Junfeng Li , Yanxu Huang , Yunyu Qiu , Shixian Wang , Qunhui Yang , Kai Wang , Yunzhong Zhu
{"title":"使用不同方法预测临界热通量:从经验关联到前沿机器学习的综述","authors":"Junfeng Li , Yanxu Huang , Yunyu Qiu , Shixian Wang , Qunhui Yang , Kai Wang , Yunzhong Zhu","doi":"10.1016/j.icheatmasstransfer.2024.108362","DOIUrl":null,"url":null,"abstract":"<div><div>Nucleate boiling effectively dissipates heat through phase change, where heat is absorbed during the transition from liquid to vapor. However, this heat dissipation is strongly limited by Critical Heat Flux (CHF). When CHF is reached, a small increase in heat flux can lead to a sudden temperature surge, potentially causing the heated surface to burn out. CHF has been extensively studied for almost 100 years, and numerous methods have been proposed to predict CHF under various working conditions. In this paper, we aim to comprehensively review the methods for predicting CHF, from initial models derived from experimental correlations to advanced numerical simulations and state-of-the-art machine learning approaches. We begin by introducing CHF models based on experimental data and discuss prediction methods that utilize CHF databases. Next, we examine wall boiling models developed through numerical simulations at different scales. Furthermore, we explore the potential of machine learning in CHF prediction, highlighting the advantages of this approach. By summarizing these studies, we aim to provide researchers with a comprehensive understanding of CHF prediction methods and offer effective strategies for accurate CHF prediction in the future.</div></div>","PeriodicalId":332,"journal":{"name":"International Communications in Heat and Mass Transfer","volume":"160 ","pages":"Article 108362"},"PeriodicalIF":6.4000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of critical heat flux using different methods: A review from empirical correlations to the cutting-edge machine learning\",\"authors\":\"Junfeng Li , Yanxu Huang , Yunyu Qiu , Shixian Wang , Qunhui Yang , Kai Wang , Yunzhong Zhu\",\"doi\":\"10.1016/j.icheatmasstransfer.2024.108362\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Nucleate boiling effectively dissipates heat through phase change, where heat is absorbed during the transition from liquid to vapor. However, this heat dissipation is strongly limited by Critical Heat Flux (CHF). When CHF is reached, a small increase in heat flux can lead to a sudden temperature surge, potentially causing the heated surface to burn out. CHF has been extensively studied for almost 100 years, and numerous methods have been proposed to predict CHF under various working conditions. In this paper, we aim to comprehensively review the methods for predicting CHF, from initial models derived from experimental correlations to advanced numerical simulations and state-of-the-art machine learning approaches. We begin by introducing CHF models based on experimental data and discuss prediction methods that utilize CHF databases. Next, we examine wall boiling models developed through numerical simulations at different scales. Furthermore, we explore the potential of machine learning in CHF prediction, highlighting the advantages of this approach. By summarizing these studies, we aim to provide researchers with a comprehensive understanding of CHF prediction methods and offer effective strategies for accurate CHF prediction in the future.</div></div>\",\"PeriodicalId\":332,\"journal\":{\"name\":\"International Communications in Heat and Mass Transfer\",\"volume\":\"160 \",\"pages\":\"Article 108362\"},\"PeriodicalIF\":6.4000,\"publicationDate\":\"2024-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Communications in Heat and Mass Transfer\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0735193324011242\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Communications in Heat and Mass Transfer","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0735193324011242","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MECHANICS","Score":null,"Total":0}
Prediction of critical heat flux using different methods: A review from empirical correlations to the cutting-edge machine learning
Nucleate boiling effectively dissipates heat through phase change, where heat is absorbed during the transition from liquid to vapor. However, this heat dissipation is strongly limited by Critical Heat Flux (CHF). When CHF is reached, a small increase in heat flux can lead to a sudden temperature surge, potentially causing the heated surface to burn out. CHF has been extensively studied for almost 100 years, and numerous methods have been proposed to predict CHF under various working conditions. In this paper, we aim to comprehensively review the methods for predicting CHF, from initial models derived from experimental correlations to advanced numerical simulations and state-of-the-art machine learning approaches. We begin by introducing CHF models based on experimental data and discuss prediction methods that utilize CHF databases. Next, we examine wall boiling models developed through numerical simulations at different scales. Furthermore, we explore the potential of machine learning in CHF prediction, highlighting the advantages of this approach. By summarizing these studies, we aim to provide researchers with a comprehensive understanding of CHF prediction methods and offer effective strategies for accurate CHF prediction in the future.
期刊介绍:
International Communications in Heat and Mass Transfer serves as a world forum for the rapid dissemination of new ideas, new measurement techniques, preliminary findings of ongoing investigations, discussions, and criticisms in the field of heat and mass transfer. Two types of manuscript will be considered for publication: communications (short reports of new work or discussions of work which has already been published) and summaries (abstracts of reports, theses or manuscripts which are too long for publication in full). Together with its companion publication, International Journal of Heat and Mass Transfer, with which it shares the same Board of Editors, this journal is read by research workers and engineers throughout the world.