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Variational Data Assimilation in Computational Fluid Dynamics
This student project is part of a project with the goal to use data assimilation for closure model parameter optimization in single- and multi-phase turbulent flow. An approach is based on the OpenFOAM computational fluid dynamics (CFD) suite and Python optimization tools.
Flow simulations performed in industry are - even today - often based on the Reynolds-averaged Navier-Stokes equations (RANS). While computational costs are low compared to more involved approaches, the accuracy is often low, too.
This project aims at improving the agreement between experimental and simulation results for a specific set-up by adjusting closure model parameters through data assimilation. To this end, the discrete adjoint method is used to obtain gradient information which, is used to optimize the closure model parameters.
Currently, the framework is being extended to deal with multi-phase problems as found for example in fuel sprays.
Flow simulations performed in industry are - even today - often based on the Reynolds-averaged Navier-Stokes equations (RANS). While computational costs are low compared to more involved approaches, the accuracy is often low, too. This project aims at improving the agreement between experimental and simulation results for a specific set-up by adjusting closure model parameters through data assimilation. To this end, the discrete adjoint method is used to obtain gradient information which, is used to optimize the closure model parameters. Currently, the framework is being extended to deal with multi-phase problems as found for example in fuel sprays.
A suitable student project will be defined based on the overall project progress, type of thesis and student interest, prior to the semester start. Possible goals may include, but are not limited to:
Identify, implement and test suited optimization methods; Parameter studies (e.g. on optimization method parameters); Develop validation cases and run validation studies; Evaluation of parameter field mapping functions.
A suitable student project will be defined based on the overall project progress, type of thesis and student interest, prior to the semester start. Possible goals may include, but are not limited to: Identify, implement and test suited optimization methods; Parameter studies (e.g. on optimization method parameters); Develop validation cases and run validation studies; Evaluation of parameter field mapping functions.
Interested candidates send an email with a recent transcript of records to: oliver.brenner@ifd.mavt.ethz.ch
Interested candidates send an email with a recent transcript of records to: oliver.brenner@ifd.mavt.ethz.ch