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Numerical optimization of Wells turbine for wave energy extraction

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초록

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.

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.

참고문헌 (45)

  1. Ansys CFX, "Release 11.0: Ansys CFX-solver Theory Guide", ANSYS, ~ (2010)
  2. Rameez Badhurshah, "Multiple surrogate based optimization of a bidirectional impulse turbine for wave energy conversion", Renewable Energy, 74, pp.749~760 (2015), Elsevier BV[DOI]
  3. Sayed Ahmed Imran Bellary, "Effectiveness of meta-models for multi-objective optimization of centrifugal impeller", Journal of Mechanical Science and Technology, 28(12), pp.4947~4957 (2014), 대한기계학회
  4. Ernesto Benini, "Aerodynamics of swept and leaned transonic compressor-rotors", Applied Energy, 84(10), pp.1012~1027 (2007), Elsevier BV[DOI]
  5. A Brito-Melo, "Analysis of Wells turbine design parameters by numerical simulation of the OWC performance", Ocean Engineering, 29(12), pp.1463~1477 (2002), Elsevier BV[DOI]
  6. Collette, Y., "Multiobjective optimization: principles and case studies", Decision Engineering. Springer Science & Business Media (2003)[DOI]
  7. R Curran, "The energy conversion performance of several types of Wells turbine designs", Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 211(2), pp.133~145 (2005), SAGE Publications[DOI]
  8. Deb, K., "Multi-objective Optimization Using Evolutionary Algorithms" (2001), John Wiley & Sons
  9. L. M C. Gato, "An experimental investigation into the effect of rotor blade sweep on the performance of the variable-pitch Wells turbine", Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 215(5), pp.611~622 (2005), SAGE Publications[DOI]
  10. Tushar Goel, "Ensemble of surrogates", Structural and Multidisciplinary Optimization, 33(3), pp.199~216 (2007), Springer Nature[DOI]
  11. Dirk Gorissen, "Multiobjective global surrogate modeling, dealing with the 5-percent problem", Engineering with Computers, 26(1), pp.81~98 (2010), Springer Nature[DOI]
  12. Paresh Halder, "High performance ocean energy harvesting turbine design–A new casing treatment scheme", Energy, 86, pp.219~231 (2015), Elsevier BV[DOI]
  13. Shinkyu Jeong, "Efficient Optimization Design Method Using Kriging Model", Journal of Aircraft, 42(5), pp.1375~1375 (2005), American Institute of Aeronautics and Astronautics (AIAA)[DOI]
  14. Taeho Kim, "Effects of blade geometry on performance of wells turbine for wave power conversion", Journal of Thermal Science, 10(4), pp.293~300 (2001), Springer Nature[DOI]
  15. T.H. Kim, "Numerical investigation on the effect of blade sweep on the performance of Wells turbine", Renewable Energy, 25(2), pp.235~248 (2002), Elsevier BV[DOI]
  16. B. Daniel Marjavaara, "Hydraulic Turbine Diffuser Shape Optimization by Multiple Surrogate Model Approximations of Pareto Fronts", Journal of Fluids Engineering, 129(9), pp.1228~ (2007), ASME International[DOI]
  17. Jay D. Martin, "Use of Kriging Models to Approximate Deterministic Computer Models", AIAA Journal, 43(4), pp.853~863 (2005), American Institute of Aeronautics and Astronautics (AIAA)[DOI]
  18. Matrices, S., "The Language of Technical Computing", pp.4~9 (2012), MathWorks, Inc.
  19. M.H. Mohamed, "Multi-objective optimization of the airfoil shape of Wells turbine used for wave energy conversion", Energy, 36(1), pp.438~446 (2011), Elsevier BV[DOI]
  20. M.H. Mohamed, "Optimization of blade pitch angle of an axial turbine used for wave energy conversion", Energy, 56, pp.229~239 (2013), Elsevier BV[DOI]
  21. Mohamed, H.M, "Numerical optimization of axial turbine with self-pitch- controlled blades used for wave energy conversion", Int. J. Energy Res, 38, pp.592~601 (2014)[DOI]
