City Scale Pollutant Dispersion Modelling Utilising A .
INTERNATIONAL JOURNAL OF MECHANICSVolume 11, 2017City Scale Pollutant Dispersion ModellingUtilising a Combination of Computational FluidDynamics and Standard Air Quality SimulationNeihad Hussen Al-Khalidy1assessment, ie through the application of validated 3-Dcomputer modelling tools such as Gaussian models(CALPUFF, Aeropol, Aermode, etc) and Lagrangian/EulerianModels (GRAL, TAPM, etc). Standard dispersion modellinguses mathematical modelling of the physics and chemistrygoverning the transport, dispersion and transformation ofpollutants in the atmosphere.Many of these models such as CALPUFF and TAPM areused for regulatory purposes [1, 2].Different types of dispersion models are overviewed andoutlined in [1] to [6]. Many of the Gaussian models have beenshown to over predict concentrations in low wind speedcondition [3]. In general, those models are not recommendedfor areas heavily influenced by turbulence such as in an urbanenvironment due to turbulence modelling approximation [6].Predicting air and pollutant in an urban environment is avery complex problem influenced by downwash andacceleration effects induced by surrounding buildings.Recently, the use of numerical techniques includingComputational Fluid Dynamics (CFD) to simulate pollutantdistribution and wind patterns caused by buildingconfigurations has received much attention. CFD can be usedas a tool to help the designer to examine the pollutant problemunder various conditions.A considerable number of CFD publications have beenpublished in the Journal of Wind Engineering & IndustrialAerodynamics in the past two decades. CFD predictions ofwind flow around bluff bodies have been compared andvalidated against wind tunnel and full scale measurements inthe open literature [ie 7, 8, and 9]. In general good agreementis obtained when best practice guidelines are used for themodelling.A CFD simulation of airflow and pollutant dispersionaround a group of real buildings configuration is presented in[10]. The need for that study was a result of a city councilrequirement prior to the issue of a construction certificate for aproposed building. The pollutant sources were located on theroof of the building. Results of the simulation were presentedfor near calm and windy conditions. The compliance withOccupational Health and Safety Commission (OHSC)requirements is also demonstrated in that study.The dispersion of gases from diesel/gas powered electricitygenerators within and then downstream of building complexesin the entire inner Brisbane city was presented in [11]. ThatAbstract— Pollutant dispersion in urban street canyons is usuallyinvestigated numerically using Standard Air Quality (AQ) modellingassessment, ie through the application of validated 3-D computermodelling tools such as CALPUFF or Advanced ComputationalFluid Dynamic (CFD) for near-field Simulation. This paper presentsa road map to use a combination of 3-D standard air qualitymodelling and Computational Fluid Dynamics (CFD) to reliablysimulate air flow and quality in city canyons on the example ofemergency ventilation smoke control in roadway tunnels. The localwind rose for a project site was created using The Air PollutionModel (TAPM) and CALMET diagnostic meteorological modellingsoftware, which reconstructs local 3D wind and temperature fieldsstarting from regional meteorological measurements, synopticweather model outputs, topography and land use data. Emissionsfrom the project site have then been initially modelled using theCALPUFF dispersion model. Boundary conditions (the worst casewind direction, wind speed and ambient temperature) resulting in thehighest pollutant concentration predictions were identified throughthis preliminary CALPUFF dispersion modelling study, which werethen used in the detailed microclimate CFD exhaust dispersionmodelling. Microclimate CFD modelling is substantially morecomputationally expensive than preliminary CALPUF simulations.The CFD analysis offers a comprehensive range of output includingpollutant concentration, velocity distribution, temperaturedistribution, pressure profile, turbulent levels, etc. allowing theidentification of sources that have unacceptable impact on the city airquality. Downwind pollutant concentrations can be further reducedby optimising the stack dimensions and/or smoke volumes andspeeds. It is anticipated that the use of CFD for entire city modellingwill be a useful tool to help urban designers and environmentalplanners. The paper also discusses some of the challenges facingCFD for modelling built environment.Keywords— CFD, Pollutant Dispersion, Wind, EmergencyVentilation Smoke, Transportation Tunnel.I. INTRODUCTIONIn order to maintain a healthy environment it is essential tobe able to predict, evaluate and understand airflow patternsand dispersion of pollutants within populated areas.Far-field pollutant dispersion is usually investigatednumerically using “Standard” operational air quality 1Author is the CFD, Wind and Energy Technical Discipline Manager ofSLR Consulting Australia Pty Limited, 2 Lincoln Street, Lane Cove, NewSouth Wales, 2066, Australia (Phone 61 2 9427 8100, Facsimile 61 2 SSN: 1998-4448210
