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Michael Graham

Michael D Graham

Steenbock Professor of Engineering
Harvey D. Spangler Professor
Vilas Distinguished Achievement Professor

The Graham group uses theory and computations to study problems in fluid dynamics, rheology and transport phenomena over a wide range of scales. We focus on problems that hold both fundamental interest in advancing basic principles as well as impact on applications. The group has two basic thrust areas, one in microscale flows and complex fluids and the other in the nonlinear dynamics of turbulent flows. In the first area, we are interested in general in the dynamics of mechanically and geometrically complex objects suspended in a flowing fluid and the interplay between microstructure and flow. Specific examples under study include the dynamics of blood cells in flow, the interplay between cell geometry and mechanics in bacterial swimming, the deformations of thin deformable sheetlike particles in flow, and the rheology and fluid dynamics of dilute micellar surfactant solutions. In the area of turbulent flows, the group aims to elucidate the complex interaction between rheology and fluid dynamics that leads to the phenomenon of turbulent drag reduction in polymer and surfactant solutions — this topic is a bridge to the group’s interest in microscale flows and rheology. We also apply ideas from nonlinear dynamical systems theory, data science and machine learning to elucidate the principles underlying the complex dynamics of turbulent shear flows, aiming toward development of control schemes that can manipulate turbulence to desired ends such as drag reduction.

Department

Chemical & Biological Engineering

Contact

3010, Engineering Hall
1415 Engineering Dr
Madison, WI

  • PhD 1992, Cornell University
  • BS 1986, University of Dayton

  • Fluid dynamics
  • Locomotion of microorganisms
  • Blood flow at the cellular level
  • Dynamics of mechanically and geometrically complex objects suspended in a flowing fluid
  • Polymer, surfactant and suspension rhology
  • Turbulent shear flows; drag reduction by polymer and surfactant additives
  • Applications of nonlinear dynamics and machine learning in fluid dynamics

Affiliated Departments

  • 2024 The Society of Rheology, Eugene C. Bingham Medal
  • 2023 American Physical Society Petroleum Research Fund, Excellence in Peer Reviewing Certificate
  • 2023 Dept. of Engineering Science and Applied Mathematics, Northwestern University, Stephen H. Davis Lecturer
  • 2022 School of Chemical and Biomolecular Engineering, Cornell University, Julian C. Smith Lecturer
  • 2020 Univ. of Wisconsin-Madison, Steenbock Professor of Engineering
  • 2020 University of Alabama-Huntsville, Distinguished Speakers Series
  • 2020 American Physical Society, Physical Review Outstanding Referee
  • 2019 American Institute of Chemical Engineers Annual Meeting, William R. Schowalter Lecturer
  • 2018 Department of Defense, Vannevar Bush Faculty Fellowship
  • 2015 American Physical Society Division of Fluid Dynamics, Stanley Corrsin Award
  • 2014 Univ. of Wisconsin-Madison, Vilas Distinguished Achievement Professorship
  • 2014 British Applied Mathematics Colloquium, Stewartson Lecturer
  • 2013 American Physical Society , Plenary lecture, American Physical Society Division of Fluid Dynamics Annual Meeting
  • 2013 European Rheology , Plenary lecturer, European Rheology Conference
  • 2013 Society of Rheology , Plenary lecturer, Society of Rheology Annual Meeting
  • 2013 MIT, Ronald F. Probstein Lecturer
  • 2012 UC-Santa Barbara, Dale Pearson Lecturer
  • 2012 Univ. of Wisconsin-Madison, Kellett Mid-Career Award
  • 2011 American Physical Society, American Physical Society Fellowship
  • 2008 National Academy of Engineering, Invitee, National Academy of Engineering Frontiers of Engineering Symposium
  • 2005 College of Engineering, Univ. of Wisconsin-Madison, Harvey D. Spangler Professorship, Dept. of Chemical and Biological Engineering
  • 2005 Univ. of Delaware, Allan P. Colburn Memorial Lecturer
  • 2004 American Physical Society, Francois Naftali Frenkiel Award for Fluid Mechanics
  • 2002 Univ. of Wisconsin-Madison, Vilas Associate in the Physical Sciences
  • 1997 3M Co., 3M Non-Tenured Faculty Award
  • 1994 Shell Faculty Fellow
  • 1990 DAAD (German Academic Exchange Service) , DAAD (German Academic Exchange Service) scholarship for research in Germany
  • 1987 Cornell University, Outstanding Graduate Teaching Assistant
  • 1986 Cornell University, McMullen Graduate Fellowship
  • 1986 AIChE, 1st Place, AIChE Environmental Division Undergraduate Student Paper Competition

