Advertisement

Optimal Design of a Marine Current Turbine Using CFD and FEA

  • Thandayutham Karthikeyan
  • Lava Kush Mishra
  • Abdus Samad
Conference paper
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 23)

Abstract

Ocean currents that are produced due to motion of tides can be utilized in power extraction by using suitable turbines. The turbine should be structurally and hydrodynamically strong. In this paper, a 0.8 m horizontal axis marine current turbine (MCT) with three blades is analyzed. A 3D CAD model of a turbine is optimized using CFD and FEA tools. The performance of the turbine is based on the coefficient of power; however, the turbine should resist the loads acting on it. The fatigue load damages the turbine which is mainly due to wave loads and it must be evaluated to avoid the cost of replacing a new turbine. Only a turbine with high power coefficient and good material strength will result in a favorable design. The parameters like pitch angles, number of blades, and turbine material are modified to study the performance and structural stability of the turbine. The detailed CFD study including boundary conditions and methodology has contributed to get an insight of the flow physics. The best suitable pitch angle and number of rotor blades for the turbine are analyzed and discussed. The optimized turbine has two rotor blades with a pitch angle of 19.5° and has achieved a significant 25% increase in CP. Later, different materials are chosen to identify the variation in stress and tip deflection of the turbine blades. This will direct toward a safe design of the turbine blades.

Keywords

Marine energy Marine current turbine CFD and FEA analysis Surrogate models 

Nomenclature

Abbreviation

BEM

Blade element momentum

CAD

Computer-aided designing

CFD

Computational fluid dynamics

FEA

Finite element analysis

GA

Genetic algorithm

KRG

Kriging

PRESS

Predicted error sum of square

RANS

Reynolds-averaged Navier–Stokes

RBF

Radial basis function

RSA

Response surface approximation

TSR

Tip speed ratio

WAS

Weighted average surrogate

Symbols

A

Rotor area (m2)

a

Axial induction factor

a′

Tangential induction factor

Cd

Drag coefficient

Cl

Lift coefficient

CP

Power coefficient

CT

Thrust coefficient

c

Chord (m)

D

Turbine tip diameter (m)

\( {\tilde{\text{e}}} \)

PRESS vector

F

Objective function

H

Total depth of water (m)

h

Installation depth from ocean surface (m)

Q

Torque (N-m)

R

Rotor radius (m)

r

Local radius (m)

T

Thrust (N)

t

Thickness (m)

UT

Free stream velocity (m/s)

Vrel

Relative velocity (m/s)

α

Angle of attack

ρ

Density (kg/m3)

Ω

Angular velocity of rotor (rad/s)

