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研究生:高晁慶
研究生(外文):Chao-Ching Kao
論文名稱:直接施力型沉浸邊界法在風車葉片的基因演算法最佳化應用
論文名稱(外文):Genetic algorithm for optimizing blade of wind turbine by direct-forcing immersed boundary modeling
指導教授:陳明志陳明志引用關係
指導教授(外文):Ming-Jyh Chern
口試委員:洪子倫林怡均陳明志
口試委員(外文):Tzyy-Leng HorngYi-Jiun LinMing-Jyh Chern
口試日期:2018-07-16
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:機械工程系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:77
中文關鍵詞:基因演算法光線投影法機翼直接施力型沉浸邊界法流固耦合
外文關鍵詞:Genetic algorithmray-casting algorithmairfoilPARSECdirect-forcing immersed boundary methodfluid-structure interaction
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由於能源短缺的問題日趨嚴重,因此再生能源的議題廣受到社會各界的重視,而在所有的再生能源當中,風能轉換器則受到了極大的關注。在本研究中,數值方法與基因演算法的結合被成功的運用在葉片的設計上,葉片的外形設計對於風能轉換器的效率存在著顯著的影響。因此,利用最佳化葉片斷面的外形來提升葉片對風車的輸出效能。另外,許多研究指出在眾多最佳化方法中,基因演算法是強而有力且全域最佳化的方法之一。實數型基因演算法可以大幅解決傳統二進位型編碼所造成染色體長度過長的缺陷,為了有效控制機翼外型的形狀,本研究運用PARSEC參數化方法的11個特徵參數來呈現機翼斷面外型。此外,直接施力沉浸邊界法為一有效模擬流固耦合運動的數值方法。本研究透過此方法來模擬流體流經旋轉的風車葉片,且運用光線投影法來捕捉隨著每個時間步旋轉的葉片位置,透過最佳化的演化結果得出,機翼外型的改變能夠提升整體輸出效能,且也顯示基因演算法與直接施力沉浸邊界法的成功結合能得到最佳化的目的。
Renewable energy is an important topic due to energy shortage. Especially wind energy converted by a wind turbine receives more attentions. In the present study, the blade design of the wind turbine using a couple method with computational fluid dynamics (CFD) and genetic algorithm (GA) is discussed. The blade shape is the most significant effective factor in the wind energy conversion. Hence, we dedicated to utilizing an optimal method for the cross-section of blade, i.e., an airfoil, in order to get the better efficiency for producing the higher lift and lower drag to drive the wind turbine. According to the previous study, the Genetic Algorithm (GA) is known to be the robust method in the optimal design area. The real-coded Genetic algorithm is considered since it is able to solve the defect of binary code. That is, the chromosomes length is too long to code. While the PARSEC parameterization method is used to represent the shape of airfoil through the eleven parameters as the control variables. Furthermore, a direct-forcing immersed boundary (DFIB) method is employed for simulations of interaction of rotating blades in a flow field at a moderate low Reynolds number. Numerical results reveal that the shape of airfoil can be optimized and the proposed DFIB model coupled with GA successfully simulates the moving blade in flow field for obtaining the high performance.
Chinese Abstract . . . . . . .i
Abstract . . . . . . .ii
Acknowledgements . . . . . . .iii
Contents . . . . . . .vi
Nomenclatures . . . . . . .ix
List . . . . . . .xii
List of Figures . . . . . . .xiii
1 INTRODUCTION 1
2 MATHEMATICAL FORMULAE AND NUMERICAL MODEL . . . . . . .9
2.1 Genetic Algorithm . . . . . . .10
2.1.1 Real-Coded Genetic Algorithm . . . . . . .11
2.1.2 Population . . . . . . .11
2.1.3 Evaluation . . . . . . .12
2.1.4 Selection and reproduction . . . . . . .13
2.1.5 Crossover . . . . . . .14
2.1.6 Mutation . . . . . . .14
2.2 Airfoil shape parameterization . . . . . . .15
2.3 Governing equations and DFIB . . . . . . .16
2.3.1 Ray-casting algorithm . . . . . . .18
2.3.2 Numerical methods for solving Navier-Stokes equations . . . . . . 19
2.4 Vertical axis wind turbine . . . . . . .21
2.5 Grid independence and verication of DFIB model . . . . . . .23
2.5.1 Computational domain and boundary condition . . . . . . .23
2.5.2 Grid independence . . . . . . .23
3 RESULTS AND DISCUSSION . . . . . . .25
3.1 Influence of elitist strategy . . . . . . .26
3.2 Optimization results for stationary airfoil . . . . . . .27
3.3 Dynamic behavior of vertical axis wind turbine . . . . . . .28
3.4 Optimization results for vertical axis wind turbine . . . . . . .30
4 CONCLUSIONS AND FUTURE WORK . . . . . . .32
4.1 Conclusions . . . . . . .32
4.2 Future Work . . . . . . .34
BIBLIOGRAPHY . . . . . . .35
CURRICULUM VITAE . . . . . . .61
1.Alaimo, A., Esposito, A., Messineo, A., Orlando, C. and Tumino, D., 2015. 3D CFD analysis of a vertical axis wind turbine. Energies 8(4), 3013-3033

