On the modelling of bubble plumes in a liquid pool

Turbulent, bubble plumes are investigated numerically using the commercial, Computational Fluid Dynamics (CFD) code CFX-F3D. A six-equation, two-fluid model approach is adopted, in which interphase momentum exchange models include buoyancy, drag, added mass, lift and turbulent dispersion effects. Particular attention is paid to turbulence modelling, in which generation and dissipation resulting from interaction between bubbles and liquid are specifically taken into account within the context of an extended k − ϵ turbulence model. Results from a number of calculations are presented and compared against published, experimental bubble plume data. It is suggested that existing bubble/liquid interaction models for plumes may be grouped into three categories: those which produce lateral bubble spreading, those which diffuse the ambient liquid velocity field, and those which couple the plume to the surrounding liquid and thereby ultimately govern the pool mixing behaviour.     

The evidential reasoning approach for multi-attribute decision analysis under interval uncertainty

The evidential reasoning (ER) approach is a method for multiple attribute decision analysis (MADA) under uncertainties. It improves the insightfulness and rationality of a decision making process by using a belief decision matrix (BDM) for problem modelling and the Dempster–Shafer (D–S) theory of evidence for attribute aggregation. The D–S theory provides scope and flexibility to deal with interval uncertainties or local ignorance in decision analysis, which is not explored in the original ER approach and will be investigated in this paper. Firstly, interval uncertainty will be defined and modelled in the ER framework. Then, an extended ER algorithm, IER, is derived, which enables the ER approach to deal with interval uncertainty in assessing alternatives on an attribute. It is proved that the original ER algorithm is a special case of the IER algorithm. The latter is demonstrated using numerical examples.     

Ground-based measurement of ash distribution in volcanic plumes

Measurement of ash particle distribution within volcanic eruption plumes is important for volcanic hazard prediction, but is difficult because of the inherent danger and inaccessibility of volcanoes, and the three-dimensional time-dependent dynamics of turbulent eruption plumes. A combined study using a novel UV camera coupled with classical analysis of the dynamics of finite-volume buoyant plumes has been used to develop a detection algorithm for ground-based volcanic ash monitoring. The method provides the capability to observe and measure the internal structure of the plume, and processes occurring during plume rise, including concentration of ash over time into the plume ‘head’, increased ash at the plume edges during early formation and dilution at the top of the plume head as entrainment of surrounding air occurs. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Similarity analysis of the interaction of bushfire plumes with ambient winds

Using dimensional analysis of the plumes from point, line and infinite plane buoyancy sources, it is shown how the different source geometries lead to different entrainment rates, vertical velocity growth rates and responses to ambient mean winds. In particular, the plume from a lateral line buoyancy source (a common idealization of a bushfire plume) has a height-independent core updraught velocity in a quiescent environment. The same source in a uniform ambient wind produces a plume which is self-similar at all downwind distances. The influence of real (rather than idealized) ambient wind fields is estimated from knowledge of wind flow in and above plant canopies. Implications are drawn for the laboratory modelling of bushfire plumes.     

Effects of the urban environment on the trajectories of stack plumes

A numerical model for plume rise is used, together with a model of the urban mixing layer, to study the effects of the inhomogeneous temperature structure of the city of Leeds on the trajectory of effluent plumes. Consideration is given to chimney location, source strength, city topography, areal sources, wind speed and rural ground level temperatures.     

Development of a photochemical airshed model for Sydney,New South Wales,Australia

This paper describes the development of an airshed modelling system for the Environment Protection Authority of NSW (EPANSW). The airshed modelling system is one of the key outcomes of the Metropolitan Air Quality Study (MAQS). It will provide the tools necessary to aid in the development of an Air Quality Management Plan for photochemical smog within the Sydney, Lower Hunter, and Illawarra regions.The three dimensional airshed model was run for a 2 day generic Sydney oxidant event using historical data and some preliminary results from other MAQS projects. Predicted maximum smog concentrations obtained from the simulations appear to be comparable with the observations.     

