Reliability-based design using variable-fidelity optimization

Abstract

Competitive marketplaces have driven the need for simulation based design optimization to produce efficient and cost effective designs. However, such design practices are typically deterministic and don't take into account model uncertainties or manufacturing tolerances. Deterministic designs may lie on failure driven constraints, resulting in designs characterized by a high probability of failure. Reliability based design optimization (RBDO) methods have been developed to obtain designs that optimize a merit function while ensuring a target reliability level. Unfortunately, these methods are notorious for the high computational expense they require to converge. There has been a considerable effort to reduce the computational cost, many of these methods have used approximations which are not guaranteed to converge to the optimal solution of the original high fidelity problem. In this research variable fidelity methods are used to reduce the cost of RBDO. Variable fidelity methods use a set of models with varying degrees of fidelity and computational expense to reduce the cost of optimization. The variable fidelity RBDO methodology developed in this investigation is demonstrated on two test cases: a nonlinear analytic problem and a high-lift airfoil design problem. For each of these problems the proposed method shows considerable savings for performing RBDO as compared with standard approaches.

Certifiable Risk-Based Engineering Design Optimization

“…Third, adaptive data-driven surrogates for the limit state failure boundary identification can improve computational efficiency for the RBDO problem [43,44,45]. Fourth, bi-fidelity RBDO methods [46,47] and recent multifidelity/multi-information-source methods for the PoF estimate [48,49,44] and the RBDO problem [50,51] have led to significant computational savings. Although significant research has been devoted to PoF and RBDO, PoF as a risk measure does not factor in how catastrophic is the failure and thus, lacks resiliency.…”

Reliability-based Topology Optimization using Stochastic Gradients

“…Taflanidis and Beck (2008a,b) explored the design and uncertain parameter space at the same time assuming uncertainty in the design parameters. Further, multi-fidelity methods (Gano et al, 2006;Chaudhuri et al, 2019), response surface methods (Foschi et al, 2002;Agarwal and Renaud, 2004), and other surrogate models (Missoum et al, 2007;Zhang and Foschi, 2004;Bichon et al, 2008;Basudhar and Missoum, 2008;Moustapha and Sudret, 2019) can be used to reduce the computational cost.…”

An Enhancement of the NSGA-II Reliability Optimization Using Extended Kalman Filter Based Initialization

“…Chakri et al [2] have stated that RBDO problems are formulated as nested optimization models that cause computational complexity in solutions. To reduce such complexities, the most popular approaches are based on either separating the reliability assessment from the optimization loop and transforming the RBDO problems into the deterministic optimization problems and reliability assessment models; or transforming the probabilistic constraints into the deterministic constraints [3]. However, it will not guarantee a fast convergence in the main optimization.…”