24 citations · 43 across the 10 of their papers we have counts for
12 papers
A Provably Robust Multi-Jet Framework applied to Active Flow Control of an Airfoil in Weakly Compressible Flow
Rohan Kaushik, Anna Schwarz, Andrea Beck
Reinforcement learning has by now become well established in finding excellent flow control strategies for a variety of scenarios. Existing literature has focused on using a simple…
In-Memory Load Balancing for Discontinuous Galerkin Methods on Polytopal Meshes
Patrick Kopper, Anna Schwarz, Jens Keim +1
High-order accurate discontinuous Galerkin (DG) methods have emerged as powerful tools for solving partial differential equations such as the compressible Navier-Stokes equations d…
Evaluating simulation techniques for lubricant distribution in gearboxes
Pawan S. Murthy, Anja Lippert, Andrea Beck
Efficient lubrication is crucial for the performance and durability of high-speed gearboxes, particularly under varying load conditions. Excess lubrication leads to increased churn…
SmartFlow: A CFD-solver-agnostic deep reinforcement learning framework for computational fluid dynamics on HPC platforms
Maochao Xiao, Yuning Wang, Felix Rodach +15
Deep reinforcement learning (DRL) is emerging as a powerful tool for fluid-dynamics research, encompassing active flow control, autonomous navigation, turbulence modeling and disco…
Entropy stable high-order discontinuous Galerkin spectral-element methods on curvilinear, hybrid meshes
Jens Keim, Anna Schwarz, Patrick Kopper +3
Hyperbolic-parabolic partial differential equations are widely used for the modeling of complex, multiscale problems. High-order methods such as the discontinuous Galerkin (DG) sch…
Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks
Marius Kurz, Andrea Beck, Benjamin Sanderse
This work proposes a novel methodology for turbulence modeling in Large Eddy Simulation (LES) based on Graph Neural Networks (GNNs), which embeds the discrete rotational, reflectio…
Comparison of Entropy Stable Collocation High-Order DG Methods for Compressible Turbulent Flows
Anna Schwarz, Daniel Kempf, Jens Keim +3
High-order methods are well-suited for the numerical simulation of complex compressible turbulent flows, but require additional stabilization techniques to capture instabilities ar…
Entropy stable shock capturing for high-order DGSEM on moving meshes
Anna Schwarz, Jens Keim, Christian Rohde +1
In this paper, a shock capturing for high-order entropy stable discontinuous Galerkin spectral element methods on moving meshes is proposed using Gauss--Lobatto nodes. The shock ca…
Invariant Control Strategies for Active Flow Control using Graph Neural Networks
Marius Kurz, Rohan Kaushik, Marcel Blind +4
Reinforcement learning has gained traction for active flow control tasks, with initial applications exploring drag mitigation via flow field augmentation around a two-dimensional c…
Efficient computation of particle-fluid and particle-particle interactions in compressible flow
Anna Schwarz, Patrick Kopper, Emilian De Staercke +1
Particle collisions are the primary mechanism of inter-particle momentum and energy exchange for dense particle-laden flow. Accurate approximation of this collision operator in fou…
GALÆXI: Solving complex compressible flows with high-order discontinuous Galerkin methods on accelerator-based systems
Daniel Kempf, Marius Kurz, Marcel Blind +6
This work presents GALAEXI as a novel, energy-efficient flow solver for the simulation of compressible flows on unstructured meshes leveraging the parallel computing power of moder…
Toward Discretization-Consistent Closure Schemes for Large Eddy Simulation Using Reinforcement Learning
Andrea Beck, Marius Kurz
This study proposes a novel method for developing discretization-consistent closure schemes for implicitly filtered Large Eddy Simulation (LES). Here, the induced filter kernel, an…