350 Int. J. Environment and Pollution Vol. 5, Nos. 3 6, 1995
|
|
- Ezra Stokes
- 6 years ago
- Views:
Transcription
1 350 Int. J. Environment and Pollution Vol. 5, Nos. 3 6, 1995 A puff-particle dispersion model P. de Haan and M. W. Rotach Swiss Federal Institute of Technology, GGIETH, Winterthurerstrasse 190, 8057 Zürich, Switzerland Abstract: A meso-scale puff diffusion model is presented which makes use of the concept of relative diffusion to disperse the puffs and moves their centres of mass by incorporating a Lagrangian stochastic dispersion model as it is normally used to describe particle dispersion. The so-called puff-particle model (PPM) retains the advantages of traditional puff models and at the same time is able to take into account the correct probability density function of the stochastic velocity components, with a computing time considerably less than that of particle models. The PPM is three-dimensional and it includes a Lagrangian approach for puff rise. The parametrizations cover stable, neutral and convective conditions. The model is shown to reproduce realistically the concentration patterns for a simple flow of homogeneous Gaussian turbulence. It is furthermore used to investigate the effect of an inhomogenity in surface roughness on dispersion characteristics. Keywords: dispersion, particle model, plume rise, puff model. Reference to this paper should be made as follows: de Haan, P., and Rotach, M.W. (1995) A puff-particle dispersion model, Int. J. Environment and Pollution, Vol. 5, Nos. 3 6, pp Introduction To take into proper account possible inhomogenities in the flow and turbulence fields when modelling pollutant dispersion, particle models (or random walk models) are generally accepted to be most appropriate. The major disadvantage of particle models, however, is their excessive need of computing time. At the other extreme, the relatively simple and fast plume models, based on one-particle statistics, yield a statistical plume, i.e. a concentration distribution that represents an ensemble average over a number of individual instantaneous plumes. This causes problems when modelling dispersion in a situation where the flow and/or turbulence field is inhomogeneous, with the scale of the inhomogenities not being symmetric with respect to the plume s axis or smaller then the plume itself. One way to circumvent this problem is the use of a puff approach with relative diffusion to disperse individual puffs. Because relative diffusion takes into consideration only the growth of a single puff, and not the average dispersion of many puffs, relatively diffused puffs can be used even over complex inhomogenities. However, in order to yield the proper spread of a plume described by many puffs (e.g., in the case of a continuously emitting source), that part of the turbulence spectrum that has no diffusive effect on the Copyright 1995 Inderscience Enterprises Ltd.
2 A puff-particle dispersion model 351 puff as a whole (i.e. the large eddies) must be included in the simulation, either by providing an updated wind-field every few seconds, or by artificially generating the effect of these eddies. Thus the idea behind the Puff-Particle Model (PPM) is to use the concept of relative diffusion to describe the spread of individual puffs as in other puff models (e.g. Mikkelsen et al., 1984), and additionally move their centres of mass using a stochastic particle model (Figure 1). With this, an ensemble average of puffs can be constructed (Figure ) without updating the meteorological fields too often. Furthermore, the effects of inhomogenity of the turbulence field can be taken into account, as it is the case in a traditional particle model. Figure 1 The concept of the PPM: The centres of mass of the individual puffs are moved along the trajectory generated by a particle model. Figure Sketch of the ensemble average of stochastically moved puffs, that are dispersed by relative diffusion, and correspond to a traditional plume. As mentioned earlier, the particle part of the PPM accounts for the effects of eddies being larger than the actual puff size and thus moving the puff as a whole. In consequence, the turbulence statistics in the particle model within the PPM should take into account only the low-frequency part of the spectrum, where the threshold frequency changes with time. For short times no large effect of this behaviour should be expected, but with increasing puff size important parts of turbulent fluctuations will have to be eliminated. However, at present, the particle model within the PPM still includes all eddy sizes in the calculation of the turbulence statistics. The puff approach is described in Section, and some details on the Lagrangian three-dimensional stochastic dispersion model are given in Section 3. The parametrizations of the turbulence statistics used in the PPM are briefly outlined in Section 4. To model the rise of polluted, warmer air within the concept of a Lagrangian puff model, a method to describe the rise of puffs without calculating an effective emission height, is presented in Section 5. As a first model validation the plume spread in homogeneous Gaussian turbulence, as obtained with the PPM, is compared to that of a Gaussian plume model (Section 6).
3 35 P. de Haan and M. W. Rotach A similar approach to combine the advantages of particle and puff models has recently been presented (Hurley, 1994). The vertical velocity components of his puffs are given by a one-dimensional Lagrangian particle model, whereas they are spread in the horizontal direction using a puff approach. In contrast to the PPM, Hurley s stochastic velocity components are used only to describe the vertical relative diffusion of the plume, and his model is therefore mainly suited to the description of dispersion in vertically inhomogeneous turbulence (e.g. a convective boundary layer). The puff model To describe the diffusion of a puff of particles (a cluster), the concept of relative diffusion (Richardson, 196) is adopted. Richardson emphasises that the rate of diffusion of two particles from each other is dependent on the distance of separation of the particles. This behaviour lies in the nature of turbulence, because only eddies smaller than the separation of two particles can contribute to an increase of this separation, i.e. are able to diffuse them. As the separation of two particles increases, larger eddies will diffuse them. Eddies that are too large to contribute to the diffusion will move both particles without diffusing them. Figure 3 shows the spreading of an ensemble averaged plume (modelled as a series of puffs using the sigma values according to one-particle or absolute diffusion characteristics), whereas Figure 4 shows the simulation of the corresponding plume by puffs dispersed with relative diffusion and additionally moved by large turbulent eddies. In the model, this additional movement is realised through the particle part of the PPM. Only a limited number of puffs are shown in Figure 4 for clarity and to highlight the size of puffs using the concept of relative dispersion (one individual plume ) compared with a statistical, ensemble-averaged plume (Figure 3). Figure 3 A series of puffs modelling a plume. Its spread is determined by one-particle or absolute diffusion. Source height 100 m.
