Open AccessBlade Shape Optimization of Savonius Wind Turbine at Low Wind Energy by Artificial Neural network
Author(s) -
Sarah Ali Al-shammari,
Abdul Hassan Karamallah,
Sattar Aljabair
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/881/1/012154
Subject(s) - turbine , wind speed , tip speed ratio , range (aeronautics) , wind power , blade (archaeology) , artificial neural network , turbine blade , maximum power principle , engineering , power (physics) , marine engineering , structural engineering , computer science , mechanical engineering , aerospace engineering , meteorology , physics , electrical engineering , artificial intelligence , quantum mechanics
Recently, vertical axis wind turbine specially Savonius type due to their positive properties and capabilities have picked up a significant consideration. This article deals with the use of artificial neural network to predict optimum blade shape design for enhanced power coefficient value for savonius wind turbine at low wind speed numerically with commercial code software ANSYS-CFX. The simulations included the analysis of many models used to learn artificial neural network to predict the optimum blade shape of savonius wind turbine at wind speed 3m/s and tip speed ratio TSR of 0.8. The performance of optimal and conventional model is studied at wide range of TSR (0.2-1.2). The obtained enhancement ratio in power coefficient is 55%. The obtained results point out that optimal blade shape Savonius wind turbine is better than semicircular blade at rang of TSR (0.6-1.1) that mean is more suitable for applying in urban area environment where the complex condition and low wind speed range.