An Enhanced Input Uncertainty Representation Method for Response Surface Models in Automotive Weight Reduction Applications

Event
SAE 2015 World Congress & Exhibition
Authors Abstract
Content
Vehicle weight reduction has become one of the viable solutions to ever-growing energy and environmental crisis. In vehicle design, response surface model (RSM) is commonly used as a surrogate of the high fidelity Finite Element (FE) model to reduce the computational time and improve the efficiency of design process. However, RSM introduces additional sources of uncertainty, such as model bias, which largely affects the reliability and robustness of the prediction results. The bias of RSM need to be addressed before the model is ready for extrapolation and design optimization. For the purpose of constructing and correcting the bias in RSMs, scheduling Design of Experiments (DOEs) must be conducted properly. This paper develops a method to arrange DOEs in order to build RSMs with high quality, considering the influence of input uncertainty. The proposed method focuses on assigning samples corresponding to the obtained Probability Density Function (PDF) of represented input, and it shows its advantage in the process of model bias correction. A real-world vehicle weight reduction design example is used to illustrate the validity of the proposed method.
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DOI
https://doi.org/10.4271/2015-01-0423
Pages
7
Citation
Liu, B., Yang, J., Zhan, Z., Zheng, L. et al., "An Enhanced Input Uncertainty Representation Method for Response Surface Models in Automotive Weight Reduction Applications," Materials and Manufacturing 8(3):616-622, 2015, https://doi.org/10.4271/2015-01-0423.
Additional Details
Publisher
Published
Apr 14, 2015
Product Code
2015-01-0423
Content Type
Journal Article
Language
English