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Numerical optimization of Wells turbine for wave energy extraction

  • Halder, Paresh (Wave Energy and Fluid Engineering Laboratory, Department of Ocean Engineering, Indian Institute of Technology Madras) ;
  • Rhee, Shin Hyung (Research Institute of Marine Systems Engineering, Department of Naval Architecture and Ocean Engineering, Seoul National University) ;
  • Samad, Abdus (Wave Energy and Fluid Engineering Laboratory, Department of Ocean Engineering, Indian Institute of Technology Madras)
  • Received : 2016.05.05
  • Accepted : 2016.06.27
  • Published : 2017.01.31

Abstract

The present work focuses multi-objective optimization of blade sweep for a Wells turbine. The blade-sweep parameters at the mid and the tip sections are selected as design variables. The peak-torque coefficient and the corresponding efficiency are the objective functions, which are maximized. The numerical analysis has been carried out by solving 3D RANS equations based on k-w SST turbulence model. Nine design points are selected within a design space and the simulations are run. Based on the computational results, surrogate-based weighted average models are constructed and the population based multi-objective evolutionary algorithm gave Pareto optimal solutions. The peak-torque coefficient and the corresponding efficiency are enhanced, and the results are analysed using CFD simulations. Two extreme designs in the Pareto solutions show that the peak-torque-coefficient is increased by 28.28% and the corresponding efficiency is decreased by 13.5%. A detailed flow analysis shows the separation phenomena change the turbine performance.

