Publications

2024

Deng, N., Yan, Y., Ji, C., & Noack, B. R. (2024). Feature-based manifold modeling for the quasiperiodic wake dynamics of a pair of side-by-side cylinders. Physics of Fluids, 36(9).
Reumschüssel, J. M., Li, Y., zur Nedden, P. M., Wang, T., Noack, B. R., & Paschereit, C. O. (2024). Experimental jet control with Bayesian optimization and persistent data topology. Physics of Fluids, 36(9).
Jiang, Z., Cornejo Maceda, G. Y., Li, Y., Shaqarin, T., Gao, N., & Noack, B. R. (2024). Jet mixing optimization using a flexible nozzle, distributed actuators, and machine learning. Physics of Fluids, 36(9).
Wang, X., Cornejo Maceda, G. Y., Liu, Y., Hu, G., Gao, N., Raps, F., & Noack, B. R. (2024). Coarse-graining characterization of the room flow circulations due to a fan-array wind generator. Physics of Fluids, 36(8).
Hou, C., Deng, N., & Noack, B. R. (2024). Dynamics-augmented cluster-based network model. Journal of Fluid Mechanics, 988, A48.
Wang, Q., Yan, L., Hu, G., Chen, W., Rabault, J., & Noack, B. R. (2024). Dynamic feature-based deep reinforcement learning for flow control of circular cylinder with sparse surface pressure sensing. Journal of Fluid Mechanics, 988, A4.
Li, S., Liu, Y., Jiang, Z., Hu, G., Noack, B. R., & Raps, F. (2024). Aerodynamic Characterization of a Fan-Array Wind Generator. AIAA Journal, 62(1), 291-301.
Li, P., Yang, Y., Li, Q., Arcondoulis, E. J., Noack, B. R., & Liu, Y. (2024). Effect of blade number on rotor efficiency and noise emission at hovering condition. Physics of Fluids, 36(2).
Yang, Y., Liang, Y., Pröbsting, S., Li, P., Zhang, H., Huang, B., … & Noack, B. R. (2024). Sizing of Multicopter Air Taxis—Weight, Endurance, and Range. Aerospace, 11(3), 200.

2023

Mendez, M. A., Ianiro, A., Noack, B. R. & Brunton, S. L. (2023) “Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning“. Cambridge University Press.
Semaan, R., Oswalds, P., Cornejo Maceda, G. Y. &l; Noack, B.~R. (2023) Aerodynamic optimization of a generic light truck under unsteady conditions using gradient‑enriched machine learning control. Experiments in Fluids 64, 59:1-27
Huang, J., Zeng, W., Xiong, J., Noack, B.R., Hu, G., Liu, S., Xu, Y. & Cao, H. (2023) Symmetry-informed reinforcement learning and its application to the attitude control of quadrotors. IEEE Transactions on Artificial Intelligence.
Shaqarin, T. & Noack, B.~R. (2022) A fast converging particle swarm optimization through targeted, position-mutated, elitism (PSO-TPME). International Journal of Computational Intelligent Systems 16 article 6:1-17.
Cornejo Maceda, G. Y., Varon, E., Lusseyran, F. & Noack, B. R. (2023) Stabilization of a multi-frequency open cavity flow with gradient-based machine learning control. Journal of Fluid Mechanics 955, A20:1–49.
Farzamnik, E., Ianiro, A., Discetti, S., Deng, N., Oberleithner, K., Noack, B. R. & Guerrero, V. (2022) From snapshots to manifolds – A tale of shear flows. Journal of Fluid Mechanics 955, A34:1:24.
Wenjie Chen, Qiulei Wang, Lei Yan, Gang Hu, Bernd R. Noack. (2023) Deep reinforcement learning-based active flow control of vortex-induced vibration of a square cylinder. Physics of Fluids May 35 (5): 053610.
Wang, T.Y., Yang, Y., Chen, X.W., Li, P.Y., Iollo, A., Cornejo Maceda, G.Y. & Noack, B.R. (2023) Cluster-based control for net drag reduction for the fluidic pinball. Physics of Fluids 35 055105:1–14.
Wang, X., Cornejo Maceda, G. Y., Deng, N. & Noack, B.R. (2023) Cluster-based control for net drag reduction for the fluidic pinball. Physics of Fluids 35 023601:1–16.

2022

Blanchard, A., Cornejo Maceda, G. Y., Fan, D., Li, Y., Zhou, Y., Noack, B. R. & Sapsis, T. (2022) Bayesian optimization of active flow control. Acta Mechanica Sinica 37, 1786-1798.
Zhang, W.W. & Noack, B. R. (2022) Artificial intelligence in fluid mechanics. Acta Mechanica Sinica 37, 1715-1717.
Cornejo Maceda, G. Y., Lusseyran, F. & Noack, B. R. (2022) “xMLC—A Toolkit for Machine Learning Control” Series ‘Machine Learning Tools for Fluid Mechanics’ 2, Technische Universität Braunschweig, Germany.
Deng, N., Noack, B. R., Morzynski, M., & Pastur, L.~R. (2022) Cluster-based hierarchical network model of the fluidic pinball — Cartographing transient and post-transient, multi-frequency, multi-attractor behaviour. Journal of Fluid Mechanics 934, A24, 1–44.
Li, S.Q., Li, W. & Noack, B.~R. (2022) Non-Linear, control-oriented flow estimation for multi-actuator multi-sensor systems exemplified for the fluidic pinball. Journal of Fluid Mechanics 952, A36.
Li, Y., Cui, W., Jia, Q., Li, Q., Yang, Z. & Noack, B. R. (2022) Optimization of active drag reduction for a slanted Ahmed body in a high-dimensional parameter space. Journal of Fluid Mechanics 932 A7:1–48.
Li, S.Q., Li, W. & Noack, B.~R. (2022) Least-order representation of control-oriented flow estimation exemplified for the fluidic pinball. Journal of Physics: Conference Series 2367, article 012024:1–8
Castellanos, R., Cornejo Maceda, G. Y., De La Fuente, I., Noack, B. R., Ianiro, A., & Discetti, S. (2022). Machine-learning flow control with few sensor feedback and measurement noise. Physics of Fluids 34(4),
Hou, C., Deng, N. & Noack, B.~R. (2022) Trajectory-optimized cluster-based network model for the sphere wake. Physics of Fluids 34, 085110:1–19.
Wang, Q.L., Yan, L., G. Hu, Li C., Xiao, Y.Q., Xiong, H., Rabault, J. & Noack, B.R. (2022) DRLinFluids—An open-source python platform of coupling Deep Reinforcement Learning and OpenFOAM. Physics of Fluids 34(8), 081801:1–15.