Effect of pollutant source location on air pollutant dispersion around a high-rise building

This article investigates the dispersion of airborne pollutants emitted from different locations near a high-rise building. A Computational Fluid Dynamics (CFD) model for simulating the wind flow field and the pollutant dispersion was developed and validated by wind tunnel data. Then the spreading of the pollutant emitted from different locations to a rectangular-shaped high-rise residential (HRR) building was numerically studied. The pollutant source location was set in a wide range of the position angle and distance between the source and the building. It was found that the pollutant concentration on the building decreases with an increase in the emission distance whereas the effect of the position angle is more complicated. Interestingly, there is a critical range of the position angle from which the emitted pollutants will not spread to the building in a significant way. The effect of the source location was linked to the wind flow field around the building, particularly with several major flows. The vertical distributions of the pollutant concentration on different faces were also investigated, and it was found that these are more affected by the vertical flow near each face. Finally, a mathematical model was developed to evaluate the pollutant concentration as a function of the emission distance and position angle. These findings are helpful to the understanding of the dispersion of airborne pollutants around high-rise buildings and the related hazard management in urban design.     

PBDW: A non-intrusive Reduced Basis Data Assimilation method and its application to an urban dispersion modeling framework

The challenges of understanding the impacts of air pollution require detailed information on the state of air quality. While many modeling approaches attempt to treat this problem, physically-based deterministic methods are often overlooked due to their costly computational requirements and complicated implementation. In this work we extend a non-intrusive Reduced Basis Data Assimilation method (known as PBDW state estimation) to large pollutant dispersion case studies relying on equations involved in chemical transport models for air quality modeling. This, with the goal of rendering methods based on parameterized partial differential equations (PDE) feasible in air quality modeling applications requiring quasi-real-time approximation and correction of model error in imperfect models. Reduced basis methods (RBM) aim to compute a cheap and accurate approximation of a physical state using approximation spaces made of a suitable sample of solutions to the model. One of the keys of these techniques is the decomposition of the computational work into an expensive one-time offline stage and a low-cost parameter-dependent online stage. Traditional RBMs require modifying the assembly routines of the computational code, an intrusive procedure which may be impossible in cases of operational model codes. We propose a less intrusive reduced order method using data assimilation for measured pollution concentrations, adapted for consideration of the scale and specific application to exterior pollutant dispersion as can be found in urban air quality studies. Common statistical techniques of data assimilation in use in these applications require large historical data sets, or time-consuming iterative methods. The method proposed here avoids both disadvantages. In the case studies presented in this work, the method allows to correct for unmodeled physics and treat cases of unknown parameter values, all while significantly reducing online computational time.     

京津冀地区PM2.5污染特征的研究————-基于函数型数据分析的视角

近年来京津冀地区的雾霾重度污染事件频发, 引起国家和社会的普遍关注. 以京津冀地区68个监测站的数据为基础, 研究了京津冀地区PM2.5小时间隔的年度数据主要变异模式、时空变化类型等污染特征. 还研究了二氧化硫、氮氧化物年度累计排放量对PM2.5浓度变化的影响. 结果表明, 氮氧化物的排放对PM2.5浓度的贡献更显著, 削减氮氧化物等污染物的排放可有效降低PM2.5浓度, 改善空气质量. 采用函数型数据分析方法, 相对于传统的统计均值方法, 能够更有效的使用所采集到的不同的数据类型, 进行更细致的分析, 从而得到更可靠的结论.     

Computation of long-term average SO2 concentration in the Venetian area

This study tests the possibility of describing the dispersion of a non-reactive atmospheric pollutant in a complex coastal zone, such as the Venetian area, by means of a Gaussian type diffusion model. The basic assumption of the model is that the pollutant has a Gaussian distribution along the vertical and is uniformly distributed crosswind in each sector of the wind rose. With the use of meteorological data recorded on an hourly basis and average evaluation of urban and industrial emission, three-monthly average SO₂ concentrations as well as the annual ones were computed for the period February 1973 to January 1975. The surface roughness and the heat island effect have been taken into account by modifying Pasquill's stability classification. The results were compared with the concentration values recorded at ten monitoring stations. The general agreement between the model results and the measured data proved the validity of the applied technique for simulating long-term average concentrations and its applicability for land planning purposes.     

