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

IF 6.4 2区 工程技术 Q1 MECHANICS
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 ,&nbsp;Yanxu Huang ,&nbsp;Yunyu Qiu ,&nbsp;Shixian Wang ,&nbsp;Qunhui Yang ,&nbsp;Kai Wang ,&nbsp;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 ,&nbsp;Yanxu Huang ,&nbsp;Yunyu Qiu ,&nbsp;Shixian Wang ,&nbsp;Qunhui Yang ,&nbsp;Kai Wang ,&nbsp;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}
引用次数: 0

摘要

核沸腾可通过相变有效散热,即在从液体转变为蒸汽的过程中吸收热量。然而,这种散热受到临界热通量 (CHF) 的极大限制。当达到 CHF 时,热通量的微小增加都会导致温度骤升,有可能导致受热表面烧毁。近百年来,人们对 CHF 进行了广泛的研究,并提出了许多方法来预测各种工作条件下的 CHF。在本文中,我们旨在全面回顾预测 CHF 的方法,包括从实验相关性得出的初始模型到先进的数值模拟和最先进的机器学习方法。我们首先介绍了基于实验数据的 CHF 模型,并讨论了利用 CHF 数据库的预测方法。接下来,我们研究了通过不同尺度的数值模拟开发的壁沸腾模型。此外,我们还探讨了机器学习在 CHF 预测中的潜力,强调了这种方法的优势。通过总结这些研究,我们旨在让研究人员全面了解 CHF 预测方法,并为未来准确预测 CHF 提供有效策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
11.00
自引率
10.00%
发文量
648
审稿时长
32 days
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信