  • Zeng, K., De Jes'us, Carlos E P'erez,, Fox, A. J., & Graham, M. D. (2024). Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems. Machine Learning: Science and Technology, 5(2), 025053.
  • Fox, A. J., & Graham, M. D. (2024). Data-driven low-dimensional model of a sedimenting flexible fiber. arXiv preprint arXiv:2405.10442.
  • Constante-Amores, C. R., & Graham, M. D. (2024). Data-driven state-space and Koopman operator models of coherent state dynamics on invariant manifolds. Journal of Fluid Mechanics, 984, R9 https://doi.org/10.1017/jfm.2024.284
  • Constante-Amores, C. R., Linot, A. J., & Graham, M. D. (2024). Enhancing predictive capabilities in data-driven dynamical modeling with automatic differentiation: Koopman and neural ODE approaches. Chaos: An Interdisciplinary Journal of Nonlinear Science, 34(4), 043119 https://doi.org/10.1063/5.0180415
  • Yu, Y., & Graham, M. D. (2024). Free-space and near-wall dynamics of a flexible sheet sedimenting in Stokes flow. Physical Review Fluids, 9(5), 054104 https://doi.org/10.1103/physrevfluids.9.054104
  • Kumar, M., & Graham, M. D. (2024). Nested traveling wave structures in elastoinertial turbulence. arXiv preprint arXiv:2403.06815.
  • P'erez De Jes'us, Carlos E,, Linot, A. J., & Graham, M. D. (2023). Building symmetries into data-driven manifold dynamics models for complex flows. arXiv preprint arXiv:2312.10235.
  • P'erez De Jes'us, Carlos E,, & Graham, M. D. (2023). Data-driven low-dimensional dynamic model of Kolmogorov flow. Physical Review Fluids, 8(4), 044402.
  • Young, C. D., & Graham, M. D. (2023). Deep learning delay coordinate dynamics for chaotic attractors from partial observable data. Physical Review E, 107(3), 034215 https://doi.org/10.1103/physreve.107.034215
  • Linot, A. J., & Graham, M. D. (2023). Dynamics of a data-driven low-dimensional model of turbulent minimal Couette flow. Journal of Fluid Mechanics, 973, A42 https://doi.org/10.1017/jfm.2023.720

  • CBE 620 - Intermediate Transport Phenomena (Spring 2025)
  • CBE 890 - Pre-Dissertator's Research (Spring 2025)
  • CBE 990 - Thesis-Research (Spring 2025)
  • CBE 562 - Special Topics in Chemical Engineering (Fall 2024)
  • CBE 599 - Special Problems (Fall 2024)
  • CBE 990 - Thesis-Research (Fall 2024)
  • CBE 990 - Thesis-Research (Summer 2024)
  • CBE 990 - Thesis-Research (Spring 2024)
  • CBE 790 - Master's Research or Thesis (Fall 2023)
  • CBE 990 - Thesis-Research (Fall 2023)
  • CBE 790 - Master's Research or Thesis (Summer 2023)
  • CBE 890 - Pre-Dissertator's Research (Summer 2023)
  • CBE 990 - Thesis-Research (Summer 2023)