A Hybrid Reliability-Based Design Optimization Approach with Adaptive Chaos Control Using Kriging Model

2016 ◽  
Vol 13 (01) ◽  
pp. 1650006 ◽  
Author(s):  
Gang Li ◽  
Zeng Meng ◽  
Peng Hao ◽  
Hao Hu
Keyword(s):  
Design Optimization ◽  
Chaos Control ◽  
Pso Algorithm ◽  
Kriging Model ◽  
Target Point ◽  
Optimization Approach ◽  
Reliability Based Design Optimization ◽  
Local Optima ◽  
Reliability Based Design ◽  
Highly Nonlinear

Traditional reliability-based design optimization (RBDO) approaches are computationally expensive for complicated problems. To cope with this challenge, a surrogate-based hybrid RBDO approach for the problems with highly nonlinear constraints and multiple local optima is proposed in this study. Specifically, the adaptive chaos control (ACC) method is used to ensure the robustness of the most probable target point (MPTP) search process, and the particle swarm optimization (PSO) algorithm is utilized to conquer the barrier caused by multiple local optima. Three illustrative benchmark examples together with a 3 m diameter orthogrid stiffened shell for current launch vehicles are employed to demonstrate the efficiency and robustness of the proposed method.

Reliability-based design optimization of electromagnetic shielding structure using neural networks and real-coded genetic algorithm

2014 ◽  
Vol 228 (18) ◽  
pp. 3471-3481 ◽  
Author(s):  
Heeralal Gargama ◽  
Sanjay K Chaturvedi ◽  
Awalendra K Thakur
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Electromagnetic Shielding ◽  
Optimization Approach ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Design Variables ◽  
Real Coded Genetic Algorithm

The conventional approaches for electromagnetic shielding structures’ design, lack the incorporation of uncertainty in the design variables/parameters. In this paper, a reliability-based design optimization approach for designing electromagnetic shielding structure is proposed. The uncertainties/variability in the design variables/parameters are dealt with using the probabilistic sufficiency factor, which is a factor of safety relative to a target probability of failure. Estimation of probabilistic sufficiency factor requires performance function evaluation at every design point, which is extremely computationally intensive. The computational burden is reduced greatly by evaluating design responses only at the selected design points from the whole design space and employing artificial neural networks to approximate probabilistic sufficiency factor as a function of design variables. Subsequently, the trained artificial neural networks are used for the probabilistic sufficiency factor evaluation in the reliability-based design optimization, where optimization part is processed with the real-coded genetic algorithm. The proposed reliability-based design optimization approach is applied to design a three-layered shielding structure for a shielding effectiveness requirement of ∼40 dB, used in many industrial/commercial applications, and for ∼80 dB used in the military applications.

Improving the fatigue life of a vehicle knuckle with a reliability-based design optimization approach

2009 ◽  
Vol 139 (5) ◽  
pp. 1619-1632 ◽  
Author(s):  
R. d’Ippolito ◽  
M. Hack ◽  
S. Donders ◽  
L. Hermans ◽  
N. Tzannetakis ◽  
...  
Keyword(s):  
Fatigue Life ◽  
Design Optimization ◽  
Optimization Approach ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design

Reliability-based design optimization of fluid–solid interaction problems

2013 ◽  
Vol 228 (10) ◽  
pp. 1724-1742 ◽  
Author(s):  
Hong-Lae Jang ◽  
Hyunkyoo Cho ◽  
Kyung K Choi ◽  
Seonho Cho
Keyword(s):  
Design Optimization ◽  
Incompressible Flows ◽  
Variational Equation ◽  
Optimization Method ◽  
Programming Algorithm ◽  
Reliability Based Design Optimization ◽  
Reliability Based Design ◽  
Highly Nonlinear ◽  
Fluid Solid Interaction ◽  
Interaction Problem

Using a sampling-based reliability-based design optimization method, we present a shape reliability-based design optimization method for coupled fluid–solid interaction problems. For the fluid–solid interaction problem in arbitrary Lagrangian–Eulerian formulation, a coupled variational equation is derived from a steady state Navier–Stokes equation for incompressible flows, an equilibrium equation for geometrically nonlinear solids, and a traction continuity condition at interfaces. The fluid–solid interaction problem is solved using the finite element method and the Newton–Raphson scheme. For the fluid mesh movement, we formulated and solved a pseudo-structural sub-problem. The shape of the solid is modeled using the Non-Uniform Rational B-Spline (NURBS) surface, and the coordinate components of the control points are selected as random design variables. The sensitivity of the probabilistic constraint is calculated using the first-order score functions obtained from the input distributions and from the Monte Carlo simulation on the surrogate model constructed by using the Dynamic Kriging method. The sequential quadratic programming algorithm is used for the optimization. In two numerical examples, the proposed optimization method is applied to the shape design problems of solid structure which is loaded by prescribed fluid flow, and this proves that the sampling-based reliability-based design optimization can be successfully utilized for obtaining a reliable optimum design in highly nonlinear multi-physics problems.

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