Reliability-Based Design Optimization Incorporating Extended Optimal Uncertainty Quantification

TL;DR

This paper integrates extended Optimal Uncertainty Quantification into reliability-based design optimization to compute sharp bounds on failure probabilities and costs, accommodating both aleatory and epistemic uncertainties without strict distribution assumptions.

Contribution

It introduces a double loop RBDO framework embedding extended OUQ, allowing for robust bounds on failure probability and cost considering uncertain data and moments.

Findings

Successfully applied to a benchmark problem with polymorphic uncertainties.

Demonstrated effectiveness in optimizing laser-hardened line placement in steel sheets.

Provides a robust approach avoiding unjustified assumptions on input data.

Abstract

Reliability-based design optimization (RBDO) approaches aim to identify the best design of an engineering problem, whilst the probability of failure (PoF) remains below an acceptable value. Thus, the incorporation of the sharpest bounds on the PoF under given constraints on the uncertain input quantities strongly strenghtens the significance of RBDO results, since unjustified assumptions on the input quantities are avoided. In this contribution, the extended Optimal Uncertainty Quantification framework is embedded within an RBDO context in terms of a double loop approach. By that, the mathematically sharpest bounds on the PoF as well as on the cost function can be computed for all design candidates and compared with acceptable values. The extended OUQ allows the incorporation of aleatory as well as epistemic uncertainties, where the definition of probability density functions is not necessarily required and just given data on the input can be included. Specifically, not only bounds on the values themselves, but also bounds on moment constraints can be taken into account. Thus, inadmissible assumptions on the data can be avoided, while the optimal design of a problem can be identified. The capability of the resulting framework is firstly shown by means of a benchmark problem under the influence of polymorphic uncertainties. Afterwards, a realistic engineering problem is analyzed, where the positioning of laser-hardened lines within a steel sheet for a car crash structure are optimized.