Stochastic particle dispersion modeling and the tracer‐particle limit

1992 ◽  
Vol 4 (12) ◽  
pp. 2809-2824 ◽  
Author(s):  
J. M. MacInnes ◽  
F. V. Bracco
Keyword(s):  
Tracer Particle ◽  
Particle Dispersion ◽  
Dispersion Modeling ◽  
Stochastic Particle

New stochastic particle dispersion modeling of a turbulent particle-laden round jet

1997 ◽  
Vol 66 (3) ◽  
pp. 207-215 ◽  
Author(s):  
Jianren Fan ◽  
Xinyu Zhang ◽  
Lihua Chen ◽  
Kefa Cen
Keyword(s):  
Particle Dispersion ◽  
Dispersion Modeling ◽  
Round Jet ◽  
Stochastic Particle

Numerical optimization of obstructed high temperature heat exchanger for recovery from the flue gases by considering ash fouling characteristics

2019 ◽  
Vol 30 (5) ◽  
pp. 2273-2303
Author(s):  
Ali Akbar Abbasian Arani ◽  
Ali Arefmanesh ◽  
Hamidreza Ehteram
Keyword(s):  
Heat Exchanger ◽  
Particle Dispersion ◽  
Flue Gases ◽  
Dispersion Modeling ◽  
Discrete Phase Model ◽  
Side Plate ◽  
Content Type ◽  
Simulation And Optimization ◽  
Ash Fouling ◽  
Fouling Characteristics

Purpose The purpose of this paper is to recommend a validated numerical model for simulation the flue gases heat recovery recuperators. Due to fulfill of this demand, the influences of ash fouling characteristics during the transient/steady-state simulation and optimization of a 3D complex heat exchanger equipped with inner plain fins and side plate fins are studied. Design/methodology/approach For the particle dispersion modeling, the discrete phase model is applied and the flow field has been solved using SIMPLE algorithm. Findings According to obtained results, for the recuperator equipped with combine inner plain and side plate fins, determination of ash fouling characteristics is really important, effective and determinative. It is clear that by underestimating the ash fouling characteristics, the achieved results are wrong and different with reality. Originality/value Finally, the configuration with inner plain fins with characteristics of: di =5 mm, do = 6 mm, dg = 2 mm, dk = 3 mm and NIPFT = 9 and side plate fins with characteristics of: TF = 3 mm, PF = 19 mm, NSPF = 17·2 = 34, WF = 10 mm, HF = 25 mm, LF = 24 mm and ß = 0° is introduced as the optimum model with the best performance among all studied configurations.

scholarly journals Improving PM2.5 Forecasting and Emission Estimation Based on the Bayesian Optimization Method and the Coupled FLEXPART-WRF Model

Atmosphere ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 428 ◽  
Author(s):  
Lifeng Guo ◽  
Baozhang Chen ◽  
Huifang Zhang ◽  
Guang Xu ◽  
Lijiang Lu ◽  
...  
Keyword(s):  
Fine Particulate Matter ◽  
Wrf Model ◽  
Optimization Method ◽  
Particle Dispersion ◽  
Bayesian Optimization ◽  
Inversion Method ◽  
Dispersion Modeling ◽  
Emission Flux ◽  
Monthly Average ◽  
Mean Square Errors

In this study, we evaluated estimates and predictions of the PM2.5 (fine particulate matter) concentrations and emissions in Xuzhou, China, using a coupled Lagrangian particle dispersion modeling system (FLEXPART-WRF). A Bayesian inversion method was used in FLEXPART-WRF to improve the emission calculation and mixing ratio estimation for PM2.5. We first examined the inversion modeling performance by comparing the model predictions with PM2.5 concentration observations from four stations in Xuzhou. The linear correlation analysis between the predicted PM2.5 concentrations and the observations shows that our inversion forecast system is much better than the system before calibration (with correlation coefficients of R = 0.639 vs. 0.459, respectively, and root mean square errors of RMSE = 7.407 vs. 9.805 µg/m3, respectively). We also estimated the monthly average emission flux in Xuzhou to be 4188.26 Mg/month, which is much higher (by ~10.12%) than the emission flux predicted by the multiscale emission inventory data (MEIC) (3803.5 Mg/month). In addition, the monthly average emission flux shows obvious seasonal variation, with the lowest PM2.5 flux in summer and the highest flux in winter. This pattern is mainly due to the additional heating fuels used in the cold season, resulting in many fine particulates in the atmosphere. Although the inversion and forecast results were improved to some extent, the inversion system can be improved further, e.g., by increasing the number of observation values and improving the accuracy of the a priori emission values. Further research and analysis are recommended to help improve the forecast precision of real-time PM2.5 concentrations and the corresponding monthly emission fluxes.

scholarly journals Penetrative convection in stratified fluids: velocity and temperature measurements

2006 ◽  
Vol 13 (3) ◽  
pp. 353-363 ◽  
Author(s):  
M. Moroni ◽  
A. Cenedese
Keyword(s):  
Feature Tracking ◽  
Tracer Particle ◽  
Mixing Layer ◽  
Pollutant Dispersion ◽  
Particle Dispersion ◽  
Layer Growth ◽  
Laboratory Model ◽  
Penetrative Convection ◽  
Stratified Lakes