New mathematical modelling and simulation of an industrial accumulator for elastic webs

This paper concerns the modelling of an accumulator used in industrial elastic web processing plant such as paper mills, fabric, rolling mills etc. Accumulators are used to allow rewind or unwind core changes while the process continues at a constant web velocity. A new nonlinear model of a pneumatic actuated industrial accumulator including pneumatic jack model, static friction representation and web weight is first detailed which enables to deduce a linear model. These models are derived from physical laws that describe web tension and velocity dynamics in each web span. In a second part, the effects of time-varying mechanical parameters, such as web Young modulus, web length and rollers inertia on accumulator dynamics are presented. The performances of the modeled accumulator are illustrated by simulations in Matlab/Simulink software environment.     

Application of hybrid RANS-LES models to the prediction of flow behaviour in an industrial crystalliser

Knowledge of the residence time and shear rates in industrial crystallisers is critical for any assessment of the performance of these vessels from a chemical engineering perspective. It is unlikely that the range of expected residence time behaviours, or the shear rates can be predicted accurately with a RANS model. In the current study, a wide range of hybrid RANS-LES models, including Stress-Blended Eddy Simulation (SBES), were used to predict the flow field in a laboratory-scale alumina precipitator with the objectives of both quantifying the accuracy of the models and assessing if the hybrid models provide significant improvement over a previous RANS modelling study. Predicted mean and fluctuating velocities have been compared with Laser Doppler Velocimetry (LDV) data from a laboratory-scale alumina precipitator. The results achieved show that hybrid RANS-LES models can accurately predict both the mean and fluctuating velocities in the precipitator vessel. Importantly, as the mesh is refined, agreement with experimental data improves and differences between model predictions reduce, showing that sensitivity to the sub-grid scale model reduces if all the relevant large-scale turbulence structures are explicitly resolved. Prediction of fluctuating velocities is found to be more accurate than that achieved in a previous RANS modelling study. The SBES results are found to be mesh-independent, and to give closer agreement with experimental data, on a coarser mesh than both the SST-DDES and SST-SAS approaches as the model formulation allows rapid transition to an explicit LES model immediately outside the wall boundary layer. This is an important result for industrial simulation due to the significant reduction in simulation times needed.     

Application of mathematical models to simulate an extreme air pollution episode in the Bulgarian city of Stara Zagora

The application of complex atmospheric transport and transformation mesoscale models is becoming more common for study complex environmental problems. In this contribution, we present a pollution episode occurred in the area of Stara Zagora (Bulgaria), where a large SO₂ industrial emitter is present. This industrial emitter accounts for more than 75% of the total SO₂ emissions in Bulgaria. Two monitoring stations located in Stara Zagora and Mogila observed high SO₂ concentration peaks for 7–11 July 2007. We have used the MM5–CMAQ air quality modelling system with CBM-IV chemical mechanism. We have used two different emission modelling tools such as SMOKE and EMIMO. In the first simulation SMOKE is used. In the second simulation, we have used EMIMO including biogenic and anthropogenic sources coming from traffic, domestic sources, etc. Additionally, in the second simulation we have used a so-called ON–OFF approach, which means that we have run two time the modelling system with and without the emissions of the large industrial plant “Maritza-Iztok”, located in the surrounding area of the two monitoring stations. The use of MM5–CMAQ air quality modelling system with two different approaches, parameterizations, spatial resolution, emission modelling systems and vertical grid levels produce two significantly different results. The results show that the parameterizations and approaches for using a complex air quality modelling system such as MM5–CMAQ, are crucial for obtaining some similarities between observed and modelled data.     