4 A puff-particle dispersion model 353 Figure 4 Sizes of relatively diffused puffs from a source at a height of 100 m. Based on the work of Richardson, Smith and Hay (1961) derive an expression for the maximum rate of diffusion, which only depends on the intensity of turbulence, L = X 8 : dσ dx max βi, (1) where β is the ratio of the Lagrangian to the Eulerian time-scale. This expression should not be used for clusters, that are initially very small, because these clusters have a significant initial sub-normal rate of growth. Pasquill and Smith (1983) have further examined Equation 1 and obtained dσ dx max 0.3i. () Equation is used for the description of relative diffusion in other puff models as well, e.g., RIMPUFF (Mikkelsen et al., 1984). 3 The stochastic dispersion model To move the centres of mass of the puffs, a three-dimensional Lagrangian stochastic dispersion model is used that fulfils the well-mixed condition for stable/neutral to convective conditions. It is based on a model described in Rotach et al. (1995) (but is expanded to three dimensions). It allows a continuous transition between correlated Gaussian turbulence on one hand, and uncorrelated skewed turbulence on the other hand, of which the latter is characteristic for the convective boundary layer.
5 354 P. de Haan and M. W. Rotach The probability density function (PDF), P tot, describing the particle velocities, is composed of an uncorrelated part P conv = P u P v P ws and a correlated part P g = P u P v P w P uw, that are linked by a transition function, f : P tot = fp conv + ( 1 f)p g. (3) In P conv above, P ws is a skewed PDF in the vertical velocity, as described by Luhar and Britter (1989). From Equation 3 it can be seen that the total PDF becomes equal to P conv (i.e. all three velocity components are uncorrelated, w is skewed) if f =1. On the other hand, if f = 0, u and w are jointly Gaussian distributed ( P u P w P uw ) and the lateral velocity component 3 is assumed to be independent of the two others. From this PDF, the model Y for the velocity increments is constructed in the usual manner (see, e.g., Thomson, 1987). The transition function f is formulated in such a way that for large w * (the convective velocity scale) it becomes unity throughout most of the boundary layer and equal to zero over large parts of a neutral or stable boundary layer w * = 0 ( ) (Rotach et al., 1995). 4 Parametrization of turbulence statistics The PPM is designed for use under all stability conditions. For stable conditions the following parametrizations have been chosen: σ w u * = z z i 3, σ v u * = 1 z σ, u z i u * = 61 z, z i (4a c) that are adopted from Pasquill and Smith (1983), Gryning et al. (1987) and Stull (1988), respectively. For neutral to convective conditions, parametrizations are used which combine the limits for neutral stability and convective conditions, σ u w * σ v u * σ u u * 3 =1.5 z exp z z z i z i z i = 0.35 z 3 i kl + z z i = 0.35 z 3 i kl z. z i u * w * (5a) (5b) (5c) as proposed by Pasquill and Smith (1983) and Rotach et al. (1995). Note that the neutral limits of the Equations 5a c do not correspond those of Equations 4a c.
6 A puff-particle dispersion model Puff rise In order to model the rise of an exhaust gas warmer than the surrounding air ( plume rise ), Briggs (1975) formulas have often been used. These expressions were derived for plume models and it is difficult to make use of them within a Lagrangian framework. Therefore, the source height is often replaced by an effective stack height for each puff. In the following, a Lagrangian approach to calculate puff rise is presented and briefly discussed. The principles on which the calculation of the puff rise is based, are briefly outlined in the following. Mainly, two forces have an effect on a parcel of air: the buoyancy and the air resistance. Additionally, a parcel of air will exchange heat with the surrounding air. The heat flux, I, through the surface area, A, of the parcel is I = λ T A x, (6) where λ denotes the thermal conductivity and [ is the distance over which the maximum temperature difference 7 is measured. The buoyancy of a parcel of air with mass m is F A = g m m * ρ T p R, (7) where J = 9.8ms, 5 = 8.134J K 1 mol 1 and p is the air pressure. In Equation 7, the density ρ and the mass per mole P are set equal for the parcel and the surrounding air. Furthermore, while rising with a velocity w, a parcel of air experiences a frictional force, F W, F W = c W ρw B, (8) where B is the area normal to the wind velocity and c W is the drag coefficient. During a time step W the parcel of air experiences a vertical acceleration a = ( ) F + F A W m, (9) but owing to the heat flux described by Equation 6 the temperature surplus according to the surrounding air is continuously reduced. At each instant after release, a puff s tendency to rise can be examined by evaluating Equation 9. Unfortunately, a puff is not a parcel of air, but rather a probability density function of a pollutant concentration. Thus c W in Equation 8 can be quite different from the corresponding value for a sphere, say. Furthermore, compared with a parcel with a closed surface, a puff has a far better exchange with ambient air, which causes an increase in the heat conductivity λ. In fact, the determination of the parameters c W and λ is the trickiest part of this new approach to describe the rising of warm air (puff rise). So far, for the use within the PPM, there stability-dependent sets of values have been determined by a parameter fit enforcing exactly the same results as by using Brigg formulas for plumes. Clearly, it would be desirable to make direct use of experimental data for a definite parametrization of c W and λ. Figure 5 shows the rise of a plume simulated by individual puffs, that reach their final height after about 100 m. On the ordinate the effective emission height after Briggs
7 356 P. de Haan and M. W. Rotach formulas for neutral stability is indicated. The values used for the parameters F and λ are : those for neutral stability. Figure 5 Rise of puffs with use of the puff-rise procedure. The puffs are initially 60 C warmer than the environment. The mean wind speed is 4 ms 1. The arrow B indicates the effective emission height after Briggs (1975). 6 Results The performance of the PPM is investigated by analysing a case of homogeneous, Gaussian turbulence. Under these conditions, a Gaussian (statistical) plume can be viewed as a reference. Thus the ability of the PPM is investigated to reproduce realistically a statistical plume by ensemble-averaging over a number of puffs that experience a stochastic velocity to move their centre of mass. Figure 6 shows the crosssection of such a statistical plume. For comparison, Figure 7 shows the corresponding plume, as obtained by relatively diffused and stochastically moved puffs. The concentration isolines to the values and show a satisfactory resemblance. The differences at the edges of the plume (i.e., the different positioning of the and contour-lines) are due to the fact that the sigma values of the relatively diffused puffs are smaller than those used to describe a statistical plume. This leads to differences at very low concentrations at the outer regions of a plume, which are of minor physical relevance, though.