2.Anitha, D., Shamili, G.K., Ravi Kumar, P. and Sabari Vihar, R., 2018. Air foil shape optimization using CFD and parametrization methods.Materials Today: Proceedings 5, 5364-5373

3.Bernitsas, M.M., Raghavan, K., Ben-Simon, Y. and Garcia, E. M. H., 2008. VIVACE(Vortex Induced Vibration Aquatic Clean Energy): A New Concept in Generation of Clean and Renewable Energy from Fluid Flow.Journal of Offshore Mechanics and Arctic Engineering, ASME Transactions.130, 041101-15

4.Borazjani, I., Ge, L. and Sotiropoulos, F., 2008. Curvilinear immersed boundary method for simulating fluid structure interaction with complex 3D rigid bodies.Journal of Computational Physics 227, 7587-7620

5.Carrigan, T.J., Dennis, B.H., Han, Z.X. and Wang, B.P., 2012. Aerodynamic shape optimization of a vertical-axis wind turbine using differential evolution. ISRN RenewableEnergy

6.Chan, C.M., Bai, H.L. and He, D.Q., 2018. Blade shape optimization of the Savonius wind turbine using a genetic algorithm. Applied Energy 213, 148-157

7.Chern, M.J., Kuan, Y.H., Nugroho, G., Lu, G.T. and Horng, T.L., 2014. Direct-forcing immersed boundary modeling of vortex-induced vibration of a circular cylinder. Journal of Wind Engineering and Industrial Aerodynamics 134, 109-121

8.Chiba, K., Obayashi, S., Nakahashi, K. and Morino, H., 2005. High-fidelity multidisciplinary design optimization of aerostructural wing shape for regional jet.23rd AIAA Applied Aerodynamics Conference, Fluid Dynamics and Co-located Conferences

9.Della Vecchia, P. and Daniele, E., 2014. An airfoil shape optimization technique coupling PARSEC parameterization and evolutionary algorithm.Aerospace Science and Technology 32, 103-110

10.Drela, M., 2000. XFoil; Massachusetts Institute of Technology: Cambridge, MA, USA.

11.Herrera, F., Lozano, M. and S ́anchez, A.M., 2003. A taxonomy for the crossover opera-tor for real-coded genetic algorithms: an experimental study. International Journal of Intelligent Systems 18(3), 309-338

12.Falc ̃ao, A.F.O., Henriques, J.C.C. and Cˆandido, J.J., 2012. Dynamics and optimization of the OWC spar buoy wave energy converter.Renewable Energy 18(3), 369-381

13.Howell, R., Qin, N., Edwards, J. and Durrani, N., 2010. Wind tunnel and numerical study of a small vertical axis wind turbine.Renewable Energy 35, 412-422

14.Ismail, M.F. and Vijayaraghavan, K., 2015. The effects of aerofoil profile modification on a vertical axis wind turbine performance. Energy 80, 20-31

15.Islam, M., Ting, D.S.K. and Fartaj, A., 2008. Aerodynamic models for Darrieus type straight-bladed vertical axis wind turbines.Renewable and Sustainable Energy Reviews 12, 1087-1099

16.Jahangirian, A. and Shahrokhi, A., 2011. Aerodynamic shape optimization using efficient evolutionary algorithms and unstructured CFD solver.Computers and Fluids 46, 270-276

17.Marco, N., Desideri, J.A. and Lanteri, S., 1999. Multi-objective optimization in CFD by genetic algorithms. Institut National De Recherce En Informatique Et En Automatique.

18.Mohd. Yusof, J, 1996. Interaction of massive particles with turbulence. Ph.D. thesis,Cornell University, USA.

19.Oyama, A., Obayashi, S. and Nakahashi, K., 2000. Real-Coded Adaptive Range Genetic Algorithm And Its Application to Aerodynamic Design. JSME International Journal Series A: Solid Mechanics and Material Engineering 43(2), 124-130

20.Peskin, C.S., 1972. Flow patterns around heart valves: A numerical method.Journal of Computational Physics 10, 252-271

21.Pehlivanoglu, Y.V. and Baysal, O., 2010. Vibrational genetic algorithm enhanced withfuzzy logic and neural networks.Aerospace Science and Technology14, 56-64

22.Ribeiro, A.F.P., Awruch, A.M. and Gomes, H.M., 2012. An airfoil optimization techniquefor wind turbines.Applied Mathematical Modelling36, 4898-4907

22.Roma, A.M., Peskin, C.S. and Berger, M.J., 1999. An adaptive version of the immersedboundary method.Journal of Computational Physics153, 509-534

23.Sashi Kumar, G.N., Mahendra, A.K. and Gouthaman, G., 2011. Multi-objective shapeoptimization using ant colony coupled computational fluid dynamics solver.Computersand Fluids46, 298-305

24.Sobieczky, H., 1998. Parametric airfoil and wings.Notes on Numerical Fluid Mechanics,Vieweg,68, 71-87

25.Song, M.X., Chen, K. and Wang, J., 2018. Three-dimensional wind turbine positioningusing Gaussian particle swarm optimization with differential evolution.Journal of WindEngineering and Industrial Aerodynamics172, 317-324
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