Evaluation of dynamic subgrid-scale models in large-eddy simulations of neutral turbulent flow over a two-dimensional sinusoidal hill

Large-eddy simulation (LES) is used to simulate neutral turbulent boundary-layer flow over a rough two-dimensional sinusoidal hill. Three different subgrid-scale (SGS) models are tested: (a) the standard Smagorinsky model with a wall-matching function, (b) the Lagrangian dynamic model, and (c) the recently developed scale-dependent Lagrangian dynamic model [Stoll, R., Porté-Agel, F., 2006. Dynamic subgrid-scale models for momentum and scalar fluxes in large-eddy simulation of neutrally stratified atmospheric boundary layers over heterogeneous terrain. Water Resources Research 42, W01409. doi:10.1029/2005WR003989]. The simulation results obtained with the different models are compared with turbulence statistics obtained from experiments conducted in the meteorological wind tunnel of the AES (Atmospheric Environment Service, Canada) [Gong, W., Taylor, P.A., Dörnbrack, A., 1996. Turbulent boundary-layer flow over fixed aerodynamically rough two-dimensional sinusoidal waves. Journal of Fluid Mechanics 312, 1–37]. We find that the scale-dependent dynamic model is able to account, without any tuning, for the local changes in the eddy-viscosity model coefficient. It can also capture the scale dependence of the coefficient associated with regions of the flow with strong mean shear and flow anisotropy. As a result, the scale-dependent dynamic model yields results that are more realistic than the ones obtained with the scale-invariant Lagrangian dynamic model.     

Numerical simulation of dispersion around an isolated cubic building: Comparison of various types of k–ɛ models

Prediction accuracy of flow and dispersion around a cubic building with a flush vent located on its roof was examined using various k–? models, and numerical results were compared with wind-tunnel data. Four types of turbulence models, i.e., the standard k–? model, the RNG k–? model, the k–? model with Launder and Kato modification and the Realizable k–? model were compared in this study. The standard k–? model provided inadequate results for the concentration field, because it could not reproduce the basic flow structure, such as the reverse flow on the roof. However, revised k–? models provided concentrations in better agreement with the experimental data. The effect of an oblique wind angle and vent locations on the prediction accuracy was also investigated. It was confirmed that the prediction accuracy of the velocity field strongly affected that of the concentration field. The RNG model showed general agreement with the experiment, and was the best of the turbulence models tested. However, it becomes clear that the results for all CFD models show poor prediction accuracy of concentration distribution at the side and leeward surfaces of the building since they all underestimate the concentration diffusion on these regions. The concentrations predicted by all CFD models were less diffusive than those of the experiment.     

Gas diffusion over an isolated hill under neutral,stable and unstable conditions

Wind tunnel experiments of gas diffusion were performed over flat terrain and over an isolated three-dimensional hill under neutral, stable and unstable (sea breeze) conditions. Conditions of airflow in the wind tunnel were determined so as to satisfy the similarity rule for the bulk Richardson number, by controlling temperature profiles and wind velocity of the thermally stratified wind tunnel. Typical characteristics were observed under each condition of atmospheric stability; reversed vortex behind the hill in neutral condition, downward slope wind in stable one and convective motion in unstable one.We compared these experiments with the results of a Direct Numerical Simulation (DNS) model for the wind velocity over the hill under neutral conditions. The numerical results showed good agreement with the experimental results.     

Some fluid modeling studies of flow and dispersion over two-dimensional low hills

The results of wind tunnel experiments on flow and dispersion over two-dimensional low and ‘gentle’ hills of different shapes and aspect ratios are discussed. The hill-induced influences on velocity and concentration fields are studied as functions of the hills‘ aspect ratio (the ratio of the half-length to the height of the hill).The speed-up of flow on the hilltop is shown to have an inverse relationship with the aspect ratio ‘a’, as predicted by several theories. However, this does not increase indefinitely as a → 0, but has a maximum value at some optimum aspect ratio greater than unity. The velocity variances and Reynolds stress increase rapidly in the near wake region in the lee of the hills. In the far wake region, beyond about five hill heights, the hill-induced perturbations in mean velocity, Reynolds stress and variances of velocity decay in inverse proportion to the distance behind the hill.Dispersion from elevated sources located on the top of ‘gentle’ hills is not significantly affected by the hills. For low-level sources on the top of steeper hills, where flow separation may occur, the ground level concentrations are reduced (by as much as a factor of 3). On the other hand, the ground level concentrations from sources on the lee side of the hills are considerably enhanced (by as much as a factor of 15) near the source, but slightly reduced far downwind. The influence of‘gentle’ hills bears an inverse relationship with the hills' aspect ratio. For steep hills, however, the hill influence is intimately related to the dimensions of the cavity region.     

