This study presents conventional and artificial neural network-based data-driven modeling (DDM) methods to model simultaneously the filtered mesoscale drag, heat transfer and reaction rate in gas–particle flows. The dataset used for developing the DDM is filtered from highly resolved simulations closed by our recently formulated microscopic drag and heat transfer coefficients (HTCs). Results reveal that the filtered drag correction is nearly independent of filter size when including the filtered gas phase pressure gradient. We further find that the filtered HTC correction critically depends on the added filtered temperature difference marker while the filtered reaction rate correction shows weak dependence on the additional markers. Moreover, compared with conventional correlations, DDM predictions agree better with filtered resolved data. Comparative analysis is also conducted between existing HTC corrections and our work. Finally, the applicability of conventional and data-driven models coupled with coarse-grid computational fluid dynamics simulations for pilot-scale (reactive) gas–particle flows is validated comprehensively.

中文翻译：

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气体-颗粒流中过滤阻力、传热和反应速率的常规和数据驱动建模

本研究提出了基于传统和人工神经网络的数据驱动建模 (DDM) 方法，以同时对气体-颗粒流中的过滤中尺度阻力、传热和反应速率进行建模。用于开发 DDM 的数据集是从我们最近制定的微观阻力和传热系数 (HTC) 封闭的高分辨率模拟中过滤出来的。结果表明，当包括过滤的气相压力梯度时，过滤阻力校正几乎与过滤器尺寸无关。我们进一步发现，过滤的 HTC 校正严重依赖于添加的过滤温差标记，而过滤的反应速率校正显示对附加标记的弱依赖。此外，与传统的相关性相比，DDM 预测与过滤后的解析数据更吻合。还对现有的 HTC 校正和我们的工作进行了比较分析。最后，综合验证了常规模型和数据驱动模型以及粗网格计算流体动力学模拟对中试规模（反应性）气体颗粒流的适用性。