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Register a new userComputational Aeroacoustics of a Generic Side View Mirror using Stress Blended Eddy Simulation
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This paper presents a numerical investigation of aerodynamic noise generated by a generic side-view mirror mounted on a flat plate using the Stress Blended Eddy Simulation (SBES) coupled with the Ffowcs Williams and Hawkings (FW-H) equation. A grid evaluation study was performed using a standardised side-view mirror with a Reynolds Number (Re) of 5.2 x10^5 based on the diameter of the model. The predictions for hydrodynamic pressure fluctuations on the mirror, the window and the sound emitted at various microphone locations are in good agreement with previously published experimental data. In addition, our numerical results indicate that yawing the mirror closer to the side window results in the flow being attached to the rear of the mirror resulting in an overall reduction in Sound Pressure Level (SPL) at several receiver locations.
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A nonlocal subgrid-scale stress (SGS) model is developed based on the convolution neural network (CNN), a powerful supervised data-driven approach. The CNN is an ideal approach to naturally consider nonlocal spatial information in prediction due to its wide receptive field. The CNN-based models used here only take primitive flow variables as input, then the flow features are automatically extracted without any $priori$ guidance. The nonlocal models trained by direct numerical simulation (DNS) data of a turbulent channel flow at $Re_{tau}=178$ are accessed in both the $priori$ and $posteriori$ test, providing physically reasonable flow statistics (like mean velocity and velocity fluctuations) closing to the DNS results even when extrapolating to a higher Reynolds number $Re_{tau}=600$. In our model, the backscatter is also predicted well and the numerical simulation is stable. The nonlocal models outperform local data-driven models like artificial neural network and some SGS models, e.g. the Smagorinsky model in actual large eddy simulation (LES). The model is also robust since stable solutions can be obtained when examining the grid resolution from one-half to double of the spatial resolution used in training. We also investigate the influence of receptive fields and suggest using the two-point correlation analysis as a quantitative method to guide the design of nonlocal physical models. To facilitate the combination of machine learning (ML) algorithms to computational fluid dynamics (CFD), a novel heterogeneous ML-CFD framework is proposed. The present study provides the effective data-driven nonlocal methods for SGS modelling in the LES of complex anisotropic turbulent flows.
We present a new approach for constructing data-driven subgrid stress models for large eddy simulation of turbulent flows. The key to our approach is representation of model input and output tensors in the filtered strain rate eigenframe. Provided inputs and outputs are selected and non-dimensionalized in a suitable manner, this yields a model form that is symmetric, Galilean invariant, rotationally invariant, reflectionally invariant, and unit invariant. We use this model form to train a simple and efficient neural network model using only one time step of filtered direct numerical simulation data from a forced homogeneous isotropic turbulence simulation. We demonstrate the accuracy of this model as well as the models ability to generalize to previously unseen filter widths, Reynolds numbers, and flow physics using a priori and a posteriori tests.
We present a high-order implicit large-eddy simulation (ILES) approach for simulating transitional turbulent flows. The approach consists of an Interior Embedded Discontinuous Galerkin (IEDG) method for the discretization of the compressible Navier-Stokes equations and a parallel preconditioned Newton-GMRES solver for the resulting nonlinear system of equations. The IEDG method arises from the marriage of the Embedded Discontinuous Galerkin (EDG) method and the Hybridizable Discontinuous Galerkin (HDG) method. As such, the IEDG method inherits the advantages of both the EDG method and the HDG method to make itself well-suited for turbulence simulations. We propose a minimal residual Newton algorithm for solving the nonlinear system arising from the IEDG discretization of the Navier-Stokes equations. The preconditioned GMRES algorithm is based on a restricted additive Schwarz (RAS) preconditioner in conjunction with a block incomplete LU factorization at the subdomain level. The proposed approach is applied to the ILES of transitional turbulent flows over a NACA 65-(18)10 compressor cascade at Reynolds number 250,000 in both design and off-design conditions. The high-order ILES results show good agreement with a subgrid-scale LES model discretized with a second-order finite volume code while using significantly less degrees of freedom. This work shows that high-order accuracy is key for predicting transitional turbulent flows without a SGS model.
We have performed Coherent-vorticity Preserving Large-Eddy simulations of a trefoil knot-shaped vortex, inspired by the experiments of Kleckner and Irvine. The flow parameter space is extended in the present study, including variations of the circulation Reynolds numbers in the range Re = 2000 - 200000, where Re = 20000 is the value used in the experiments. The vortex line corresponding to the trefoil knot is defined using a parametric equation and the Biot-Savart law is employed to initialize the velocity field. The CvP LES computation displays a good qualitative match with the experiment. In particular, the vortex entanglement process is accurately represented as well as the subsequent separation of the main vortex in two distinct structures - a small and a large vortex - with different self-advection speeds that have been quantified. The small vortex propagates faster than the large oscillatory vortex which carries an important amount of vorticity. The advection velocity of the vortex before bursting is found to be independent of the Reynolds number. The low Reynolds number computation leads to a decrease of the separated vortices velocity after bursting, compared to the higher Reynolds computations. The computation of energy spectra emphasizes intense energy transfers from large to small scales during the bursting process. The evolution of volume-averaged enstrophy shows that the bursting leads to the creation of small scales that are sustained a long time in the flow, when a sufficiently large Reynolds number is considered (Re>20000). The low Reynolds number case Re = 2000 hinders the generation of small scales during the bursting process and yields essentially laminar dynamics. The onset of background turbulence due to the entanglement process can be observed at Re = 200000
The Multiple Mapping Conditioning / Large Eddy Simulation (MMC-LES) approach is used to simulate a supersonic lifted hydrogen jet flame, which features shock-induced autoignition, shock-flame interaction, lifted flame stabilization, and finite-rate chemistry effects. The shocks and expansion waves, shock-reaction interactions and overall flame characteristics are accurately reproduced by the model. Predictions are compared with the detailed experimental data for the mean axial velocity, mean and root-mean-square temperature, species mole fractions, and mixture fraction at various locations. The predicted and experimentally observed flame structures are compared through scatter plots of species mole fractions and temperature against mixture fraction. Unlike most past MMC-LES which has been applied to low-Mach flames, in this supersonic flame case pressure work and viscous heating are included in the stochastic FDF equations. Analysis indicates that the pressure work plays an important role in autoignition induction and flame stabilization, whereas viscous heating is only significant in shear layers (but still negligibly small compared to the pressure work). The evolutions of particle information subject to local gas dynamics are extracted through trajectory analysis on representative fuel and oxidizer particles. The particles intermittently enter the extinction region and may be deviated from the full burning or mixing lines under the effects of shocks, expansion waves and viscous heating. The chemical explosive mode analysis performed on the Lagrangian particles shows that temperature, the H and OH radicals contribute dominantly to CEM respectively in the central fuel jet, fuel-rich and fuel-lean sides. The pronounced particle Damkohler numbers first occur in the fuel jet / coflow shear layer, enhanced at the first shock intersection point and peak around the flame stabilization point.
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