An atmospheric dispersion model for the environmental impact assessment of thermal power plants in Japan--a method for evaluating topographical effects

An atmospheric dispersion model was developed for the environmental impact assessment of thermal power plants in Japan, and a method for evaluating topographical effects using this model was proposed. The atmospheric dispersion model consists of an airflow model with a turbulence closure model based on the algebraic Reynolds stress model and a Lagrangian particle dispersion model (LPDM). The evaluation of the maximum concentration of air pollutants such as SO2, NOx, and suspended particulate matter is usually considered of primary importance for environmental impact assessment. Three indices were therefore estimated by the atmospheric dispersion model: the ratios (alpha and beta, respectively) of the maximum concentration and the distance of the point of the maximum concentration from the source over topography to the respective values over a flat plane, and the relative concentration distribution [gamma(x)] along the ground surface projection of the plume axis normalized by the maximum concentration over a flat plane. The atmospheric dispersion model was applied to the topography around a power plant with a maximum elevation of more than 1,000 m. The values of alpha and beta evaluated by the atmospheric dispersion model varied between 1 and 3 and between 1 and 0.4, respectively, depending on the topographical features. These results and the calculated distributions of y(x) were highly similar to the results of the wind tunnel experiment. Therefore, when the slope of a hill or mountain is similar to the topography considered in this study, it is possible to evaluate topographical effects on exhaust gas dispersion with reasonable accuracy using the atmospheric dispersion model as well as wind tunnel experiments.     

Computational Fluid Dynamics (CFD) Simulations on the Effect of Rough Surface on Atmospheric Turbulence Flow Above Hilly Terrain Shapes

The behavioral distribution of the atmospheric turbulence flow over the terrain with changes in a rough surface has become one of the most important topics of air pollution research, among such other topics as transportation and dispersion pollutants. In this study, a computational model on atmospheric turbulence flow over a terrain hill shaped with rough surface was investigated under neutral atmospheric conditions. The flow was assumed to be 2D and modeled using computational fluid dynamics (CFD) models, which were numerically solved using Reynolds-averaged Navier-Stokes equations. Rough surface conditions were modeled using a number of windbreak fences regularly spaced on the hill. The mean velocity and turbulent structures such as turbulence intensity and turbulent kinetic energy were investigated in the upwind and downwind regions over the hill, and the numerical models were validated against the wind-tunnel results to optimize the turbulence model. The computational results agreed well with the results obtained from the wind tunnel experiments. The computational results indicate that the mean velocity was observed to increase dramatically around the crest of the upwind slope of the hill. A thick internal boundary layer was observed with a fence on the crest and downwind region of the hill. The reversed flow and recirculation zone were formed in the wake region behind the hill. It was thus determined that turbulent kinetic energy decreases as the mean velocity increases.     

公路交通噪声预测模型FHWA与RLS90的比较

为了了解我国长期以来使用的两种公路交通噪声预测模型,即美国联邦公路局(FHWA)模型和德国RLS90模型的预测精确性,通过理论分析,比较了RLS90分段模型、RLS90长直线模型、FHWA模型的差异,并通过实际测量值对3种模型的计算结果进行了验证,表明RLS90模型在高速公路交通噪声预测中略优于FHWA模型。     

Development of Artificial Neural Network Based Metamodels for Inactivation of Anthrax Spores in Ventilated Spaces Using Computational Fluid Dynamics

