Multi-fidelity transfer learning for complex bund overtopping prediction with varying input dimensions

IF 3.6 3区 工程技术 Q2 ENGINEERING, CHEMICAL
{"title":"Multi-fidelity transfer learning for complex bund overtopping prediction with varying input dimensions","authors":"","doi":"10.1016/j.jlp.2024.105477","DOIUrl":null,"url":null,"abstract":"<div><div>This study introduces a novel multi-fidelity transfer learning framework designed to predict the complex bund overtopping fraction in catastrophic tank failure scenarios. The framework addresses the challenge of varying input dimensions between low-fidelity and high-fidelity datasets by effectively integrating these disparate sources of data. In this case, low-fidelity data, generated from empirical formulas based on simple bund configurations, is first used for initial model pre-training. The model is then fine-tuned using a smaller, high-fidelity dataset obtained through computational fluid dynamics simulations, which account for more complex bund configurations, including additional breakwater parameters. This approach enhances the model's predictive accuracy and generalization capability, particularly in scenarios with limited high-fidelity data. Case studies demonstrate that the transfer learning model outperforms traditional models trained solely on high-fidelity data, offering significant reductions in computational cost while maintaining robust predictive performance. The proposed framework not only advances the understanding and prediction of bund effectiveness but also provides a versatile tool applicable to a wide range of engineering problems involving multi-fidelity data.</div></div>","PeriodicalId":16291,"journal":{"name":"Journal of Loss Prevention in The Process Industries","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Loss Prevention in The Process Industries","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950423024002353","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0

Abstract

This study introduces a novel multi-fidelity transfer learning framework designed to predict the complex bund overtopping fraction in catastrophic tank failure scenarios. The framework addresses the challenge of varying input dimensions between low-fidelity and high-fidelity datasets by effectively integrating these disparate sources of data. In this case, low-fidelity data, generated from empirical formulas based on simple bund configurations, is first used for initial model pre-training. The model is then fine-tuned using a smaller, high-fidelity dataset obtained through computational fluid dynamics simulations, which account for more complex bund configurations, including additional breakwater parameters. This approach enhances the model's predictive accuracy and generalization capability, particularly in scenarios with limited high-fidelity data. Case studies demonstrate that the transfer learning model outperforms traditional models trained solely on high-fidelity data, offering significant reductions in computational cost while maintaining robust predictive performance. The proposed framework not only advances the understanding and prediction of bund effectiveness but also provides a versatile tool applicable to a wide range of engineering problems involving multi-fidelity data.
针对不同输入维度的复杂外滩翻浆预测的多保真度迁移学习
本研究介绍了一种新颖的多保真度迁移学习框架,旨在预测灾难性油箱失效场景中复杂的外堤倾覆率。该框架通过有效整合这些不同的数据源,解决了低保真和高保真数据集之间不同输入维度的难题。在这种情况下,根据简单外堤配置的经验公式生成的低保真数据首先用于初始模型的预训练。然后使用通过计算流体动力学模拟获得的较小的高保真数据集对该模型进行微调,该数据集考虑了更复杂的外滩配置,包括额外的防波堤参数。这种方法提高了模型的预测精度和概括能力,尤其是在高保真数据有限的情况下。案例研究表明,迁移学习模型优于仅根据高保真数据训练的传统模型,在保持稳健的预测性能的同时显著降低了计算成本。所提出的框架不仅促进了对外滩效果的理解和预测,而且还提供了一种通用工具,适用于涉及多保真数据的各种工程问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
7.20
自引率
14.30%
发文量
226
审稿时长
52 days
期刊介绍: The broad scope of the journal is process safety. Process safety is defined as the prevention and mitigation of process-related injuries and damage arising from process incidents involving fire, explosion and toxic release. Such undesired events occur in the process industries during the use, storage, manufacture, handling, and transportation of highly hazardous chemicals.
×
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