We demonstrate how incorporating physics constraints into convolutional neural networks
(CNNs) enables learning subgrid-scale (SGS) closures for stable and accurate large-eddy …

Combustion science is an interdisciplinary study that involves nonlinear physical and
chemical phenomena in time and length scales, including complex chemical reactions and …

大涡模拟方法(LES) 是研究复杂湍流问题的重要工具, 在航空航天, 湍流燃烧, 气动声学,
大气边界层等众多工程领域中具有广泛的应用前景. 大涡模拟方法采用粗网格计算大尺度上的 …

In this work, artificial neural network-based nonlinear algebraic models (ANN-NAMs) are
developed for the subgrid-scale (SGS) stress in large eddy simulation (LES) of turbulence at …

Deconvolutional artificial neural network (DANN) models are developed for subgrid-scale
(SGS) stress in large eddy simulation (LES) of turbulence. The filtered velocities at different …

Large eddy simulation (LES) is an important method to investigate different types of complex
turbulent flows, which has been widely applied to the turbulent flows in aerospace …

Highlights•The development of a new dynamic Smagorinsky model which obtains coefficient
by ANN.•The ANN model is trained from a simple case and is generalizable to other …

Numerical weather prediction is an established weather forecasting technique in which
equations describing wind, temperature, pressure and humidity are solved using the current …

Dynamic iterative approximate deconvolution (DIAD) models with Galilean invariance are
developed for subgrid-scale (SGS) stress in the large-eddy simulation (LES) of turbulence …

Contemporary research on the application of data-driven techniques to modeling subgrid
closure in two-dimensional turbulence has been limited. Furthermore, the incorporation of …