Mathematical Model Studies on Dispersion of Fine Non-Spherical Particles in Enclosed Spaces

2013 ◽  
Vol 756-759 ◽  
pp. 4699-4702
Author(s):  
Zi Biao Song ◽  
Chun Ge ◽  
Shu Xiong Zhang ◽  
Xiao Lu Wu
Keyword(s):  
Fine Particle ◽  
Indoor Environment ◽  
Dispersion Model ◽  
Random Force ◽  
Particle Dispersion ◽  
Spherical Particles ◽  
Theory Analysis ◽  
Dispersion Process ◽  
Model Studies ◽  
Better Than

Study on the fine particle dispersion in the room is very important for creating and maintaining a healthy indoor environment. The paper presented a new dispersion model of fine non-spherical particles formulated in a Lagrangian way based on its dynamic characteristics. In this model, the effects of gravity, resistance, particles shape and random force were taken into account. The influences of gravity and non-spherical particles shape on its dispersion process were analyzed in theory. Simulation result and theory analysis showed the models established in the paper performed better than the random walk model.

Experimental Studies on Dispersion of Fine Non-Spherical Particles in Enclosed Spaces

2013 ◽  
Vol 310 ◽  
pp. 3-6 ◽  
Author(s):  
Zi Biao Song ◽  
Xiao Lu Wu ◽  
Yu Qian Ye ◽  
Chang Jun Rong
Keyword(s):  
Fine Particle ◽  
Indoor Environment ◽  
Carbon Nanofiber ◽  
Dispersion Model ◽  
Experimental Studies ◽  
Particle Dispersion ◽  
Spherical Particles ◽  
Dispersion Process ◽  
Aerosol Dispersion ◽  
Mass Concentrations

Study on the fine particle dispersion in the room is very important for creating and maintaining a healthy indoor environment. An experiment of a carbon nanofiber material blown in smoke box was taken and the mass concentrations of the aerosol formed by this material were measured. Dispersion process of this material in the smoke box was simulated by random walk model, spherical particles aerosol dispersion model and non-spherical particles aerosol dispersion model, respectively. The setting velocities of the aerosol in the smoke box were calculated according to the mass concentrations at different times and the influences of gravity and non-spherical particles’ shape on its dispersion process were analyzed in theory.

The Use of Large-Eddy Simulations in Lagrangian Particle Dispersion Models

2004 ◽  
Vol 61 (23) ◽  
pp. 2877-2887 ◽  
Author(s):  
Jeffrey C. Weil ◽  
Peter P. Sullivan ◽  
Chin-Hoh Moeng
Keyword(s):  
Boundary Layer ◽  
Surface Layer ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Large Eddy Simulations ◽  
Surface Source ◽  
Mean Velocity ◽  
Convective Boundary ◽  
Large Eddy ◽  
Better Than

Abstract A Lagrangian dispersion model driven by velocity fields from large-eddy simulations (LESs) is presented for passive particle dispersion in the planetary boundary layer (PBL). In this combined LES–Lagrangian stochastic model (LSM), the total velocity is divided into resolved or filtered and unresolved or subfilter-scale (SFS) velocities. The random SFS velocity is modeled using an adaptation of Thomson's LSM in which the ensemble-mean velocity and velocity variances are replaced by the resolved velocity and SFS variances, respectively. The random SFS velocity forcing has an amplitude determined by the SFS fraction of the total turbulent kinetic energy (TKE); the fraction is about 0.15 in the bulk of the simulated convective boundary layer (CBL) used here and reaches values as large as 0.31 and 0.37 in the surface layer and entrainment layer, respectively. For the proposed LES–LSM, the modeled crosswind-integrated concentration (CWIC) fields are in good agreement with the 1) surface-layer similarity (SLS) theory for a surface source in the CBL and 2) convection tank measurements of the CWIC for an elevated release in the CBL surface layer. The second comparison includes the modeled evolution of the vertical profile shape with downstream distance, which shows the attainment of an elevated CWIC maximum and a vertically well-mixed CWIC far downstream, in agreement with the tank data. For the proposed model, the agreement with the tank data and SLS theory is better than that obtained with an earlier model in which the SFS fraction of the TKE is assumed to be 1, and significantly better than a model that neglects the SFS velocities altogether.