INTERNATIONAL JOURNAL OF MECHANICSstudy accounted for effect of buildings geometry, upwindbuildings configuration, canyon orientation, wind speed, winddirection and pollutant sources are accounted for.In this study, a combination of standard air qualitydispersion modelling and advanced Computational FluidDynamics (CFD) assessment was used to reliably assess thedispersion characteristics in city canyons. The main objectivesof this study are to:1) Develop a 3D CAD model of building complexes for CFDdispersion modelling.2) Incorporates the emission sources into the model.3) Develop localised weather data for the project site.4) Develop procedures to integrate data from the standard(Mesoscale metrological) model into the CFDmicroclimate numerical model.5) Predict pollutant concentrations on the ground andfacades of buildings where intakes of air conditioningsystems are located.6) Provide recommendations to improve air quality of theproject pollutant sources.Volume 11, 2017 T( h) (u i h ) (k effective) S xi xi xi xiWhere ρ is the density, u is the velocity, p is the staticpressure, ρg and F are the gravitational body and externalbody forces, ij is the stress tensor, h is the enthalpy and keffectiveis the effective thermal conductivity, C is the pollutantconcentration and v is the kinematic viscosity and S is thevolumetric heat source.Turbulence is predicted using one of the followingmethods: Direct Numerical Simulation (DNS) Large Eddy Simulation (LES) Reynolds-Averaged Navier-Stokes (RANS) EquationsFor most real world building problems turbulence is, inprinciple, described by the Navier-Stokes equations [12].Commercial CFD codes provides a wide range of turbulencemodels including Spalart-Allmaras model,k-epsilon (ke)models, k-w models, v2f model, Reynolds Stress models,Scale Adaptive Simulation (SAS) model, Detached EddySimulation, Large eddy simulation (LES) models. The qualityof CFD simulation depends on the selected turbulence model.In practical problems the turbulence model should be assimple as the relevant physics will permitII. PROBLEM FORMULATIONThe data of interest in this study are the maximum 1-houraverage concentrations of pollutant, eg CO, PM10 and NOX(µg/m3) experienced at the surrounding buildings during anemergency fire event in a transportation tunnel.Preliminary modelling of emissions from a project site hasbeen performed using a combination of the TAPM, CALMETand CALPUFF models. CALPUFF is a transport anddispersion model that ejects “puffs” of material emitted frommodelled sources, simulating dispersion and transformationprocesses along the way. In doing so it typically uses thefields generated by a meteorological pre-processor CALMET,discussed further below in Section 3. The CALPUFFdispersion model has the ability to handle calm wind speedsand complicated terrain and was considered appropriate for theprediction of the worst case wind conditions for microclimatepollutant dispersion analysis. The objective of this preliminarymodelling was to identify the worst case meteorologicalconditions that give rise to the worst-case downwindconcentrations, which were then used in the detailed CFDexhaust dispersion modelling.A detailed Computational Fluid Dynamic (CFD) pollutantsimulation was then used to quantify pollutant dispersion onthe ground level and on the façades of the nearby buildingsunder the identified worst case meteorological conditions.The CFD model solves the continuity, momentum, energyand species concentration equations. The equations for asteady state case can be written as follows:III. CASE STUDY – EMERGENCY TUNNEL VENTILATIONThe design fire parameters used for the design of tunnelemergency ventilation have a significant impact on the tunneldesign and users safety. In order to improve the safety oftransportation tunnels, it is essential to be able to predict,evaluate and understand airflow patterns and dispersion ofpollutants within populated areas during fire