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reliability-based design optimization using convex approximations and sequential optimization and reliability assessment method

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2010年第1期24卷 279-283页

作者： Cho, Tae Min Lee, Byung Chai Korea Adv Inst Sci & Technol Dept Mech Engn Taejon 305701 South Korea

In this study, an effective method for reliability-based design optimization (RBDO) is proposed enhancing sequential optimization and reliability assessment (SORA) method by convex approximations. In SORA, reliability estimation and deterministic optimization are performed sequentially. The sensitivity and function value of probabilistic constraint at the most probable point (MPP) are obtained in the reliability analysis loop. In this study, the convex approximations for probabilistic constraint are constructed by utilizing the sensitivity and function value of the probabilistic constraint at the MPP. Hence, the proposed method requires much less function evaluations of probabilistic constraints in the deterministic optimization than the original SORA method. The efficiency and accuracy of the proposed method were verified through numerical examples.

关键词： Convex approximations Most probable point reliability-based design optimization Sequential optimization and reliability assessment method

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reliability-based design optimization for the lattice boom of crawler crane

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STRUCTURES 2021年 29卷 1111-1118页

作者： Li, Jinping Bai, Lin Gao, Wei Shi, Ning Wang, Ning Ye, Min Gu, Hairong Xu, Xinxin Liu, Jian Changan Univ Highway Maintenance Equipment Natl Engn Lab Xian 710064 Shaanxi Peoples R China Univ New South Wales Sch Civil & Environm Engn Ctr Infrastruct Engn & Safety Sydney NSW 2052 Australia Henan Gaoyuan Maintenance Technol Highway Co Ltd Xinxiang 453003 Henan Peoples R China Fourth Construct Co LTD China Construct Eighth Engn Div Qingdao 266000 Shandong Peoples R China

The reliability-based design optimization (RBDO) problem involving discrete design variables and multi-working constraints is tackled with the consideration of the uncertainty factors in the optimal design of crawler crane's lattice boom. In the optimization model, the uncertainties of material property, geometric parameters and loads are represented by random variables. The geometrical dimensions, performance functions' reliability indexes and the minimum weight of lattice boom are defined respectively as the random design variables, probabilistic constraints and objective function. Integrating the reliability analysis method based on response surface method (RSM) with multi-island genetic algorithm (MIGA), an efficient reliability-based design optimization process for the lattice boom is developed under the typical constrained working conditions. Several verification cases are presented to demonstrate the effectiveness of optimization results. It is shown that the reliability-based design optimization method can not only satisfy the reliability constraints of the lattice boom, but also ensure the safety and economy of crawler crane boom as well as minimize the structure weight. The developed method is capable to be extended to the reliability-based design optimization of other large and complex construction machinery structures.

关键词： Crawler crane Lattice boom reliability-based design optimization Response surface method Multi-island genetic algorithm

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reliability-based design optimization Using Confidence-based Model Validation for Insufficient Experimental Data

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JOURNAL OF MECHANICAL design 2017年第3期139卷 031404页

作者： Moon, Min-Yeong Choi, K. K. Cho, Hyunkyoo Gaul, Nicholas Lamb, David Gorsich, David Univ Iowa Dept Mech & Ind Engn Coll Engn Iowa City IA 52242 USA RAMDO Solut LLC Iowa City IA 52240 USA US Army RDECOM TARDEC Warren MI 48397 USA

The conventional reliability-based design optimization (RBDO) methods assume that a simulation model is able to represent the real physics accurately. However, this assumption may not always hold as the simulation model could be biased. Accordingly, designed product based on the conventional RBDO optimum may either not satisfy the target reliability or be overly conservative design. Therefore, simulation model validation using output experimental data, which corrects model bias, should be integrated in the RBDO process. With particular focus on RBDO, the model validation needs to account for the uncertainty induced by insufficient experimental data as well as the inherent variability of the products. In this paper, a confidence-based model validation method that captures the variability and the uncertainty, and that corrects model bias at a user-specified target confidence level, has been developed. The developed model validation helps RBDO to obtain a conservative RBDO optimum design at the target confidence level. The RBDO with model validation may have a convergence issue because the feasible domain changes as the design moves (i.e., a moving-target problem). To resolve the issue, a practical optimization procedure is proposed. Furthermore, the efficiency is achieved by carrying out deterministic design optimization (DDO) and RBDO without model validation, followed by RBDO with confidence-based model validation. Finally, we demonstrate that the proposed RBDO approach can achieve a conservative and practical optimum design given a limited number of experimental data.

