Browse Publications Technical Papers 2016-01-0279
2016-04-05

Quantification of Meta-model and Parameter Uncertainties in Robust Design 2016-01-0279

To reduce the computational time of the iterations in robust design, meta-models are frequently utilized to approximate time-consuming computer aided engineering models. However, the bias of meta-model uncertainty largely affects the robustness of the prediction results, this uncertainty need to be addressed before design optimization. In this paper, an efficient uncertainty quantification method considering both model and parameter uncertainties is proposed. Firstly, the uncertainty of parameters are characterized by statistical distributions. The Bayesian inference is then performed to improve the predictive capabilities of the surrogate models, meanwhile, the model uncertainty can also be quantified in the form of variance. Monte Carlo sampling is finally utilized to quantify the compound uncertainties of model and parameter. Furthermore, the proposed uncertainty quantification method is used for robust design. A numerical example and a real-world vehicle lightweight case study are used to demonstrate the validity of the proposed method.

SAE MOBILUS

Subscribers can view annotate, and download all of SAE's content. Learn More »

Access SAE MOBILUS »

Members save up to 43% off list price.
Login to see discount.
Special Offer: Purchase more aerospace standards and aerospace material specifications and save! AeroPaks off a customized subscription plan that lets you pay for just the documents that you need, when you need them.
X