Enhancements to AERMOD's building downwash algorithms based on wind-tunnel and Embedded-LES modeling
Knowing the fate of effluent from an industrial stack is important for assessing its impact on human health. AERMOD is one of several Gaussian plume models containing algorithms to evaluate the effect of buildings on the movement of the effluent from a stack. The goal of this study is to improve AER...
Gespeichert in:
Veröffentlicht in: | Atmospheric environment (1994) 2018-04, Vol.179, p.321-330 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Knowing the fate of effluent from an industrial stack is important for assessing its impact on human health. AERMOD is one of several Gaussian plume models containing algorithms to evaluate the effect of buildings on the movement of the effluent from a stack. The goal of this study is to improve AERMOD's ability to accurately model important and complex building downwash scenarios by incorporating knowledge gained from a recently completed series of wind tunnel studies and complementary large eddy simulations of flow and dispersion around simple structures for a variety of building dimensions, stack locations, stack heights, and wind angles. This study presents three modifications to the building downwash algorithm in AERMOD that improve the physical basis and internal consistency of the model, and one modification to AERMOD's building pre-processor to better represent elongated buildings in oblique winds. These modifications are demonstrated to improve the ability of AERMOD to model observed ground-level concentrations in the vicinity of a building for the variety of conditions examined in the wind tunnel and numerical studies.
•AERMOD downwash module simulates stack effluent distribution around a building.•New wind-tunnel and LES studies allow for analysis of the concentration fields.•Modifications to the building downwash algorithms improve model estimates.•Model estimates improved for a variety of stack-building configurations. |
---|---|
ISSN: | 1352-2310 1873-2844 |
DOI: | 10.1016/j.atmosenv.2018.02.022 |