Integrated Framework for Design Optimization Under Aleatory and/or Epistemic Uncertainties Using Adaptive-Loop Method

Abstract

In last two decades, significant attentions have been paid to develop design optimization methodologies under various uncertainties: reliability-based design optimization (RBDO), possibility-based design optimization (PBDO), etc. As a result, a variety of methods of uncertainty-based design optimization have been proposed and are mainly catagorized as: parallel-loop method, serial-loop method, and single-loop method. It has been reported that each method has its own strong and weak points. Thus, this paper attempts to understand various methods better, and proposes to develop an integrated framework for uncertainty-based design optimization. In short, the integrated design framework timely involves three phases (deterministic design optimization, parallel-loop method, single-loop method) to maximize numerical efficiency without losing computational stability and accuracy in the process of uncertainty-based design optimization. While the parallel-loop method maintains numerical stability well with a minimal computation, deterministic design optimization and single-loop method will improve numerical efficiency at the beginning and end of uncertainty-based design optimization. Thus, the proposed method is called adaptive-loop method. It will be shown that integrated framework using the proposed method is applicable for various design optimization methodologies under aleatory or epistemic uncertainties, such as RBDO, PBDO, etc. Examples are used to demonstrate the effectiveness of integrated framework using the adaptive-loop method in terms of numerical efficiency.