Prediction of gas–solid erosion wear of bionic surfaces based on machine learning and unimodal intelligent optimization algorithm

Abstract

Solid particle erosion wear is an inevitable phenomenon in industrial production, with erosion removal mass serving as a crucial metric for assessing the wear rate per unit area on the impacted surface. Developing predictive models to estimate the degree of mass removal is crucial for effectively controlling, evaluating, and preventing severe damage resulting from solid particle erosion wear. In this study, we constructed a comprehensive dataset comprising smooth and bionic surfaces, encompassing inner, outer, and planar surfaces. The dataset was used in a multifactorial erosion experiment, considering adjustable erosion angles, solid particle incident gas velocities, and solid particle diameter sizes. Through visualization and analysis of the obtained dataset, we identified optimal scenarios for bionic surface erosion resistance, offering insights for structural design against bionic erosion resistance. Furthermore, we compared machine learning algorithms to address the prediction problem, resulting in the selection of the best-performing regression algorithm, SVR (Support Vector Regression). Additionally, we compared the performance of other advanced intelligent optimization algorithms using unimodal benchmark functions, finding that HHO (Harris Hawks Optimization) emerged as the optimal choice for unimodal optimization. Building on HHO, we developed the IHHO (Improved Harris Hawks Optimization)-SVR model, using experimental data from erosion tests as the training dataset. This model can predict gas–solid two-phase flow erosion patterns, encompassing various wall types, solid particle sizes, solid incident gas velocities, and impact angles. Due to its robustness and rapid prediction capabilities, the model is expected to serve as a cost-effective tool for predicting erosion removal mass in gas–solid two-phase flow scenarios.