DOI QR코드

DOI QR Code

2차원 사각주 주위 유동의 플라즈마 능동제어에 대한 연구

Active control of flow around a 2D square cylinder using plasma actuators

  • Paraskovia Kolesova (School of Mechanical Engineering, Pusan National University) ;
  • Mustafa G. Yousif (School of Mechanical Engineering, Pusan National University) ;
  • Hee-Chang Lim (School of Mechanical Engineering, Pusan National University)
  • 투고 : 2024.05.14
  • 심사 : 2024.07.12
  • 발행 : 2024.07.31

초록

This study investigates the effectiveness of using a plasma actuator for active control of turbulent flow around a finite square cylinder. The primary objective is to analyze the impact of plasma actuators on flow separation and wake region characteristics, which are critical for reducing drag and suppressing vortex-induced vibrations. Direct Numerical Simulation (DNS) was employed to explore the flow dynamics at various operational parameters, including different actuation frequencies and voltages. The proposed methodology employs a neural network trained using the Proximal Policy Optimization (PPO) algorithm to determine optimal control policies for plasma actuators. This network is integrated with a computational fluid dynamics (CFD) solver for real-time control. Results indicate that this deep reinforcement learning (DRL)-based strategy outperforms existing methods in controlling flow, demonstrating robustness and adaptability across various flow conditions, which highlights its potential for practical applications.

키워드

과제정보

이 과제는 부산대학교 기본연구지원사업(2년)에 의하여 연구되었음.

