Structure estimation of vertical axis wind turbine using artificial neural network

TEKŞİN S., Azginoglu N., AKANSU S. O.

ALEXANDRIA ENGINEERING JOURNAL, vol.61, no.1, pp.305-314, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 61 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1016/j.aej.2021.05.002
  • Journal Name: ALEXANDRIA ENGINEERING JOURNAL
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Directory of Open Access Journals
  • Page Numbers: pp.305-314
  • Keywords: ANN, Estimation, Sustainable energy, VAWT, Wind, Environment, SHEAR-STRENGTH, PERFORMANCE, SPEED, POWER, PREDICTION, PARAMETERS, ALGORITHM, SELECTION, DESIGN, BEAMS
  • Erciyes University Affiliated: Yes

Abstract

Vertical axis wind turbines (VAWTS) can be a suitable choice for usage in urban areas. Wind conditions and structural parameters are critical to power production. In this study, some variations of a darrieus vawt was attempted via wind tunnel testing. A modified naca4412 blade profile was selected for this investigation. The influence of aspect ratios, angle of attack, chord length, etc. were investigated experimentally. After taken more than 800 data artificial neural network (ANN) was applied to estimate the other range of scales using different algorithms that are utilized the aforementioned experimental parameters. This study focuses on the design criteria and application of VAWTS inbuilt real environments without testing. The results have been promising and will provide ease of design for similar designs. Moreover, this work will contribute to environmental cleanup and a more livable world by increasing renewable energy resources. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.