Work

CFD and ANN surrogate modelling for fixing angle optimisation of a Darrieus vertical axis wind turbine

Year: 2026

Type: article

Abstract: Global decarbonisation requires efficient and reliable renewable energy systems, with Vertical Axis Wind Turbines (VAWTs) offering advantages in turbulent and urban environments. This study examines the effect of blade Fixing Angle (FA) on the aerodynamic performance of a three-bladed Darrieus VAWT using Unsteady Reynolds-Averaged Navier–Stokes(URANS) based Computational Fluid Dynamics (CFD) simulations coupled with an Artificial Neural Network (ANN) surrogate model.A parametric analysis was carried out for seven FA values across a Tip-Speed Ratio (TSR) range of 0.5−3.0. Results show that a small positive FA (toe-out) improves efficiency by delaying stall onset and extending upwind torque production. The optimum case (FA=+1.5∘,TSR=2.0) achieved a 15.3% increase in power coefficient (CP) compared to the baseline (FA=0∘,TSR=2.0). In contrast, negative FA reduced performance due to earlier separation and greater downwind losses.The ANN surrogate, trained on CFD data, effectively captured the nonlinear dependence of CP on TSR and FA, achieving R2=0.98 and a Mean Absolute Error (MAE) below 0.01. Once validated, the ANN enabled rapid optimisation of the design space, locating the global optimum with negligible computational cost.Flow-field and force-coefficient analyses further reveal that positive FA modifies the incidence-angle distribution, mitigates torque ripple, and suppresses dynamic stall, thereby explaining the observed performance enhancement. Overall, the integration of CFD and ANN provides both physical insight and computational efficiency, establishing a solid foundation for future multi-objective optimisation of VAWTs considering parameters such as CP, torque ripple, and self-starting capability.

Source: Engineering Applications of Computational Fluid Mechanics

DOI: 10.1080/19942060.2026.2654918

Authors: Amir Tamaddon, Ma Mohin, Yakub Iqbal Mogul, Jaimon Dennis Quadros, Mamadou Ndiaye

Institution: University of Greater Manchester

Language: English

Topic: Wind Energy Research and Development

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $1,993