关键词： reliability-based design optimization confidence-based model validation confidence-based model bias correction insufficient output experimental data conservative design

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reliability-based design optimization for elastoplastic mechanical structures

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COMPUTERS & STRUCTURES 2007年第10期85卷 615-625页

作者： Kaymaz, Irfan Marti, Kurt Ataturk Univ Dept Mech Engn TR-25240 Erzurum Turkey Fed Armed Forces Univ Munich Aero Space Engn & Technol D-85577 Munich Germany

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 beta-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. (c) 2006 Civil-Comp Ltd. and Elsevier Ltd. All rights reserved.

关键词： reliability-based design optimization reliability analysis elastoplastic mechanical structures direct computation of the beta-point

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reliability-based design optimization of Additive Manufacturing for Lithium Battery Silicon Anode

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ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART B-MECHANICAL ENGINEERING 2024年第3期10卷 031104页

作者： Liu, Zheng Wu, Hao Wang, Pingfeng Li, Yumeng Univ Illinois Dept Ind & Enterprise Syst Engn 104 S Mathews Ave Urbana IL 61801 USA

With the blooming of the electric vehicle market and the advancement in the lithium-ion battery industry, silicon anode has shown great potential for the next-generation battery. Using the state-of-the-art additive manufacturing technique (three-dimensional (3D) holographic lithography), researchers have demonstrated that silicon anode can be fabricated as a three-dimensional bicontinuous porous microstructure. However, the volume fluctuation of the silicon anode caused by lithiation during the discharging process causes continuous capacity decay and poor cycling life. Besides, uncertainties are inherent in the manufacturing and usage processes, making it crucial to systematically consider them in the silicon anode design to improve its performance and reliability. To fill the gap between current silicon anode research and future industrial need, this study established a digital twin to investigate the optimal design for silicon anode under the uncertainties of additive manufacturing and battery usage. This study started with developing multiphysics finite element models of the silicon anode lithiation process to investigate the volume fluctuation of silicon. Then, surrogate models were built based on the results from the finite element models to reduce computational cost. The reliability-based design optimization (RBDO) was employed to find the best design point for the silicon anode, in which an outer optimization loop maximized the objective function and an inner loop dedicated to reliability analysis. Finally, the Pareto optimal front of the silicon anode designs was obtained and validated, which shows over 10% improvements in the silicon anode's total capacity and rate capability.

关键词： lithium-ion battery additive manufacturing reliability-based design optimization

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reliability-based design optimization using local sensitivity with application to magnetic nano switch

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2013年第1-2期43卷 103-113页

作者： Phuong Pham-Quang Delinchant, Benoit Coulomb, Jean-Louis Grenoble Elect Engn Lab F-38402 St Martin Dheres France

This paper presents a method and a tool for solving the reliability-based design optimization (RBDO). The RBDO aims to minimize a cost function by changing the value of design parameters while ensuring a level of reliability. Uncertainties propagation is a key concept in reliable studies and it can be associated with a sensitivity analysis in order to sort parameter influences. Global and local sensitivities are compared in this study in order to keep a reasonable cost versus accuracy ratio. A software tool has been also developed to automate reliable studies. It is applied to the reliable optimization of a magnetic nano switch with SQP algorithm using Jacobian calculated by composition of automatic and symbolic differentiation.

关键词： Sensitivity analysis automatic differentiation Jacobian reliability-based design optimization magnetic nano switch

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reliability-based design optimization with Confidence Level for Non-Gaussian Distributions Using Bootstrap Method

Reliability-based Design Optimization with Confidence Level ...