events andemergency tunnel ventilation.The flow patterns that develop around buildings govern thedistribution of pressure and consequently the concentrationdistribution of pollutants in a built environment. Given thecomplexity of built environments, the dispersion of pollutantsresulting from a city scale project will be complex, influencedby downwash and acceleration effects induced by surroundingbuildings, wind tunnelling through the aligned open areas, thelocation of the source point, ambient meteorologicalconditions, etc.Pollutant dispersion is therefore a function of the followingparameters:1) Upstream wind characteristics: wind speed and direction,turbulence intensity, etc.2) Stack position relative to surrounding buildings, stackheight, exit velocity and temperature.3) Surrounding building configuration, both upwind anddownwind.4) Interaction of flow patterns associated with adjacent ( ui ) 0 xi p ij( ui u j ) ) g i Fi x j xi x jISSN: 1998-4448211
INTERNATIONAL JOURNAL OF MECHANICSVolume 11, 2017terrain features and land use. A summary of the annualwind behavior predicted by CALMET for the project sitefor one calendar year is presented in Figure 1.buildings.This study provides a roadmap to optimise the design ofemergency tunnel ventilation utilising advanced numericaltechniques. The proposed methodology involved thefollowings:1) Obtain or predict the wind rose for the project site. In thisstudy the TAPM prognostic model [13], developed by theCommonwealth Scientific and Industrial ResearchOrganisation (CSIRO) was used to generate the upper airdata required for CALMET modelling. TAPM predictswind speed and direction, temperature, pressure, watervapour, cloud, rain water and turbulence. The programallows the user to generate synthetic observations byreferencing databases (covering terrain, vegetation andsoil type, sea surface temperature and synoptic scalemeteorological analyses) which are subsequently used inthe model input to generate one full year of hourlymeteorological observations at user-defined levels withinthe atmosphere. Additionally, the TAPM model mayassimilate actual local wind observations so that they canoptionally be included in a model solution. The windspeed and direction observations are used to realign thepredicted solution towards the observation values.However, for this study no data assimilation has been usedto nudge (i.e. influence) the TAPM predictions and themodel was instead run without any observational dataassimilation. TAPM generated three dimensionalmeteorological data was used as the initial guess windfield for a CALMET meteorological model that developshourly wind and other meteorological fields on a threedimensional gridded modelling domain that are requiredas inputs to the CALPUFF dispersion model. CALMETmodelling was conducted using the nested CALMETapproach, where the final results from a coarse-grid runwere used as the initial guess of a fine-grid run. This hasthe advantage that off-domain terrain features includingslope flows, blocking effect are allowed to take effect andthe larger –scale wind flow provides a better start in thefine-grid run. Associated two dimensional fields such asmixing height, surface characteristics and dispersionproperties are also included in the file produced byCALMET. The interpolated wind field is then modifiedwithin the model to account for the influences oftopography, sea breeze, as well as differential heating andsurface roughness associated with different land usesacross the modelling domain. These modifications areapplied to the winds at each grid point to develop a finalwind field. The final hourly varying wind field thusreflects the influences of local topography and land uses.2) Hourly surface meteorological data from Bureau ofMeteorology (BOM) stations located near the project sitewere incorporated in the CALMET modelling. The outputfrom the outer domain CALMET modelling was then usedas the initial guess field for the inner domain CALMETmodelling. The computational domain encompassed anarea of 6 km 6 km. A horizontal grid spacing of 0.1 kmwas used to adequately represent the important localISSN: 1998-4448Fig.1 G Wind speed Frequency Distribution for the Project Site, aspredicted by CALMET for one Calendar Year1) Conduct a preliminary CALPUFF atmospheric dispersionmodelling. Boundary conditions (eg the worst case winddirection, wind speed and ambient temperature) resultingin the highest pollutant concentration predictions wereidentified through this preliminary CALPUFF dispersionmodelling study, which were then used in the