Macroporous materials exhibit outstanding properties in heat and mass transfer due to their high pore volume, high surface area, and high Young's modulus. Consequently, understanding their thermofluidic properties is crucial in the design, synthesis, and optimal application of these materials. Therefore, this study, premieres, the use of a machine learning (ML) backpropagation network to develop and train a series of datasets for permeability, Forchheimer coefficient, and effective thermal conductivity of variable macroporous foam–fluid systems with respect to degrees of interstices, fluid and solid properties. To account for permeability values for flowing fluids in the Darcy regime, numerical simulations of slow–moving fluids were implemented over the materials' interstices. In comparison to similarly substantiated values of permeability in the Forchheimer regime, these values were a bit lower. The ML-based backpropagation algorithm was used to analyze data, which produced predictions (output signals) that are more than 90 % in correlation to CFD datasets. This provided insight into the effect of porosity and reduced mean pore openings on macroporous structures' thermofluidic behaviour. Material porosity was observed to play a dominant role in estimating Forchheimer coefficients and effective thermal conductivities for these foam-fluid systems. However, reduced mean pore openings were observed to be more critical for estimating permeability. The contributory effects of reduced mean pore openings on the effective thermal conductivity for these macroporous foam–fluid systems were determined to vary between 5.8 and 13.2 percent. Furthermore, the effective thermal conductivity of macroporous foam–fluid systems was also evaluated in relation to changes in the interstitial fluid and solid matrix thermal conductivity.

中文翻译：

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大孔泡沫流体系统渗透率、福希海默系数和有效导热系数的机器学习反向传播网络分析

大孔材料由于其高孔体积、高表面积和高杨氏模量而在传热传质方面表现出优异的性能。因此，了解它们的热流体特性对于这些材料的设计、合成和优化应用至关重要。因此，本研究首次使用机器学习 (ML) 反向传播网络来开发和训练一系列数据集，用于测量可变大孔泡沫流体系统的渗透率、Forchheimer 系数和有效导热系数（与间隙程度相关），流体和固体特性。为了解释达西状态下流动流体的渗透率值，在材料的间隙上进行了缓慢移动流体的数值模拟。与 Forchheimer 体系中类似证实的渗透率值相比，这些值略低。基于机器学习的反向传播算法用于分析数据，产生的预测（输出信号）与 CFD 数据集的相关性超过 90%。这提供了对孔隙率和平均孔隙开口减少对大孔结构热流体行为的影响的深入了解。据观察，材料孔隙率在估计这些泡沫流体系统的 Forchheimer 系数和有效导热系数方面发挥着主导作用。然而，观察到平均孔隙开口的减少对于估计渗透率更为关键。平均孔径减小对这些大孔泡沫流体系统的有效导热率的影响确定在 5.8% 到 13.2% 之间。此外，还根据间隙流体和固体基质导热系数的变化评估了大孔泡沫-流体系统的有效导热系数。