8 A puff-particle dispersion model 357 Figure 6 Cross-section of a Gaussian plume at x = 77.4 m. Source height, 100 m. The sigma values describing the plume are those of stability class D. Figure 7 Cross-section at x = 77.4 m of a plume simulated by the PPM under neutral stability conditions.
9 358 P. de Haan and M. W. Rotach As an example for the ability of the PPM to simulate the dispersion of pollutants over a surface inhomogenity, a roughness strip with ] = 1 0 m within smooth terrain (] = m) is examined. Figure 8 shows the simulation with the PPM, whereas in Figure 9 the result for a Gaussian plume model is depicted, where the same source has been used in both cases. Figure 8 Concentration isolines (vertical cross-section at z = 0 m) of a plume released at (0, 0, 0 m), modelled by the PPM. The strip between x = 400 and x = 600 m has a roughness length 50 times larger than the surrounding terrain. The wind and turbulence field for the PPM was provided by a simulation with a k-ε model. For the plume model, the equilibrium upwind conditions of this simulation were used as input. It can be seen that the spread within the plume as modelled with the PPM increases as soon as the strip with the enhanced roughness is reached (Figure 8). For the case of a surface release closer to the roughness strip, the difference between the plume model and the PPM can be expected to be even larger than for the case depicted in Figures 8 and 9.
10 A puff-particle dispersion model 359 Figure 9 The same situation as in Figure 8, but modelled with a Gaussian plume model. 7 Acknowledgements Many thanks go to D. Bünzli, GGIETH, for doing the simulation of the k-ε wind-field. References Briggs, G.A. (1975) Plume rise predictions, in Lectures on Air Pollution and Environmental Impact Analyses, AMS, Boston, pp Gryning, S.-E., Holtslag, A.A.M., Irwin, J.S. and Sivertsen, B. (1987) Applied dispersion modelling based on meteorological scaling parameters, Atmospheric Env., Vol. 1, pp Hurley, P. (1994) Partpuff A Lagrangian particle-puff approach for plume dispersion modelling applications, Appl. Meteorol., Vol. 33, pp Luhar, A.K. and Britter, R.E. (1989) A random walk model for dispersion in inhomogeneous turbulence in a convective boundary layer, Atmospheric Env., Vol. 3, pp Mikkelsen, T., Larsen, S.-E. and Thykier-Nielsen, S. (1984) Description of the Risø puff diffusion model, Nuclear Technology, Vol. 67, pp Pasquill, F. and Smith, F.B. (1983) Atmospheric Diffusion, 3rd edn, John Wiley & Sons, New York. Richardson, L.F. (196) Atmospheric diffusion shown on a distance-neighbour graph, Proc. Royal Soc., Series A, Vol. 110, pp Rotach, M.W., Gryning, S.-E. and Tassone, C. (1995) A two-dimensional Lagrangian stochastic dispersion model for daytime conditions, submitted to Quart. J. Roy. Meteorol. Soc. (Copies are available from the authors of this paper.) Smith, F.B. and Hay, J.S. (1961) The expansion of clusters of particles in the atmosphere, Quart. J. Roy. Meteorol. Soc., Vol. 87, pp Stull, R.B. (1988) An Introduction to Boundary Layer Meteorology, Kluwer Academic Publishers, Dordrecht. Thomson, D.J. (1987) Criteria for the selection of stochastic models of particle trajectories in turbulent flows, J. Fluid Mech., Vol. 180, pp
Predicting concentration fluctuations with a puffparticle
Int. J. Environment and Pollution, Vol. 16, Nos. 1 6, 2001 49 Predicting concentration fluctuations with a puffparticle model P. de Haan INFRAS, Muehlemattstr. 45, 3007 Bern, Switzerland e-mail: peter.dehaan@infras.ch
More informationA THREE-DIMENSIONAL BACKWARD LAGRANGIAN FOOTPRINT MODEL FOR A WIDE RANGE OF BOUNDARY-LAYER STRATIFICATIONS. 1. Introduction
A THREE-DIMENSIONAL BACKWARD LAGRANGIAN FOOTPRINT MODEL FOR A WIDE RANGE OF BOUNDARY-LAYER STRATIFICATIONS N. KLJUN 1,, M.W.ROTACH 1 and H. P. SCHMID 2 1 Institute for Atmospheric and Climate Science ETH,
More information1.18 EVALUATION OF THE CALINE4 AND CAR-FMI MODELS AGAINST THE DATA FROM A ROADSIDE MEASUREMENT CAMPAIGN
.8 EVALUATION OF THE CALINE4 AND CAR-FMI MODELS AGAINST THE DATA FROM A ROADSIDE MEASUREMENT CAMPAIGN Joseph Levitin, Jari Härkönen, Jaakko Kukkonen and Juha Nikmo Israel Meteorological Service (IMS),
More informationWind and turbulence experience strong gradients in vegetation. How do we deal with this? We have to predict wind and turbulence profiles through the
1 2 Wind and turbulence experience strong gradients in vegetation. How do we deal with this? We have to predict wind and turbulence profiles through the canopy. 3 Next we discuss turbulence in the canopy.
