通过蒙特卡洛和人工神经网络耦合量化非封闭溢出火灾数据的不确定性

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-01-27 DOI:10.1080/00223131.2024.2310564

Elvan Sahin, Brian Lattimer, Mohammad Amer Allaf, Juliana Pacheco Duarte

引用次数: 0

摘要

由于溢出火灾的复杂性，预测热释放率（HRR）是一个具有挑战性的方面，因此，确定造成不确定性的关键因素对于开发可靠的火灾模型至关重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Uncertainty quantification of unconfined spill fire data by coupling Monte Carlo and artificial neural networks

Due to the complexity of spill fire, predicting heat release rate (HRR) is a challenging aspect, therefore, identifying key contributors to uncertainty is essential to develop reliable models for f...

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.