Numerical Evaluation of Two k-εTurbulence Model for Predicting Flow and Solidification in Continuous Casting Slab

A steady three-dimensional fluid flow and solidification model was built based on CFD software by high-Reynolds-number and Lam-Bremhorst low-Reynolds-number k-ε model.During the simulation,the fixed-grid enthalpy-porosity technique was used to represent the solidification,and Darcy law was adopted to simulate the flow in mushy region.The prediction for steel flow and solidification was evaluated by the comparison of two turbulence models.It is found that both Lam-Bremhorst low-Reynolds-number and high-Reynolds-number k-ε models predict the same trend of the steel flow and temperature distribution.However,due to the effect of turbulent flow on heat transfer,the low-Reynolds-number turbulence model predicts longer penetration depth of molten steel in sub-mold region,less shell growth and higher shell surface temperature at the narrow face compared with standard k-ε model.

An assessment of k-εturbulence models for gas distribution analysis

This paper presents the gas distribution analysis by injecting air fountain into the containment and simulations with the HYDRAGON code. Turbulence models of standard k-ε(SKE), re-normalization group k-ε(RNG) and a realizable k-ε(RLZ) are used to assess the effects on the gas distribution analysis during a severe accident in a nuclear power plant. By comparing with experimental data,the simulation results of the RNG and SKE turbulence models agree well with the experimental data on the prediction of dimensionless density distributions. The results illustrate that the turbulence model choice had a small effect on the simulation results, particularly the region near to the air fountain source.

Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction

Accurately predicting fluid forces acting on the sur-face of a structure is crucial in engineering design.However,this task becomes particularly challenging in turbulent flow,due to the complex and irregular changes in the flow field.In this study,we propose a novel deep learning method,named mapping net-work-coordinated stacked gated recurrent units(MSU),for pre-dicting pressure on a circular cylinder from velocity data.Specifi-cally,our coordinated learning strategy is designed to extract the most critical velocity point for prediction,a process that has not been explored before.In our experiments,MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.This method significantly reduces the workload of data measure-ment in practical engineering applications.Our experimental results demonstrate that MSU predictions are highly similar to the real turbulent data in both spatio-temporal and individual aspects.Furthermore,the comparison results show that MSU predicts more precise results,even outperforming models that use all velocity field points.Compared with state-of-the-art methods,MSU has an average improvement of more than 45%in various indicators such as root mean square error(RMSE).Through comprehensive and authoritative physical verification,we estab-lished that MSU’s prediction results closely align with pressure field data obtained in real turbulence fields.This confirmation underscores the considerable potential of MSU for practical applications in real engineering scenarios.The code is available at https://github.com/zhangzm0128/MSU.

Robust autofocusing propagation in turbulence

Turbulence in complex environments such as the atmosphere and biological media has always been a great challenge to the application of beam propagation in optical communication, optical trapping and manipulation. To overcome this challenge, this study comprehensively investigates the robust propagation of traditional Gaussian and autofocusing beams in turbulent environments. In order to select stable beams that exhibit high intensity and high field gradient at the focal position in complex environments, Kolmogorov turbulence theory is used to simulate the propagation of beams in atmospheric turbulence based on the multi-phase screen method. We systematically analyze the intensity fluctuations, the variation of the coherence factor and the change in the scintillation index with propagation distance. The analysis reveals that the intensity fluctuations of autofocusing beams are significantly smaller than those of Gaussian beams, and the coherence of autofocusing beams is better than that of Gaussian beams under turbulence. Moreover, autofocusing beams exhibit less oscillation than Gaussian beams, indicating that autofocusing beams propagate in complex environments with less distortion and intensity fluctuation. Overall, this work clearly demonstrates that autofocusing beams exhibit higher stability in propagation compared with Gaussian beams, showing great promise for applications such as optical trapping and manipulation in complex environments.

An improved TDE technique for derivation of 2D turbulence structures based on GPI data in toroidal plasma

This paper reports an improved time-delay estimation(TDE)technique for the derivation of turbulence structures based on gas-puff imaging data.The improved TDE technique,integrating an inverse timing search and hierarchical strategy,offers superior accuracy in calculating turbulent velocity field maps and analyzing blob dynamics,which has the power to obtain the radial profiles of equilibrium poloidal velocity,blob size and its radial velocity,even the fluctuation analysis,such as geodesic acoustic modes and quasi-coherent mode,etc.This improved technique could provide important 2D information for the study of edge turbulence and blob dynamics,advancing the understanding of edge turbulence physics in fusion plasmas.