ABSTRACT

Linear, quadratic, and artificial neural network (ANN)-based metamodels were developed for predicting the extent of anthrax spore inactivation by chlorine dioxide in a ventilated three-dimensional space over time from computational fluid dynamics model (CFD) simulation data. Dimensionless groups were developed to define the design space of the problem scenario. The Hammersley sequence sampling (HSS) method was used to determine the sampling points for the numerical experiments within the design space. A CFD model, comprised of multiple submodels, was applied to conduct the numerical experiments. Large eddy simulation (LES) with the Smagorinsky subgrid-scale model was applied to compute the airflow. Anthrax spores were modeled as a dispersed solid phase using the Lagrangian treatment. The disinfectant transport was calculated by solving a mass transport equation. Kinetic decay constants were included for spontaneous decay of the disinfectant and for the reaction of the disinfectant with the surfaces of the three-dimensional space. To enhance the mixing of the disinfectant with the room air, a momentum source was included in the simulation. An inactivation rate equation accounted for the reaction between the spores and the disinfectant. The ANN-based metamodels were most successful in predicting the number of viable bioaerosols remaining in an arbitrary enclosed space. Sensitivity analysis showed that the mass fraction of the disinfectant, inactivation rate constant, and contact time had the most influence on the inactivation of the spores.

IMPLICATIONS This investigation presents a framework for the development of user-friendly models; metamodels for the prediction of the number of viable spores remaining in an indoor room during disinfection from accurate but time-consuming CFD studies. During any decontamination event, to know when to stop pumping in the disinfectant and to know what level of log reduction of the spores have been achieved before even starting decontamination would provide valuable guidance. The neural network based metamodels can be applied to obtain quick and relatively accurate answers. This would be necessary when immediate information is required during emergencies.     

An Atmospheric Dispersion Model for the Environmental Impact Assessment of Thermal Power Plants in Japan—A Method for Evaluating Topographical Effects

Abstract

An atmospheric dispersion model was developed for the environmental impact assessment of thermal power plants in Japan, and a method for evaluating topographical effects using this model was proposed. The atmospheric dispersion model consists of an airflow model with a turbulence closure model based on the algebraic Reynolds stress model and a Lagrangian particle dispersion model (LPDM). The evaluation of the maximum concentration of air pollutants such as SO₂, NO_x, and suspended particulate matter is usually considered of primary importance for environmental impact assessment. Three indices were therefore estimated by the atmospheric dispersion model: the ratios (α and β, respectively) of the maximum concentration and the distance of the point of the maximum concentration from the source over topography to the respective values over a flat plane, and the relative concentration distribution [γ(x)] along the ground surface projection of the plume axis normalized by the maximum concentration over a flat plane. The atmospheric dispersion model was applied to the topography around a power plant with a maximum elevation of more than 1000 m. The values of α and β evaluated by the atmospheric dispersion model varied between 1 and 3 and between 1 and 0.4, respectively, depending on the topographical features. These results and the calculated distributions of γ(x) were highly similar to the results of the wind tunnel experiment. Therefore, when the slope of a hill or mountain is similar to the topography considered in this study, it is possible to evaluate topographical effects on exhaust gas dispersion with reasonable accuracy using the atmospheric dispersion model as well as wind tunnel experiments.     

A wind tunnel study of the flow structure and dispersion from sources upwind of three-dimensional hills

The flow fields around moderately steep hills of triangular cross section and varying crosswind aspect ratio and around a bell-shaped hill were examined by using models immersed in a simulated neutral atmospheric boundary layer in a meteorological wind tunnel. The triangular hills ranged from an axisymmetric cone to a two-dimensional ridge. Concentration patterns resulting from sources of three heights placed upwind of each of these hills were examined to determine plume deformations and terrain amplification factors. The separated flow fields, increasing in size with increasing aspect ratio, appeared to have dominating influences on the entire flow structure; changes in several flow parameters were plausibly explained in terms of the notion that the effective hill shape was the hill-plus-recirculation region rather than the actual hill shape. The concentration measurements showed strong distortions of plume shapes effected by the hills, with convergence in vertical planes and divergence in horizontal planes. Plumes from elevated sources approached the hill surfaces much more closely the smaller the aspect ratio; streamline displacements were generally within the limits suggested by potential flow theory. The terrain amplification factor A, defined as the ratio of the maximum surface concentration in the presence of the hill to the maximum in flat terrain, was found to decrease with increasing aspect ratio. For the half-hill-height sources, values of A ranged from 4 for the bell-shaped hill down to 1.5 for the two-dimensional ridge; for the hill-height source, from 1.8 for the cone down to 0.6 for the two-dimensional ridge. The latter value is suspect, however, because larger concentrations are expected downwind of the reattachment point, a region not probed in the current study.     

