Applications of An Eddy-Viscosity Eliminator Based on Sigmoid Functions in Reynolds-Averaged Navier-Stokes Simulations of Sloshing Flow

Reynolds-averaged Navier-Stokes(RANS)turbulence modeling can lead to the excessive turbulence level around the interface in two-phase flow,which causes the unphysical motion of the interface in sloshing simulation.In order to avoid the unphysical motion of the interface,a novel eddy-viscosity eliminator based on sigmoid functions is designed to reduce the excessive turbulence level,and the eddy-viscosity eliminator based on polynomials is extracted from the cavitation simulations.Surface elevations by combining the eddy-viscosity eliminators and classical two-equation closure models are compared with the experiments,the ones by using the adaptive asymptotic model(AAM)and the ones by using the modified two-equation closure models.The root-mean-squared error(RMSE)is introduced to quantify the accuracies of surface elevations and the forces.The relation between the turbulence level in the transition layer and RMSEs of surface elevations is studied.Besides,the parametric analysis of the eddy-viscosity eliminators is carried out.The studies suggest that(1)the excessive turbulence level in the transition layer around the interface has a significant influence on the accuracies of surface elevations and the forces;(2)the eddy-viscosity eliminators can effectively reduce the excessive turbulence level in the transition layer to avoid the unphysical motion of the interface;(3)the k-ωSST model combined with the eddy-viscosity eliminators is appropriate for predicting surface elevations and forces in RANS simulations of sloshing flow.

Artificial neural network-based one-equation model for simulation of laminar-turbulent transitional flow

A mapping function between the Reynolds-averaged Navier-Stokes mean flow variables and transition intermittency factor is constructed by fully connected artificial neural network(ANN),which replaces the governing equation of the intermittency factor in transition-predictive Spalart-Allmaras(SA)-γmodel.By taking SA-γmodel as the benchmark,the present ANN model is trained at two airfoils with various angles of attack,Mach numbers and Reynolds numbers,and tested with unseen airfoils in different flow states.The a posteriori tests manifest that the mean pressure coefficient,skin friction coefficient,size of laminar separation bubble,mean streamwise velocity,Reynolds shear stress and lift/drag/moment coefficient from the present two-way coupling ANN model almost coincide with those from the benchmark SA-γmodel.Furthermore,the ANN model proves to exhibit a higher calculation efficiency and better convergence quality than traditional SA-γmodel.

Derivation of a Nonlinear Reynolds Stress Model Using Renormalization Group Analysis and Two-Scale Expansion Technique

Adopting Yoshizawa＇s two-scale expansion technique, the fluctuating field is expanded around the isotropic field. The renormalization group method is applied for calculating the covariance of the fluctuating field at the lower order expansion. A nonlinear Reynolds stress model is derived and the turbulent constants inside are evaluated analytically. Compared with the two-scale direct interaction approximation analysis for turbulent shear flows proposed by Yoshizawa, the calculation is much more simple. The analytical model presented here is close to the Speziale model, which is widely applied in the numerical simulations for the complex turbulent flows.

A new dynamic subgrid-scale model using artificial neural network for compressible flow

The subgrid-scale(SGS)kinetic energy has been used to predict the SGS stress in compressible flow and it was resolved through the SGS kinetic energy transport equation in past studies.In this paper,a new SGS eddy-viscosity model is proposed using artificial neural network to obtain the SGS kinetic energy precisely,instead of using the SGS kinetic energy equation.Using the infinite series expansion and reserving the first term of the expanded term,we obtain an approximated SGS kinetic energy,which has a high correlation with the real SGS kinetic energy.Then,the coefficient of the modelled SGS kinetic energy is resolved by the artificial neural network and the modelled SGS kinetic energy is more accurate through this method compared to the SGS kinetic energy obtained from the SGS kinetic energy equation.The coefficients of the SGS stress and SGS heat flux terms are determined by the dynamic procedure.The new model is tested in the compressible turbulent channel flow.From the a posterior tests,we know that the new model can precisely predict the mean velocity,the Reynolds stress,the mean temperature and turbulence intensities,etc.

Rational subgrid-scale modelling: a short survey

We review the previous attempts of rational subgrid-scale （SGS） modelling by employing theKolmogorov equation of filtered quantities. Aiming at explaining and solving the underlyingproblems in these models, we also introduce the recent methodological investigations for therational SGS modelling technique by defining the terms of assumption and restriction. Thesemethodological works are expected to provide instructive criterions for not only the rational SGSmodelling, but also other types of SGS modelling practices.

Prediction of Boundary Layer Transition Based on Modeling of Laminar Fluctuations Using RANS Approach

This article presents a linear eddy-viscosity turbulence model for predicting bypass and natural transition in boundary layers by using Reynolds-averaged Navier-Stokes (RANS) equations. The model includes three transport equations, separately, to compute laminar kinetic energy, turbulent kinetic energy, and dissipation rate in a flow field. It needs neither correlations of intermittency factors nor knowledge of the transition onset. Two transition tests are carried out: flat plate boundary layer under zero ...

APPLICATION OF QUADRATIC AND CUBIC TURBULENCE MODELS ON CAVITATING FLOWS AROUND SUBMERGED OBJECTS

Quadratic and cubic Non-Linear Eddy-Viscosity Models （NLEVMs） at low Reynolds number （Re） correction were introduced into the present Computational Fluid Dynamics （CFD） framework, to provide better numerical treatment about the anisotropic turbulence stress in cavitating flows, which have large density ratio and large-scaled swirling flow structures. The applications of these NLEVMs were carried out through a self-developed cavitation code, coupled with a cavitation model based on the transport equation of liquid phase. These NLEVMs were firstly validated by the benchmark of disk supercavity, and found able to obtain more accurate capture of the hydrodynamic properties than the linear models. One of such models was further applied on the cavitation problem of submerged vehicles. Ultimately, the supercavitating flows around an especially designed underwater vehicle were predicted using the cubic k - e turbulence model, and its cavitation behaviors were studied.