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Reconstruction model for heat release rate based on artificial neural network

文献类型：期刊论文

作者Li B(李波); Yao W(姚卫); Li YC(李亚超); Fan XJ(范学军)
刊名INTERNATIONAL JOURNAL OF HYDROGEN ENERGY ; 37
出版日期2021-05
卷号46页码:19599-19616
关键词Heat release rate (HRR) Artificial neural network (ANN) Proper orthogonal -12omposition (POD) Chemiluminescence Supersonic hydrogen flame
ISSN号0360-3199
DOI10.1016/j.ijhydene.2021.03.074
英文摘要Optimizing the distribution of heat release rate (HRR) is the key to improve the performance of various combustors. However, limited by current diagnostic techniques, the spatial measurement of HRR in many realistic combustion devices is often difficult or even impossible. HRR prediction is theoretically possible through establishing correlations between HRR and other quantities (e.g., chemiluminescence intensity) that can be experimentally determined; however, up to now, few universal correlations have been established. A novel artificial neural network (ANN) approach was adopted to build the mapping relationship between the combustion heat release rate and the measurable chemiluminescent species. Proper orthogonal -12omposition (POD) technology is used to extract the combustion physics and reduce the data of the spatial-temporally high-resolution combustion field. The correlation between the reduced-order HRR and chemiluminescent species is built using an ANN model. A unique segmentation approach was proposed to improve the training efficiency and accuracy. Validation in a supersonic hydrogen-oxygen nonpremixed flame proves the accuracy and efficiency of the proposed HRR reconstruction model based on the reduced-order POD method and data-driven ANN model. (c) 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
学科主题Chemistry, Physical ; Electrochemistry ; Energy & Fuels
分类号二类
语种英语
WOS记录号WOS:000653094800001
资助机构National Key Research and Development Program of China [2019YFB1704202] ; Strategic Priority Research Program of Chinese Academy of Sciences [XDA17030X00] ; National Natural Science Foundation of China [91641110]
其他责任者Yao, W ; Fan, XJ (corresponding author), Chinese Acad Sci, Inst Mech, Key Lab High Temp Gas Dynam, Inst Mech CAS, Beijing 100190, Peoples R China.
源URL[http://dspace.imech.ac.cn/handle/311007/90231]  
专题力学研究所_高温气体动力学国家重点实验室
作者单位1.Univ Chinese Acad Sci, Sch Engn Sci, Inst Mech CAS, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Mech, Key Lab High Temp Gas Dynam, Inst Mech CAS, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Li B,Yao W,Li YC,et al. Reconstruction model for heat release rate based on artificial neural network[J]. INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 37,2021,46:19599-19616.
APA 李波,姚卫,李亚超,&范学军.(2021).Reconstruction model for heat release rate based on artificial neural network.INTERNATIONAL JOURNAL OF HYDROGEN ENERGY,46,19599-19616.
MLA 李波,et al."Reconstruction model for heat release rate based on artificial neural network".INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 46(2021):19599-19616.

入库方式： OAI收割

来源：力学研究所

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