Effect of Inter Turbine Spacing in Omnidirectional Wind for Savonius Cluster: A Computational Fluid Dynamics and Artificial Neural Network Approach

Abstract

This research explores the potential of wind energy as a sustainable solution to the environmental challenges by focusing on the deployment of vertical axis wind turbines in confined spaces, such as urban rooftops, ship decks and gas stations etc. It aims to identify the optimal configuration for a cluster of three rotors by varying geometric parameters, assessing the omnidirectionality of the rotors, and using an artificial neural network as an optimization tool. The methodology involves two-dimensional unsteady Reynolds-averaged Navier-Stokes computational fluid dynamics simulations followed by building and training an artificial neural network. The results indicate that the best cluster configuration, achieving a power coefficient of 0.2517, consists of a horizontal distance of 0.08D, vertical distance of 0.1D, and wind direction of 232°. The accuracy of neural network is demonstrated by a mere 1.206% discrepancy with the computational analysis, thus highlighting its potential as a time-saving tool in the optimization process. Additionally, the average power coefficient across all wind directions was found to be 0.194, thus confirming the strong omnidirectional characteristics of cluster. The study also notes a significant speed-up region between the rotors, which creates a low-pressure area, enhancing the power coefficient of the downstream rotor and demonstrating the advantages of this pressure-based turbine configuration. This comprehensive study establishes the efficacy of neural network in optimizing wind turbine clusters, thus significantly expediting their design and development process.