Properties of focused Laguerre–Gaussian beam propagating in anisotropic ocean turbulence

We analyze the properties of a focused Laguerre–Gaussian(LG)beam propagating through anisotropic ocean turbulence based on the Huygens–Fresnel principle.Under the Rytov approximation theory,we derive the analytical formula of the channel capacity of the focused LG beam in the anisotropic ocean turbulence,and analyze the relationship between the capacity and the light source parameters as well as the turbulent ocean parameters.It is found that the focusing mirror can greatly enhance the channel capacity of the system at the geometric focal plane in oceanic turbulence.The results also demonstrate that the communication link can obtain high channel capacity by adopting longer beam wavelength,greater initial beam waist radius,and larger number of transmission channels.Further,the capacity of the system increases with the decrease of the mean squared temperature dissipation rate,temperature-salinity contribution ratio and turbulence outer scale factor,and with the increase of the kinetic energy dissipation rate per unit mass of fluid,turbulence inner scale factor and anisotropy factor.Compared to a Hankel–Bessel beam with diffraction-free characteristics and unfocused LG beam,the focused LG beam shows superior anti-turbulence interference properties,which provide a theoretical reference for research and development of underwater optical communication links using focused LG beams.

Gyro-Landau-fluid simulations of impurity effects on ion temperature gradient driven turbulence transport

The effects of impurities on ion temperature gradient(ITG)driven turbulence transport in tokamak core plasmas are investigated numerically via global simulations of microturbulence with carbon impurities and adiabatic electrons.The simulations use an extended fluid code(ExFC)based on a four-field gyro-Landau-fluid(GLF)model.The multispecies form of the normalized GLF equations is presented,which guarantees the self-consistent evolution of both bulk ions and impurities.With parametric profiles of the cyclone base case,well-benchmarked ExFC is employed to perform simulations focusing on different impurity density profiles.For a fixed temperature profile,it is found that the turbulent heat diffusivity of bulk ions in a quasi-steady state is usually lower than that without impurities,which is contrary to the linear and quasilinear predictions.The evolutions of the temperature gradient and heat diffusivity exhibit a fast relaxation process,indicating that the destabilization of the outwardly peaked impurity profile is a transient state response.Furthermore,the impurity effects from different profiles can obviously influence the nonlinear critical temperature gradient,which is likely to be dominated by linear effects.These results suggest that the improvement in plasma confinement could be attributed to the impurities,most likely through adjusting both heat diffusivity and the critical temperature gradient.

The Turbulent Schmidt Number for Transient Contaminant Dispersion in a Large Ventilated Room Using a Realizable k-εModel

Buildings with large open spaces in which chemicals are handled are often exposed to the risk of explosions.Computational fluid dynamics is a useful and convenient way to investigate contaminant dispersion in such large spaces.The turbulent Schmidt number(Sc_(t))concept has typically been used in this regard,and most studies have adopted a default value.We studied the concentration distribution for sulfur hexafluoride(SF_(6))assuming different emission rates and considering the effect of Sc_(t).Then we examined the same problem for a light gas by assuming hydrogen gas(H_(2))as the contaminant.When SF_(6) was considered as the contaminant gas,a variation in the emission rate completely changed the concentration distribution.When the emission rate was low,the gravitational effect did not take place.For both low and high emission rates,an increase in S_(ct) accelerated the transport rate of SF_(6).In contrast,for H_(2) as the contaminant gas,a larger S_(ct) could induce a decrease in the H_(2) transport rate.

Development of a 2D spatial displacement estimation method for turbulence velocimetry of the gas puff imaging system on EAST

A gas puff imaging(GPI)diagnostic has been developed and operated on EAST since 2012,and the time-delay estimation(TDE)method is used to derive the propagation velocity of fluctuations from the two-dimensional GPI data.However,with the TDE method it is difficult to analyze the data with fast transient events,such as edge-localized mode(ELM).Consequently,a method called the spatial displacement estimation(SDE)algorithm is developed to estimate the turbulence velocity with high temporal resolution.Based on the SDE algorithm,we make some improvements,including an adaptive median filter and super-resolution technology.After the development of the algorithm,a straight-line movement and a curved-line movement are used to test the accuracy of the algorithm,and the calculated speed agrees well with preset speed.This SDE algorithm is applied to the EAST GPI data analysis,and the derived propagation velocity of turbulence is consistent with that from the TDE method,but with much higher temporal resolution.