A new hybrid system of QSAR models for predicting bioconcentration factors (BCF)

The aim was to develop a reliable and practical quantitative structure–activity relationship (QSAR) model validated by strict conditions for predicting bioconcentration factors (BCF). We built up several QSAR models starting from a large data set of 473 heterogeneous chemicals, based on multiple linear regression (MLR), radial basis function neural network (RBFNN) and support vector machine (SVM) methods. To improve the results, we also applied a hybrid model, which gave better prediction than single models. All models were statistically analysed using strict criteria, including an external test set. The outliers were also examined to understand better in which cases large errors were to be expected and to improve the predictive models. The models offer more robust tools for regulatory purposes, on the basis of the statistical results and the quality check on the input data.     

A new hybrid system of QSAR models for predicting bioconcentration factors (BCF)

The aim was to develop a reliable and practical quantitative structure-activity relationship (QSAR) model validated by strict conditions for predicting bioconcentration factors (BCF). We built up several QSAR models starting from a large data set of 473 heterogeneous chemicals, based on multiple linear regression (MLR), radial basis function neural network (RBFNN) and support vector machine (SVM) methods. To improve the results, we also applied a hybrid model, which gave better prediction than single models. All models were statistically analysed using strict criteria, including an external test set. The outliers were also examined to understand better in which cases large errors were to be expected and to improve the predictive models. The models offer more robust tools for regulatory purposes, on the basis of the statistical results and the quality check on the input data.     

The importance of outlier detection and training set selection for reliable environmental QSAR predictions

Empirical QSAR models are only valid in the domain they were trained and validated. Application of the model to substances outside the domain of the model can lead to grossly erroneous predictions. Partial least squares (PLS) regression provides tools for prediction diagnostics that can be used to decide whether or not a substance is within the model domain, i.e. if the model prediction can be trusted. QSAR models for four different environmental end-points are used to demonstrate the importance of appropriate training set selection and how the reliability of QSAR predictions can be increased by outlier diagnostics. All models showed consistent results; test set prediction errors were very similar in magnitude to training set estimation errors when prediction outlier diagnostics were used to detect and remove outliers in the prediction data. Test set prediction errors for substances classified as outliers were much larger. The difference in the number of outliers between models with a randomly and systematically selected training illustrates well the need of representative training data.     

Development of artificial neural network based metamodels for inactivation of anthrax spores in ventilated spaces using computational fluid dynamics

Linear, quadratic, and artificial neural network (ANN)-based metamodels were developed for predicting the extent of anthrax spore inactivation by chlorine dioxide in a ventilated three-dimensional space over time from computational fluid dynamics model (CFD) simulation data. Dimensionless groups were developed to define the design space of the problem scenario. The Hammersley sequence sampling (HSS) method was used to determine the sampling points for the numerical experiments within the design space. A CFD model, comprised of multiple submodels, was applied to conduct the numerical experiments. Large eddy simulation (LES) with the Smagorinsky subgridscale model was applied to compute the airflow. Anthrax spores were modeled as a dispersed solid phase using the Lagrangian treatment. The disinfectant transport was calculated by solving a mass transport equation. Kinetic decay constants were included for spontaneous decay of the disinfectant and for the reaction of the disinfectant with the surfaces of the three-dimensional space. To enhance the mixing of the disinfectant with the room air, a momentum source was included in the simulation. An inactivation rate equation accounted for the reaction between the spores and the disinfectant. The ANN-based metamodels were most successful in predicting the number of viable bioaerosols remaining in an arbitrary enclosed space. Sensitivity analysis showed that the mass fraction of the disinfectant, inactivation rate constant, and contact time had the most influence on the inactivation of the spores.     

Pollution control by reduction of drag on cars and buses through platooning

Platooning is synchronised movement of two or more vehicles, as a unit, travelling at the same speed with relatively small inter-vehicle spacing. It is generally done in order to increase the highway capacity (vehicles/lane/hour). It was recognised early on that close follow up would likely decrease the average vehicle drag. The authors also expected minimisation of drag on cars and buses moving in platoons. Therefore, estimation of drag on a maximum four-vehicle platoon model was carried out inside Jadavpur University Subsonic Low Turbulence Closed Circuit Wind Tunnel test section. Several car models and bus models were used as platoon members in this experiment. The results show significant drag minimisation, which in turn decreases fuel consumption. Less fuel consumption indicates less air pollution, which is the talk of the day in large cities.     