  22. Myers, R. H., "Response Surface Methodology:Process and Product Optimization Using Designed Experiments" (1995), Wiley. John Wiley & Sons
  23. Orr, M.J.L, "Introduction to Radial Basis Function Networks", ~ (1996)
  24. S. Raghunathan, "The wells air turbine for wave energy conversion", Progress in Aerospace Sciences, 31(4), pp.335~386 (1995), Elsevier BV[DOI]
  25. S. Raghunathan, "Effect of blade profile on the performance of the Wells self-rectifying air turbine", International Journal of Heat and Fluid Flow, 6(1), pp.17~22 (1985), Elsevier BV[DOI]
  26. Sacks, J., "Design and analysis of computer experiments", Stat. Sci, 4, pp.409~423 (2012)[DOI]
  27. A Samad, "Shape optimization of an axial compressor blade by multi-objective genetic algorithm", Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 222(6), pp.599~611 (2008), SAGE Publications[DOI]
  28. Abdus Samad, "Multiple Surrogate Modeling for Axial Compressor Blade Shape Optimization", Journal of Propulsion and Power, 24(2), pp.301~310 (2008), American Institute of Aeronautics and Astronautics (AIAA)[DOI]
  29. T. W. Simpson, "Kriging models for global approximation in simulation-based multidisciplinary design optimization", AIAA Journal, 39, pp.2233~2241 (2001), American Institute of Aeronautics and Astronautics (AIAA)[DOI]
  30. Suzuki, M, "Influence of blade profiles on flow around Wells turbine", Int. J. Fluid Mach. Syst, 1, pp.148~154 (2008)[DOI]
  31. Zahari Taha, "A comparison of computational and experimental results of Wells turbine performance for wave energy conversion", Applied Ocean Research, 32(1), pp.83~90 (2010), Elsevier BV[DOI]
  32. Manabu Takao, "Wells turbine with end plates for wave energy conversion", Ocean Engineering, 34(11-12), pp.1790~1795 (2007), Elsevier BV[DOI]
  33. Manabu Takao, "Effect of blade profile on the performance of a large-scale Wells turbine for wave-energy conversion", International Journal of Sustainable Energy, 25(1), pp.53~61 (2006), Informa UK Limited[DOI]
  34. Thakker, A., "Effect of blade profile on the performance of Wells turbine under unidirectional sinusoidal and real sea flow conditions", Int. J. Rotat. Mach, 2007 (2007)[DOI]
  35. Torresi, M., "Fluid Dynamic Analysis of a Low Solidity Wells Turbine", 59 Congresso ATI (2004)
  36. M. Torresi, "Accurate numerical simulation of a high solidity Wells turbine", Renewable Energy, 33(4), pp.735~747 (2008), Elsevier BV[DOI]
  37. Valipour, M., "Monthly inflow forecasting using autoregressive artificial neural network", J. Appl. Sci, 12, pp.2139~2147 (2012)
  38. Mohammad Valipour, "Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir", Journal of Hydrology, 476, pp.433~441 (2013), Elsevier BV[DOI]
  39. Valipour, M., "An evaluation of SWDC and WinSRFR models to optimize of infiltration parameters in furrow irrigation", Am. J. Sci. Res, 69, pp.128~142 (2012)
  40. Valipour, M., "Optimize of all effective infiltration parameters in furrow irrigation using visual basic and genetic algorithm programming", Aust. J. Basic Appl. Sci, 6, pp.132~137 (2012)
  41. Valipour, M., "Sensitive analysis of optimized infiltration parameters in SWDC model", Adv. Environ. Biol, 6, pp.2574~2581 (2012)
  42. Wang, D., "Aerodynamic shape optimization design of the swept wing based on the kriging surrogate model", Appl. Mech. Mater, 444, pp.1277~1282 (2014)
  43. Webster, M., "The effect of rotor blade shape on the performance of the Wells turbine", Int. J. Offshore Polar Eng, 11, pp.227~230 (2001)
  44. Webster, M., "The effect of rotor blade sweep on the performance of the Wells turbine", Int. J. Offshore Polar Eng, 9, pp.233~240 (1999)[DOI]
  45. Webster, M, "The effect of rotor blade shape on the performance of the Wells turbine", Proceedings of the Ninth (1999)International Offshore and Polar Engineering Conference, 1999, pp.169~173 (1999)

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