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Random forest method for estimation of brake specific fuel consumption

The internal combustion engine is a widely used power equipment in various fields, and its energy utilization is measured using brake specific fuel consumption (BSFC). BSFC map plays a crucial role in the analysis, optimization, and assessment of internal combustion engines. However, due to cost con...

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Detalles Bibliográficos
Autores principales: Yun, Qinsheng, Wang, Xiangjun, Yao, Chen, Wang, Haiyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584861/
https://www.ncbi.nlm.nih.gov/pubmed/37853230
http://dx.doi.org/10.1038/s41598-023-45026-1
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author Yun, Qinsheng
Wang, Xiangjun
Yao, Chen
Wang, Haiyan
author_facet Yun, Qinsheng
Wang, Xiangjun
Yao, Chen
Wang, Haiyan
author_sort Yun, Qinsheng
collection PubMed
description The internal combustion engine is a widely used power equipment in various fields, and its energy utilization is measured using brake specific fuel consumption (BSFC). BSFC map plays a crucial role in the analysis, optimization, and assessment of internal combustion engines. However, due to cost constraints, some values on the BSFC map are estimated using techniques like K-nearest neighbor, inverse distance weighted interpolation, and multi-layer perceptron, which are recognized for their limited accuracy, particularly when dealing with distributed sampled data. To address this, an improved random forest method is proposed for the estimation of BSFC. Polynomial features are employed to increase higher dimensions of features for random forest by nonlinear transformation, and critical parameters are optimized by particle swarm optimization algorithms. The performance of different methods was compared on two datasets to estimate 20%, 30%, and 40% of BSFC data, and the results reveal that the method proposed in this paper outperforms other common methods and is suitable for estimating the BSFC map.
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spelling pubmed-105848612023-10-20 Random forest method for estimation of brake specific fuel consumption Yun, Qinsheng Wang, Xiangjun Yao, Chen Wang, Haiyan Sci Rep Article The internal combustion engine is a widely used power equipment in various fields, and its energy utilization is measured using brake specific fuel consumption (BSFC). BSFC map plays a crucial role in the analysis, optimization, and assessment of internal combustion engines. However, due to cost constraints, some values on the BSFC map are estimated using techniques like K-nearest neighbor, inverse distance weighted interpolation, and multi-layer perceptron, which are recognized for their limited accuracy, particularly when dealing with distributed sampled data. To address this, an improved random forest method is proposed for the estimation of BSFC. Polynomial features are employed to increase higher dimensions of features for random forest by nonlinear transformation, and critical parameters are optimized by particle swarm optimization algorithms. The performance of different methods was compared on two datasets to estimate 20%, 30%, and 40% of BSFC data, and the results reveal that the method proposed in this paper outperforms other common methods and is suitable for estimating the BSFC map. Nature Publishing Group UK 2023-10-18 /pmc/articles/PMC10584861/ /pubmed/37853230 http://dx.doi.org/10.1038/s41598-023-45026-1 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Yun, Qinsheng
Wang, Xiangjun
Yao, Chen
Wang, Haiyan
Random forest method for estimation of brake specific fuel consumption
title Random forest method for estimation of brake specific fuel consumption
title_full Random forest method for estimation of brake specific fuel consumption
title_fullStr Random forest method for estimation of brake specific fuel consumption
title_full_unstemmed Random forest method for estimation of brake specific fuel consumption
title_short Random forest method for estimation of brake specific fuel consumption
title_sort random forest method for estimation of brake specific fuel consumption
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10584861/
https://www.ncbi.nlm.nih.gov/pubmed/37853230
http://dx.doi.org/10.1038/s41598-023-45026-1
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