• Energy Research

Abstract A simple way to improve its power coefficient (cp) of a Savonius turbine is by its installation above a cuboidal building as the building will redirect the wind and increase its speed significantly. To determine the gain, a turbine was constructed and installed above a bluff body and tow tested. Detailed measurements of vehicle speed and turbine power were made. Tow test speeds were 8, 10 and 12 m/s, while TSR range was 0.6–1.1. Most importantly, wind speed at the position beside and slightly above the turbine was measured during test runs. The cp calculated using this measured wind speed was used to validate CFD simulation results. Simulation results were also used to obtain the relationships between the wind speed of the free stream and at the anemometer position. Typically, wind speed at the anemometer position is about 9% higher than those of the free stream. These relationships were used to derive the free stream wind speed of each experimental run. The cp calculated using these derived free stream wind speeds showed an increase of 25% at 12 m/s wind speed, compared to the cp reported by previous researchers for a similar turbine operating in unmodified air flow.

Abstract This study presents a model to determine the aerodynamic forces on an irregularly shaped object for prediction of trajectories of debris. We classify the debris according to shape and use wind tunnel tests to create a database of aerodynamic forces of similarly sized objects. We have then validated the database results against wind tunnel measurements of aerodynamic coefficients of rock-like debris objects. Although the individual errors may be significant, the results show a generally good agreement with our model prediction and a small average error.

Renewable energy sources are essential to address climate change, fossil fuel depletion, and stringent environmental regulations in the subsequent decades. Horizontal-axis wind turbines (HAWTs) are particularly suited to meet this demand. However, their efficiency is affected by environmental factors because they operate in open areas. Adverse weather conditions like rain reduce their aerodynamic performance. This study investigates wind turbine power prediction under rainy conditions by integrating Blade Element Momentum (BEM) theory with explainable artificial intelligence (XAI). The S809 airfoil’s aerodynamic characteristics, used in NREL wind turbines, were analyzed using ANSYS FLUENT and symbolic regression under varying rain intensities. Simulations at a Reynolds number (Re) of 1 × 106 were performed using the Discrete Phase Model (DPM) and k–ω SST turbulence model, with liquid water content (LWC) values of 0 (dry), 10, 25, and 39 g/m3. The lift and drag coefficients were calculated at various angles of attack for all the conditions. The results indicated that rain led to reduced lift and increased drag. The innovative aspect of this research is the development of machine learning models predicting changes in the airfoil coefficients under rain with an R2 value of 0.97. The proposed XAI framework models rain effects at a lower computational time, enabling efficient wind farm performance assessment in rainy conditions compared to conventional CFD simulations. It was found that a heavy rain LWC of 39 g/m3 could reduce power output by 5.7% to 7%. These findings highlight the impact of rain on aerodynamic performance and the importance of advanced predictive models for optimizing renewable energy generation.