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Erschienen in: Structural and Multidisciplinary Optimization 3/2019

28.03.2019 | Research Paper

Match-based pseudo-MAP full-operation-range optimization method for a turbocharger compressor

verfasst von: Qinqing Chen, Jimin Ni, Qiwei Wang, Xiuyong Shi

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 3/2019

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Abstract

To achieve full-operation-range performance optimization of a compressor and better matching performance with an engine in an accurate and efficient way, a novel pseudo-MAP optimization method is proposed. The pseudo-MAP is a contour-map (MAP) with the performance of only nine compressor characteristic operating points. As these nine points all represent the extreme and intermediate operation conditions in a compressor MAP, there are strong similarities between the compressor MAP and its pseudo-MAP. To verify that the compressor full-operation-range optimization can be replaced by the optimization of the pseudo-MAP, the performance of all the nine points are set as the optimization objectives. The approximation relation between the optimization objectives and the factorial analysis-screened compressor parameters is constructed via experimental design (DoE) and the radial basis function (RBF), and then, a multi-objective optimization for 18 optimization objectives is conducted by using a genetic algorithm (NSGA-II). After optimization, the choke flow of one compressor increases by 20%, and its maximum efficiency increases to 80%. Moreover, the pressure ratio significantly increases at medium or high flow, and the variation within the entire flow range is suppressed. As a result, the engine matched with the optimized compressor provides more power with higher efficiency and stability, which verifies the feasibility of the pseudo-MAP optimization method.

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Metadaten
Titel
Match-based pseudo-MAP full-operation-range optimization method for a turbocharger compressor
verfasst von
Qinqing Chen
Jimin Ni
Qiwei Wang
Xiuyong Shi
Publikationsdatum
28.03.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Structural and Multidisciplinary Optimization / Ausgabe 3/2019
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI
https://doi.org/10.1007/s00158-019-02262-2

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