scholarly journals Large-Scale Atmospheric Dispersal Simulations Identify Likely Airborne Incursion Routes of Wheat Stem Rust Into Ethiopia

2017 ◽  
Vol 107 (10) ◽  
pp. 1175-1186 ◽  
Author(s):  
M. Meyer ◽  
L. Burgin ◽  
M. C. Hort ◽  
D. P. Hodson ◽  
C. A. Gilligan
Keyword(s):  
Stem Rust ◽  
Large Scale ◽  
Dispersion Model ◽  
Fungal Spores ◽  
Disease Outbreaks ◽  
Particle Dispersion ◽  
East African ◽  
Wheat Stem Rust ◽  
Sub Saharan ◽  
African Rift

In recent years, severe wheat stem rust epidemics hit Ethiopia, sub-Saharan Africa’s largest wheat-producing country. These were caused by race TKTTF (Digalu race) of the pathogen Puccinia graminis f. sp. tritici, which, in Ethiopia, was first detected at the beginning of August 2012. We use the incursion of this new pathogen race as a case study to determine likely airborne origins of fungal spores on regional and continental scales by means of a Lagrangian particle dispersion model (LPDM). Two different techniques, LPDM simulations forward and backward in time, are compared. The effects of release altitudes in time-backward simulations and P. graminis f. sp. tritici urediniospore viability functions in time-forward simulations are analyzed. Results suggest Yemen as the most likely origin but, also, point to other possible sources in the Middle East and the East African Rift Valley. This is plausible in light of available field surveys and phylogenetic data on TKTTF isolates from Ethiopia and other countries. Independent of the case involving TKTTF, we assess long-term dispersal trends (>10 years) to obtain quantitative estimates of the risk of exotic P. graminis f. sp. tritici spore transport (of any race) into Ethiopia for different ‘what-if’ scenarios of disease outbreaks in potential source countries in different months of the wheat season.

scholarly journals Atmospheric rivers moisture sources from a Lagrangian perspective

2016 ◽  
Vol 7 (2) ◽  
pp. 371-384 ◽  
Author(s):  
Alexandre M. Ramos ◽  
Raquel Nieto ◽  
Ricardo Tomé ◽  
Luis Gimeno ◽  
Ricardo M. Trigo ◽  
...  
Keyword(s):  
North Atlantic ◽  
Dispersion Model ◽  
Local Maximum ◽  
Detection Algorithm ◽  
Ocean Basin ◽  
Particle Dispersion ◽  
Western Coast ◽  
Moisture Uptake ◽  
Lagrangian Analysis ◽  
Data Set

Abstract. An automated atmospheric river (AR) detection algorithm is used for the North Atlantic Ocean basin, allowing the identification of the major ARs affecting western European coasts between 1979 and 2012 over the winter half-year (October to March). The entire western coast of Europe was divided into five domains, namely the Iberian Peninsula (9.75° W, 36–43.75° N), France (4.5° W, 43.75–50° N), UK (4.5° W, 50–59° N), southern Scandinavia and the Netherlands (5.25° E, 50–59° N), and northern Scandinavia (5.25° E, 59–70° N). Following the identification of the main ARs that made landfall in western Europe, a Lagrangian analysis was then applied in order to identify the main areas where the moisture uptake was anomalous and contributed to the ARs reaching each domain. The Lagrangian data set used was obtained from the FLEXPART (FLEXible PARTicle dispersion) model global simulation from 1979 to 2012 and was forced by ERA-Interim reanalysis on a 1° latitude–longitude grid. The results show that, in general, for all regions considered, the major climatological areas for the anomalous moisture uptake extend along the subtropical North Atlantic, from the Florida Peninsula (northward of 20° N) to each sink region, with the nearest coast to each sink region always appearing as a local maximum. In addition, during AR events the Atlantic subtropical source is reinforced and displaced, with a slight northward movement of the sources found when the sink region is positioned at higher latitudes. In conclusion, the results confirm not only the anomalous advection of moisture linked to ARs from subtropical ocean areas but also the existence of a tropical source, together with midlatitude anomaly sources at some locations closer to AR landfalls.

scholarly journals A regional CO2 observing system simulation experiment for the ASCENDS satellite mission