More informationPLUME RISE MODEL SPECIFICATION
August 2017 P11/02Q/17 PLUME RISE MODEL SPECIFICATION University of Surrey (A G Robins), National Power (D D Apsley) and CERC In this document ADMS refers to ADMS 5.2, ADMS-Roads 4.1, ADMS-Urban 4.1 and
More informationBOUNDARY LAYER STRUCTURE SPECIFICATION
August 2017 P09/01X/17 BOUNDARY LAYER STRUCTURE SPECIFICATION CERC In this document ADMS refers to ADMS 5.2, ADMS-Roads 4.1, ADMS-Urban 4.1 and ADMS-Airport 4.1. Where information refers to a subset of
More informationA simple operative formula for ground level concentration from a point source
Air Pollution XX 3 A simple operative formula for ground level concentration from a point source T. Tirabassi, M. T. Vilhena & D. Buske 3 Institute of Atmospheric cience and Climate (IAC-CNR), Bologna,
More informationDepartment of Meteorology University of Nairobi. Laboratory Manual. Micrometeorology and Air pollution SMR 407. Prof. Nzioka John Muthama
Department of Meteorology University of Nairobi Laboratory Manual Micrometeorology and Air pollution SMR 407 Prof. Nioka John Muthama Signature Date December 04 Version Lab : Introduction to the operations
More informationINTER-COMPARISON AND VALIDATION OF RANS AND LES COMPUTATIONAL APPROACHES FOR ATMOSPHERIC DISPERSION AROUND A CUBIC OBSTACLE. Resources, Kozani, Greece
INTER-COMPARISON AND VALIDATION OF AND LES COMPUTATIONAL APPROACHES FOR ATMOSPHERIC DISPERSION AROUND A CUBIC OBSTACLE S. Andronopoulos 1, D.G.E. Grigoriadis 1, I. Mavroidis 2, R.F. Griffiths 3 and J.G.
More informationFootprints: outline Üllar Rannik University of Helsinki
Footprints: outline Üllar Rannik University of Helsinki -Concept of footprint and definitions -Analytical footprint models -Model by Korman and Meixner -Footprints for fluxes vs. concentrations -Footprints
More informationOFFLINE APPROACH FOR HIGHER ORDER CONCENTRATION MOMENTS Andrea Bisignano 1, Luca Mortarini 2, Enrico Ferrero 1, Stefano Alessandrini 3
OFFLINE APPROAC FOR IGER ORDER CONCENTRATION MOMENTS Andrea Bisignano 1, Luca Mortarini, Enrico Ferrero 1, Stefano Alessandrini 3 1 Università del Piemonte Orientale, Dipartimento di Scienze e Innovazione
More informationH A NOVEL WIND PROFILE FORMULATION FOR NEUTRAL CONDITIONS IN URBAN ENVIRONMENT
HARMO13-1- June 1, Paris, France - 13th Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes H13-178 A NOVEL WIND PROFILE FORMULATION FOR NEUTRAL CONDITIONS IN URBAN
More information5S: Atmospheric Diffusion Model
1. Physical model experiment (wind tunnel experiment case) Wind tunnel experiment is one of the proven methods for the estimation of atmospheric diffusion. The topography/ buildings models are set up into
More informationAnalytical Model for Dispersion of Rocket Exhaust Source Size Impact Assessment
American Journal of Environmental Engineering 28, 8(4): 35-39 DOI:.5923/j.ajee.2884.8 Analytical Model for Dispersion of Rocket Exhaust Source Size Impact Assessment Bruno K. Bainy,*, Bardo E. J. Bodmann
More informationAn intercomparison of two turbulence closure schemes and four parameterizations for stochastic dispersion models (*)
IL NUOVO CIMENTO VOL. 20 C, N. 3 Maggio-Giugno 1997 An intercomparison of two turbulence closure schemes and four parameterizations for stochastic dispersion models (*) E. FERRERO ( 1 ), D. ANFOSSI ( 2
More informationUse of footprint modelling for the characterisation of complex measurement sites
Use of footprint modelling for the characterisation of complex measurement sites 1, Tiina Markkanen 2, Charlotte Hasager 3, Thomas Foken 1 1, 2 Department of Physical Sciences, University of Helsinki,
More informationAbstract. 1 Introduction
Simulation of nocturnal drainage flows and dispersion of pollutants in a complex valley D. Boucoulava, M. Tombrou, C. Helmis, D. Asimakopoulos Department ofapplied Physics, University ofathens, 33 Ippokratous,
More informationMARINE BOUNDARY-LAYER HEIGHT ESTIMATED FROM NWP MODEL OUTPUT BULGARIA
MARINE BOUNDARY-LAYER HEIGHT ESTIMATED FROM NWP MODEL OUTPUT Sven-Erik Gryning 1 and Ekaterina Batchvarova 1, 1 Wind Energy Department, Risø National Laboratory, DK-4 Roskilde, DENMARK National Institute
More informationA VALIDATION EXERCISE ON THE SAFE-AIR VIEW SOFTWARE. Joint Research Centre NDFM Ispra, Italy 2
A VALIDATION EXERCISE ON THE SAFE-AIR VIEW SOFTWARE F. D Alberti 1, F. d Amati 1, E. Canepa 2, G. Triacchini 3 1 Joint Research Centre NDFM Ispra, Italy 2 CNR INFM CNISM Department of Physics, University
More informationInstituto di Fisica, Universitd 'La Sapienza', Rome, Italy
Development and application of a longterm atmospheric dispersion model for environmental impact assessment purposes F. Desiato", R. Inghilesi^ ^National Environmental Protection Agency Instituto di Fisica,
More informationCambridge Using Plume Rise Schemes To Model Highly Buoyant Plumes From Large Fires
Using Plume Rise Schemes To Model Highly Buoyant Plumes From Large Fires Helen Webster, Robert Beare, Benjamin Devenish, James Haywood, Adrian Lock and David Thomson Crown copyright 2007 Page 1 Outline
More informationThe Atmospheric Boundary Layer. The Surface Energy Balance (9.2)
The Atmospheric Boundary Layer Turbulence (9.1) The Surface Energy Balance (9.2) Vertical Structure (9.3) Evolution (9.4) Special Effects (9.5) The Boundary Layer in Context (9.6) Fair Weather over Land
More informationSTREET CANYON MODEL MISKAM AND
STREET CANYON MODEL MISKAM AND CONCENTRATION ESTIMATION COUPLING THE MICROMIXING LAGRANGIAN MODEL Giovanni Leuzzi 1, Márton Balczó 2, Andrea Amicarelli 3, Paolo Monti 1, Joachim Eichhorn 3 and David J.
