Reliability based design optimization of coupled acoustic-structure system using generalized polynomial chaos

2017 ◽  
Vol 134 ◽  
pp. 75-84 ◽  
Author(s):  
K. Dammak ◽  
A. El Hami ◽  
S. Koubaa ◽  
L. Walha ◽  
M. Haddar
Keyword(s):  
Design Optimization ◽  
Polynomial Chaos ◽  
Reliability Based Design Optimization ◽  
Generalized Polynomial Chaos ◽  
Acoustic Structure ◽  
Structure System ◽  
Reliability Based Design ◽  
Generalized Polynomial

scholarly journals Time-Dependent Reliability-Based Design Optimization Utilizing Nonintrusive Polynomial Chaos

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
Yao Wang ◽  
Shengkui Zeng ◽  
Jianbin Guo
Keyword(s):  
Design Optimization ◽  
Structural Reliability ◽  
Polynomial Chaos ◽  
Time Dependent ◽  
Fe Model ◽  
Reliability Based Design Optimization ◽  
Wear Process ◽  
Reliability Based Design ◽  
Optimization Methodology ◽  
Wear Law

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.

A Reliability-Based Formulation for Simulation-Based Control Co-design Using Generalized Polynomial Chaos Expansion

2021 ◽  
pp. 1-15
Author(s):  
Mohammad Behtash ◽  
Michael J. Alexander-Ramos
Keyword(s):  
Dynamic System ◽  
Design Optimization ◽  
Reliability Analysis ◽  
Polynomial Chaos ◽  
Probabilistic Constraints ◽  
Stochastic Dynamic ◽  
Generalized Polynomial Chaos ◽  
Generalized Polynomial ◽  
And Control ◽  
Stochastic Dynamic Systems

Abstract Combined plant and control design (control co-design, or CCD) methods are often used during product development to address the synergistic coupling between the plant and control parts of a dynamic system. Recently, a few studies have started applying CCD to stochastic dynamic systems. In their most rigorous approach, reliability-based design optimization (RBDO) principles have been used to ensure solution feasibility under uncertainty. However, since existing reliability-based CCD (RBCCD) algorithms use all-at-once (AAO) formulations, only most-probable-point (MPP) methods can be used as reliability analysis techniques. Though effective for linear/quadratic RBCCD problems, the use of such methods for highly nonlinear RBCCD problems introduces solution error that could lead to system failure. A multidisciplinary feasible (MDF) formulation for RBCCD problems would eliminate this issue by removing the dynamic equality constraints and instead enforcing them through forward simulation. Since the RBCCD problem structure would be similar to traditional RBDO problems, any of the well-established reliability analysis methods could be used. Therefore, in this work, a novel reliability-based MDF formulation of multidisciplinary dynamic system design optimization (RB-MDF-MDSDO) has been proposed for RBCCD. To quantify the uncertainty propagated by the random decision variables, Monte Carlo simulation has been applied to the generalized polynomial chaos (gPC) expansion of the probabilistic constraints. The proposed formulation is applied to two engineering test problems, with the results indicating the effectiveness of both the overall formulation as well as the reliability analysis technique for RBCCD.

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