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Improvement of the weighted multi-point (WMP) radiation model for diffusive flames by the application of a set of stochastic optimisation algorithms
Fire Safety Journal ( IF 3.1 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.firesaf.2020.103243
B.H. Silva , F.M. Pereira , P.R. Pagot , F.H.R. França

Abstract The weighted multi-point source model (WMP) has been proposed to model flame radiation from far to near distances, encompassing regions where the single point (SP) model cannot predict it adequately. To develop its formulation, the WMP has been studied as an inverse problem, being optimised to minimise the error between experimental and numerical data. Efficient optimisation methods are thus necessary, and a performance study applied to the WMP model is essential for its development. This study aims to evaluate which types of stochastic algorithms are the best for this problem and to improve the current radiation model comparing the results to those by previous studies. Five algorithms with different characteristics are chosen and tuned for the WMP problem with a design of experiments (DoE) methodology, and applied to each configuration of the model. The best performance was shown by the grey wolf optimiser (GWO), allying stability to fast convergence. The best solution improved previous results by 23.7%, and was also 82.72% better than the solution calculated with the SP model, and 76.24% better than the one calculated with the canonical WMP. A trend in radiation emission distribution is observed with results by previous studies, guiding better formulations in weight distribution.

中文翻译:

通过应用一组随机优化算法改进扩散火焰的加权多点 (WMP) 辐射模型

摘要 已提出加权多点源模型 (WMP) 来模拟从远到近的火焰辐射,包括单点 (SP) 模型无法充分预测的区域。为了开发它的公式,WMP 作为一个逆问题被研究,被优化以最小化实验和数值数据之间的误差。因此,有效的优化方法是必要的,应用于 WMP 模型的性能研究对其开发至关重要。本研究旨在评估哪种类型的随机算法最适合解决此问题,并将结果与​​先前研究的结果进行比较,以改进当前的辐射模型。使用实验设计 (DoE) 方法为 WMP 问题选择并调整了五种具有不同特征的算法,并应用于模型的每个配置。灰狼优化器 (GWO) 显示了最佳性能,将稳定性与快速收敛相结合。最佳解决方案将之前的结果提高了 23.7%,也比使用 SP 模型计算的解决方案好 82.72%,比使用规范 WMP 计算的解决方案好 76.24%。先前研究的结果观察到了辐射发射分布的趋势,指导了更好的重量分布公式。
更新日期:2021-01-01
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