More informationIN THE PRESENCE OF A GRADIENT IN
CALCULATION OF PARTICLE TRAJECTORIES IN THE PRESENCE OF A GRADIENT IN TURBULENT-VELOCITY VARIANCE J. D. WILSON New Zealand Meteorological Service, P.O. Box 722 Wellington, New Zealand (Present address,
More informationWind Flow Modeling The Basis for Resource Assessment and Wind Power Forecasting
Wind Flow Modeling The Basis for Resource Assessment and Wind Power Forecasting Detlev Heinemann ForWind Center for Wind Energy Research Energy Meteorology Unit, Oldenburg University Contents Model Physics
More informationAbstract. 1 Introduction
Convective boundary layer height evaluations R. Stlibi, Ph. Tercier, Ch. Haberli Environmental Meteorology, Swiss Meteorological Institute, CH-1530Payerne, Switzerland Abstract The convective boundary
More informationDispersion for point sources CE 524 February
Dispersion for point sources CE 524 February 2011 1 Concentration Air pollution law in most industrial countries based on concentration of contaminants NAAQS in US Need method dto predict concentrations
More informationPlumes and jets with time-dependent sources in stratified and unstratified environments
Plumes and jets with time-dependent sources in stratified and unstratified environments Abstract Matthew Scase 1, Colm Caulfield 2,1, Stuart Dalziel 1 & Julian Hunt 3 1 DAMTP, Centre for Mathematical Sciences,
More informationAtrium assisted natural ventilation of multi storey buildings
Atrium assisted natural ventilation of multi storey buildings Ji, Y and Cook, M Title Authors Type URL Published Date 005 Atrium assisted natural ventilation of multi storey buildings Ji, Y and Cook, M
More informationChristophe DUCHENNE 1, Patrick ARMAND 1, Maxime NIBART 2, Virginie HERGAULT 3. Harmo 17 Budapest (Hungary) 9-12 May 2016
Validation of a LPDM against the CUTE experiments of the COST ES1006 Action Comparison of the results obtained with the diagnostic and RANS versions of the flow model Christophe DUCHENNE 1, Patrick ARMAND
More informationSupporting Information for. Measuring Emissions from Oil and Natural Gas. Well Pads Using the Mobile Flux Plane Technique
Supporting Information for Measuring Emissions from Oil and Natural Gas Well Pads Using the Mobile Flux Plane Technique Chris W. Rella*, Tracy R. Tsai, Connor G. Botkin, Eric R. Crosson, David Steele This
More informationJ3.7 MEASURING METEOROLOGY IN HIGHLY NON-HOMOGENEOUS AREAS. Ekaterina Batchvarova* 1 and Sven-Erik Gryning 2. Denmark ABSTRACT
J3.7 MEASURING METEOROLOGY IN HIGHLY NON-HOMOGENEOUS AREAS Ekaterina Batchvarova* 1 and Sven-Erik Gryning 2 1 National Institute of Meteorology and Hydrology, Sofia, Bulgaria, 2 Risø National Laboratory/DTU,
More informationThe refinement of a meteorological preprocessor for the urban environment. Ari Karppinen, Sylvain M. Joffre and Jaakko Kukkonen
Int. J. Environment and Pollution, Vol. 14, No. 1-6, 000 1 The refinement of a meteorological preprocessor for the urban environment Ari Karppinen, Slvain M. Joffre and Jaakko Kukkonen Finnish Meteorological
More informationA mechanistic model for seed dispersal
A mechanistic model for seed dispersal Tom Robbins Department of Mathematics University of Utah and Centre for Mathematical Biology, Department of Mathematics University of Alberta A mechanistic model
More informationAir Pollution Meteorology
Air Pollution Meteorology Government Pilots Utilities Public Farmers Severe Weather Storm / Hurricane Frost / Freeze Significant Weather Fog / Haze / Cloud Precipitation High Resolution Weather & Dispersion
More informationAnalytical air pollution modeling T. Tirabassi ABSTRACT
Analytical air pollution modeling T. Tirabassi ABSTRACT In this paper we present some advanced practical models that use solutions of the advection-diffusion equation that accept wind and eddy diffusivity
More informationAtmospheric stability parameters and sea storm severity
Coastal Engineering 81 Atmospheric stability parameters and sea storm severity G. Benassai & L. Zuzolo Institute of Meteorology & Oceanography, Parthenope University, Naples, Italy Abstract The preliminary
More informationEnvironmental Fluid Dynamics
Environmental Fluid Dynamics ME EN 7710 Spring 2015 Instructor: E.R. Pardyjak University of Utah Department of Mechanical Engineering Definitions Environmental Fluid Mechanics principles that govern transport,
More informationAbstract. 1 Estimation of wind fields
Fall-out estimation by lagrangian random walk model J. L. Polo, J. Barquin Universidad Pontificia Camillas, Alberto Aguilera, 23, 28015 Madrid (Spain) Email: barquin@iit.upco.es Abstract The purpose of
More informationPart III: Modeling atmospheric convective boundary layer (CBL) Evgeni Fedorovich School of Meteorology, University of Oklahoma, Norman, USA
Physical modeling of atmospheric boundary layer flows Part III: Modeling atmospheric convective boundary layer (CBL) Outline Evgeni Fedorovich School of Meteorology, University of Oklahoma, Norman, USA
More informationEAS rd Scored Assignment (20%) Due: 7 Apr. 2016
EAS 471 3 rd Scored Assignment (0%) Due: 7 Apr. 016 Option A: Lagrangian Simulation of Project Prairie Grass In the Project Prairie Grass tracer gas dispersion trials (Barad, 1958; Haugen, 1959), sulphur
More informationFollow this and additional works at:
International Congress on Environmental Modelling and Software Brigham Young University BYU ScholarsArchive 6th International Congress on Environmental Modelling and Software - Leipzig, Germany - July
