Erosive wear and particle attrition in multi-stage solar particle receivers and screw conveyors: A CFD-DEM approach with machine learning and artificial neural networks

Abstract

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.