Design Potential Method for Robust System Parameter Design

TLDR

The study introduces a design potential method that tightly integrates probabilistic constraint evaluation into the design optimization process for robust system parameter design. The method couples performance probability analysis with design optimization in a unified system space, integrating probabilistic constraint evaluation directly into the optimization loop. The authors demonstrate that the proposed performance‑measure approach is robust and preferable when constraints are inactive, while the traditional reliability‑index method is better for violated constraints but may fail when inactive; the design potential method also speeds up convergence of reliability‑based optimization.

Abstract

A novel design potential method that integrates the probabilistic constraint evaluation closely into the design optimization process is presented for robust system parameter design. From a broader perspective, it is shown that the probabilistic constraints can be evaluated using either the conventional reliability index approach or the proposed performance measure approach. The performance measure approach is inherently robust and is more effective when the prohahilistic constraint is inactive. The reliability index approach is more effective for the violated probabilistic constraint, but it could yield singularity when the probabilistic constraint is inactive. Moreover, the close coupling of performance probability analysis and design optimization is illustrated in a proposed unified system space. The design potential method, which is developed to take full advantage of the important design information obtained from the previous probabilistic constraint evaluation, can significantly accelerate the convergence of the reliability-based design optimization process.