collaborators

5 papers

physics.flu-dyn2026

Physics-guided surrogate learning enables zero-shot control of turbulent wings

Yuning Wang, Pol Suarez, Mathis Bode +1

Turbulent boundary layers over aerodynamic surfaces are a major source of aircraft drag, yet their control remains challenging due to multiscale dynamics and spatial variability, p…

physics.flu-dyn2026

Modeling subgrid scale production rates on complex meshes using graph neural networks

Priyabrat Dash, Mathis Bode, Konduri Aditya

Large-eddy simulations (LES) require closures for filtered production rates because the resolved fields do not contain all correlations that govern chemical source terms. We develo…

physics.flu-dyn2026

Super-resolution of turbulent reacting flows on complex meshes using graph neural networks

Priyabrat Dash, Konduri Aditya, Christos E. Frouzakis +1

State-of-the-art deep learning models have been extensively utilized to reconstruct small-scale structures from coarse-grained data in turbulent flows. However, their application h…

physics.flu-dyn2025

Analysis of In-cylinder Flow Structures and Turbulence in a Laboratory Scale Engine using Direct Numerical Simulations

Bogdan A. Danciu, George K. Giannakopoulos, Mathis Bode +1

In-cylinder flow structures and turbulence characteristics are investigated using direct numerical simulations (DNS) in a laboratory-scale engine at technically relevant engine spe…

physics.flu-dyn2025

Direct Numerical Simulation of Hydrogen Combustion in a Real-Size IC Engine

Bogdan A. Danciu, George K. Giannakopoulos, Mathis Bode +1

This study presents the first Direct Numerical Simulation (DNS) of hydrogen combustion in a real-size internal combustion engine, investigating the complex dynamics of ignition, fl…