2014 ◽  
Vol 14 (23) ◽  
pp. 12897-12914 ◽  
Author(s):  
J. S. Wang ◽  
S. R. Kawa ◽  
J. Eluszkiewicz ◽  
D. F. Baker ◽  
M. Mountain ◽  
...  
Keyword(s):  
Measurement Errors ◽  
Dispersion Model ◽  
A Priori ◽  
Wrf Model ◽  
Particle Dispersion ◽  
Instrument Design ◽  
Satellite Mission ◽  
Model Framework ◽  
Satellite Systems ◽  
Novel Approach

Abstract. Top–down estimates of the spatiotemporal variations in emissions and uptake of CO2 will benefit from the increasing measurement density brought by recent and future additions to the suite of in situ and remote CO2 measurement platforms. In particular, the planned NASA Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) satellite mission will provide greater coverage in cloudy regions, at high latitudes, and at night than passive satellite systems, as well as high precision and accuracy. In a novel approach to quantifying the ability of satellite column measurements to constrain CO2 fluxes, we use a portable library of footprints (surface influence functions) generated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in combination with the Weather Research and Forecasting (WRF) model in a regional Bayesian synthesis inversion. The regional Lagrangian particle dispersion model framework is well suited to make use of ASCENDS observations to constrain weekly fluxes in North America at a high resolution, in this case at 1° latitude × 1° longitude. We consider random measurement errors only, modeled as a function of the mission and instrument design specifications along with realistic atmospheric and surface conditions. We find that the ASCENDS observations could potentially reduce flux uncertainties substantially at biome and finer scales. At the grid scale and weekly resolution, the largest uncertainty reductions, on the order of 50%, occur where and when there is good coverage by observations with low measurement errors and the a priori uncertainties are large. Uncertainty reductions are smaller for a 1.57 μm candidate wavelength than for a 2.05 μm wavelength, and are smaller for the higher of the two measurement error levels that we consider (1.0 ppm vs. 0.5 ppm clear-sky error at Railroad Valley, Nevada). Uncertainty reductions at the annual biome scale range from ~40% to ~75% across our four instrument design cases and from ~65% to ~85% for the continent as a whole. Tests suggest that the quantitative results are moderately sensitive to assumptions regarding a priori uncertainties and boundary conditions. The a posteriori flux uncertainties we obtain, ranging from 0.01 to 0.06 Pg C yr−1 across the biomes, would meet requirements for improved understanding of long-term carbon sinks suggested by a previous study.

scholarly journals A new Lagrangian in-time particle simulation module (Itpas v1) for atmospheric particle dispersion

2021 ◽  
Vol 14 (4) ◽  
pp. 2205-2220
Author(s):  
Matthias Faust ◽  
Ralf Wolke ◽  
Steffen Münch ◽  
Roger Funk ◽  
Kerstin Schepanski
Keyword(s):  
Boundary Layer ◽  
Particle Transport ◽  
Dispersion Model ◽  
Weather Forecast ◽  
Forecast Model ◽  
Particle Dispersion ◽  
Particle Simulation ◽  
High Temporal Resolution ◽  
Atmospheric Particle ◽  
Trajectory Models

Abstract. Trajectory models are intuitive tools for airflow studies. But in general, they are limited to non-turbulent, i.e. laminar flow, conditions. Therefore, trajectory models are not particularly suitable for investigating airflow within the turbulent atmospheric boundary layer. To overcome this, a common approach is handling the turbulent uncertainty as a random deviation from a mean path in order to create a statistic of possible solutions which envelops the mean path. This is well known as the Lagrangian particle dispersion model (LPDM). However, the decisive factor is the representation of turbulence in the model, for which widely used models such as FLEXPART and HYSPLIT use an approximation. A conceivable improvement could be the use of a turbulence parameterisation approach based on the turbulent kinetic energy (TKE) at high temporal resolution. Here, we elaborated this approach and developed the LPDM Itpas, which is coupled online to the German Weather Service's mesoscale weather forecast model COSMO. It benefits from the prognostically calculated TKE as well as from the high-frequency wind information. We demonstrate the model's applicability for a case study on agricultural particle emission in eastern Germany. The results obtained are discussed with regard to the model's ability to describe particle transport within a turbulent boundary layer. Ultimately, the simulations performed suggest that the newly introduced method based on prognostic TKE sufficiently represents the particle transport.

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