Optimizing Windbreak Geometry for Enhanced Wind Erosion Control using Machine Learning-Accelerated CFD

Abstract Wind erosion is a major geophysical crisis, threatening global food security, ecological stability, and critical infrastructure. The crisis manifests as large-scale dust events and land degradation. While vegetation cover is the sustainable solution, severe drought often makes its establishment infeasible in arid regions. Consequently, engineered, non-living windbreaks remain a vital mitigation tool. Their performance critically relies on achieving a precise, optimized geometric design. This study introduces a fast and efficient computational framework for optimizing windbreak geometry to maximize the erosion-protected area. The method employs a hybrid approach, integrating Computational Fluid Dynamics (CFD) simulations to generate datasets with advanced Machine Learning (ML) models serving as high-fidelity surrogates. Among the tested models, the Random Forest (RF) model achieved the highest performance (R2 =0.98, MAE = 0.09), establishing it as a reliable surrogate for optimizing five key geometric parameters (porosity, shape, area, distribution, and angle). Subsequently, using the Random Forest (RF) model as a surrogate fitness function, the Genetic Algorithm (GA) identified the optimal geometry. This design characterized by 34.53% porosity, square pores (0.5 cm2), homogeneous distribution, and a porosity angle of 11.54֯, This optimized design resulted in a 22.78% increase in protection efficiency compared to non-porous barriers and achieved a 9.1% higher wind-proofing efficiency. This innovative approach offers a powerful, time-efficient tool for windbreak design, and may help to enhance ecological sustainability and protect soil resources from the impacts of climate change and drought. Keywords: Wind erosion, Windbreak, Computational Fluid Dynamics (CFD), Machine Learning (ML), Optimization, Genetic Algorithm (GA)