Marcel Blind- Dr.-Ing.
- Researcher at University of Stuttgart
Marcel Blind
- Dr.-Ing.
- Researcher at University of Stuttgart
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13
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Introduction
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Publications
Publications (13)
Reinforcement learning (RL) has recently gained traction for active flow control tasks, with initial applications exploring drag mitigation via flow field augmentation around a two-dimensional cylinder. RL has since been extended to more complex turbulent flows and has shown significant potential in learning complex control strategies. However, suc...
This work presents GALÆXI as a novel, energy-efficient flow solver for the simulation of compressible flows on unstructured hexahedral meshes leveraging the parallel computing power of modern Graphics Processing Units (GPUs). GALÆXI implements the high-order Discontinuous Galerkin Spectral Element Method (DGSEM) using shock capturing with a finite-...
In this study, the interaction between a turbulent wake and the boundary layer of a horizontal tail plane (HTP) in the transonic flow regime is investigated. The setup considered corresponds to a generic tandem wing configuration with an OAT15A airfoil as the main wing and a National Advisory Committee for Aeronautics (NACA) 64A-110 as an HTP. Due...
This work presents GALÆXI as a novel, energy-efficient flow solver for the simulation of compressible flows on unstructured meshes leveraging the parallel computing power of modern Graphics Processing Units (GPUs). GALÆXI implements the high-order Discontinuous Galerkin Spectral Element Method (DGSEM) using shock capturing with a finite-volume subc...
Large-scale simulations pose significant challenges not only to the solver itself but also to the pre- and postprocessing framework. Hence, we present generally applicable improvements to enhance the performance of those tools and thus increase the feasibility of large-scale jobs and convergence studies. To accomplish this, we use a shared memory a...
Modern high-order discretizations bear considerable potential for the exascale era due to their high fidelity and the high, local computational load that allows for computational efficiency in massively parallel simulations. To this end, the discontinuous Galerkin (DG) framework FLEXI was selected to demonstrate exascale readiness within the Center...
The High Fidelity CFD Workshop was held on January 8-9, 2022, and covered a range of test cases focused on verification. The present paper contains a summary of the test case focused on smooth body separation prediction using wall-modeled large eddy simulation, to which 11 participant teams submitted blind predictions.
In this paper we present experimental and numerical reference data for a NACA 64A-110 airfoil at two angles of attack for $Ma=0.72$ and a Reynolds number of $Re_c=930.000$ with respect to the chord length. The test cases are designed to provide data for an uninclined airfoil at $0^\circ$ and for the case of a stable, steady shock at $3^\circ$. For...
Generating turbulent inflow data is a challenging task in zonal large eddy simulation (zLES) and often relies on predefined DNS data to generate synthetic turbulence with the correct statistics. The more accurate, but more involved alternative is to use instantaneous data from a precursor simulation. Using instantaneous data as an inflow condition...
Turbulent inflow methods offer new possibilities for an efficient simulation by reducing the computational domain to the interesting parts. Typical examples are turbulent flow over cavities, around obstacles or in the context of zonal large eddy simulations. Within this work, we present the current state of two turbulent inflow methods implemented...
Generating turbulent inflow data is a challenging task in zonal Large Eddy Simulation (zLES) and often relies on predefined DNS data to generate synthetic turbulence with the correct statistics. The more accurate, but more involved alternative is to use instantaneous data from a precursor simulation. Using instantaneous data as an inflow condition...
The accuracy and computational cost of a large eddy simulation are highly dependent on the computational grid. Building optimal grids manually from a priori knowledge is not feasible in most practical use cases; instead, solution-adaptive strategies can provide a robust and cost-efficient method to generate a grid with the desired accuracy. We adap...
