Multi-objective optimization of a CRDI assisted diesel engine with predictive regression model developed by uniform design Article Swipe

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Xiaoxiao Niu , Wang Zhengxiang , Yinyan Wang , Yang Song ·

YOU? · · 2016 · Open Access · · DOI: https://doi.org/10.2991/wartia-16.2016.211

This paper is dedicated to find a unique solution for the optimization of engine performance and emissions which have a trade-off between NOx-soot-BSFC.To develop regression models for engine performance and emissions, multiple nonlinear regression with uniform design is employed.And the accuracy of the predictive models has revealed the good performance and high efficiency of uniform design.The multi-objective optimization with weighted sum method is conducted for multi-objective optimization and weights which represent relative importance of different objective can be suitably varied to meet the different desire of engine designers.Finally, four optimization objectives have conducted and validated which confirmed the feasible of this optimization approach.

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Automotive Engineering

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Diesel engine Regression analysis Computer science Diesel fuel Regression Automotive engineering Engineering Mathematics Statistics Machine learning

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Type: article
Language: en
Landing Page: https://doi.org/10.2991/wartia-16.2016.211
PDF: https://www.atlantis-press.com/article/25855874.pdf
OA Status: gold
References: 19
Related Works: 10
OpenAlex ID: https://openalex.org/W2414723304

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OpenAlex ID: https://openalex.org/W2414723304

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DOI: https://doi.org/10.2991/wartia-16.2016.211

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Title: Multi-objective optimization of a CRDI assisted diesel engine with predictive regression model developed by uniform design

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Language: en

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Publication year: 2016

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Publication date: 2016-01-01

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Authors: Xiaoxiao Niu, Wang Zhengxiang, Yinyan Wang, Yang Song

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Landing page: https://doi.org/10.2991/wartia-16.2016.211

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PDF URL: https://www.atlantis-press.com/article/25855874.pdf

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Concepts: Diesel engine, Regression analysis, Computer science, Diesel fuel, Regression, Automotive engineering, Engineering, Mathematics, Statistics, Machine learning

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Cited by: 0

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References (count): 19

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