detailedmicroclimate CFD exhaust dispersion modelling.2) Develop a detailed 3D CAD model of the proposedproject site for CFD modelling.3) Incorporate the emergency ventilation shafts (stacks) intothe model.4) Add topography and surrounding buildings to a minimumradius of 500 m.5) Develop mixed meshed cells (tetrahedral, hexahedra andpyramids) or polyhedral mesh for the computationaldomain.6) Integrate the previously predicted local weather data forthe worst case scenarios with the developed CFD model.In this case number of runs for microclimate CFD runs issignificantly reduced. It is important to understand thatmicroclimate CFD modelling is substantially morecomputationally expensive than preliminary CALPUFsimulations.7) Establish the emission rates for pollutant (CO, PM10, etc.)at the ventilation shafts. Data are obtained from the firemodel for the project.8) Calculate initial turbulence quantities (kinetic energy anddissipation rate) required for the upwind free boundaryfrom empirical relationships.9) Select a proper turbulence model and numerical schemefor the assessment based on available best practiceguidelines and/or validated studies.212
INTERNATIONAL JOURNAL OF MECHANICSVolume 11, 2017B. DiscretizationThe software package utilised in the current CFD analysisis the commercially available code ANSYS-Fluent [14]. TheCFD model solves continuity, momentum, energy and speciesequations in the computational domain to predict the steadystate airflow and pollutants dispersion at and around theproject site.1) The quality of the mesh is a critical aspect of the overallnumerical simulation and it has a significant impact on theaccuracy of the results and solver run time. For the currentanalysis, polyhedral elements with a total number of14,212,493 nodes were used to cover the computationaldomain (Refer Figure 3). Polyhedral cells are especiallybeneficial for handling complex flows and used to providemore accurate results than even hexahedra mesh. For ahexahedral cell, there are three optimal flow directionswhich lead to the maximum accuracy while for apolyhedron with 12 faces; there are six optimal directionswhich together with large number of neighbors lead to amore accurate solution with a lower cell count.2) The following techniques were used for discretization: A second order numerical scheme for discretization ofpressure to obtain more accurate results. A second order numerical scheme for discretization ofmomentum to obtain more accurate results.3) A Realizable k-epsilon (rke) turbulence model was usedfor all analysed cases due to ability to handlerecirculation, high gradients and computational timeadvantages.4) The solution is combined with a wall function to avoidusing very fine elements near the wall.5) An iterative procedure was used to estimate the airvelocity in terms of three directions, pollutantsconcentration, pressure profile and turbulence parameters.The normalised residuals of continuity for all cases werereduced by at least three orders of magnitude while thenormalised residual of x-, y-, and z-velocity, species,temperature, k and epsilon was reduced between four and sixorders of magnitude demonstrating a valid solution (ReferFigure 4).10) Predict pollutants concentration (or dilution factors) andassess the Emergency Ventilation Smoke (EVS) impact ofthe project site on surrounding buildings andinfrastructure.11) Provide guidance as to the areas where adopted pollutantsacceptability criterion had the potential to be exceededand an indication as to the likely local optimum treatmentstrategy.12) In consultation with the project team revise the designwhere required by optimising the stack location,dimensions and/or smoke volumes and speeds.The above procedure allows assessing design modificationoptions including all parameters of interest (Buildingconfiguration, detailed stack geometry and position,topography, etc.) in a timely manner.A. Modelling ConfigurationA 3D model of the project site and surrounding buildingsand structure blocks was created from the architecturaldrawings and CAD models supplied by the client. The 3Dmodel of the stacks and surrounding blocks is shown in Figure2. The CFD model incorporated the following:1) A calculation domain of 2,000 m length, 2,000 m wideand 500 m high was used for the CFD analysis.2) Four rail tunnel stacks with three stacks are operatedsimultaneously as per the proposed design.3) Accurate terrain effect of the modelled area.Fig.2 Geometry for CFD ModellingFig.3 Polyhedral MeshISSN: 1998-4448213
