Robust deep learning for emulating turbulent viscosities
From the simplest models to complex deep neural networks, modeling turbulence with
machine learning techniques still offers multiple challenges. In this context, the present …
machine learning techniques still offers multiple challenges. In this context, the present …
Reconstruction of proper numerical inlet boundary conditions for draft tube flow simulations using machine learning
This paper discusses an innovative strategy to determine appropriate mean and fluctuating
inlet boundary conditions for the numerical flow simulations of a hydraulic turbine draft tube …
inlet boundary conditions for the numerical flow simulations of a hydraulic turbine draft tube …
Dimension reduced turbulent flow data from deep vector quantisers
Analysing large-scale data from simulations of turbulent flows is memory intensive, requiring
significant resources. This major challenge highlights the need for data compression …
significant resources. This major challenge highlights the need for data compression …
Inflow turbulence generation for compressible turbulent boundary layers
RX Li, WX Huang, CX Xu - Physics of Fluids, 2024 - pubs.aip.org
It is still challenging to generate high-quality inflow turbulence for the direct numerical and
large-eddy simulations of compressible turbulent boundary layers (CTBL). Recently, Wang …
large-eddy simulations of compressible turbulent boundary layers (CTBL). Recently, Wang …