Ensemble data assimilation-based mixed subgrid-scale model for large-eddy simulations
arXiv:2305.11112 · doi:10.1063/5.0160482
Abstract
An ensemble Kalman filter (EnKF)-based mixed model (EnKF-MM) is proposed for the subgrid-scale (SGS) closure in the large-eddy simulation (LES) of turbulence. The model coefficients are determined through the EnKF-based data assimilation technique. The direct numerical simulation (DNS) results are filtered to obtain the benchmark data for LES. Reconstructing the correct kinetic energy spectrum of the filtered DNS (fDNS) data has been adopted as the target for the EnKF to optimize the coefficient of the functional part in the mixed model. The proposed EnKF-MM framework is subsequently tested in the LES of both the incompressible homogeneous isotropic turbulence (HIT) and turbulent mixing layer (TML). The performance of LES is comprehensively examined through the predictions of the flow statistics including the velocity spectrum, the probability density functions (PDFs) of the SGS stress, the PDF of the strain rate and the PDF of the SGS energy flux. The structure functions, the evolution of turbulent kinetic energy, the mean flow and the Reynolds stress profile, and the iso-surface of the Q-criterion are also examined to evaluate the spatial-temporal predictions by different SGS models. The results of the EnKF-MM framework are consistently more satisfying compared to the traditional SGS models, including the dynamic Smagorinsky model (DSM), the dynamic mixed model (DMM) and the velocity gradient model (VGM), demonstrating its great potential in the optimization of SGS models for LES of turbulence.
References in corpus (17)
- Turbulence Modeling in the Age of Data
- Deep Neural Networks for Data-Driven Turbulence Models
- Sub-grid modelling for two-dimensional turbulence using neural networks
- Searching for turbulence models by artificial neural network
- Grid-point and time-step requirements for direct numerical simulation and large-eddy simulation
- Physics-guided deep learning framework for predictive modeling of the Reynolds stress anisotropy
- Ensemble Kalman method for learning turbulence models from indirect observation data
- Deconvolutional artificial neural network models for large eddy simulation of turbulence
- Data-driven subgrid-scale modeling of forced Burgers turbulence using deep learning with generalization to higher Reynolds numbers via transfer learning
- Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows
- Ensemble Neural Networks (ENN): A gradient-free stochastic method
- Mean-flow Data Assimilation based on minimal correction of turbulence models: application to turbulent high-Reynolds number backward-facing step
- Effect of filter type on the statistics of energy transfer between resolved and subfilter scales from a-priori analysis of direct numerical simulations of isotropic turbulence
- Reconstruction of turbulent flow fields from lidar measurements based on large-eddy simulation
- Synchronizing subgrid scale models of turbulence to data
- Log-law recovery through reinforcement-learning wall model for large-eddy simulation
- Adjoint-based variational optimal mixed models for large-eddy simulation of turbulence