More informationFINITE ELEMENT METHOD IN
FINITE ELEMENT METHOD IN FLUID DYNAMICS Part 6: Particles transport model Marcela B. Goldschmit 2 3 Lagrangean Model The particles movement equations are solved. The trajectory of each particles can be
More informationRogers and Yau Chapter 10: Drop breakup, snow, precip rate, and bulk models
Rogers and Yau Chapter 10: Drop breakup, snow, precip rate, and bulk models One explanation for the negative exponential (M-P) distribution of raindrops is drop breakup. Drop size is limited because increased
More informationτ xz = τ measured close to the the surface (often at z=5m) these three scales represent inner unit or near wall normalization
τ xz = τ measured close to the the surface (often at z=5m) these three scales represent inner unit or near wall normalization Note that w *3 /z i is used to normalized the TKE equation in case of free
More informationAtmospheric Boundary Layers
Lecture for International Summer School on the Atmospheric Boundary Layer, Les Houches, France, June 17, 2008 Atmospheric Boundary Layers Bert Holtslag Introducing the latest developments in theoretical
More informationMODELS FOR ASSESSING AIR POLLUTION IN CITIES
The Eighth Asia-Pacific Conference on Wind Engineering, December 10 14, 013, Chennai, India MODELS FOR ASSESSING AIR POLLUTION IN CITIES S. Kar 1 and M. Damodaran 1 Graduate Student of Chemical Engineering,
More informationModeling of dispersed phase by Lagrangian approach in Fluent
Lappeenranta University of Technology From the SelectedWorks of Kari Myöhänen 2008 Modeling of dispersed phase by Lagrangian approach in Fluent Kari Myöhänen Available at: https://works.bepress.com/kari_myohanen/5/
More informationCHAM Case Study CFD Modelling of Gas Dispersion from a Ruptured Supercritical CO 2 Pipeline
CHAM Limited Pioneering CFD Software for Education & Industry CHAM Case Study CFD Modelling of Gas Dispersion from a Ruptured Supercritical CO 2 Pipeline 1. INTRODUCTION This demonstration calculation
More informationLab #3: Stability and Dispersion. Fall 2014 Due Tuesday, November 25, 2014
NAME ID number Disc. Day and Time Lab #3: Stability and Dispersion Atmospheric Sciences 2L Fall 2014 Due Tuesday, November 25, 2014 In this lab, we will test the stability of a simulated atmospheric environment
More informationConvective Fluxes: Sensible and Latent Heat Convective Fluxes Convective fluxes require Vertical gradient of temperature / water AND Turbulence ( mixing ) Vertical gradient, but no turbulence: only very
More informationPARTICLE DISPERSION IN ENCLOSED SPACES USING A LAGRANGIAN MODEL
IV Journeys in Multiphase Flows (JEM 217) March 27-31, 217, São Paulo, SP, Brazil Copyright 217 by ABCM Paper ID: JEM-217-4 PARTICLE DISPERSION IN ENCLOSED SPACES USING A LAGRANGIAN MODEL Ana María Mosquera
More informationROTATION OF TRAJECTORIES IN LAGRANGIAN STOCHASTIC MODELS OF TURBULENT DISPERSION
ROTATION OF TRAJECTORIES IN LAGRANGIAN STOCHASTIC MODELS OF TURBULENT DISPERSION B.L. SAWFORD CSIRO Atmospheric Research, Aspendale, Vic 3195, Australia Received in final form 27 July 1999) Abstract. We
More informationConvection. forced convection when the flow is caused by external means, such as by a fan, a pump, or atmospheric winds.
Convection The convection heat transfer mode is comprised of two mechanisms. In addition to energy transfer due to random molecular motion (diffusion), energy is also transferred by the bulk, or macroscopic,
More informationEAS 572 Assignment 3 (25%) 2012
EAS 572 Assignment 3 (25%) 2012 Lagrangian Simulation of Project Prairie Grass In the Project Prairie Grass tracer gas dispersion trials(barad, 1958; Haugen, 1959), sulphur dioxide was released continuously
More informationFLACS CFD Model Evaluation with Kit Fox, MUST, Prairie Grass, and EMU L-Shaped Building Data
FLACS CFD Model Evaluation with Kit Fox, MUST, Prairie Grass, and EMU L-Shaped Building Data Steven Hanna (Harvard Univ., Boston, MA) Olav Hansen (Gexcon, Bergen, Norway) Seshu Dharmavaram (Dupont Corp.,
More informationINDEX. (The index refers to the continuous pagination)
(The index refers to the continuous pagination) Accuracy in physical models methods for assessing overall assessment acquisition of information acrylonitrile hazards polymerisation toxic effects toxic
More informationAn introductory overview of observations in geophysical flows
Diffusion, mixing, dispersion An introductory overview of observations in geophysical flows Alberto Maurizi a.maurizi@isac.cnr.it Institute of Atmospheric Sciences and Climate - CNR, Bologna, Italy stratified
More informationMeteorological Data Collection, X/Q and D/Q, Critical Receptors
Meteorological Data Collection, X/Q and D/Q, Critical Receptors Ken Sejkora Entergy Nuclear Northeast Pilgrim Station Presented at the 23rd Annual RETS-REMP Workshop Training Session Westminster, CO /
More informationStrategy in modelling irregular shaped particle behaviour in confined turbulent flows
Title Strategy in modelling irregular shaped particle behaviour in confined turbulent flows M. Sommerfeld F L Mechanische Verfahrenstechnik Zentrum Ingenieurwissenschaften 699 Halle (Saale), Germany www-mvt.iw.uni-halle.de
More informationNaka-Gun, Ibaraki, , Japan
Examination of Atmospheric Dispersion Model s Performance - Comparison with the Monitoring Data under the Normal Operation of the Tokai Reprocessing Plant - M. Takeyasu 1, M. Nakano 1, N. Miyagawa 1, M.
