Machine learning-based multi-objective optimization and thermodynamic evaluation of organic Rankine cycle (ORC) system for vehicle engine under road condition - Details

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Abstract：

Organic　Rankine　cycle　(ORC)　cooperative　multi-objective　optimization　under　dynamic　driving　cycle　is　the　key　to　obtain　the　potential　of　waste　heat　recovery　of　internal　combustion　(IC)　engine.　The　exhaust　waste　heat　energy　of　IC　engine　has　the　characteristics　of　complex　change　and　strong　fluctuation　under　actual　road　conditions.　The　analysis　and　optimization　of　the　performance　of　ORC　system　under　single　engine　operation　has　some　limitations　in　evaluating　the　actual　waste　heat　recovery　characteristics.　Based　on　Japan10-15　road　condition,　this　paper　builds　ORC　system　for　vehicle　engine　model　oriented　to　driving　environment.　The　interaction　between　operating　parameters　of　ORC　system　and　thermodynamic　and　thermal　economic　performance　is　analyzed.　Based　on　the　operating　characteristics　of　the　ORC　system,　the　thermal　efficiency　and　power　output　per　unit　heat　transfer　area　(POPA)　data　driven　models　are　established,　and　the　structural　parameters　of　the　network　are　optimized.　Based　on　the　multi-objective　optimization　algorithm,　the　thermodynamic　performance　of　ORC　system　under　road　condition　is　optimized.　The　results　show　that　the　ORC　system　has　a　maximum　thermal　efficiency　of　2.23%　and　a　maximum　POPA　of　0.55　kW/m2　under　Japan10-15　road　condition.　In　this　paper,　the　performance　evaluation　and　multi-objective　optimization　results　of　ORC　system　under　road　condition　can　provide　a　certain　reference　for　the　practical　application　potential　of　ORC　system.　©　2023　Elsevier　Ltd

Keyword：

Organic Rankine cycle Machine learning Performance evaluation Multi-objective optimization Vehicle engine

Author Community：

[ 1 ] [Xing C.]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing, Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Ping X.]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing, Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
[ 3 ] [Guo R.]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing, Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
[ 4 ] [Zhang H.]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing, Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
[ 5 ] [Yang F.]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing, Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
[ 6 ] [Yu M.]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing, Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
[ 7 ] [Yang A.]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing, Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China
[ 8 ] [Wang Y.]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing, Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing, 100124, China

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Related Keywords：

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Source ：

Applied Thermal Engineering

ISSN： 1359-4311

Year： 2023

Volume： 231

6 . 4 0 0

JCR@2022

ESI Discipline： ENGINEERING;

ESI HC Threshold：19

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 13

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 11

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