| Reconstruction model for heat release rate based on artificial neural network | |
Li B(李波); Yao W(姚卫) ; Li YC(李亚超); Fan XJ(范学军)
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| Source Publication | INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
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| 2021-05 | |
| Volume | 46Issue:37Pages:19599-19616 |
| ISSN | 0360-3199 |
| Abstract | 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. |
| Keyword | Heat release rate (HRR) Artificial neural network (ANN) Proper orthogonal -12omposition (POD) Chemiluminescence Supersonic hydrogen flame |
| Subject Area | Chemistry, Physical ; Electrochemistry ; Energy & Fuels |
| DOI | 10.1016/j.ijhydene.2021.03.074 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:000653094800001 |
| Funding Organization | 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] |
| Classification | 二类 |
| Ranking | 1 |
| Contributor | 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. |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/90231 |
| Collection | 高温气体动力学国家重点实验室 |
| Affiliation | 1.Chinese Acad Sci, Inst Mech, Key Lab High Temp Gas Dynam, Inst Mech CAS, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Engn Sci, Inst Mech CAS, Beijing 100049, Peoples R China |
| Recommended Citation 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,2021,46,37,:19599-19616.Rp_Au: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. |
| 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(37),19599-19616. |
| MLA | 李波,et al."Reconstruction model for heat release rate based on artificial neural network".INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 46.37(2021):19599-19616. |
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| Reconstruction model（5169KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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