A5051284248- Fluid Dynamics and Turbulent Flows
- Aerodynamics and Acoustics in Jet Flows
- Wind and Air Flow Studies
- Model Reduction and Neural Networks
- Fluid Dynamics and Vibration Analysis
- Computational Fluid Dynamics and Aerodynamics
Technical University of Munich
2024
University of Manchester
2022
In this work, we developed a novel framework for incorporating the near-wall non-overlapping domain decomposition (NDD) method with machine learning technique. It allows solution to be calculated Robin-type (slip) wall boundary condition on relatively coarse mesh and then corrected in region by solving thin boundary-layer equations fine subgrid. Through an estimated turbulent viscosity profile provided neural network, proposed can easily extended different turbulence models achieve...
Near-wall turbulence modeling represents one of challengers in the computational fluid dynamics. To tackle this problem, near-wall non-overlapping domain decomposition (NDD) method proved to be very efficient. It has been successfully used with different Reynolds-averaged Navier–Stokes models. In NDD is split into two sub-domains: an inner region near wall, which characterized by high gradients, and outer region. simplify solution, thin-layer model can used. case, a trade-off between...
To mitigate the high computational cost associated with resolving small near-wall eddies in large eddy simulation (LES) while achieving acceptable accuracy, this work extends implicit domain decomposition (INDD) method to a hybrid Reynolds-averaged Navier–Stokes (RANS)/LES zonal approach. In framework, LES solution is first computed using Robin-type (slip) wall boundary condition on coarse mesh. This then iteratively corrected region based an updated derived from simplified one-dimensional...