More informationThe effect of turbulence and gust on sand erosion and dust entrainment during sand storm Xue-Ling Cheng, Fei Hu and Qing-Cun Zeng
The effect of turbulence and gust on sand erosion and dust entrainment during sand storm Xue-Ling Cheng, Fei Hu and Qing-Cun Zeng State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric
More informationDelia Arnold 1, Arturo Vargas 1,Milagros Montero 2, Alla Dvorzhak 2 and Petra Seibert 3
COMPARISON OF THE DISPERSION MODEL IN RODOS-LX AND MM5-V3.7-FLEXPART(V6.2). A CASE STUDY FOR THE NUCLEAR POWER PLANT OF ALMARAZ Delia Arnold 1, Arturo Vargas 1,Milagros Montero 2, Alla Dvorzhak 2 and Petra
More informationOverview of Meteorology and Atmospheric Dispersion
2 Overview of Meteorology and Atmospheric Dispersion This overview is intended to be consistent with similar sections in other CCPS books such as DeVaull, King, Lantzy and Fontaine (1995) and Hanna, Drivas
More informationBuoyancy Fluxes in a Stratified Fluid
27 Buoyancy Fluxes in a Stratified Fluid G. N. Ivey, J. Imberger and J. R. Koseff Abstract Direct numerical simulations of the time evolution of homogeneous stably stratified shear flows have been performed
More information16th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes 8-11 September 2014, Varna, Bulgaria
16th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes 8-11 September 2014, Varna, Bulgaria INVESTIGATION OF THE DISPERSION OF AIR POLLUTANTS BY
More informationWildland Fire Modelling including Turbulence and Fire spotting
Wildland Fire Modelling including Turbulence and Fire spotting Inderpreet Kaur 1 in collaboration with Andrea Mentrelli 1,2, Frederic Bosseur 3, Jean Baptiste Filippi 3, Gianni Pagnini 1,4 1 BCAM, Bilbao,
More informationOn the validation study devoted to stratified atmospheric flow over an isolated hill
On the validation study devoted to stratified atmospheric flow over an isolated hill Sládek I. 2/, Kozel K. 1/, Jaňour Z. 2/ 1/ U1211, Faculty of Mechanical Engineering, Czech Technical University in Prague.
More informationLecture 1. Equations of motion - Newton s second law in three dimensions. Pressure gradient + force force
Lecture 3 Lecture 1 Basic dynamics Equations of motion - Newton s second law in three dimensions Acceleration = Pressure Coriolis + gravity + friction gradient + force force This set of equations is the
More informationSensitivity analysis of a concentration fluctuation model to dissipation rate estimates. Andrea Amicarelli* and Pietro Salizzoni
164 Int. J. Environment and Pollution, Vol. 48, Nos. 1/2/3/4, 2012 Sensitivity analysis of a concentration fluctuation model to dissipation rate estimates Andrea Amicarelli* and Pietro Salizzoni Laboratory
More informationSurface layer parameterization in WRF
Surface layer parameteriation in WRF Laura Bianco ATOC 7500: Mesoscale Meteorological Modeling Spring 008 Surface Boundary Layer: The atmospheric surface layer is the lowest part of the atmospheric boundary
More informationPart I: Dry Convection
Turbulent dispersion and chemical transformation in the atmospheric boundary layer: Part I: Dry Convection DISPERSION Thanks: Alessandro Dosio Jordi Vilà-Guerau de Arellano WA G E N I N G E N U N I VE
More informationProbability density function (PDF) methods 1,2 belong to the broader family of statistical approaches
Joint probability density function modeling of velocity and scalar in turbulence with unstructured grids arxiv:6.59v [physics.flu-dyn] Jun J. Bakosi, P. Franzese and Z. Boybeyi George Mason University,
More informationPollutant dispersion in urban geometries
Pollutant dispersion in urban geometries V. Garbero 1, P. Salizzoni 2, L. Soulhac 2 1 Politecnico di Torino - Department of Mathematics 2 Ecole Centrale de Lyon - Laboratoire de Méchaniques des Fluides
More informationLatest thoughts on stochastic kinetic energy backscatter - good and bad
Latest thoughts on stochastic kinetic energy backscatter - good and bad by Glenn Shutts DARC Reading University May 15 2013 Acknowledgments ECMWF for supporting this work Martin Leutbecher Martin Steinheimer
More informationSergej S. Zilitinkevich 1,2,3. Helsinki 27 May 1 June Division of Atmospheric Sciences, University of Helsinki, Finland 2
Atmospheric Planetary Boundary Layers (ABLs / PBLs) in stable, neural and unstable stratification: scaling, data, analytical models and surface-flux algorithms Sergej S. Zilitinkevich 1,2,3 1 Division
More informationChapter 7. Three Dimensional Modelling of Buoyancy-Driven Displacement Ventilation: Point Source
Chapter 7 Three Dimensional Modelling of Buoyancy-Driven Displacement Ventilation: Point Source 135 7. Three Dimensional Modelling of Buoyancy- Driven Displacement Ventilation: Point Source 7.1 Preamble
More informationNumerical simulation of dispersion around an isolated cubic building: Model evaluation of RANS and LES. Yoshihide Tominaga a and Ted Stathopoulos b
Accepted on 3 April for publication in the Building and Environment Numerical simulation of dispersion around an isolated cubic building: Model evaluation of RANS and Yoshihide Tominaga a and Ted Stathopoulos
More information19 Pollutant Dispersion
Copyright 015 by Roland Stull. Practical Meteorology: An Algebra-based Survey of Atmospheric Science. 19 Pollutant Dispersion Contents Dispersion Factors 74 Air Quality Standards 75 urbulence Statistics
More informationSome data on the distance-neighbow function for relative diffusion
J. Fluid Mech. (97), vol. 47, part 3, pp. 60607 Printed in Great Britain 60 Some data on the distanceneighbow function for relative diffusion By PAUL J. SULLIVAN? W. M. Keck Laboratory, California Institute
More information4.13 EVALUATION OF NEAR FIELD DISPERSION LAGRANGIEN MODELLING WITH KRYPTON 85 MEASUREMENTS AROUND COGEMA LA HAGUE NUCLEAR REPROCESSING PLANT (FRANCE)
