A novel time-dependent system constraint boundary sampling technique for solving time-dependent reliability-based design optimization problems

Time-dependent reliability-based design optimization (RBDO) has been acknowledged as an advance optimization methodology since it accounts for time-varying stochastic nature of systems. This paper proposes a time-dependent RBDO method considering both of the time-dependent kinematic reliability and the time-dependent structural reliability as constrains. Polynomial chaos combined with the moving least squares (PCMLS) is presented as a nonintrusive time-dependent surrogate model to conduct uncertainty quantification. Wear is considered to be a critical failure that deteriorates the kinematic reliability and the structural reliability through the changing kinematics. According to Archard’s wear law, a multidiscipline reliability model including the kinematics model and the structural finite element (FE) model is constructed to generate the stochastic processes of system responses. These disciplines are closely coupled and uncertainty impacts are cross-propagated to account for the correlationship between the wear process and loads. The new method is applied to an airborne retractable mechanism. The optimization goal is to minimize the mean and the variance of the total weight under both of the time-dependent and the time-independent reliability constraints.

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Global kriging surrogate modeling for general time-variant reliability-based design optimization problems

Structural and Multidisciplinary Optimization ◽

10.1007/s00158-018-1938-y ◽

2018 ◽

Vol 58 (3) ◽

pp. 955-968 ◽

Cited By ~ 14

Author(s):

Lara Hawchar ◽

Charbel-Pierre El Soueidy ◽

Franck Schoefs

Keyword(s):

Design Optimization ◽

Optimization Problems ◽

Surrogate Modeling ◽

Reliability Based Design Optimization ◽

Reliability Based Design ◽

Kriging Surrogate Modeling ◽

General Time

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A decoupling algorithm with first-order asymptotic integration for reliability-based design optimization

Advances in Mechanical Engineering ◽

10.1177/1687814018793336 ◽

2018 ◽

Vol 10 (9) ◽

pp. 168781401879333 ◽

Cited By ~ 1

Author(s):

Zhiliang Huang ◽

Tongguang Yang ◽

Fangyi Li

Keyword(s):

Design Optimization ◽

Reliability Analysis ◽

Optimization Problems ◽

Asymptotic Integration ◽

Probabilistic Constraints ◽

Reliability Based Design Optimization ◽

First Order Reliability Method ◽

First Order ◽

Reliability Based Design ◽

Reliability Method

Conventional decoupling approaches usually employ first-order reliability method to deal with probabilistic constraints in a reliability-based design optimization problem. In first-order reliability method, constraint functions are transformed into a standard normal space. Extra non-linearity introduced by the non-normal-to-normal transformation may increase the error in reliability analysis and then result in the reliability-based design optimization analysis with insufficient accuracy. In this article, a decoupling approach is proposed to provide an alternative tool for the reliability-based design optimization problems. To improve accuracy, the reliability analysis is performed by first-order asymptotic integration method without any extra non-linearity transformation. To achieve high efficiency, an approximate technique of reliability analysis is given to avoid calculating time-consuming performance function. Two numerical examples and an application of practical laptop structural design are presented to validate the effectiveness of the proposed approach.

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Reliability-based design optimization for vertical vibrations of a modified electric vehicle using fourth-moment polynomial standard transformation method

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407020907488 ◽

2020 ◽

Vol 234 (10-11) ◽

pp. 2649-2664

Author(s):

Sheng Wang ◽

Lin Hua ◽

Xinghui Han ◽

Zhuoyu Su

Keyword(s):

Design Optimization ◽

Electric Vehicle ◽

Optimization Problems ◽

Computing Time ◽

Optimization Procedure ◽

Transformation Method ◽

Vertical Vibration ◽

Fourth Moment ◽

Reliability Based Design Optimization ◽

Reliability Based Design

This article presents a new reliability-based design optimization procedure for the vertical vibration issues raised by a modified electric vehicle using fourth-moment polynomial standard transformation method. First, the fourth-moment polynomial standard transformation method with polynomial chaos expansion is used to obtain the reliability index of uncertain constraints in the reliability-based design optimization which is highly precise and saves computing time compared with other common methods. Next, the half-car model with nonlinear suspension parameters for the modified electric vehicle is investigated, and the response surface methodology is adopted to approximate the complex and time-consuming vertical vibration calculation to the polynomial expressions, and the approximation is validated for reliability-based design optimization results within permissible error level. Then, reliability-based design optimization results under both deterministic and uncertain load parameters are shown and analyzed. Unlike the traditional vertical vibration optimization that only considers one or several sets of load parameters, which lacks versatility, this article presents the reliability-based design optimization with uncertain load parameters which is more suitable for engineering. The results show that the proposed reliability-based design optimization procedure is an effective and efficient way to solve vertical vibration optimization problems for the modified electric vehicle, and the optimization statistics, including the maximum probability interval, can provide references for other suspension dynamical optimization.

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