Journal Of Applied and Computational Sciences in Mechanics

Journal Of Applied and Computational Sciences in Mechanics

Optimal Closed-Loop Control of Step Flow Separation Using Genetic Programming

Document Type : Original Article

Authors
Siroos Kasmaiee 1
Mehran Tadjfar 2
saman kasmaiee 3
1 Department of Aerospace Engineering, Amirkabir University of Technology, Tehran, Iran.
2 Aerospace engineering department, AmirKabir university. Tehran. Iran
3 Department of Aerospace Engineering, Amirkabir University of Technology, Tehran, Iran
10.22067/jacsm.2024.88143.1261
Abstract
In this research, novel model-free method based on machine learning for closed-loop control of flow separation behind a step is introduced. The primary goal of this study is to reduce the recirculation zone behind the step at a Reynolds number of 1350, which is achieved by a jet slot. In this study, the flow was simulated as steady and two-dimensional based on finite volume discretization. Feedback control rules have been optimized based on a cost function that includes the area of the recirculation flow and the costs associated with the injection. This optimization process was carried out using genetic programming algorithms. A tree-based genetic programming was used to construct various injection model functions and create a closed-loop control system. After evolving through 8 generations with 500 samples in each generation, the algorithm arrives at a feedback rule capable of reducing the recirculation flow area by up to 60%. This machine learning-based control system was compared with the best open-loop jet based on Kelvin-Helmholtz frequency pulsation.
Keywords
Flow control
Genetic programming
Numerical simulation
Machine learning
Computational fluid mechanics
Subjects
[1]    S. Kasmaiee and M. Tadjfar, “Influence of injection angle on liquid jet in crossflow,” International Journal of Multiphase Flow, vol. 153, p. 104128, 2022. https://doi.org/10.1016/j.ijmultiphaseflow.2022.104128
[2]    S. Kasmaiee and M. Tadjfar, “Experimental study of the injection angle impact on the column waves: wavelength, frequency and drop size,” Experimental Thermal and Fluid Science, vol. 148, p. 110989, 2023. https://doi.org/10.1016/j.expthermflusci.2023.110989
[3]    S. Kasmaiee, S. Kasmaiee, and M. Homayounpour, “Correcting spelling mistakes in Persian texts with rules and deep learning methods,” Scientific Reports, vol. 13, no. 1, p. 19945, 2023. https://doi.org/10.1038/s41598-023-47295-2
[4]    R. Özkan and M. S. Genç, “Aerodynamic design and optimization of a small-scale wind turbine blade using a novel artificial bee colony algorithm based on blade element momentum (ABC-BEM) theory,” Energy Conversion and Management, vol. 283, p. 116937, 2023. https://doi.org/10.1016/j.enconman.2023.116937
[5]    A. Pollard, T. J. Hacker, and S. Dyke, “Whither turbulence and big data for the twenty-first century: Discussion 2: Large data: Opportunities for collaborations,” in Whither Turbulence and Big Data in the 21st Century?, A. Pollard, L. Castillo, L. Danaila, and M. Glauser, Eds. Cham: Springer International Publishing, 2016. https://doi.org/10.1007/978-3-319-41217-7_31
[6]    E. Perlman, R. Burns, Y. Li, and C. Meneveau, “Data exploration of turbulence simulations using a database cluster,” in Proceedings of the 2007 ACM/IEEE Conference on Supercomputing, Reno, NV, USA, Nov. 2007, pp. 1–11. https://doi.org/10.1145/1362622.1362654
[7]    X. Wu and P. Moin, “A direct numerical simulation study on the mean velocity characteristics in turbulent pipe flow,” Journal of Fluid Mechanics, vol. 608, pp. 81–112, 2008. https://doi.org/10.1017/S0022112008002085
[8]    S. L. Brunton, B. R. Noack, and P. Koumoutsakos, “Machine learning for fluid mechanics,” Annual Review of Fluid Mechanics, vol. 52, no. 1, pp. 477–508, 2020. https://doi.org/10.1146/annurev-fluid-010719-060214
[9]    S. Kasmaiee, M. Tadjfar, S. Kasmaiee, and G. Ahmadi, “Linear stability analysis of surface waves of liquid jet injected in transverse gas flow with different angles,” Theoretical and Computational Fluid Dynamics, vol. 38, no. 1, pp. 107–138, 2024. https://doi.org/10.1007/s00162-024-00685-2