Abstract. The flux through the interface between a mixing layer and a stable layer plays a fundamental role in characterizing and forecasting the quality of water in stratified lakes and in the oceans, and the quality of air in the atmosphere. The evolution of the mixing layer in a stably stratified fluid body is simulated in the laboratory when "Penetrative Convection" occurs. The laboratory model consists of a tank filled with water and subjected to heating from below. The methods employed to detect the mixing layer growth were thermocouples for temperature data and two image analysis techniques, namely Laser Induced Fluorescence (LIF) and Feature Tracking (FT). LIF allows the mixing layer evolution to be visualized. Feature Tracking is used to detect tracer particle trajectories moving within the measurement volume. Pollutant dispersion phenomena are naturally described in the Lagrangian approach as the pollutant acts as a tag of the fluid particles. The transilient matrix represents one of the possible tools available for quantifying particle dispersion during the evolution of the phenomenon.

Accelerating Convergence in Stochastic Particle Dispersion Simulation Codes

2001 ◽  
Vol 173 (1) ◽  
pp. 231-255 ◽  
Author(s):  
Richard R. Picard ◽  
Mark Fitzgerald ◽  
Michael J. Brown
Keyword(s):  
Particle Dispersion ◽  
Stochastic Particle ◽  
Simulation Codes

scholarly journals Contrasting local and long-range-transported warm ice-nucleating particles during an atmospheric river in coastal California, USA

2019 ◽  
Vol 19 (7) ◽  
pp. 4193-4210 ◽  
Author(s):  
Andrew C. Martin ◽  
Gavin Cornwell ◽  
Charlotte M. Beall ◽  
Forest Cannon ◽  
Sean Reilly ◽  
...  
Keyword(s):  
Long Range ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Mixed Phase ◽  
Dispersion Modeling ◽  
Time Resolved ◽  
Atmospheric Rivers ◽  
Atmospheric River ◽  
Cloud Properties ◽  
The Impact

Abstract. Ice-nucleating particles (INPs) have been found to influence the amount, phase and efficiency of precipitation from winter storms, including atmospheric rivers. Warm INPs, those that initiate freezing at temperatures warmer than −10 ∘C, are thought to be particularly impactful because they can create primary ice in mixed-phase clouds, enhancing precipitation efficiency. The dominant sources of warm INPs during atmospheric rivers, the role of meteorology in modulating transport and injection of warm INPs into atmospheric river clouds, and the impact of warm INPs on mixed-phase cloud properties are not well-understood. In this case study, time-resolved precipitation samples were collected during an atmospheric river in northern California, USA, during winter 2016. Precipitation samples were collected at two sites, one coastal and one inland, which are separated by about 35 km. The sites are sufficiently close that air mass sources during this storm were almost identical, but the inland site was exposed to terrestrial sources of warm INPs while the coastal site was not. Warm INPs were more numerous in precipitation at the inland site by an order of magnitude. Using FLEXPART (FLEXible PARTicle dispersion model) dispersion modeling and radar-derived cloud vertical structure, we detected influence from terrestrial INP sources at the inland site but did not find clear evidence of marine warm INPs at either site. We episodically detected warm INPs from long-range-transported sources at both sites. By extending the FLEXPART modeling using a meteorological reanalysis, we demonstrate that long-range-transported warm INPs were observed only when the upper tropospheric jet provided transport to cloud tops. Using radar-derived hydrometeor classifications, we demonstrate that hydrometeors over the terrestrially influenced inland site were more likely to be in the ice phase for cloud temperatures between 0 and −10 ∘C. We thus conclude that terrestrial and long-range-transported aerosol were important sources of warm INPs during this atmospheric river. Meteorological details such as transport mechanism and cloud structure were important in determining (i) warm INP source and injection temperature and (ii) ultimately the impact of warm INPs on mixed-phase cloud properties.

Aerosol chemical composition and formation of secondary aerosol during haze events at the SORPES station in China

2020 ◽  
Author(s):  
Jiaping Wang ◽  
Jinbo Wang ◽  
Wei Nie ◽  
Xuguang Chi ◽  
Jiandong Wang ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Eastern China ◽  
Cold Season ◽  
Particle Dispersion ◽  
Dispersion Modeling ◽  
Formation Processes ◽  
Aerosol Formation ◽  
Secondary Aerosol ◽  
Haze Pollution ◽  
Aerosol Chemical Composition

<p>Haze pollution is a serious air quality concern in China up to now, which occurs frequently in mega city clusters, e.g. Beijing-Tianjin-Hebei and Yangtze River Delta region, especially in the cold season. Understanding the dominating secondary aerosol formation processes is vital for improving the prediction and emission control strategy of haze pollution. In this study, we reported measurements of aerosol chemical composition using the soot particle aerosol mass spectrometer (SP-AMS) at a regional background station, the Station for Observing Regional Processes of the Earth System (SORPES), in Nanjing, eastern China. Characteristics of aerosol chemical composition and dominating secondary aerosol formation processes were analyzed during typical haze events and compared with that in clean episodes. Sources and transportation of organic aerosol were performed using positive matrix factorization (PMF) together with backward Lagrangian particle dispersion modeling (LPDM).</p>

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