4.13 EVALUATION OF NEAR FIELD DISPERSION LAGRANGIEN MODELLING WITH KRYPTON 85 MEASUREMENTS AROUND COGEMA LA HAGUE NUCLEAR REPROCESSING PLANT (FRANCE) Christine Lac 1, Denis Maro 2, Didier Hebert 2, François
More informationMODEL TYPE (Adapted from COMET online NWP modules) 1. Introduction
MODEL TYPE (Adapted from COMET online NWP modules) 1. Introduction Grid point and spectral models are based on the same set of primitive equations. However, each type formulates and solves the equations
More informationEffects of different terrain on velocity standard deviations
Atmospheric Science Letters (2001) doi:10.1006/asle.2001.0038 Effects of different terrain on velocity standard deviations M. H. Al-Jiboori 1,2, Yumao Xu 1 and Yongfu Qian 1 1 Department of Atmospheric
More information3D experiments with a stochastic convective parameterisation scheme
3D experiments with a stochastic convective parameterisation scheme R. J. Keane and R. S. Plant 3D experiments with a stochastic convective parameterisation scheme p.1/17 Outline Introduction to the Plant-Craig
More informationDIAGNOSTIC WIND FIELD
seminar DIAGNOSTIC WIND FIELD author: Matic Ivančič mentor: prof. dr. Jože Rakovec May 23, 2010 Abstract Diagnostic wind field procedure is calculation of wind on small scale and in complex terrain. Diagnostic
More informationValidation of Boundary Layer Winds from WRF Mesoscale Forecasts over Denmark
Downloaded from orbit.dtu.dk on: Dec 14, 2018 Validation of Boundary Layer Winds from WRF Mesoscale Forecasts over Denmark Hahmann, Andrea N.; Pena Diaz, Alfredo Published in: EWEC 2010 Proceedings online
More informationLES of wind turbulence and heat environment around dense tall buildings
EACWE 5 Florence, Italy 19 th 23 rd July 2009 LES of wind turbulence and heat environment around dense tall buildings Flying Sphere image Museo Ideale L. Da Vinci Tsuyoshi Nozu 1, Takeshi Kishida 2, Tetsuro
More information6.1 ON THE TURBULENCE STRUCTURE OVER HIGHLY TERRAIN: KEY FINDINGS FROM THE MAP-RIVIERA PROJECT
6.1 ON THE TURBULENCE STRUCTURE OVER HIGHLY TERRAIN: KEY FINDINGS FROM THE MAP-RIVIERA PROJECT Mathias W Rotach (1), (2), Marco Andretta (1), Pierluigi Calanca (1), (3), Andreas P Weigel (1), Roland Vogt
More informationCFD modeling of dust dispersion through Najaf historic city centre
INTERNATIONAL JOURNAL OF ENERGY AND ENVIRONMENT Volume 5, Issue 6, 2014 pp.723-728 Journal homepage: www.ijee.ieefoundation.org TECHNICAL PAPER CFD modeling of dust dispersion through Najaf historic city
More informationMODEL EVALUATION OF RIMPUFF WITHIN COMPLEX TERRAIN USING AN 41 AR RADIOLOGICAL DATASET. Leisa L. Dyer 1 and Poul Astrup 2
MODEL EVALUATION OF RIMPUFF WITHIN COMPLEX TERRAIN USING AN 41 AR RADIOLOGICAL DATASET Leisa L. Dyer 1 and Poul Astrup 2 1 Australian Nuclear Science and Technology Organisation (ANSTO), Quality, Safety,
More informationADMS 5 Flat Terrain Validation Kincaid, Indianapolis and Prairie Grass
ADMS 5 Flat Terrain Validation Kincaid, Indianapolis and Prairie Grass Cambridge Environmental Research Consultants November 2016 1 Introduction This document presents a summary of ADMS model results compared
More informationA Discussion on The Effect of Mesh Resolution on Convective Boundary Layer Statistics and Structures Generated by Large-Eddy Simulation by Sullivan
耶鲁 - 南京信息工程大学大气环境中心 Yale-NUIST Center on Atmospheric Environment A Discussion on The Effect of Mesh Resolution on Convective Boundary Layer Statistics and Structures Generated by Large-Eddy Simulation
More informationGFD 2013 Lecture 10: Gravity currents on slopes and in turbulent environments
GFD 2013 Lecture 10: Gravity currents on slopes and in turbulent environments Paul Linden; notes by Gregory Wagner and Barbara Zemskova June 28, 2013 1 Introduction Natural gravity currents are often found
More informationModule No. # 02 Lecture No. # 06 Dispersion models (continued)
Health, Safety and Environmental Management in Petroleum and offshore Engineering Prof. Dr. Srinivasan Chandrasekaran Department of Ocean Engineering Indian Institute of Technology, Madras Module No. #
More informationStable Boundary Layer Parameterization
Stable Boundary Layer Parameterization Sergej S. Zilitinkevich Meteorological Research, Finnish Meteorological Institute, Helsinki, Finland Atmospheric Sciences and Geophysics,, Finland Nansen Environmental
More informationTHE EFFECT OF STRATIFICATION ON THE ROUGHNESS LENGTH AN DISPLACEMENT HEIGHT
THE EFFECT OF STRATIFICATION ON THE ROUGHNESS LENGTH AN DISPLACEMENT HEIGHT S. S. Zilitinkevich 1,2,3, I. Mammarella 1,2, A. Baklanov 4, and S. M. Joffre 2 1. Atmospheric Sciences,, Finland 2. Finnish
More informationDaniel J. Jacob, Models of Atmospheric Transport and Chemistry, 2007.
1 0. CHEMICAL TRACER MODELS: AN INTRODUCTION Concentrations of chemicals in the atmosphere are affected by four general types of processes: transport, chemistry, emissions, and deposition. 3-D numerical
More informationCharacteristics of the night and day time atmospheric boundary layer at Dome C, Antarctica
Characteristics of the night and day time atmospheric boundary layer at Dome C, Antarctica S. Argentini, I. Pietroni,G. Mastrantonio, A. Viola, S. Zilitinchevich ISAC-CNR Via del Fosso del Cavaliere 100,
More informationO. A Survey of Critical Experiments
O. A Survey of Critical Experiments 1 (A) Visualizations of Turbulent Flow Figure 1: Van Dyke, Album of Fluid Motion #152. Generation of turbulence by a grid. Smoke wires show a uniform laminar stream
More informationCommissariat à l Energie Atomique, Département Analyse, Surveillance, Environnement Bruyères-le-Châtel, France 2
ANALYSIS OF ATMOSPHERIC RADIOXENON ACTIVITIES MEASURED BY A RADIONUCLIDE GAS STATION LOCATED IN FRANCE: SIMULATION OF THE ATMOSPHERIC TRANSPORT WITH A MESOSCALE MODELLING SYSTEM Patrick Armand 1, Pascal
More information