A novel approach for solid particle erosion prediction based on Gaussian Process Regression

Abstract

Based on Gaussian Process Regression (GPR) method, a novel non-linear approach is adopted to predict solid particle erosion in standard elbows. Several data sets extracted from Computational Fluid Dynamics (CFD) results and experimental tests are used to evaluate the effectiveness of the approach for gas-solid as well as gas-liquid-solid flows. Elbow diameter, particle size and density, fluid density and viscosity, and flow velocity are considered as input parameters. By combining input parameters into dimensionless groups, dimensionality reduction is performed. This is conducted to understand the dimensionless numbers governing the erosion phenomenon and effect of dimensionality reduction on erosion prediction. Results and error analysis show that for gas-solid flow, over 80% of the predictions have relative error less than 20% compared to the experimental data. In gas-liquid-solid flow conditions, effect of mixture Reynolds number and ratio of the superficial velocities on erosion rate for both churn and annular flows are investigated. A GPR-based framework is demonstrated to predict erosion distributions over the elbow and results are compared with experimental measurements. Results demonstrate that the GPR approach can accurately and reliably predict solid particle erosion.