MODELING THE EFFECTS OF URBAN HEAT ISLANDS GENERATED MESOSCALE WIND ON AIR POLLUTION DISPERSION IN A PATCHY ATMOSPHERE

Abstract The paper presents a steady‐state two‐dimensional mathematical model to study the dispersion of air pollutants continuously emitting from a point source in a patchy atmosphere. The first patch, which extends to some downwind distance from the source (present in first patch), is the urban center, whereas the second patch is its adjacent countryside. For simulating the effects of urban heat islands on pollutant distribution, a local mesoscale wind is introduced in the first patch along with the usual large‐scale wind. Keeping in mind the real‐life situations, the meteorological, deposition, and removal parameters are taken to be different in different patches. The concentration of pollutants in both the patches has been computed under different stability conditions of the atmosphere through finite difference scheme. The results of the model show that the urban heat island (mesoscale wind) has a tremendous impact on air pollution distribution, both over the urban center as well as its adjacent countryside (rural). The model solutions also reveal about the concentration distribution of pollutants in different parts of the atmosphere in different atmospheric conditions.     

Forecasting concentrations of air pollutants by logarithm support vector regression with immune algorithms

The need to minimize the potential impact of air pollutants on humans has made the accurate prediction of concentrations of air pollutants a crucial subject in environmental research. Support vector regression (SVR) models have been successfully employed to solve time series problems in many fields. The use of SVR models for forecasting concentrations of air pollutants has not been widely investigated. Data preprocessing procedures and the parameter selection of SVR models can radically influence forecasting performance. This study proposes a support vector regression with logarithm preprocessing procedure and immune algorithms (SVRLIA) model which takes advantage of the structural risk minimization of SVR models, the data smoothing of preprocessing procedures, and the optimization of immune algorithms, in order to more accurately forecast concentrations of air pollutants. Three pollutants, namely particulate matter (PM₁₀), nitrogen oxide, (NO_x), and nitrogen dioxide (NO₂), are collected and examined to determine the feasibility of the developed SVRLIA model. Experimental results reveal that the SVRLIA model can accurately forecast concentrations of air pollutants.     

Sets and indices in linear programming modelling and their integration with relational data models

LP models are usually constructed using index sets and data tables which are closely related to the attributes and relations of relational database (RDB) systems. We extend the syntax of MPL, an existing LP modelling language, in order to connect it to a given RDB system. This approach reuses existing modelling and database software, provides a rich modelling environment and achieves model and data independence. This integrated software enables Mathematical Programming to be widely used as a decision support tool by unlocking the data residing in corporate databases.     

Oxidation of reactive solute released into shear flow

When a plume of oxygen-demanding reactive solute is releasedinto a shear flow, a corresponding plume of oxidant depletionis created. In this paper the effect of nonlinear interactionbetween the two plumes is investigated. The principal resultis that the spread of reagent obeys a t power law instead of the usual diffusive t law.     

UNSTEADY STATE DISPERSION OF AIR POLLUTANTS UNDER THE EFFECTS OF DELAYED AND NONDELAYED REMOVAL MECHANISMS

Abstract In this paper, we present a two‐dimensional time‐dependent mathematical model for studying the unsteady state dispersion of air pollutants emitted from an elevated line source in the atmosphere under the simultaneous effects of delayed (slow) and nondelayed (instantaneous) removal mechanisms. The wind speed and coefficient of diffusion are taken as functions of the vertical height above the ground. The deposition of pollutants on the absorptive ground and leakage into the atmosphere at the inversion layer are also included in the model by applying appropriate boundary conditions. The model is solved numerically by the fractional step method. The Lagrangian approach is used to solve the advection part, whereas the Eulerian finite difference scheme is applied to solve the part with the diffusion and removal processes. The solutions are analyzed to observe the effects of coexisting delayed and nondelayed removal mechanisms on overall dispersion. Comparison of delayed and nondelayed removal processes of equal capacity shows that the latter (nondelayed) process is more effective than the former (delayed removal) in the removal of pollutants from the atmosphere.