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Leeikjin - One of the best experts on this subject based on the ideXlab platform.

Irfan Kaymaz - One of the best experts on this subject based on the ideXlab platform.

  • Approximation methods for reliability‐Based Design Optimization problems
    Gamm-mitteilungen, 2007
    Co-Authors: Irfan Kaymaz
    Abstract:

    Deterministic optimum Designs are obtained without considering of uncertainties related to the problem parameters such as material parameters (yield stress, allowable stresses, moment capacities, etc.), external loadings, manufacturing errors, tolerances, cost functions, which could lead to unreliable Designs, therefore several methods have been developed to treat uncertainties in engineering analysis and, more recently, to carry out Design Optimization with the additional requirement of reliability, which referred to as reliability-Based Design Optimization. In this paper, two most common approaches for reliability-Based Design Optimization are reviewed, one of which is reliability-index Based approach and the other performancemeasure approach. Although both approaches can be used to evaluate the probabilistic constraint, their use can be prohibitive when the associated function evaluation required by the probabilistic constraint is expensive, especially for real engineering problems. Therefore, an adaptive response surface method is proposed by which the probabilistic constraint is replaced with a simple polynomial function, thus the computational time can be reduced significantly as presented in the example given in this paper. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

  • Reliability-Based Design Optimization for elastoplastic mechanical structures
    Computers & Structures, 2007
    Co-Authors: Irfan Kaymaz, Kurt Marti
    Abstract:

    In this paper, two special formulations to carry out a reliability-Based Design Optimization of elastoplastic mechanical structures are introduced. The first approach is Based on a well-known two-level method where the first level involves the Optimization for the Design parameters whereas the evaluation of the probabilistic constraints is carried out in a sub-Optimization level. Because the evaluation of the probabilistic constraints in a sub-Optimization level causes non-convergence behavior for some problems as indicated in the literature, an alternative formulation Based on one-level is developed considering the optimality conditions of the @b-computation by which the probabilistic constraint appears in the first level reliability-Based Design Optimization formulation. In both approaches, an explicit parameter Optimization problem is proposed for the computation of a Design point z"x^* for elastoplastic structures. Three examples in this paper demonstrate that the one-level reliability-Based Design Optimization formulation is superior in terms of convergence to an optimal Design than the two-level reliability-Based Design Optimization formulation.

  • A probabilistic Design system for reliability-Based Design Optimization
    Structural and Multidisciplinary Optimization, 2004
    Co-Authors: Irfan Kaymaz, Chris Mcmahon
    Abstract:

    A probabilistic Design system for reliability-Based Design Optimization problems called ADAPRES_NET is presented in this paper. ADAPRES_NET includes two main features, one of which is the use of an adaptive response surface method by which the probabilistic constraints are replaced with response functions, the other a distributed computing environment by which the computational applications are distributed on a network. The proposed system is presented with an example in which the well-known mechanical part, the connecting rod, is selected. Finally, the evaluation of the probabilistic constraints is also compared with that of the classical reliability methods, and the results indicate the benefit of using ADAPRES_NET.

Byeng D Youn - One of the best experts on this subject based on the ideXlab platform.

  • Reliability-Based Design Optimization
    Springer Series in Reliability Engineering, 2018
    Co-Authors: Chao Hu, Byeng D Youn, Pingfeng Wang
    Abstract:

    As mentioned in earlier chapters, many system failures can be traced back to various difficulties in evaluating and Designing complex systems under highly uncertain manufacturing and operational conditions. Our attempt to address this challenge continues with the discussion of reliability-Based Design Optimization (RBDO). RBDO is a probabilistic approach to engineering system Design that accounts for the stochastic nature of engineered systems. Our discussion of RBDO will cover the problem statement and formulation of RBDO as well as several probabilistic Design approaches for RBDO.

  • Bayesian reliability-Based Design Optimization using eigenvector dimension reduction (EDR) method
    Structural and Multidisciplinary Optimization, 2008
    Co-Authors: Byeng D Youn, Pingfeng Wang
    Abstract:

    In practical engineering Design, most data sets for system uncertainties are insufficiently sampled from unknown statistical distributions, known as epistemic uncertainty. Existing methods in uncertainty-Based Design Optimization have difficulty in handling both aleatory and epistemic uncertainties. To tackle Design problems engaging both epistemic and aleatory uncertainties, reliability-Based Design Optimization (RBDO) is integrated with Bayes theorem. It is referred to as Bayesian RBDO. However, Bayesian RBDO becomes extremely expensive when employing the first- or second-order reliability method (FORM/SORM) for reliability predictions. Thus, this paper proposes development of Bayesian RBDO methodology and its integration to a numerical solver, the eigenvector dimension reduction (EDR) method, for Bayesian reliability analysis. The EDR method takes a sensitivity-free approach for reliability analysis so that it is very efficient and accurate compared with other reliability methods such as FORM/SORM. Efficiency and accuracy of the Bayesian RBDO process are substantially improved after this integration.