基于数据流的K-S变化检测的动态多目标规划算法

为了更加准确地判断环境是否发生变化并快速追踪动态多目标规划问题(dynamicmulti-objectiveoptimization problem,DMOP)当前时刻的Pareto前沿,提出了一种基于数据流的Kolmogorov-Smirnov(K-S)变化检测的动态多目标规划(DSK-SDMOP)算法。该算法以NSGA-Ⅱ为基础,通过数据流建立2个时刻的检验窗口,再利用K-S检验基于数据流的Pareto最优前沿是否发生变化,检测2个窗口的数据是否服从同一分布来判断环境是否发生变化,并就环境变化的剧烈程度实行相应的应答机制,以提高对环境的适应程度。利用基于数据流的K-S检测方法,对环境变化不会过于敏感,而且不用提前假设对应目标值的分布,易于操作。通过5个动态多目标规划标准测试函数对该算法进行测试,并和现有的2种算法进行对比分析,结果表明该算法处理动态多目标规划问题具有良好的性能。

基于数字孪生与k-近邻算法的车间设备运行状态预测研究

由于传统车间设备运行状态预测不能有效利用历史数据进行学习,实时响应能力有限,难以在复杂调度环境中取得良好效果,因此文章提出一种数字孪生与k-近邻算法相结合的车间设备运行状态预测模型。构建车间设备实体在信息空间的数字孪生模型,并建立设备实体与模型之间的映射关系,从而获取实时特征数据,即设备的运行状态特征数据。运用k-近邻算法计算实时特征数据与历史数据之间的欧几里得距离,即计算设备当前运行状态与历史已知状态的相似度,最终通过前k个距离所对应的设备历史运行状态数据,预测设备的当前运行状态。该模型的本质是通过数字孪生的实时数据采集,获取指定设备运行状态特征数据,运用k-近邻算法预测设备的实时运行状态。相较以往研究,本研究贡献在于提高设备实时运行状态预测的准确率。如果将数字孪生、k-近邻算法与具备自我学习能力的相关算法相结合,模型的预测效果会更好。

一种分配查询的k-匿名位置隐私保护方案

针对基于位置服务中移动终端用户位置隐私保护存在时空关联和背景知识攻击的问题,提出一种分配查询的k-匿名位置隐私保护方案。基于查询概率和欧氏距离,在相邻网格区域内筛选假位置,合并真实用户位置形成k-匿名;通过兴趣点语义四叉树为假位置分配合理的查询,从时间、位置及查询3个维度提升用户被识别的不确定性,从而增强用户隐私度。安全性分析表明,提出的方案满足匿名性、不可伪造性和抵抗查询服务跟踪等安全特性。仿真实验表明,提出的方案具有较高的隐私保护度和效率以及较低的通信开销。

Influence of the upper mixed layer depth on Langmuir turbulence characteristics

The upper mixed layer depth(h)has a significant seasonal variation in the real ocean and the low-order statistics of Langmuir turbulence are dramatically influenced by the upper mixed layer depth.To explore the influence of the upper mixed layer depth on Langmuir turbulence under the condition of the wind and wave equilibrium,the changes of Langmuir turbulence characteristics with the idealized variation of the upper mixed layer depth from very shallow(h=5 m)to deep enough(h=40 m)are studied using a non-hydrostatic large eddy simulation model.The simulation results show that there is a direct entrainment depth induced by Langmuir turbulence(h_(LT))within the thermocline.The normalized depthaveraged vertical velocity variance is smaller and larger than the downwind velocity variance for the ratio of the upper mixed layer to a direct entrainment depth induced by Langmuir turbulence h/h_(LT)<1 and h/h_(LT)>1,respectively,indicating that turbulence characteristics have the essential change(i.e.,depth-averaged vertical velocity variance(DAVV)DADV for Langmuir turbulence)between h/h_(LT)<1 and h/h_(LT)>1.The rate of change of the normalized depth-averaged low-order statistics for h/h_(LT)<1 is much larger than that for h/h_(LT)>1.The reason is that the downward pressure perturbation induced by Langmuir cells is strongly inhibited by the upward reactive force of the strong stratified thermocline for h/h_(LT)<1 and the eff ect of upward reactive force on the downward pressure perturbation becomes weak for h/h_(LT)>1.Hence,the upper mixed layer depth has significant influences on Langmuir turbulence characteristics.

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.

