We study the effect of sub-filter scale (SFS) dynamics on the accuracy of direct deconvolution models (DDM) in large eddy simulation of isotropic turbulence at different filter-to-grid ratios (FGR), by using several types of invertible filters including the Gaussian, Helmholtz I and II, Butterworth, Chebyshev I and II, Cauchy, Pao, and rapidly decaying filters. We show that the FGR is crucial in controlling errors to ensure an accurate prediction of SFS stresses. In the case of FGR of 1, the DDM models cannot accurately reconstruct SFS stress, since the effect of SFS dynamics on SFS stress is not properly resolved by the coarse grid. The prediction abilities of most DDM models are significantly improved at FGR of 2, giving rise to quite an accurate reconstruction of SFS stresses, except for the situation of Helmholtz I and II filters. All the DDM models give very accurate results at FGR of 4. Moreover, the DDM models are comprehensively compared against various traditional SFS models, including the velocity gradient model, dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and the approximate deconvolution model. In the a priori study, the correlation coefficients of SFS stress for the DDM are much larger than those of the traditional models. In the a posteriori study, DDM outperforms DSM and DMM models in the prediction of various velocity statistics and instantaneous flow structures. These results indicate that the DDM framework with an appropriate FGR has much potential in developing high-fidelity SFS models in the LES of turbulence.

We study the effect of filter anisotropy and sub-filter scale (SFS) dynamics on the accuracy of large eddy simulation (LES) of turbulence, by using several types of SFS models including the dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and the direct deconvolution model (DDM) with the anisotropic filter. The aspect ratios (AR) of the filters for LES range from 1 to 16. We show that the DDM is capable of predicting SFS stresses accurately at highly anisotropic filter. In the a priori study, the correlation coefficients of SFS stress reconstructed by the DDM are over 90%, which are much larger than those of the DSM and DMM models. The correlation coefficients decrease as the AR increases. In the a posteriori studies, the DDM outperforms DSM and DMM models in the prediction of various turbulence statistics, including the velocity spectra, and probability density functions of the vorticity, SFS energy flux, velocity increments, strain-rate tensors and SFS stress. As the anisotropy increases, the results of DSM and DMM become worse, but DDM can give satisfactory results for all the filter-anisotropy cases. These results indicate that the DDM framework is a promising tool in developing advanced SFS models in the LES of turbulence in the presence of anisotropic filter.