Design Optimization and Reliability Estimation with Incomplete Uncertainty Information

Abstract

Existing methods for design optimization under uncertainty assume that a high level of information is available, typically in the form of data. In reality, however, insufficient data prevents correct inference of probability distributions, membership functions, or interval ranges. In this article we use an engine design example to show that optimal design decisions and reliability estimations depend strongly on uncertainty characterization. We contrast the reliability-based optimal designs to the ones obtained using worst-case optimization, and ask the question of how to obtain nonconservative designs with incomplete uncertainty information. We propose an answer to this question through the use of Bayesian statistics. We estimate the truck's engine reliability based only on available samples, and demonstrate that the accuracy of our estimates increases as more samples become available. Finally, we use this information-based reliability assessment to optimize the engine while maximizing the confidence that the design will meet or exceed a prespecified reliability target.

Software Integration for Simulation-Based Analysis and Robust Design Automation of HMMWV Rollover Behavior

“…The purpose of the deterministic study is to investigate the effect of different stabilizer bars on the HMMWV rollover behavior. The uncertainty-based analysis accounts for the uncontrollable variations that exist in reality [4,5]. It is performed to study the robustness of the HMMWV design under the influence of uncertainty.…”

Impact of Uncertainty Quantification on Design Decisions for a Hydraulic-Hybrid Powertrain Engine

“…All random design variables and parameters were assumed to be normally distributed (the effect of probability distribution type on design decisions was investigated in Gunawan et al 7 ). The standard deviations of the random design variables and the means and standard deviations of the random parameters are summarized in Table 2.…”

Modelo para gestão do desenvolvimento e produção de pneus fornecidos para industria automobilística.

“…Gunawan et al [354] apresentam um modelo de cálculo para otimização de projeto e previsão de confiabilidade para situações cujos dados de incertezas estão incompletos. Esse modelo foi usado para otimizar o projeto de um motor maximizando a probabilidade de que o projeto atenda ou exceda os objetivos de confiabilidade pré-estabelecidos.…”