Optimization of k-ε RNG turbulence model parameters using genetic algorithm for enhanced wind field simulation in complex mountainous terrain

A coupled approach combining computational fluid dynamics (CFD) and a genetic algorithm (GA) is proposed to optimize the k-ε RNG turbulence model parameters for improved wind field simulation over the complex terrain of Askervein Hill. The GA optimizes key parameters including C μ , C 1 , and C 2 . After four generations, the GA minimizes the error between CFD results and field measurements to obtain optimized parameter values. Compared to the default k-ε RNG model, the optimized model reduces the total relative error from 38.1% to 25.2%, the wind speed error from 24.6% to 14.8%, and the turbulent kinetic energy error from 69.4% to 49.4%. The GA-optimized model also better captures the turbulent wake structure behind the hill. This automated optimization approach efficiently improves the accuracy of CFD wind simulations over complex terrain, showing promise for wind resource assessment and environmental applications.