Reliability-Based Design Optimization of a Large-Scale Truss Structure Using Polynomial Chaos Expansion Metamodel

2019 ◽  
pp. 481-488
Author(s):  
Subhrajit Dutta ◽  
Chandrasekhar Putcha
Keyword(s):  
Design Optimization ◽  
Large Scale ◽  
Polynomial Chaos ◽  
Polynomial Chaos Expansion ◽  
Truss Structure ◽  
Chaos Expansion ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design

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.

scholarly journals Intelligent Approach to Robust Design Optimization of a Rotor System due to Its Support Stiffness Uncertainty

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Bensheng Xu ◽  
Chaoping Zang ◽  
Genbei Zhang
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
System Performance ◽  
Polynomial Chaos ◽  
Low Pressure ◽  
Polynomial Chaos Expansion ◽  
Rotor System ◽  
Robust Design Optimization ◽  
Chaos Expansion ◽  
Vibration Responses

In this paper, an intelligent robust design approach combined with different techniques such as polynomial chaos expansion (PCE), radial basis function (RBF) neural network, and evolutionary algorithms is presented with a focus on the optimization of the dynamic response of a rotor system considering support stiffness uncertainty. In the proposed method, the PCE method instead of the traditional Monte Carlo uncertainty analysis is applied to analyze the uncertain propagation of system performance. The RBF network is introduced to establish the approximate models of the objective and constraint functions. Taking the low-pressure rotor of a gas turbine with support stiffness uncertainty as an example, the optimization model is established with the mean and variance of unbalanced response of the rotor system at different operating speeds as the objective function, and the maximum unbalance response is less than the upper limit as the constraint function. The polynomial chaos expansion is generated to facilitate a rapid analysis of robustness in the presence of support stiffness uncertainties that is defined in terms of tolerance with good accuracy. The optimal Hypercubus are used as experimental plans for building RBF approximation models of the objective and constraint functions. Finally, the robust solutions are obtained with the multiobject optimization algorithm NSGA-II. Monte Caro simulation analysis demonstrates that the qualified rate of maximum vibration responses of the low-pressure rotor system can be increased from 83.6% to over 99%. This approach to robust design optimization is shown to lead to designs that significantly decrease vibration responses of the rotor system and improved system performance with reduced sensitivity to support stiffness uncertainty.

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