INTERNATIONAL JOURNAL OF MECHANICSVolume 11, 2017A: General Flow CharacteristicFig. 4 Scaled Residual HistoryC. CFD Results and DiscussionIn the wind and pollutant dispersion CFD modelling, windspeed and pollutant concentration can be reported at any pointon the ground, podium and at any vertical elevations. The areaof interest therefore includes all surrounding buildings shownin Figure 2. Results of simulations in Figure 5 to Figure 13 arepresented for the following worst case atmospheric condition:Wind Speed 2.30 m/sWind Angle 92 Ambient Temperature 299.3K.Lower temperature exhaust gas emissions data are related tosmall fire sizes. The highest exhaust gas temperature at theinlet to the central fans located closest to the fire foundthrough literature research was163 C for a 100 MW fire and107 C for a 20 MW fire [15]. In this study small and large firesizes are considered. Results in Figure 5 to Figure 13 are for asmoke (exhaust) temperature of 315 K (15 degrees aboveambient temperature).Exhaust speed of 5 m/s is proposed and was applied to allmodelled stacks. A pollutant concentration of unity wasassumed at the sources and dilution factors are predicted in theareas of interest.Figure 5 shows the vectors of mean airflow velocitiesthrough a two-dimensional section above the ground of theproject site. Velocity vectors are plotted on a colour codedscale between 0 and 2.5 m/s. Dark blue represents stillconditions at 0 m/s and red representing the strongest windspeed. The following conclusions can be reached from Figure5:1) General flow characteristics for the modelled builten
modelling and Computational Fluid Dynamics (CFD) to reliably simulate air flow and quality in city canyons on the example of emergency ventilation smoke control in roadway tunnels. The local wind rose for a project site was created using The Air Pollution Model (TAPM) and CALMET diagnostic meteorological modelling
Dispersion is generally divided into two individual contributions, known as material dispersion and waveguide dispersion [2]. Material dispersion is related to the wavelength dependency of the refractive index of silica (SiO2) glass, which is the host material of optical flbres used for transmission purposes. Waveguide dispersion, on the other 1
1550 nm region. This dispersion limits the possible transmission length without compensation on OC-768/STM-256 DWDM networks. ITU G.653 is a dispersion-shifted fiber (DSF), designed to minimize chromatic dispersion in the 1550 nm window with zero dispersion between 1525 nm and 1575 nm. But this type of fiber has several drawbacks,
GIS Pollutant Loading Application— Users Manual 1. Pollutant Loading Application Overview PLOAD is a simplified, GIS-based model to calculate pollutant loads for watersheds1. PLOAD estimates nonpoint sources (NPS) of pollution on an annual average basis, for any user-specified pollutant. The user may calculate the NPS loads using either the .
CCC-466/SCALE 3 in 1985 CCC-725/SCALE 5 in 2004 CCC-545/SCALE 4.0 in 1990 CCC-732/SCALE 5.1 in 2006 SCALE 4.1 in 1992 CCC-750/SCALE 6.0 in 2009 SCALE 4.2 in 1994 CCC-785/SCALE 6.1 in 2011 SCALE 4.3 in 1995 CCC-834/SCALE 6.2 in 2016 The SCALE team is thankful for 40 years of sustaining support from NRC
CFD modeling using RANS and LES of pollutant dispersion in a three-dimensional street canyon is investi- gated by comparison with measurements. The purpose of this study is to confirm the accuracy of LES in mod- eling plume dispersion in a simple street canyon model and to clarify the mechanism of the discrepancy in re- lation to RANS computation.
Svstem Amounts of AaCl Treated Location Scale ratio Lab Scale B en&-Scale 28.64 grams 860 grams B-241 B-161 1 30 Pilot-Plant 12500 grams MWMF 435 Table 2 indicates that scale up ratios 30 from lab-scale to bench scale and 14.5 from bench scale to MWMW pilot scale. A successful operation of the bench scale unit would provide important design .
Interim Guidance for Estimating Pre- and Post-Development Stormwater Pollutant Loads, available from NHDES. At this time, NHDES requires pollutant loading analysis to include only total suspended solids (TSS), total phosphorus (TP), and total nitrogen (TN). Although projects may have other pollutants of concern, TSS, TP,
development—year 1 (2012–13) FINAL PROJECT REPORT by Qing Shen, P.I.*; Peng Chen*; Peter Schmiedeskamp*; Alon Bassok*; Suzanne Childressy *University of Washington yPuget Sound Regional Council for Pacific Northwest Transportation Consortium (PacTrans) USDOT University Transportation Center for Federal Region 10 University of Washington More Hall 112, Box 352700 Seattle, WA 98195-2700 .