2021

Fernex, D., Semaan, R. & Noack, B. R. (2021) “Generalized cluster-based network model for an actuated turbulent boundary layer.” Paper at the Proceedings of the 2021 AIAA SciTech Forum, Nashville, TN, USA.
Fernex, D., Weiner, A., Noack, B., & Semaan, R. (2021). Sparse Spatial Sampling: A mesh sampling algorithm for efficient processing of big simulation data. In AIAA Scitech 2021 Forum (p. 1484).
Blanchard, A. B., Cornejo Maceda, G. Y., Fan, D., Li, Y., Zhou, Y., Noack, B. R., & Sapsis, T. P. (2021). Bayesian optimization for active flow control. Acta Mechanica Sinica, 1-13.
Cornejo Maceda, G.~Y., Li, Y., Lusseyran, F., Morzynski, M. & Noack, B. R. (2021) Stabilization of the fluidic pinball with gradient-based machine learning control. Journal of Fluid Mechanics 917, article A42:1-43.
Deng, N., Noack, B. R., Morzynski, M., & Pastur, L. R. (2021) Galerkin force model for transient dynamics of the fluidic pinball. Journal of Fluid Mechanics 918, article A04:1–37
Hao Li, Daniel Fernex, JianGuo Tan, Marek Morzynski & Noack, B. R. (2021) Cluster-based network model. Journal of Fluid Mechanics 906, article A21, 1-41.
Qiao, Z. Minelli, G., Noack, B. R., Krajnovic, S., & Chernoray, V. (2021) “Multi-frequency aerodynamic control of a yawed bluff body optimized with a genetic algorithm.” Journal for Wind Engineering and Industrial Applications 212, article 104600
Shaqarin, T., Oswald, P., Noack, B. R., & Semaan, R. (2021). Drag reduction of a D-shaped bluff-body using linear parameter varying control. Physics of Fluids, 33(7).
Fernex, D., Noack, B. R., & Semaan, R. (2021) “Cluster-based network models-From snapshots to complex dynamical systems” Science Advances 7 (25), eabf5006:1..10.

2020

Brunton, S. L., Noack, B. R. & Koumoutsakos, P. (2020) “Machine learning for fluid mechanics”. Annual Reviews of Fluid Mechanics 52., 477–508
Albers, M., Meysonnat, P. S., Fernex, D., Semaan, R., Noack, B. R. & Schröder, W. (2020) Drag reduction and energy savings by spanwise travelling transversal surface waves for flat plate flow. Flow, Turbulence and Combustion (online).
Deng, N., Noack, B. R., Morzynski, M., & Pastur, L.~R. (2020) Low-order model for successive bifurcations of the fluidic pinball. Journal of Fluid Mechanics 884, A37.
Minelli, G., Dong, T., Noack, B. R. & Krajnovic, S. (2020) Upstream actuation for bluff body wake control driven by a genetically inspired optimization. Journal of Fluid Mechanics, 893.
Zhou, Y, Fan, D., Zhang, B., Li, R. & Noack, B. R. (2020) Artificial intelligence control of a turbulent jet. Journal of Fluid Mechanics 897, article A27, 1-46.
Raibaudo, C, Zhong, P., Noack, B. R., & Martinuzzi, R. J.. (2020) “Machine learning strategies applied to the control of a fluidic pinball.” Physics ofFluids 32, article 015108.
Tan, J., Li, H., & Noack, B. R. (2020). On the cavity-actuated supersonic mixing layer downstream a thick splitter plate. Physics of Fluids, 32(9).
Fernex, D., Semaan, R., Albers, M., Meysonnat, P., Schröder, W. & Noack, B. R. (2020) Actuation response model from sparse data for wall turbulence drag reduction. Physics Reviews Fluids 5 (7), article 073901.
Zheng, Q., Wang, J., Noack, B. R., Li, H., Wan, M., & Chen, S. (2020). Vibrational relaxation in compressible isotropic turbulence with thermal nonequilibrium. Physical Review Fluids, 5(4), 044602.
Lennie, M., Steenbuck, J., Noack, B. R., & Paschereit, C. O. (2019). Cartographing dynamic stall with machine learning. Wind Energy Science Discussions, 2019, 1-31.

Books

Review articles

Patents

Y. T. Liu, B. R. Noack, N. Deng, X. Wang, S. Q. Li, and Z. T. Jiang. A fan array device for generating wind fields. China Utility Model Patent, CN 219865511U, 2023.