ABSTRACT
Hydrokinetic turbines can generate electricity in remote rural locations using the kinetic energy of nearby rivers or canals. The Savonius hydrokinetic turbine (SHKT) is the easiest to design and manufacture. Optimization of blade shape factor (p/q) and blade arc angle (Φ) can contribute significantly to enhancing the efficiency of a turbine. The present work proposes a time-saving and reliable method to design an optimized SHKT by using a blend of experimentally validated 3D computational fluid dynamics (CFD) simulations and Artificial Neural Network (ANN). To optimize the turbine blade, the turbine performance needs to be analyzed for a number of values of blade parameters selected at very small intervals. Performing so many CFD simulations is a costly task. Application of an ANN tool trained using a smaller number of CFD results should significantly curtail the costs while maintaining reliability as well. The power coefficient (Cp) of SHKT was obtained using CFD simulations for some selected sets of Φ and p/q. These results were used to train the ANN by creating a parametric map between the input parameters viz. Φ, p/q, and the output parameter Cp. The trained ANN tool was further used to predict the turbine’s performance for sets of input parameters varying at very small intervals. The blade with p/q of 0.2 and Φ of 148° provides a maximum Cp of 0.209 at a TSR of 0.8. This optimal blade was 10.5% more efficient than the standard semicircular blade.
Nomenclature and abbreviation
H | = | Height of the blades [m] |
D | = | Height of the blades [m] |
Do | = | Diameter of endplates [m] |
θ | = | Rotational angle [degree] |
e | = | Distance between the blades [m] |
t | = | Turbine blades thickness [m] |
A | = | Swept area (H×D) [m2] |
U | = | Average velocity of water [m/s] |
g | = | Acceleration due to gravity[m/s2] |
ω | = | Angular velocity [rad/s] |
Φ | = | Blade arc angle [-] |
p/q | = | Blade shape factor [-] |
= | Arithmetic average value [-] | |
= | Estimated value [-] | |
= | Target value [-] | |
N | = | Number of data [-] |
ρ | = | Water density [kg/m3] |
CP | = | Power coefficient [-] |
Cm | = | Moment coefficient [-] |
Cm max | = | Maximum moment coefficient [-] |
CP max | = | Maximum power coefficient [-] |
Abbreviations | = | |
AR | = | Aspect ratio [H/D] |
OR | = | Overlap ratio [e/D] |
TSR | = | Tip speed ratio [ωD/2 U] |
CFD | = | Computational Fluid Dynamics |
FVM | = | Finite volume method |
MRF | = | Multiple reference frame |
SMM | = | Sliding mesh motion |
ANN | = | Artificial neural network |
R | = | Regression |
ANN | = | Mean square error |
2D | = | Two dimensional |
3D | = | Three dimensional |
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Notes on contributors
Thochi Seb Rengma
Mr. Thochi Seb Rengma is a PhD Research Scholar in the department of Mechanical Engineering, IIT Delhi. He is working on design and performance analysis of Savonius type vertical axis hydrokinetic turbine.
Shubham Kumar
Mr. Shubham Kumar is a PhD Research Scholar in the department of Mechanical Engineering, IIT Delhi. He is working on Thermal analysis and cooling of Solar PV systems in real operating conditions.
Mahendra Kumar Gupta
Mr. Mahendra Kumar Gupta is a PhD Research Scholar in the department of Mechanical Engineering, IIT Delhi. He is working on Design, Development and performance analysis of Pico-capacity hydrokinetic turbine.
P.M.V. Subbarao
Dr. P.M.V. Subbarao, Professor (Department of Mechanical Engineering), IIT Delhi. His research interest includes computational and experimental micro fluid mechanics, computational study of high-speed gas dynamics, turbulent flow fluid mechanics and heat transfer.