A coupled finite volume and discrete element method (CFD-DEM) numerical model is developed to unravel the fundamental mechanisms of granular transport, surface erosive wear and particle attrition in multistage solar particle receivers (MSPR) and screw conveyors (SC). Additionally, various regression-based supervised machine learning (ML) and artificial neural networks (ANN) are trained and optimized with the goal of quickly quantifying erosion and attrition thereby overcoming (a) the inherent challenges of high computational expense of CFD-DEM simulations and (b) exorbitant time required to post-process and extract DEM results. This study is the first of its kind to harness a synergistic numerical framework (CFD-DEM, ML, ANN) to unravel the fundamental mechanisms of erosion and attrition in MSPRs, non-vibrationally induced SCs, and vibrationally-induced SCs. It is found that the packing distribution, particle curtain width, and severity of particle scattering in a MSPR exhibits a subtle variation with particle diameter. In the context of SCs, the magnitudes of erosion and attrition are contingent on the particle diameter and flow operating conditions such as screw blade velocity, vibrational frequency and vibrational amplitude. Interestingly, a vibrationally-induced SC (VI-SC) exhibits superior performance in terms of low erosion and attrition unlike non-vibrationally induced SC (NVI-SC). It is found that MSPRs with 750–1000 µm particles and SCs with 750–1000 µm coupled with vibrational frequencies and vibrational amplitudes of 5 Hz and 0.007 m, respectively, are the preferred configurations due to minimum particle scattering, low erosive wear and low particle attrition. The trained and optimized ML models such as XGBoost, CatBoost, and Multilayer Perceptron (MLP) exhibit superior performance in predicting erosive wear and particle attrition on unseen DEM data, whereas the Multiple Linear Regression (MLR) and Gaussian Process Regression (GPR) ML exhibit poor performance prediction. For the first time, new information on fundamental granular flow dynamics, erosive wear and particle attrition in MSPRs and SCs is discerned. The methodology allows scientists to quickly examine material degradation for a myriad of industrial applications such as concentrated solar thermal (CST), among others.

中文翻译：

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多级太阳能粒子接收器和螺旋输送机中的侵蚀磨损和粒子磨损：采用机器学习和人工神经网络的 CFD-DEM 方法


开发了耦合有限体积和离散元方法 (CFD-DEM) 数值模型，以揭示多级太阳能粒子接收器 (MSPR) 和螺旋输送机 (SC) 中颗粒输送、表面侵蚀磨损和颗粒磨损的基本机制。此外，各种基于回归的监督机器学习 (ML) 和人工神经网络 (ANN) 都经过训练和优化，目标是快速量化侵蚀和磨损，从而克服 (a) CFD-DEM 模拟计算成本高的固有挑战和(b) 后处理和提取 DEM 结果需要大量时间。这项研究首次利用协同数值框架（CFD-DEM、ML、ANN）来揭示 MSPR、非振动诱发 SC 和振动诱发 SC 中侵蚀和磨损的基本机制。研究发现，MSPR 中的堆积分布、颗粒幕宽度和颗粒散射的严重程度随着颗粒直径的变化而呈现出微妙的变化。在 SC 的背景下，侵蚀和磨损的程度取决于颗粒直径和流动操作条件，例如螺旋叶片速度、振动频率和振动幅度。有趣的是，与非振动诱导 SC (NVI-SC) 不同，振动诱导 SC (VI-SC) 在低侵蚀和磨损方面表现出优异的性能。结果发现，具有 750–1000 µm 颗粒的 MSPR 和具有 750–1000 µm 的 SC，振动频率和振动幅度分别为 5 Hz 和 0.007 m，由于颗粒散射最小、侵蚀磨损和颗粒含量低，因此是首选配置。磨损。 经过训练和优化的 ML 模型（例如 XGBoost、CatBoost 和多层感知器 (MLP)）在预测看不见的 DEM 数据上的侵蚀磨损和颗粒磨损方面表现出卓越的性能，而多元线性回归 (MLR) 和高斯过程回归 (GPR) ML 则表现出卓越的性能性能预测不佳。首次发现有关 MSPR 和 SC 中基本颗粒流动力学、侵蚀磨损和颗粒磨损的新信息。该方法使科学家能够快速检查多种工业应用的材料降解情况，例如聚光太阳能热 (CST) 等。