Measurement-driven Large Eddy Simulation of dispersion in street canyons of variable building height

Papp, Bálint; Kristóf, Gergely; Istók, Balázs; Koren, Márton; Bálczó, Marton; Balogh, Miklós

doi:10.1016/j.jweia.2020.104495

Cited by 7 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1, using a Cambustion HFR400 FFID (Fast Flame Ionization Detection) analyzer, for 60-150 [s] in each measurement point, with a sampling frequency of 1000 [Hz] and a response time of 2.05 [ms], having a Δ rel = 12% relative and Δ abs = 0.28 c * absolute measurement uncertainty. The complete list of the measurement instrumentation can be found in [18].…”

Section: Wind Tunnel Experimentsmentioning

confidence: 99%

Predicting Concentration Fluctuations of Locally Emitted Air Pollutants in Urban-like Geometry Using Deep Learning

Papp,

Kristóf

2024

Period. Polytech. Mech. Eng.

View full text Add to dashboard Cite

The accurate quantification of concentration fluctuations is crucial when evaluating the exposure to toxic, infectious, reactive, flammable, or explosive substances, as well as for the estimation of odor nuisance. However, in the field of Computational Fluid Dynamics (CFD), the industry currently relies predominantly on steady-state RANS turbulence models for simulating near-field pollutant dispersion, which are only capable of producing the time-averaged concentration field. This paper presents a regression relationship for calculating the standard deviation of the local concentration based on the mean concentration and the downstream distance from a point source, over a city-like surface, in the case of the wind direction perpendicular to the streets. The desired peak values and other statistical characteristics can be predicted by assuming a gamma distribution which is fitted based on the average and standard deviation. To obtain the regression function, a deep neural network model was used. The model was trained using time-resolved concentration data obtained from wind tunnel experiments. The validation results show that the concentration fluctuations predicted by the DNN-based model are in satisfactory agreement with the measurement data in terms of the skewness, the kurtosis, the median, and the peak concentrations. Furthermore, the present paper suggests a workflow for estimating the concentration fluctuations based on RANS CFD results, as well as recommendations for generating further training data for specific applications.

show abstract

Section: Wind Tunnel Experimentsmentioning

confidence: 99%