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ASME Internationl design Engineering Technical Conferences / Computers and Information in Engineering Conference

作者： Noh, Yoojeong Choi, Kyung K. Lee, Ikjin Gorsich, David Univ Iowa Coll Engn Dept Mech & Ind Engn Iowa City IA 52242 USA

ISBN: (纸本)9780791844090

For reliability-based design optimization (RBDO), generating an input statistical model with confidence level has been recently proposed to offset the inaccurate estimation of the input statistical model with Gaussian distributions. For this, the confidence intervals of mean and standard deviation are calculated using the Gaussian distributions of input random variables. However, if the input random variables are non-Gaussian, the use of the Gaussian distributions of input variables will provide inaccurate confidence intervals, and thus, yield undesirable confidence level of the reliability-based optimum design meeting the target reliability A. In this paper, the RBDO method using the bootstrap method, which does not use the Gaussian distributions of input variables to calculate the confidence intervals of mean and standard deviation, are proposed to obtain the desirable confidence level of output performance for non-Gaussian distributions.

关键词： reliability-based design optimization input statistical model confidence level non-Gaussian distribution bootstrap method

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reliability-based design optimization using probabilistic sufficiency factor

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2004年第5期27卷 314-325页

作者： Qu, X Haftka, RT Univ Florida Dept Mech & Aerosp Engn Gainesville FL 32611 USA

A probabilistic sufficiency factor approach is proposed that combines safety factor and probability of failure. The probabilistic sufficiency factor approach represents a factor of safety relative to a target probability of failure. It provides a measure of safety that can be used more readily than the probability of failure or the safety index by designers to estimate the required weight increase to reach a target safety level. The probabilistic sufficiency factor can be calculated from the results of Monte Carlo simulation with little extra computation. The paper presents the use of probabilistic sufficiency factor with a design response surface approximation, which fits it as a function of design variables. It is shown that the design response surface approximation for the probabilistic sufficiency factor is more accurate than that for the probability of failure or for the safety index. Unlike the probability of failure or the safety index, the probabilistic sufficiency factor does not suffer from accuracy problems in regions of low probability of failure when calculated by Monte Carlo simulation. The use of the probabilistic sufficiency factor accelerates the convergence of reliability-based design optimization.

关键词： Monte Carlo simulation probabilistic sufciency factor reliability-based design optimization response surface approximation

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reliability-based design optimization of a Large-Scale Truss Structure Using Polynomial Chaos Expansion Metamodel

Reliability-Based Design Optimization of a Large-Scale Truss...

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International Conference on reliability, Safety and Hazard 2019 (ICRESH)

作者： Dutta, Subhrajit Putcha, Chandrasekhar Natl Inst Technol Silchar Dept Civil Engn Silchar India Calif State Univ Fullerton Dept Civil & Environm Engn Fullerton CA 92634 USA

ISBN: (纸本)9789811390081;9789811390074

Optimal design of engineering systems considering uncertainties is broadly dealt in the area of reliability-based design optimization (RBDO). These RBDO problems involve a costly inner loop reliability estimate involving a computation-intensive true model solver. The current work is aimed at reducing this cost of computation in RBDO. To this end, a polynomial chaos expansion (PCE) metamodel is used. This PCE metamodel is later used to substitute the actual expensive true model in the reliability computation phase. A stochastic optimizer-particle swarm optimization (PSO) is used for the outer optimization loop. The effectiveness of PCE metamodel combined with PSO is demonstrated for a large-scale truss structural system in reducing the overall computation cost involved in RBDO.

关键词： reliability-based design optimization Metamodel Polynomial chaos expansion Structural optimization

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reliability-based design optimization using local sensitivity with application to magnetic nano switch

Reliability-based design optimization using local sensitivit...

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12th International Workshop on optimization and Inverse Problems in Electromagnetism (OIPE)

作者： Phuong Pham-Quang Delinchant, Benoit Coulomb, Jean-Louis Grenoble Elect Engn Lab F-38402 St Martin Dheres France

关键词： Sensitivity analysis automatic differentiation Jacobian reliability-based design optimization magnetic nano switch

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