Keywords

References

  1. Ansys CFX, 2010. Release 11.0: Ansys CFX-solver Theory Guide. ANSYS.
  2. Badhurshah, R., Samad, A., 2015. Multiple surrogate based optimization of a bidirectional impulse turbine for wave energy conversion. Renew. Energy 74, 749-760. http://dx.doi.org/10.1016/j.renene.2014.09.001.
  3. Bellary, S.A.I., Husain, A., Samad, A., 2014. Effectiveness of meta-models for multi-objective optimization of centrifugal impeller. J. Mech. Sci. Technol. 28, 4947-4957. https://doi.org/10.1007/s12206-014-1116-0
  4. Benini, E., Biollo, R., 2007. Aerodynamics of swept and leaned transonic compressor-rotors. Appl. Energy 84, 1012-1027. http://dx.doi.org/10.1016/j.apenergy.2007.03.003.
  5. Brito-Melo, A., Gato, L.M.C., Sarmento, A.J.N.A., 2002. Analysis of Wells turbine design parameters by numerical simulation of the OWC performance. Ocean. Eng. 29, 1463-1477. http://dx.doi.org/10.1016/S0029-8018(01)00099-3.
  6. Collette, Y., Siarry, P., 2003. Multiobjective optimization: principles and case studies. Decision Engineering. Springer Science & Business Media. http://dx.doi.org/10.1007/978-3-662-08883-8.
  7. Curran, R., Gato, L.M.C., 1997. The energy conversion performance of several types of Wells turbine designs. Proc. Inst. Mech. Eng. Part A J. Power Energy 211, 133-145. http://dx.doi.org/10.1243/0957650971537051.
  8. Deb, K., 2001. Multi-objective Optimization Using Evolutionary Algorithms. John Wiley & Sons.
  9. Gato, L.M.C.,Webster, M., 2001. An experimental investigation into the effect of rotor blade sweep on the performance of the variable-pitch Wells turbine. Proc. Inst. Mech. Eng. Part A J. Power Energy 215, 611-622. http://dx.doi.org/10.1243/0957650011538848.
  10. Goel, T., Haftka, R.T., Shyy, W., Queipo, N.V., 2007. Ensemble of surrogates. Struct. Multidiscip. Optim. 33, 199-216. http://dx.doi.org/10.1007/s00158-006-0051-9.
  11. Gorissen, D., Couckuyt, I., Laermans, E., Dhaene, T., 2010. Multiobjective global surrogate modeling, dealing with the 5-percent problem. Eng. Comput. 26, 81-98. http://dx.doi.org/10.1007/s00366-009-0138-1.
  12. Halder, P., Samad, A., Kim, J.H., Choi, Y.S., 2015. High performance ocean energy harvesting turbine design-a new casing treatment scheme. Energy 86, 219-231. http://dx.doi.org/10.1016/j.energy.2015.03.131.
  13. Jeong, S., Murayama, M., Yamamoto, K., 2005. Efficient optimization design method using kriging model. J. Aircr. 42, 413-420. http://dx.doi.org/10.2514/1.C10485E.
  14. Kim, T., Setoguchi, T., Kinoue, Y., Kaneko, K., 2001. Effects of blade geometry on performance of wells turbine for wave power conversion. J. Therm. Sci. 10, 293-300. http://dx.doi.org/10.1007/s11630-001-0035-4.
  15. Kim, T.H., Setoguchi, T., Kaneko, K., Raghunathan, S., 2002. Numerical investigation on the effect of blade sweep on the performance of Wells turbine. Renew. Energy. http://dx.doi.org/10.1016/S0960-1481(00)00210-X.
  16. Marjavaara, D.B., Lundstr€om, S.T., Goel, T., Mack, Y., Shyy, W., 2007. Hydraulic turbine diffuser shape optimization by multiple surrogate model approximations of pareto fronts. J. Fluids Eng. 129, 1228. http://dx.doi.org/10.1115/1.2754324.
  17. Martin, J.D., Simpson, T.W., 2005. Use of kriging models to approximate deterministic computer models. AIAA J. 43, 853-863. http://dx.doi.org/10.2514/1.8650.
  18. Matrices, S., 2012. The Language of Technical Computing. MathWorks, Inc., pp. 4-9. http//www.mathworks.com
  19. Mohamed, M.H., Janiga, G., Pap, E., Thevenin, D., 2011. Multi-objective optimization of the airfoil shape of Wells turbine used for wave energy conversion. Energy 36, 438-446. http://dx.doi.org/10.1016/j.energy.2010.10.021.
  20. Mohamed, M.H., Shaaban, S., 2013. Optimization of blade pitch angle of an axial turbine used for wave energy conversion. Energy 56, 229-239. http://dx.doi.org/10.1016/j.energy.2013.04.035.
  21. Mohamed, H.M., Shaaban, S., 2014. Numerical optimization of axial turbine with self-pitch- controlled blades used for wave energy conversion. Int. J. Energy Res. 38, 592-601. http://dx.doi.org/10.1002/er.
  22. Myers, R.H., Montgomery, D.C., 1995. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. Wiley. John Wiley & Sons, New York. http://dx.doi.org/10.1016/S0378-3758(97)81631-X.
  23. Orr, M.J.L., 1996. Introduction to Radial Basis Function Networks.
  24. Raghunathan, S., 1995. The Wells air turbine for wave energy conversion. Prog. Aerosp. Sci. 31, 335-386. http://dx.doi.org/10.1016/0376-0421(95)00001-F.
