Reliability-Based Robust Design Optimization Method for Engineering Systems with Uncertainty Quantification

TL;DR

This paper presents a robust design optimization method for engineering systems that integrates uncertainty quantification using polynomial chaos with genetic algorithms to enhance reliability and robustness.

Contribution

It introduces a framework combining polynomial chaos-based uncertainty analysis with gradient-free genetic algorithms for robust optimization of engineering systems.

Findings

Effective uncertainty quantification with polynomial chaos.

Genetic algorithms provide global search capabilities.

Improved reliability in optimized engineering designs.

Abstract

Robust optimization is a method for optimization under uncertainties in engineering systems and designs for applications ranging from aeronautics to nuclear. In a robust design process, parameter variability (or uncertainty) is incorporated into the engineering systems' optimization process to assure the systems' quality and reliability. This chapter focuses on a robust optimization approach for developing robust and reliable advanced systems and explains the framework for using uncertainty quantification and optimization techniques. For the uncertainty analysis, a polynomial chaos-based approach is combined with the optimization algorithms MOSA (Multi-Objective Simulated Annealing), and the process is discussed with a simplified test function. For the optimization process, gradient-free genetic algorithms are considered as the optimizer scans the whole design space, and the optimal values are not always dependent on the initial values.