  • Inverse Possibility Analysis Method for Possibility-Based Design Optimization
    AIAA Journal, 2006
    Co-Authors: Kyung K. Choi, Byeng D Youn
    Abstract:

    DOI: 10.2514/1.16546 Structural analysis and Design Optimization have recently been extended to the stochastic approach to consider variousuncertainties.However,inareaswhereitisnotpossibletoproduceaccurate statistical information forinput data, the probabilistic method is not appropriate for stochastic structural analysis and Design Optimization, because improper modeling of uncertainty could cause a greater degree of statistical uncertainty than those of physical uncertainty. For systems with insufficient information for input data, possibility-Based (or fuzzy set) methods have recently been introduced in structural analysis and Design Optimization. Using possibility methods, the extended fuzzy operations are much simpler than of random variables. Possibility-Based Design Optimization will provide more conservative Designs than those from probability methods, andwill provide a system level of failure possibility automatically. This paper proposes a new formulation of possibility-Based Design Optimization using the performance measure approach. For the inverse possibility analysis, the maximal possibility search method is proposed to improve numerical efficiency and accuracy comparing with the vertex method and the multilevel-cut method. Two mathematical examples, including a nonmonotonic response and a physical example of vehicle side impact, are used to demonstrate the proposed maximal possibility search method and possibility-Based Design Optimization.

  • enriched performance measure approach for reliability Based Design Optimization
    AIAA Journal, 2005
    Co-Authors: Byeng D Youn, Kyung K. Choi, Liu Du
    Abstract:

    An enriched performance measure approach is presented for reliability-Based Design Optimization to substantially improve computational efficiency when applied to large-scale applications. In the enriched performance measure approach, four improvements are made over the original performance measure approach: as a way to launch reliability-Based Design Optimization at a deterministic optimum Design, as a new enhanced hybrid-mean value method, as an efficient probabilistic feasibility check, and as a fast reliability analysis under the condition of Design closeness. It is found that deterministic Design Optimization helps improve numerical efficiency by reducing some reliability-Based Design Optimization iterations. In reliability-Based Design Optimization, a computational burden on the feasibility check of constraints can be significantly reduced by using a mean value first-order method and by carrying out the refined reliability analysis using the enhanced hybrid-mean value method for e-active and violated constraints. The enhanced hybrid-mean value method is developed to handle nonlinear and/or nonmonotonic constraints in reliability analysis. The fast reliability analysis method is proposed to efficiently evaluate probabilistic constraints under the condition of Design closeness. Moreover, two numerical examples are provided to compare the enriched performance measure approach to existing reliability-Based Design Optimization methods from a numerical efficiency and stability point of view.

Keshtegarbehrooz - One of the best experts on this subject based on the ideXlab platform.

Teng Fang - One of the best experts on this subject based on the ideXlab platform.

  • A Single-Loop Approach for Time-Variant Reliability-Based Design Optimization
    IEEE Transactions on Reliability, 2017
    Co-Authors: Zhiliang Huang, Xiao-ming Li, Teng Fang
    Abstract:

    In the process of long-term use, the uncertainty of an engineering structure often presents time-variant or dynamic characteristics due to the influence of stochastic loads and material performance degradations. In such a situation, the structural Design Optimization will involve an important problem of time-variant reliability-Based Design Optimization (TRBDO). Performing TRBDO involves a nested Optimization, which will lead to extremely low computational efficiency. In this paper, a single-loop approach (SLA) is proposed to convert the nested Optimization in TRBDO into a sequence iterative process composed of the time-variant reliability analysis (TRA), constraint discretization, and Design Optimization. In each iteration step, the TRA method Based on stochastic process discretization is first used to calculate the time-variant reliability of constraints; second, through introducing the concept of the target reliability index of discretized time period and proposing the corresponding algorithm, each time-variant constraint is discretized into a series of time-invariant constraints to formulate a conventional reliability-Based Design Optimization problem. The approach exhibits a good comprehensive performance in terms of efficiency and convergence. The validity and practicality of the SLA are validated by two numerical examples and a Design problem for the chassis of a self-balancing vehicle.