Aero-Hydrodynamic Coupled Dynamic Characteristics of Semi-Submersible Floating Offshore Wind Turbines Under Inflow Turbulence

In this study,the frequency characteristics of the turbulent wind and the effects of wind-wave coupling on the low-and high-frequency responses of semi-submersible floating offshore wind turbines(FOWT)are investigated.Various wave load components,such as first-order wave loads,combined first-and second-order difference-frequency wave loads,combined first-and second-order sum-frequency wave loads,and first-and complete second-order wave loads are taken into consideration,while different turbulent environments are considered in aerodynamic loads.The com-parison is based on time histories and frequency spectra of platform motions and structural load responses and statistical values.The findings indicate that the second-order difference-frequency wave loads will significantly increase the natural frequency of low-frequency motion in the responses of the platform motion and structure load of the semi-submersible platform,which will cause structural fatigue damage.Under the action of turbulent wind,the influences of second-order wave loads on the platform motion and structural load response cannot be ignored,especially under extreme sea conditions.Therefore,in order to evaluate the dynamic responses of semi-submersible FOWT more accurately,the actual environment should be simulated more realistically.

Artificial neural network-based subgrid-scale models for LES of compressible turbulent channel flow

Fully connected neural networks(FCNNs)have been developed for the closure of subgrid-scale(SGS)stress and SGS heat flux in large-eddy simulations of compressible turbulent channel flow.The FCNNbased SGS model trained using data with Mach number Ma=3.0 and Reynolds number Re=3000 was applied to situations with different Mach numbers and Reynolds numbers.The input variables of the neural network model were the filtered velocity gradients and temperature gradients at a single spatial grid point.The a priori test showed that the FCNN model had a correlation coefficient larger than 0.91 and a relative error smaller than 0.43,with much better reconstructions of SGS unclosed terms than the dynamic Smagorinsky model(DSM).In a posteriori test,the behavior of the FCNN model was marginally better than that of the DSM in predicting the mean velocity profiles,mean temperature profiles,turbulent intensities,total Reynolds stress,total Reynolds heat flux,and mean SGS flux of kinetic energy,and outperformed the Smagorinsky model.

Numerical simulation of a combined oxidation ditch flow using 3D k-ε turbulence model

The standard three dimensional（3D） k-ε turbulence model was applied to simulate the flow field of a small scale combined oxidation ditch. The moving mesh approach was used to model the rotor of the ditch. Comparison of the computed and the measured data is acceptable. A vertical reverse flow zone in the ditch was found, and it played a very important role in the ditch flow behavior. The flow pattern in the ditch is discussed in detail, and approaches are suggested to improve the hydrodynamic performance in the ditch.

Numerical simulation of gas–liquid flow in the bubble column using Wray–Agarwal turbulence model coupled with population balance model

In this paper,an improved computational fluid dynamic(CFD)model for gas-liquid flow in bubble column was developed using the one-equation Wary-Agarwal(WA)turbulence model coupled with the population balance model(PBM).Through 18 orthogonal test cases,the optimal combination of interfacial force models,including drag force,lift force,turbulent dispersion force.The modified wall lubrication force model was proposed to improve the predictive ability for hydrodynamic behavior near the wall of the bubble column.The values simulated by optimized CFD model were in agreement with experimental data,and the errors were within±20%.In addition,the axial velocity,turbulent kinetic energy,bubble size distribution,and the dynamic characteristic of bubble plume were analyzed at different superficial gas velocities.This research work could provide a theoretical basis for the extension of the CFD-PBM coupled model to other multiphase reactors..

Numerical prediction of inner turbulent flow in conical diffuser by using a new five-point scheme and DLR k-ε turbulence model

The internal turbulent flow in conical diffuser is a very complicated adverse pressure gradient flow.DLR k-ε turbulence model was adopted to study it.The every terms of the Laplace operator in DLR k-ε turbulence model and pressure Poisson equation were discretized by upwind difference scheme.A new full implicit difference scheme of 5-point was constructed by using finite volume method and finite difference method.A large sparse matrix with five diagonals was formed and was stored by three arrays of one dimension in a compressed mode.General iterative methods do not work wel1 with large sparse matrix.With algebraic multigrid method(AMG),linear algebraic system of equations was solved and the precision was set at 10-6.The computation results were compared with the experimental results.The results show that the computation results have a good agreement with the experiment data.The precision of computational results and numerical simulation efficiency are greatly improved.

REVISED k-ε TURBULENCE MODEL IN ELECTROMAGNETIC CONTINUOUS CASTING OF MELT

The research is motivated by the ongoing the electromagnetic continuous casting of molten metal. The revised k-ε model considering the effect of magnetic field application was derived. The specific model equations for the electromagnetic braking were used to calculate the velocity distribution in the continuous casting mold of steel. The results show that the revised k-ε model considering the effect of magnetic field application tends to suppress the production of turbulence and difference between the conventional and revised k-e model is small.