[10]  S. Kasmaiee and M. Tadjfar, “Non-circular pressure swirl nozzles injecting into stagnant air,” International Journal of Multiphase Flow, vol. 175, p. 104798, 2024. https://doi.org/10.1016/j.ijmultiphaseflow.2024.104798
[11]  L. Nissim, S. Karnik, P. A. Smith, Y. Wang, O. H. Frazier, and K. H. Fraser, “Machine learning based on computational fluid dynamics enables geometric design optimisation of the NeoVAD blades,” Scientific Reports, vol. 13, no. 1, p. 7183, 2023. https://doi.org/10.1038/s41598-023-33708-9
[12]  L. Zhu, W. Zhang, J. Kou, and Y. Liu, “Machine learning methods for turbulence modeling in subsonic flows around airfoils,” Physics of Fluids, vol. 31, no. 1, 2019. https://doi.org/10.1063/1.5061693
[13]  S. Kasmaiee, M. Tadjfar, and S. Kasmaiee, “Machine learning-based optimization of a pitching airfoil performance in dynamic stall conditions using a suction controller,” Physics of Fluids, vol. 35, no. 9, 2023. https://doi.org/10.1063/5.0164437
[14]  T. Duriez, S. L. Brunton, and B. R. Noack, Machine Learning Control – Taming Nonlinear Dynamics and Turbulence. vol. 116. Cham: Springer, 2017. https://doi.org/10.1007/978-3-319-40624-4
[15]  T. R. Bewley, P. Moin, and R. Temam, “DNS-based predictive control of turbulence: an optimal benchmark for feedback algorithms,” Journal of Fluid Mechanics, vol. 447, pp. 179–225, 2001. https://doi.org/10.1017/S0022112001005821
[16]  G. Novati, L. Mahadevan, and P. Koumoutsakos, “Controlled gliding and perching through deep-reinforcement-learning,” Physical Review Fluids, vol. 4, no. 9, p. 093902, 2019. https://doi.org/10.1103/PhysRevFluids.4.093902
[17]  S. Kasmaiee, M. Tadjfar, and S. Kasmaiee, “Optimization of blowing jet performance on wind turbine airfoil under dynamic stall conditions using active machine learning and computational intelligence,” Arabian Journal for Science and Engineering, vol. 49, no. 2, pp. 1771–1795, 2024. https://doi.org/10.1007/s13369-023-07892-9
[18]  S. Kasmaiee, S. Kasmaiee, and A. Farshad, “Unsteady CFD simulation of a rotor blade under various wind conditions,” Scientific Reports, vol. 14, no. 1, p. 19176, 2024. https://doi.org/10.1038/s41598-024-70350-5
[19]  S. Kasmaiee and M. Tadjfar, “Elliptical pressure swirl jet issuing into stagnant air,” Physics of Fluids, vol. 36, no. 7, 2024. https://doi.org/10.1063/5.0198105
[20]  S. Kasmaiee, M. Tadjfar, and S. Kasmaiee, “Investigation of suction jet parameters in flow control of dynamic stall,” Journal of Applied and Computational Sciences in Mechanics, vol. 32, no. 2, pp. 181–200, 2021. (In Persian) https://doi.org/10.22067/jacsm.2021.71526.1042
[21]  S. Kasmaiee, M. Tadjfar, and S. Kasmaiee, “Investigating the impacts of blowing jet on the dynamic stall of NACA0012,” Journal of Applied and Computational Sciences in Mechanics, vol. 34, pp. 1–20, 2022. (In Persian) https://doi.org/10.22067/jacsm.2022.74008.1074   
[22]  N. Gautier and J. L. Aider, “Effects of pulsed actuation upstream a backward-facing step,” Comptes Rendus Mécanique, vol. 342, no. 6–7, pp. 382–388, 2013. https://doi.org/10.1016/j.crme.2013.04.003
[23]  M. Pastoor, L. Henning, B. R. Noack, R. King, and G. Tadmor, “Feedback shear layer control for bluff body drag reduction,” Journal of Fluid Mechanics, vol. 608, pp. 161–196, 2008. https://doi.org/10.1017/S0022112008002073
[24]  G. Tadmor, O. Lehmann, B. R. Noack, L. Cordier, J. Delville, J.-P. Bonnet, and M. Morzyński, “Reduced-order models for closed-loop wake control,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 369, no. 1940, pp. 1513–1524, 2011. https://doi.org/10.1098/rsta.2010.0367
[25]  L. Brandt, D. Sipp, J. O. Pralits, and O. Marquet, “Effect of base-flow variation in noise amplifiers: the flat-plate boundary layer,” Journal of Fluid Mechanics, vol. 687, pp. 503–528, 2011. https://doi.org/10.1017/jfm.2011.382
[26]  O. Semeraro, S. Bagheri, L. Brandt, and D. S. Henningson, “Transition delay in a boundary layer flow using active control,” Journal of Fluid Mechanics, vol. 731, pp. 288–311, 2013. https://doi.org/10.1017/jfm.2013.299
[27]  N. Gautier and J.-L. Aider, “Control of the separated flow downstream of a backward-facing step using visual feedback,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 469, no. 2160, p. 20130404, 2013. https://doi.org/10.1098/rspa.2013.0404