참고문헌

  1. M. Gad-eI-Hak, 1996, "Modern developments in flow control," Applied Mechanics Reviews, 42(10), 261. 
  2. L.E. Jones, R. D. Sandberg, and N. D. Sandham, 2008, "Direct numerical simulations of forced separation bubbles on an airfoil at incidence," Journal of Fluid Mechanics, vol. 602, pp.175-207.  https://doi.org/10.1017/S0022112008000864
  3. G. Huang, Y. Dai, Ch. Yang, Y. Wu, and Yi. Xia, 2021, "Effect of dielectric barrier discharge plasma actuator on the dynamic moment behavior of pitching airfoil at low Reynolds number," Physics of Fluids, 33, 043603. 
  4. S. Huang, M. Yousif, and H.C. Lim, 2022, "Experimental study of natural transition in natural convection boundary layer," Journal of the Korean Society of Visualization, vol.20, No.1, 29-37.  https://doi.org/10.5407/JKSV.2022.20.1.029
  5. Y.W. Yi, D.S.Lee, K.K. Shin, C.S. Hong, and H.C. Lim, 2021, "Effects of Synthetic Turbulent Boundary Layer on Fluctuating Pressure on the Wall," Journal of the Korean Society of Visualization, vol.19, No.3, 92-98.  https://doi.org/10.5407/JKSV.2021.19.3.092
  6. D. Kim, J. Hwang, T.J.Min, W.M. Jo, "Numerical Analysis of Transitional Flow in a Stenosed Carotid Artery," Journal of the Korean Society of Visualization, vol.20, No.1, 52-63.  https://doi.org/10.5407/JKSV.2022.20.1.052
  7. E. B. Thompsonk and M. Gunasekaran, 2021, "Review analysis on laminar separation bubble at low Reynolds numbers," Journal of Physics: Conference Series 2054, 012005. 
  8. D. Poirel, Y. Harris, and A. Benaissa, 2008, "Self-sustained aeroelastic oscillations of a naca0012 airfoil at low-to-moderate Reynolds numbers," Journal of Fluids and Structures 24, 700-719.  https://doi.org/10.1016/j.jfluidstructs.2007.11.005
  9. D. Poirel and W. Yuan, 2010, "Aerodynamics of laminar separation flutter at a transitional Reynolds number," Journal of Fluids and Structures 26, 1174-1194.  https://doi.org/10.1016/j.jfluidstructs.2010.06.005
  10. J.N. Kutz, 2017, "Deep learning in fluid dynamics," Journal of Fluid Mechanics, Vol.814, 1-4.  https://doi.org/10.1017/jfm.2016.803
  11. M. Z. Yousif, L. Yu, and H.-C. Lim, 2021, "High-fidelity reconstruction of turbulent flow from spatially limited data using enhanced super-resolution generative adversarial network," Physics of Fluid, Vol.33, 125119 
  12. H. Kim, J. Kim, and S. Won, and C. Lee, "Unsupervised deep learning for super-resolution reconstruction of turbulence," Journal of Fluid Mechanics, Vol.910. 
  13. M. Z. Yousif, and H.-C. Lim, 2022, "Reduced-order modeling for turbulent wake of a finite wall-mounted square cylinder based on artificial neural network," Physics of Fluid, Vol.34, 015116. 
  14. M. Z. Yousif, L. Yu, and H.-C. Lim, 2022, "Physics-guided deep learning for generating turbulent inflow conditions," Journal of Fluid Mechanics, Vol.936, A21. 
  15. M. Z. Yousif, L. Yu, and H.-C. Lim, 2022, "Super-resolution reconstruction of turbulent flow fields at various Reynolds numbers based on generative adversarial networks," Physics of Fluid, Vol.34, 015130. 
  16. M. Z. Yousif, L. Yu, S. Hoyas, R. Vinuesa, and H.-C. Lim, 2023, "A deep-learning approach for reconstructing 3D turbulent flows from 2D observation data," Scientific Report, Vol.13, 2029. 
  17. J. Rabault and A. Kuhnle, 2019, "Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach," Physics of Fluids 31, 094105. 
  18. J. Rabault, M. Kuchta, A. Jensen, U. Reglade, and N. Cerardi, 2019, "Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control," Journal of Fluid Mechanics 865, 281-302.  https://doi.org/10.1017/jfm.2019.62
  19. Y. Wang, Y. Mei, and N. Aubry, 2022, "Deep reinforcement learning based synthetic jet control on distributed flow over airfoil," Physics of Fluids, 34, 033606. 
  20. H. Tang, J. Rabault, A. Kuhnle, Y. Wang, and T. Wang, "Robust active flow control over a range of reynolds numbers using an artificial neural network trained through deep reinforcement learning," 2020, Physics of Fluids 32, 053605. 
  21. D. Greenblatt and D. R. Williams, 2022, "Flow control for unmanned air vehicles," Annual Review of Fluid Mechanics 54, 383- 412.  https://doi.org/10.1146/annurev-fluid-032221-105053
  22. B.-Z. Han and W.-X. Huang, 2020, "Active control for drag reduction of turbulent channel flow based on convolutional neural networks," Physics of Fluids, 32(9), 095108. 
  23. J. Rabault and M. Kuchta and A. Jensen and U. Reglade and N. Cerardi, 2019, "Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control," Journal of Fluid Mechanics, Vol.865, 281-302.  https://doi.org/10.1017/jfm.2019.62
  24. J. Li and M. Zhang, 2021, "Reinforcement-learning-based control of confined cylinder wakes with stability analyses," Journal of Fluid Mechanics, Vol.932, A44. 
  25. L. Guastoni and J. Rabault and P. Schlatter and H. Azizpour and R. Vinuesa, 2023, "Deep reinforcement learning for turbulent drag reduction in channel flows," Vol.46, A27. 
  26. N. Benard, M. Caron, and E. Moreau, 2015, "Evaluation of the time-resolved EHD force produced by a plasma actuator by particle image velocimetry-a parametric study," J. Phys. 646(1), 12055-12058.  https://doi.org/10.1088/1742-6596/646/1/012055
  27. W. Shyy, B. Jayaraman, and A. Andersson, 2002, "Modelling of glow discharge-induced fluid dynamics," Journal of Applied Physics 92, pp.6434-6443.  https://doi.org/10.1063/1.1515103
  28. Y. Suzen, G. Huang, J. Jacob, and D. Ashpis, 2005, "Numerical Simulations of Plasma Based Flow Control Applications," 35th AIAA Fluid Dynamics Conference and Exhibit. American Institute of Aeronautics and Astronautics. 
  29. Y. Suzen, G. Huang, and D. Ashpis, 2007, "Numerical Simulations of Flow Separation Control in Low-Pressure Turbines Using Plasma Actuators," 45th AIAA Aerospace Sciences Meeting and Exhibit. American Institute of Aeronautics and Astronautics. 
  30. M. Forte, J, Jolibois, J. Pons, E. Moreau, G. Touchard, and M. Cazalens, 2007, "Optimization of a dielectric barrier discharge actuator by stationary and non-stationary measurements of the induced flow velocity: application to airflow control," Experiments in Fluids, 43:917-928.  https://doi.org/10.1007/s00348-007-0362-7
  31. V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou., D. Wierstra., and M. Riendmiller, 2013, " Playing Atari with Deep Reinforcement Learning," NIPS Deep Learning Workshop 2013.W. Shyy, B. Jayaraman, and A. Andersson, 2002, "Modeling of glow discharge-induced fluid dynamics," J. Appl. Phys. 92(11), 6434-6443.  https://doi.org/10.1063/1.1515103
  32. J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, 2017, "Proximal Policy Optimization Algorithms," Arxiv:1707.06347. 
  33. R. Paris, S. Beneddine, and J. Dandois, 2021, "Robust flow control and optimal sensor placement using deep reinforcement learning," Journal of Fluid Mechanics, 913. 
  34. Y. Anzai, K. Fukagata, P. Meliga, E. Boujo, and F. Gallaire, 2017, "Numerical simulation and sensitivity analysis of a low-Reynolds-number flow around a square cylinder controlled using plasma actuators," Physical review fluids 2, 043901. 
  35. S. Sen, S. Mittal and G. Biswas, 2011, "Flow past a square cylinder at low Reynolds numbers," International Journal for Numerical Methods in Fluids, 67, 1160-1174  https://doi.org/10.1002/fld.2416
  36. A. Sohankar, C. Norberg, and L. Davidson, 1998, "Low-Reynolds-number flow around a square cylinder at incidence: study of blockage, onset of vortex shedding and outlet boundary condition," Numerical Methods in Fluids, 26, 39-56.  https://doi.org/10.1002/(SICI)1097-0363(19980115)26:1<39::AID-FLD623>3.0.CO;2-P