Tools
Tools
SURI, Yatin, PEGG, C, ISLAM, S and ASTHANA, Abhishek (2019). Computational Fluid Dynamics Modelling to design and optimise Power Kites for Renewable Power Generation. In: AL-HABIBEH, Amin, ASTHANA, Abhishek and VUKOVIC, Vladimir, (eds.) The International Conference on Energy and Sustainable Futures (ICESF). Nottingham Trent University Publications.
|
PDF
Asthana_ComputationalFluidDynamics(VoR).pdf - Published Version All rights reserved. Download (1MB) | Preview |
Abstract
Power kites provide the potential rewards of obtaining the disused energy supply from high altitude wind. This paper aims to provide a design of Power kite and optimise the potential for renewable power generation. The Power kite was modelled using Computational Fluid Dynamics (CFD) to study its characteristics. The numerical modelling results were compared against the wind tunnel experimental study and two 3D printed Power kites. The design was optimised using several variables, including aerofoil choice, surface roughness, wind speed and operating parameters. Attempts at optimising the kite design were implemented. The results suggest that operating the kites at minimum 15 m horizontal separation is favourable, with the trailing kite operating below the leading, removing the potential for this kite to operate in the wake turbulence of the first. As the wind speed is generally very low at low altitudes, it is recommended to use a symmetrical aerofoil for the kite design, as these tend to produce greater lift with low Reynolds airflow. This paper presents relevant, applicable data which can be used for predicting the performance, and potentially optimising further Power kite designs.
| Item Type: | Book Section |
|---|---|
| Identification Number: | 9781912253012 |
| SWORD Depositor: | Symplectic Elements |
| Depositing User: | Symplectic Elements |
| Date Deposited: | 01 Apr 2020 14:33 |
| Last Modified: | 18 Mar 2021 02:00 |
| URI: | https://shura.shu.ac.uk/id/eprint/26025 |
Actions (login required)
 |
View Item |
Downloads
Downloads per month over past year