Professorial Faculty
Technical University of Munich

Prof. Dr. Makrand Khanwale

Professorship

Computational Fluid Dynamics

School

TUM School of Engineering and Design

Contact Details

Business card at TUMonline

Academic Career and Research Areas

Prof. Khanwale focuses on developing computational frameworks for multiphysics fluid dynamics, combining rigorous numerical analysis, high-fidelity simulation, and high-performance computing. This work bridges physics, applied mathematics, and computer science to enable predictive modeling of complex fluid dynamical systems. Building on this foundation, his research group develops computational tools to study multiphysics multiscale transport phenomena

Prof. Khanwale was a Physical Science Research Scientist in the Department of Mechanical Engineering at Stanford University, where he worked with Prof. Ali Mani and Prof. Gianluca Iaccarino. Before that he received his PhD from Iowa State University, with a co-major in Mechanical Engineering and Applied Mathematics, under the supervision of Prof. Baskar Ganapathysubramanian and Prof. James Rossmanith.  His doctoral research focused on developing energy-stable numerical methods for multiphase flows.

    Awards

    • Finalist in the Scientific Visualization and Data Analytics Showcase at Super Computing ’22, International Conference for High-Performance Computing, Networking, Storage, and Analysis (2022)
    • Research Excellence Award from Iowa State Graduate College (2021)
    • Teaching Excellence Award from Iowa State Graduate College (2019)
    • Recipient of Dean’s fellowship from the College of Engineering at Iowa State University for incoming graduate students

    Key Publications (all publications)

    Sungu Kim, Kumar Saurabh, Makrand A. Khanwale, Ali Mani, Robbyn K Anand, and Baskar Ganapathysubramanian. “Direct numerical simulation of electrokinetic transport phenomena in fluids: Variational multi-scale stabilization and octree-based mesh refinement”. Journal of Computational Physics. 2024; 500.

    Abstract

    Kumar Saurabh, Masado Ishii, Makrand A. Khanwale, Hari Sundar, and Baskar Ganapathysubramanian. “Scalable adaptive algorithms for next-generation multiphase flow simulations”. 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS). 2023, pp. 590–601.

    Abstract

    Makrand A. Khanwale, Kumar Saurabh, Masado Ishii, Hari Sundar, James A. Rossmanith, and Baskar Ganapathysubramanian. “A projection-based, semi-implicit time-stepping approach for the Cahn-Hilliard Navier-Stokes equations on
    adaptive octree meshes”. Journal of Computational Physics. 2023; 475.  

    Abstract

    Makrand A. Khanwale, Kumar Saurabh, Milinda Fernando, Victor M. Calo, Hari Sundar, James A. Rossmanith, and Baskar Ganapathysubramanian. “A fully-coupled framework for solving Cahn-Hilliard Navier-Stokes equations: Second-order, energy-stable numerical methods on adaptive octree based meshes”. Computer Physics Communications. 2022; 280.

    Abstract

    Makrand A. Khanwale, Alec D. Lofquist, Hari Sundar, James A. Rossmanith, and Baskar Ganapathysubramanian. “Simulating two-phase flows with thermodynamically consistent energy stable Cahn-Hilliard Navier-Stokes equations on parallel adaptive octree based meshes”. Journal of Computational Physics. 2020; 419. 

    Abstract

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