Atmospheric Diffusion Modeling

Schnelle, Karl B.

doi:10.1016/b0-12-227410-5/00036-3

Cited by 12 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dispersion caused by wind, which has a convection and a mixing component, has been studied in detail, both physically and computationally [40][41][42]. The modern modelling approach of this mechanism is based on Monin-Obukhov similarity theory [43][44][45], which uses a characteristic scale, the Monin-Obukhov length, L, to represent the height of the dynamic turbulence layer, and a related parameter, u*, to account for friction velocity.…”

Section: Wind Speedmentioning

confidence: 99%

Exploring Input Parameter Effects on Air Pollution Dispersion Models: Uncertainty and Implications for Environmental Assessments in Urban Infrastructures

Koulidis,

Progiou,

Sebos

et al. 2024

Preprint

View full text Add to dashboard Cite

Air pollution poses a significant risk to public health and ecosystems. The use of sophisticated models to simulate the transformation and dispersion of pollutants in the atmosphere is a recognized method to assess air pollution levels and its sources and inform policy decisions for effective mitigation strategies in urban and industrial environments. The reliability of models depends, in part, on the sensitivity to the variance of input parameters, which can introduce uncertainty into the model's predictions. This uncertainty is especially challenging when the input parameters exhibit inherent variability. In this study, we analyse the uncertainty of an integrated emission/dispersion model by evaluating the relationship between input parameters and model outcomes. We uncover new aspects of this relationship while clarifying some previously known dependencies. Wind speed was found to have the most significant effect on calculated concentrations, with small differences leading to substantial concentration variations. The time of day, which reflects prevailing vertical turbulence conditions, was also found to have a notable impact on concentrations, whereas ambient temperature can also significantly influence model’s results. Based on these findings, we offer specific recommendations for regulators and scientists involved in environmental impact assessment to improve the accuracy and reliability of air pollution modelling.

show abstract

Section: Wind Speedmentioning

confidence: 99%

Exploring Input Parameter Effects on Air Pollution Dispersion Models: Uncertainty and Implications for Environmental Assessments in Urban Infrastructures

Koulidis,

Progiou,

Sebos

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Negative Ri indicates unstable atmosphere stratification hence Brasseur scheme was used, whereas positive Ri indicates stable atmosphere hence TKE scheme was used. This instability index can be described as the ratio of buoyancy term and flow shear term (Leelőssy et al, 2014;Schnelle, 2003) as shown in the Equation ( 7):…”

Section: Hybrid Schemementioning

confidence: 99%

Wind Gust Parameterization Assessment under Convective and Non-convective Events: A Case Study at the Kertajati International Airport

Zulfikar,

Abdillah,

Sarli

2023

J. Ilmu Fis.

View full text Add to dashboard Cite

Wind gusts (gusts) are sudden increases in wind speed that potentially cause severe damage to infrastructure. Gusts occur within several seconds but numerical weather models typically predict future wind with a time step of tens of seconds or minutes. Therefore, a parameterization is needed to estimate gust. Gusts can be produced convectively and non-convectively depending on the presense of thunderstorm. The gust parameterization schemes may perform differently in both cases. In this study, five wind gust parameterization schemes were evaluated at the Kertajati International Airport. Based on simulations of three convective gust and three non-convective gust events using several evaluation metrics, we find that the best scheme for non-convectively driven gusts is the Turbulent Kinetic Energy (TKE) scheme, while the Hybrid scheme performs best for convectively driven gusts. However, the performance of Hybrid scheme during non-convective event is not so far behind TKE scheme. The Hybrid scheme was developed to work on both non-convective and convective events and this capability is evidently shown. The result could be useful to develop mitigation measures for strong wind incident that frequently occurs in Indonesia.

show abstract

“…For the general case of simulating the dispersion in an urban area, a power-law profile is normally used in conjunction with a linear-mixing length model to prescribe the velocity and turbulence fields. The powerlaw profile is used [9,40] to describe the velocity variation with height, where…”

Section: Boundary Conditionsmentioning

confidence: 99%

“…The exponent, p, can be related to the stability class and roughness length [9]. Typically, the exponent p is taken to be 0.29, which represents an urban area under neutrally stable conditions.…”

Section: Boundary Conditionsmentioning

confidence: 99%

A Computational Fluid Dynamics Approach for Urban Area Transport and Dispersion Modeling

et al. 2005

View full text Add to dashboard Cite

A simulation tool has been developed to model the wind fields, turbulence fields, and the dispersion of Chemical, Biological, Radiological and Nuclear (CBRN) substances in urban areas on the building to city blocks scale. A Computational Fluid Dynamics (CFD) approach has been taken that naturally accounts for critical flow and dispersion processes in urban areas, such as channeling, lofting, vertical mixing and turbulence, by solving the steady-state, Reynolds-Averaged Navier-Stokes (RANS) equations. Rapid generation of high quality cityscape volume meshes is attained by a unique voxel-based model generator that directly interfaces with common Geographic Information Systems (GIS) file formats. The flow and turbulence fields are obtained by solving the steady-state RANS equations using a collocated, pressure-based approach formulated for unstructured and polyhedral mesh elements. Turbulence modeling is based upon the Renormalization Group variant of the k-ε model (k-ε RNG). Neutrally buoyant simulations are made by prescribing velocity boundary condition profiles found by a power-law relationship, while turbulence quantities boundary conditions are defined by a prescribed mixing length in conjunction with the assumption of turbulence equilibrium. Dispersion fields are computed by solving an unsteady transport equation of a dilute gas, formulated in a Eulerian framework, using the velocity and turbulence fields found from the steady-state RANS solution. In this paper the model is explained and detailed comparisons of predicted to experimentally obtained velocity, turbulence and dispersion fields are made to neutrally stable wind tunnel and hydraulic flume experiments.Key words: Urban area transport and dispersion, computational fluid dynamics Nomenclature C tracer gas concentration k turbulence kinetic energy l turbulence length scale P turbulence kinetic energy production rate Q tracer mass source rate ε turbulence kinetic energy dissipation rate µ laminar viscosity coefficient

show abstract

Atmospheric Diffusion Modeling

Cited by 12 publications

References 6 publications

Exploring Input Parameter Effects on Air Pollution Dispersion Models: Uncertainty and Implications for Environmental Assessments in Urban Infrastructures

Exploring Input Parameter Effects on Air Pollution Dispersion Models: Uncertainty and Implications for Environmental Assessments in Urban Infrastructures

Wind Gust Parameterization Assessment under Convective and Non-convective Events: A Case Study at the Kertajati International Airport

A Computational Fluid Dynamics Approach for Urban Area Transport and Dispersion Modeling

Contact Info

Product

Resources

About