Performance investigation on blade arc angle and blade shape factor of a Savonius hydrokinetic turbine using artificial neural network

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 (C_p) 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 C_p. 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 C_p of 0.209 at a TSR of 0.8. This optimal blade was 10.5% more efficient than the standard semicircular blade.

KEYWORDS:

• Blade arc angle
• blade shape factor
• artificial neural network
• hydrokinetic turbine
• savonius turbine

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 [-]
${{\mathrm{C}}_{\mathrm{p}}}_{\mathrm{a}\mathrm{v}\mathrm{g}}$	=	Arithmetic average value [-]
${{\mathrm{C}}_{\mathrm{p}}}_{\mathrm{e}\mathrm{s}\mathrm{t}}$	=	Estimated value [-]
${{\mathrm{C}}_{\mathrm{p}}}_{\mathrm{t}\mathrm{a}\mathrm{r}}$	=	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.