A computational fluid dynamics based artificial neural network model to predict solid particle erosion

This study employs machine learning approach along with CFD-based methodology to develop robust erosion models. A generalized model is developed based on experiments conducted on 90-degree elbows of 1-in. diameter and made from Inconel 718, Nickel Alloy 825, 25% Cr, Nickel Alloy 925, and 13% Cr L-80 materials. The Baker Hughes erosion model developed in 2008 is studied as a baseline. Statistical analysis was performed on CFD output parameters to identify those that most affect erosion rates. A correlation analysis and non-parametric statistical analysis is performed resulting in the development of two new regression models based on turbulent kinetic energy, and surface shear stress was developed. A 25-% improvement is observed in the predictions of cumulative erosion rate error compared to baseline. An artificial neural network with multilayer feed-forward model with the back-propagation algorithm and Levenberg-Marquardt training was developed. This model, along with Bayesian regularization, reduced cumulative error to less than 10%, compared to more than 40% in the baseline Baker Hughes model.