[28]  M. Wahde, Biologically Inspired Optimization Methods: An Introduction, Southampton, UK: WIT Press, 2008. https://doi.org/10.1007/978-1-84564-344-7
[29]  C. Lee, J. Kim, D. Babcock, and R. Goodman, “Application of neural networks to turbulence control for drag reduction,” Physics of Fluids, vol. 9, no. 6, pp. 1740–1747, 1997. https://doi.org/10.1063/1.869290
[30]  I. Rechenberg, “Evolution strategy: Nature’s way of optimization,” in Optimization: Methods and Applications, Possibilities and Limitations, H. W. Bergmann, Ed., Lecture Notes in Engineering, vol. 47. Berlin, Heidelberg: Springer, 1989, pp. 106–126. https://doi.org/10.1007/978-3-642-83814-9_6
[31]  M. Milano and P. Koumoutsakos, “A clustering genetic algorithm for cylinder drag optimization,” Journal of Computational Physics, vol. 175, no. 1, pp. 79–107, 2002. https://doi.org/10.1006/jcph.2001.6882
[32]  R. Mäkinen, J. Toivanen, and J. Periaux, “Multidisciplinary shape optimization in aerodynamics and electromagnetics using genetic algorithms,” International Journal for Numerical Methods in Fluids, vol. 30, no. 2, pp. 149–159, 1999. https://doi.org/10.1002/(SICI)1097-0363(19990530)30:2<149::AID-FLD829>3.0.CO;2-B
[33]  K. Morimoto, K. Iwamoto, Y. Suzuki, and N. Kasagi, “Genetic algorithm-based optimization of feedback control scheme for wall turbulence,” in Proceedings of the Third Symposium Smart Control of Turbulence, Tokyo, Japan, 2002, pp. 107–113. http://thtlab.jp/Doc/SMART3-Morimoto.pdf
[34]  G. J. Berman and Z. J. Wang, “Energy-minimizing kinematics in hovering insect flight,” Journal of Fluid Mechanics, vol. 582, pp. 153–168, 2007. https://doi.org/10.1017/S0022112007006209
[35]  T. Duriez, V. Parezanovic and et. al, “Closed-loop control of experimental shear flows using machine learning,” in Proceedings 7th AIAA Flow Control Conference, Atlanta, GA, USA, 2014, p. 2219. https://doi.org/10.2514/6.2014-2219
[36]  N. Gautier, J.-L. Aider, T. Duriez, B. R. Noack, M. Segond, and M. Abel, “Closed-loop separation control using machine learning,” Journal of Fluid Mechanics, vol. 770, pp. 442–457, 2015. https://doi.org/10.1017/jfm.2015.95
[37]  H. Zarabadipour and M. Farhangranjbar, “Robust-adaptive sliding mode controller design with fault tolerance for active suspension of half-car model,” Journal of Applied and Computational Sciences in Mechanics, vol. 34, no. 3, pp. 79–96, 2022. (In Persian) https://doi.org/10.22067/jacsm.2022.71589.1043
[38]  A. Khorshidi and H. Moeenfard, “Beyond pull-in stabilization of a 2-DOF torsional micro-actuator using a fuzzy controller,” Journal of Applied and Computational Sciences in Mechanics, vol. 27, no. 2, pp. 99–112, 2016. (in Persian) https://doi.org/10.22067/fum_mech.v27i2.40523
[39]  M. Bamdad and A. Mardani, “Multimode wheeled mobile robot with height control on uneven surfaces,” Journal of Applied and Computational Sciences in Mechanics, vol. 27, no. 1, pp. 173–184, 2016. (In Persian) https://doi.org/10.22067/fum-mech.v27i1.34471
[40]  M. Mosalsal and M. Khodabandeh, “Variable-pitch control of a quadrotor using feedback linearization controller and direct adaptive feedback linearization controller,” Journal of Applied and Computational Sciences in Mechanics, vol. 31, no. 2, pp. 67–90, 2020. (In Persian) https://doi.org/10.22067/fum-mech.v31i2.84510 
[41]  M. Aalipour, A. Mokhtarian, and H. Karimpour, “Nonlinear control of motion of a spherical robot on inclined surfaces based on feedback linearization method,” Journal of Applied and Computational Sciences in Mechanics, vol. 31, no. 2, pp. 91–104, 2020. (In Persian) https://doi.org/10.22067/fum-mech.v31i2.74433
[42]  C. Chettri, “Numerical solution of a 2D flow over a backward facing step,” 2018. https://doi.org/10.13140/RG.2.2.28668.64646
[43]  D. M. Driver and H. L. Seegmiller, “Features of a reattaching turbulent shear layer in divergent channel flow,” AIAA Journal, vol. 23, no. 2, pp. 163–171, 1985. https://doi.org/10.2514/3.8890
[44]  D. C. Jespersen, T. H. Pulliam, and M. L. Childs, “OVERFLOW turbulence modeling resource validation results,” NASA Technical Report ARC-E-DAA-TN35216, NASA Ames Research Center, Moffett Field, CA, USA, 2016.
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