliable design space. However, the computational cost of an RBDO task is often expensive compared to a deterministic optimization, which is mainly due to the reliability analysis performed inside the optimization loop. Theoretically, the reliability of a given design point can be obtained through a multidimensional integration. Integration with multiple variables over the safety domain is, unfortunately, formidable in most cases. MonteCarlo simulation (MCS) is often used to solve this difficulty. However, the inherit statistic uncertainty associated with MCS sometimes causes an unstable RBDO solution. To avoid this unstable solution, this study transforms a multi-variable constraint into a single variable constraint using an exponential function with a polynomial coefficient (EPM). The adaptive Gauss-Kronrod quadrature is used to compute the constraint reliability. The calculated reliability and its derivative are incorporated with an optimizer such as sequential quadratic programming (SQP) or most probable point particle swarm optimization (MPP-based PSO) to conduct the RBDO task. To ensure the design accuracy, the stability of the RBDO algorithm with respect to the initial point is investigated through several numerical examples.

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Design Potential Method for Robust System Parameter Design

Cited by 108 publications

References 15 publications

Reliability-based aeroelastic optimization of a composite aircraft wing via fluid-structure interaction of high fidelity solvers

Reliability-based aeroelastic optimization of a composite aircraft wing via fluid-structure interaction of high fidelity solvers

An Iterative Topology Optimization Algorithm That Considers Randomness in Design Parameters

A single loop reliability-based design optimization using EPM and MPP-based PSO

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