Mathematical modeling and performance evaluation of Ducted Horizontal-axis Helical Wind Turbines: Insights into aerodynamics and efficiency

• Published in: PloS one 2024-06, Vol.19 (6), p.e0303526
• Main Authors: Shaikh, Zishan, Fazlizan, Ahmad, Razali, Halim, Wong, Kok Hoe, Molla, Altaf Hossain, Abdulkadir, Rabiu Aliyu, Baleanu, Dumitru, Ibrahim, Rabha W
• Format: Article
• Language: English
• Subjects: Aerodynamics; Air-turbines; Alternative energy; Analysis; Artificial neural networks; Biology and Life Sciences; Computer and Information Sciences; Computer Simulation; Correlation coefficient; Correlation coefficients; Diffusers; Efficiency; Engineering and Technology; Force and energy; Hypotheses; Kinematics; Mathematical models; Models, Theoretical; Neural networks; Neural Networks, Computer; Performance evaluation; Physical Sciences; Power Plants; Root-mean-square errors; Shear stress; Simulation methods; Statistical methods; Theoretical analysis; Turbines; Turbulence models; Velocity; Vertical axis wind turbines; Wind; Wind power; Wind turbines
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0303526

With the escalating demand for energy, there is a growing focus on decentralized, small-scale energy infrastructure. The success of new turbines in this context is notable. However, many of these turbines do not follow many of the basic ideas established to evaluate their performance, leaving no precise technique or mathematical model. This research developed a Ducted Horizontal-axis Helical Wind Turbine (DHAHWT). The DHAHWT is a duct-mounted helical savonius turbine with a venturi and diffuser to improve flow. Unlike a vertical axis helical savonius turbine, DHAHWT revolves roughly parallel to the wind, making it a horizontal turbine. This complicates mathematical and theoretical analysis. This study created a DHAHWT mathematical model. COMSOL simulations utilizing Menter's Shear Stress Transport model (SST) across an incoming velocity range of 1m/s to 4m/s were used to evaluate the turbine's interaction with the wind. MATLAB was used to train an artificial neural network (ANN) utilizing COMSOL data to obtain greater velocity data. The Mean Average Percentage Error (MAPE) and Root Mean Square Error (RMSE) of ANN data were found to be 3%, indicating high accuracy. Further, using advanced statistical methods the Pearson's correlation coefficient was calculated resulting in a better understanding of the relationship of between incoming velocity and velocity at different sections of the wind turbine. This study will shed light on the aerodynamics and working of DHAHWT.