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Hybrid uncertainties-based analysis and optimization design of powertrain mounting systems

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Abstract

In this study, a hybrid uncertainties-based analysis and optimization method is presented for the designs of the powertrain mounting system (PMS) involving mixed uncertainties. In the presented method, the PMS parameters with sufficient data are treated as random variables, while those with limited information are defined as interval variables. Then, an uncertainty-based analysis method called as hybrid interval-random perturbation-central difference method (HIRP-CDM), is proposed to compute the hybrid interval-random outputs of the inherent characteristics of the PMS in concerned directions. In addition, the hybrid interval-random-Monte Carlo method (HIR-MCM) is developed to verify the computational accuracy of HIRP-CDM. Next, an optimization model mixed uncertainties is built up for the PMS design based on HIRP-CDM, in which the hybrid intervalrandom outputs of the concerned inherent characteristics are adopted to construct the design objective and constrains. The complex optimization problem can be effectively settled by means of HIRP-CDM. The effectiveness of the presented method is verified by a numerical example.

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Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510641, China

    BoHao Cai, Wen-Bin Shangguan, Hui Lü & Tao Bo

Authors
  1. BoHao Cai
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  2. Wen-Bin Shangguan
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  3. Hui Lü
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  4. Tao Bo
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Corresponding author

Correspondence to Hui Lü.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant Nos. 51605167, 51975217), the Science and Technology Program of Guangzhou, China (Grant No. 201804010092), and the Fundamental Research Funds for the Central Universities, SCUT (Grant No. 2019MS058).

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Cai, B., Shangguan, WB., Lü, H. et al. Hybrid uncertainties-based analysis and optimization design of powertrain mounting systems. Sci. China Technol. Sci. 63, 838–850 (2020). https://doi.org/10.1007/s11431-019-1477-8

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  • DOI: https://doi.org/10.1007/s11431-019-1477-8

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