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Accelerating Predictions of Turbulent Combustion and Nonequilibrium Flows Using Solver-Embedded Deep Learning
To join this seminar online: https://teams.microsoft.com/l/meetup-join/19%3ameeting_N2QyNzVlMDAtN2MyMi00MjFkLTk3NTgtZWI2MTcyNDVlOTEw%40thread.v2/0?context=%7b%22Tid%22%3a%22cc95de1b-97f5-4f93-b4ba-fe68b852cf91%22%2c%22Oid%22%3a%22e6ced614-5673-458c-832d-5d4ada66f593%22%7d
Predictions of complex flows remain a significant challenge for engineering systems. Computationally affordable predictions of turbulent flows generally require Reynolds-Averaged Navier–Stokes (RANS) simulations and Large-Eddy Simulation (LES), the predictive accuracy of which can be insufficient due to non-Boussinesq turbulence and/or unresolved multiphysics that preclude qualitative fidelity in certain regimes. For example, in turbulent combustion, flame–turbulence interactions can lead to inverse-cascade energy transfer, which violates the assumptions of many RANS and LES closures. We present an adjoint-based, solver-embedded data assimilation method to augment the RANS and LES equations using trusted data. This is accomplished using Python-native flow solvers that leverage differentiable programming techniques to construct the adjoint equations needed for optimization. We present applications to shock-tube ignition delay predictions, turbulent premixed jet flames, and shock-dominated nonequilibrium flows and discuss the potential of adjoint-based approaches for future machine learning applications.
Date:
22 May 2025, 12:00
Venue:
L3
Speaker:
Jonathan F. MacArt (University of Notre Dame)
Organiser:
Alain Goriely (University of Oxford)
Organiser contact email address:
Kirkham@maths.ox.ac.uk
Part of:
Industrial & Applied Mathematics Seminar
Booking required?:
Not required
Cost:
Free
Audience:
Public
Editor:
Nicola Kirkham