ϕ

Local blade pitch angle

φ

Angle between the plane of rotation

Subscripts

ERR

Error

OPT

Optimal

RMS

Root mean square

SUR

Surrogate

References

  1. 1.
    Bahaj AS, Myers LE (2003) Fundamentals applicable to the utilization of marine current turbines for energy production. Renew Energy 28(14):2205–2211CrossRefGoogle Scholar
  2. 2.
    Myers LE, Bahaj AS (2007) Wake studies of a 1/30th scale horizontal axis marine current turbine. Ocean Eng 34(5/6):758–762CrossRefGoogle Scholar
  3. 3.
    Rosli R, Norman R, Atlar M (2016) Experimental investigations of the Hydro-Spina turbine performance. Renew Energy 99:1227–1234CrossRefGoogle Scholar
  4. 4.
    Nishino T, Willden RHJ (2012) Effects of 3-D channel blockage and turbulent wake mixing on the limit of power extraction by tidal turbines. Int J Heat Fluid Flow 37:123–135CrossRefGoogle Scholar
  5. 5.
    Wimshurst A, Willden RHJ (2016) Computational analysis of blockage designed tidal turbine rotors. Progress in Renewable Energies Offshore, Taylor & Francis Group, pp 587–597Google Scholar
  6. 6.
    Amet E, Maitre T, Pellone C, Achard JL (2009) 2D numerical simulations of blade vortex interactions in a darrieus turbine. J Fluids Eng 131(11):1–15CrossRefGoogle Scholar
  7. 7.
    Priegue L, Stoesser T, Runge S (2015) Effect of blade parameters on the performance of a cross flow turbine. In: Proceedings of the 36th IAHR world congress, 28th June to 3rd July, The NetherlandsGoogle Scholar
  8. 8.
    Schluntz J, Willden RHJ (2015) The effect of blockage on tidal turbine rotor design and performance. Renew Energy 81:432–441CrossRefGoogle Scholar
  9. 9.
    Mukherji SS, Kolkar N, Banerjee A, Mishra R (2011) Numerical investigation and evaluation of optimum hydrodynamic performance of a horizontal axis hydrokinetic turbine. J Renew Sustain Energy 3(063105):1–17Google Scholar
  10. 10.
    Kolekar N, Banerjee A (2013) A coupled hydro-structural design optimization for hydrokinetic turbines. J Renew Sustain Energy 5(053146):1–22Google Scholar
  11. 11.
    Selig MS, Carroll VLC (1996) Application of a genetic algorithm to wind turbine design. J Energy Res Technol 118(1):22–28CrossRefGoogle Scholar
  12. 12.
    Belessis MA, Stamos DG (1996) Investigation of the capabilities of a genetic optimization algorithm in designing wind turbine rotors. In: Proceedings of European union wind energy conference and exhibition, May 20–24, Goteborg, SwedenGoogle Scholar
  13. 13.
    Fuglsang P, Madsen HA (1999) Optimization method for wind turbine rotors. J Wind Eng Ind Aerodyn 80(1/2):191–206CrossRefGoogle Scholar
  14. 14.
    McEwen LN, Evans R, Meunier M (2012) Cost effective tidal turbine blades. In: 4th international conference on ocean energy, 17th October, DublinGoogle Scholar
  15. 15.
    Bahaj AS, Molland AF, Chaplin JR, Battern WMJ (2007) Power and thrust measurements of marine current turbine under various hydrodynamic flow conditions in a cavitation tunnel and a towing tank. Renew Energy 32(3):407–426CrossRefGoogle Scholar
  16. 16.
    Blackmore T, Myers LE, Bahaj AS (2016) Effects of turbulence on tidal turbines: Implications to performance, blade loads and condition monitoring. Int J Marine Energy 14:1–26CrossRefGoogle Scholar
  17. 17.
    Karthikeyan T, Avital EJ, Venkatesan N, Samad A (2017) Design and analysis of marine current turbine. In: Proceedings of ASME 2017 gas turbine India conference and exhibition, 7th and 8th December, Bangalore, IndiaGoogle Scholar
  18. 18.
    Menter FR (2014) Two- equation eddy- viscosity turbulence models for engineering applications. AIAA J 32(8):1598–1605CrossRefGoogle Scholar
  19. 19.
    Bai X, Avital EJ, Munjiza A, Williams JJR (2014) Numerical simulation of a marine current turbine in free surface flow. Renew Energy 63:715–723CrossRefGoogle Scholar
  20. 20.
    Danao LA, Abuan B, Howell R (2016) Design analysis of a horizontal axis tidal turbine. In: 3rd Asian wave and tidal conference, 24–28th October, Marina Bay Sands, SingaporeGoogle Scholar
  21. 21.
    Grogan DM, Leen SB, Kennedy CR, Bradaigh CMO (2013) Design of composite tidal turbine blades. Renew Energy 57:151–162CrossRefGoogle Scholar
  22. 22.
    Thakker A, Jarvis J, Buggy M, Sahed A (2008) A novel approach to materials selection strategy case study: wave energy extraction impulse turbine. Mater Des 29:1973–1980CrossRefGoogle Scholar
  23. 23.
    Hansen MOL (2008) Aerodynamics of wind turbines – Second edition. Earthscan publication, The United KingdomGoogle Scholar
  24. 24.
    Batten WMJ, Bahaj AS, Molland AF, Chaplin JR (2008) The prediction of the hydrodynamic performance of marine current turbines. Renew Energy 33:1085–1096CrossRefGoogle Scholar
  25. 25.
    Zhu GJ, Guo PC, Luo XQ, Feng JJ (2012) Multiple objective optimization of the horizontal-axis marine current turbine based on NSGA-II algorithm. Earth Environ Sci 15:1–8Google Scholar
  26. 26.
    Badhurshah R, Samad A (2015) Multiple surrogate based optimization of a bidirectional impulse turbine for wave energy conversion. Renew Energy 74:749–760CrossRefGoogle Scholar
  27. 27.
    Koo GW, Lee SM, Kim KY (2014) Shape optimization of inlet part of a printed circuit heat exchanger using surrogate modeling. Appl Therm Eng 72(1):90–96CrossRefGoogle Scholar
  28. 28.
    Jiang Y, Lin H, Yue G, Zheng Q, Xu X (2017) Aero-thermal optimization on multi-rows film cooling of a realistic marine high pressure turbine vane. Appl Therm Eng 111:537–549CrossRefGoogle Scholar
  29. 29.
    Tsoukalas I, Kossieris P, Efstratiadis A, Makropoulos C (2016) Surrogate-enhanced evolutionary annealing simplex algorithm for effective and efficient optimization of water resources problems on a budget. Environ Model Softw 77:122–142CrossRefGoogle Scholar
  30. 30.
    Lee H, Jo Y, Lee DJ, Choi S (2016) Surrogate model based design optimization of multiple wing sails considering flow interaction effect. J Propul Power 24(2):422–436Google Scholar
  31. 31.
    Samad A, Kim KY (2008) Multiple surrogate modeling for axial compressor blade shape optimization. Struct Multidisciplinary Optim 39(4):439–457Google Scholar
  32. 32.
    Jiang Y, Lin H, Yue G, Zheng Q, Xu X (2017) Aero-thermal optimization on multi-rows film cooling of a realistic marine high pressure turbine vane. Appl Therm Eng 111:537–549CrossRefGoogle Scholar
  33. 33.
    Batten WMJ, Bahaj AS, Molland AF, Chaplin JR (2007) Experimentally validated numerical method for the hydrodynamic design of horizontal axis tidal turbines. Ocean Eng 34(7):1013–1020CrossRefGoogle Scholar
  34. 34.
    Delafin PL, Nishino T, Wang L, Kolios A (2016) Effect of the number of blades and solidity on the performance of a vertical axis wind turbine. J Phys: Conf Ser 753:1–8Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Thandayutham Karthikeyan
    • 1
  • Lava Kush Mishra
    • 1
  • Abdus Samad
    • 1
  1. 1.Wave Energy and Fluids Engineering Lab, Department of Ocean EngineeringIIT MadrasChennaiIndia

Personalised recommendations