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• Multi-objective optimization and regression models utilized for wind lens design • Regression model predicted wind lens’s performance and identified its determinants • Expansion ratio, length, and contraction ratio most impacted performance • MOO balanced wind lens parameters for efficient and lightweight designs • Optimal wind lens boosts turbine Cp by 86% with only a 1.793kg weight penalty Wind lenses (WLs) have gained popularity in enhancing the wind turbine performance but at the cost of adding weight to the system, which in turn influences its practicality. This study aims at optimizing wind lens parameters for enhancing performance and weight reduction. Furthermore, Multiple Linear Regression analysis was applied to build predictive surrogate models and to identify the significant design parameters that impact the wind lens performance and weight. ANSYS 2025 was utilized to evaluate the performance and structural weight. A software package commonly used for experimental projects, Design-Expert v 13, was used for statistical analysis. Response Surface Method was used for solving the optimization problem. The study shows that all geometric parameters, except the wind lens thickness have significant impact the WL performance. While the thickness effect on WL performance is minimal, it is the most significant variable that affects system weight. In contrast, diffuser and nozzle radius curvatures significantly improve performance but contribute little to overall weight. Optimum performance and weight are attained at Expansion Ratio = 1.24, diffuser length = 0.16D, contraction ratio = 1.052, nozzle length = 0.055D, brim height = 0.1D, and lens thickness = 0.0015D. This design achieves a 4.6% increase in velocity ratio and a 6.6% weight reduction over the baseline geometry, and 86% increase in power coefficient with a 1.793 kg weight penalty over the bare turbine. The findings provide practical guidelines for engineers to optimize wind lens configurations, emphasizing the importance of considering trade-offs in real-world applications.
Kassa et al. (Mon,) studied this question.