Spatial artificial neural network model for subgrid-scale stress and heat flux of compressible turbulence

The subgrid-scale(SGS)stress and SGS heat flux are modeled by using an artificial neural network(ANN)for large eddy simulation(LES)of compressible turbulence.The input features of ANN model are based on the first-order and second-order derivatives of filtered velocity and temperature at different spatial locations.The proposed spatial artificial neural network(SANN)model gives much larger correlation coefficients and much smaller relative errors than the gradient model in an a priori analysis.In an a posteriori analysis,the SANN model performs better than the dynamic mixed model(DMM)in the prediction of spectra and statistical properties of velocity and temperature,and the instantaneous flow structures.

Viscous Rayleigh-Taylor instability with and without diffusion effect

The approximate but analytical solution of the viscous Rayleigh-Taylor insta- bility （RTI） has been widely used recently in theoretical and numerical investigations due to its clarity. In this paper, a modified analytical solution of the growth rate for the viscous RTI of incompressible fluids is obtained based on an approximate method. Its accuracy is verified numerically to be significantly improved in comparison with the previous one in the whole wave number range for different viscosity ratios and Atwood numbers. Fur- thermore, this solution is expanded for viscous RTI including the concentration-diffusion effect.

Deconvolutional artificial-neural-network framework for subfilter-scale models of compressible turbulence

We establish a deconvolutional artificial-neural-network(D-ANN)approach in large-eddy simulation(LES)of compressible turbulent flow.Filtered variables in the neighboring locations are taken as the inputs of D-ANN to recover original(unfiltered)variables,including density,momentum and pressure.The scale-similarity form is adopted to reconstruct subfilter-scale(SFS)terms.The proposed D-ANN models can give better a priori predictions of the sub-filter stress and heat flux than the classical approximate-deconvolution method(ADM)and the velocity-gradient model(VGM).The predicted SFS terms with the D-ANN models have correlation coefficients larger than 98.4%and relative errors smaller than 18%.In the a posteriori analysis,the D-ANN model compares against the implicit LES(ILES),the dynamic-Smagorinsky model(DSM),and the dynamic-mixed model(DMM).The D-ANN model predicts better than these classical models for velocity spectra,statistical properties of SFS kinetic energy flux and velocity increments.The turbulence statistics and transient velocity divergence are also accurately reconstructed.The type of explicit filter and the impact of compressibility do not significantly affect a posteriori accuracy of the D-ANN model.Results showthat the proposed D-ANN approach has a great potential in developing highly accurate SFS models for large-eddy simulation of complex compressible turbulent flow.

Dynamic nonlinear algebraic models with scale-similarity dynamic procedure for large-eddy simulation of turbulence

A dynamic nonlinear algebraic model with scale-similarity dynamic procedure(DNAM-SSD)is proposed for subgrid-scale(SGS)stress in large-eddy simulation of turbulence.The model coefficients of the DNAM-SSD model are adaptively calculated through the scale-similarity relation,which greatly simplifies the conventional Germano-identity based dynamic procedure(GID).The a priori study shows that the DNAM-SSD model predicts the SGS stress considerably better than the conventional velocity gradient model(VGM),dynamic Smagorinsky model(DSM),dynamic mixed model(DMM)and DNAM-GID model at a variety of filter widths ranging from inertial to viscous ranges.The correlation coefficients of the SGS stress predicted by the DNAM-SSD model can be larger than 95%with the relative errors lower than 30%.In the a posteriori testings of LES,the DNAM-SSD model outperforms the implicit LES(ILES),DSM,DMM and DNAM-GID models without increasing computational costs,which only takes up half the time of the DNAM-GID model.The DNAM-SSD model accurately predicts plenty of turbulent statistics and instantaneous spatial structures in reasonable agreement with the filtered DNS data.These results indicate that the current DNAM-SSD model is attractive for the development of highly accurate SGS models for LES of turbulence.