  25. Raghunathan, S., Tan, C.P., 1985. Effect of blade profile on the performance of the Wells self-rectifying air turbine. Int. J. Heat. Fluid Flow. 6, 369-379. http://dx.doi.org/10.1016/0142-727X(85)90026-8.
  26. Sacks, J., Welch, W.J., Mitchell, T.J., Wynn, H.P., 2012. Design and analysis of computer experiments. Stat. Sci. 4, 409-423. http://dx.doi.org/10.1214/ss/1177012413.
  27. Samad, A., Kim, K.Y., 2008. Shape optimization of an axial compressor blade by multi-objective genetic algorithm. Proc. Inst. Mech. Eng. Part A J. Power Energy 222, 599-611. http://dx.doi.org/10.1243/09576509JPE596.
  28. Samad, A., Kim, K.Y., Goel, T., Haftka, R.T., Shyy, W., 2008. Multiple surrogate modeling for axial compressor blade shape optimization. J. Propuls. Power 24, 301-310. http://dx.doi.org/10.2514/1.28999.
  29. Simpson, T.W., Mauery, T.M., Korte, J., Mistree, F., 2001. Kriging models for global approximation in simulation-based multidisciplinary design optimization. AIAA J. 39, 2233-2241. http://dx.doi.org/10.2514/3.15017.
  30. Suzuki, M., Arakawa, C., 2008. Influence of blade profiles on flow around Wells turbine. Int. J. Fluid Mach. Syst. 1, 148-154. http://dx.doi.org/10.5293/IJFMS, 1.1.148.
  31. Taha, Z., Sugiyono, Sawada, T., 2010. A comparison of computational and experimental results of Wells turbine performance for wave energy conversion. Appl. Ocean. Res. 32, 83-90. http://dx.doi.org/10.1016/j.apor.2010.04.002.
  32. Takao, M., Setoguchi, T., Kinoue, Y., Kaneko, K., 2007. Wells turbine with end plates for wave energy conversion. Ocean. Eng. 34, 1790-1795. http://dx.doi.org/10.1016/j.oceaneng.2006.10.009.
  33. Takao, M., Thakker, A., Abdulhadi, R., Setoguchi, T., 2006. Effect of blade profile on the performance of a large-scale Wells turbine for wave-energy conversion. Int. J. Sustain. Energy 25, 53-61. http://dx.doi.org/10.1080/14786450600593295.
  34. Thakker, A., Abdulhadi, R., 2007. Effect of blade profile on the performance of Wells turbine under unidirectional sinusoidal and real sea flow conditions. Int. J. Rotat. Mach. 2007 http://dx.doi.org/10.1155/2007/51598.
  35. Torresi, M., Camporeale, S.M., Pascazio, G., Fortunato, B., 2004. Fluid Dynamic Analysis of a Low Solidity Wells Turbine. In: 59 Congresso ATI, Genova, Italy.
  36. Torresi, M., Camporeale, S.M., Strippoli, P.D., Pascazio, G., 2008. Accurate numerical simulation of a high solidity Wells turbine. Renew. Energy 33, 735-747. http://dx.doi.org/10.1016/j.renene.2007.04.006.
  37. Valipour, M., Banihabib, E., Behbahani, R., 2012. Monthly inflow forecasting using autoregressive artificial neural network. J. Appl. Sci. 12, 2139-2147. https://doi.org/10.3923/jas.2012.2139.2147
  38. Valipour, M., Banihabib, M.E., Behbahani, S.M.R., 2013. Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J. Hydrol. 476, 433-441. http://dx.doi.org/10.1016/j.jhydrol.2012.11.017.
  39. Valipour, M., Montazar, A., 2012a. An evaluation of SWDC and WinSRFR models to optimize of infiltration parameters in furrow irrigation. Am. J. Sci. Res. 69, 128-142.
  40. Valipour, M., Montazar, A.A., 2012b. Optimize of all effective infiltration parameters in furrow irrigation using visual basic and genetic algorithm programming. Aust. J. Basic Appl. Sci. 6, 132-137.
  41. Valipour, M., Montazar, A.A., 2012c. Sensitive analysis of optimized infiltration parameters in SWDC model. Adv. Environ. Biol. 6, 2574-2581.
  42. Wang, D., Bai, J.Q., Hua, J., Sun, Z.W., Qiao, L., 2014. Aerodynamic shape optimization design of the swept wing based on the kriging surrogate model. Appl. Mech. Mater. 444, 1277-1282.
  43. Webster, M., Gato, L.M.C., 2001. The effect of rotor blade shape on the performance of the Wells turbine. Int. J. Offshore Polar Eng. 11, 227-230. http://dx.doi.org/10.1016/S0960-1481(00)00163-4.
  44. Webster, M., Gato, L.M.C., 1999a. The effect of rotor blade sweep on the performance of the Wells turbine. Int. J. Offshore Polar Eng. 9, 233-240. http://dx.doi.org/10.1016/S0960-1481(00)00163-4.
  45. Webster, M., Gato, L.M.C., 1999b. The effect of rotor blade shape on the performance of the Wells turbine. In: Proceedings of the Ninth (1999) International Offshore and Polar Engineering Conference. Brest, France, May 30-June 4, 1999, pp. 169-173. http://dx.doi.org/10.1016/S0960-1481(00)00163-4.

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