微滤膜垢分形生长的过程模拟

基于微滤膜系统污垢形成机制和分形理论,建立微滤过程膜表面混合垢生长DLA模型,并通过实验验证了模型模拟的可行性和准确性。选取不同运行周期条件下微滤膜系统中的受污染膜丝,进行膜垢污染生长的实时测试,并与不同运行条件下模型的动态模拟结果进行实际比较分析,结果表明两者分形维数相近,且分形维数与膜污染程度呈正相关,说明该模型能够动态表征膜污染水平,可揭示出微滤过程中膜垢生长的动态变化规律,预测出膜材料的受污染水平。     

A prognostic physico-chemical model of secondary and marine inorganic multicomponent aerosols II. Model tests

The accuracy and efficiency of the sectional multicomponent aerosol model SEMA, described in the first part of this paper, are tested. Comparisons of results of an equilibrium version of SEMA with results of the equilibrium models SEQUILIB and AIM show good agreement for relative humidities above 60% and thus validate the thermodynamic portion of the model. Tests of the dynamic portion of SEMA show the reliability of the model down to a minimum number of four sections. The results of a model application give evidence that kinetic limitations may be important in the formation of secondary aerosol species by condensation of sulphuric acid, nitric acid, and ammonia on sea salt aerosol. The concentrations of the chemical components of marine aerosol may be substantially different from their thermodynamic equilibrium concentrations in the polluted coastal atmosphere.     

多元回归和BP人工神经网络在预测混合厌氧消化产气量过程中的应用比较

对脂肪类单基质和城市污水厂剩余污泥混合厌氧消化过程的产气阶段进行基于多元回归和BP人工神经网络的产气量预测模型比较研究。实验数据分别取自反应过程的第1～16天和第17～70天。结果表明:多元回归模型的预测平均准确率分别为75.69%和79.29%;BP神经网络模型的预测平均准确率为79.05%。通过对比2种模型的预测结果可知,两种模型都有较高的预测准确率,但BP模型的预测准确率更高,更适用于混合厌氧消化产气量预测。     

Experimental verification for real-time environmental emergency response system: WSPEEDI by European tracer experiment

Japan Atomic Energy Research Institute has developed an emergency response system WSPEEDI to forecast long-range atmospheric dispersions of radionuclides discharged into the atmosphere. The latest version of WSPEEDI consists of an atmospheric dynamic model MM5 for calculating meteorological fields and a particle random-walk model for atmospheric dispersion. The performance of WSPEEDI was evaluated by data obtained from a field tracer experiment over Europe (ETEX) in this paper. The model validation was done with respect to the following points: (1) the dependence of model accuracy on the temporal and spatial resolutions of the meteorological fields and (2) the superiority of an atmospheric dynamic model over a mass-consistent wind model. Regarding (1), it was shown that the calculation accuracy of the new version with high temporal resolution was improved, especially at the edge of the plume. Moreover, although the increase in horizontal spatial resolution of the old version had no substantial effect on the model performance, increase in horizontal resolution of the new version contributed to the significant improvement of the calculation accuracy. These results showed that the dynamically calculated meteorological field with the spatial resolution of the meso-β–γ scale greatly improved calculation accuracy.     

Multi-model vs. EPS-based ensemble atmospheric dispersion simulations: A quantitative assessment on the ETEX-1 tracer experiment case

Several techniques have been developed over the last decade for the ensemble treatment of atmospheric dispersion model predictions. Among them two have received most of the attention, the multi-model and the ensemble prediction system (EPS) modeling. The multi-model approach relies on model simulations produced by different atmospheric dispersion models using meteorological data from potentially different weather prediction systems. The EPS-based ensemble is generated by running a single atmospheric dispersion model with the ensemble weather prediction members. In the paper we compare both approaches with the help of statistical indicators, using the simulations performed for the ETEX-1 tracer experiment. Both ensembles are also evaluated against measurement data. Among the most relevant results is that the multi-model median and the mean of EPS-based ensemble produced the best results, hence we consider a combination of multi-model and EPS-based approaches as an interesting suggestion for further research.