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Author |
García Sánchez, C.; Van Tendeloo, G.; Gorle, C. |
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Title |
Quantifying inflow uncertainties in RANS simulations of urban pollutant dispersion |
Type |
A1 Journal article |
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Year  |
2017 |
Publication |
Atmospheric environment : an international journal |
Abbreviated Journal |
Atmos Environ |
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Volume |
161 |
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Pages |
263-273 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
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Abstract |
Numerical simulations of flow and pollutant dispersion in urban environments have the potential to support design and policy decisions that could reduce the population's exposure to air pollution. Reynolds-averaged Navier-Stokes simulations are a common modeling technique for urban flow and dispersion, but several sources of uncertainty in the simulations can affect the accuracy of the results. The present study proposes a method to quantify the uncertainty related to variability in the inflow boundary conditions. The method is applied to predict flow and pollutant dispersion in downtown Oklahoma City and the results are compared to field measurements available from the Joint Urban 2003 measurement campaign. Three uncertain parameters that define the inflow profiles for velocity, turbulence kinetic energy and turbulence dissipation are defined: the velocity magnitude and direction, and the terrain roughness length. The uncertain parameter space is defined based on the available measurement data, and a non-intrusive propagation approach that employs 729 simulations is used to quantify the uncertainty in the simulation output. A variance based sensitivity analysis is performed to identify the most influential uncertain parameters, and it is shown that the predicted tracer concentrations are influenced by all three uncertain variables. Subsequently, we specify different probability distributions for the uncertain inflow variables based on the available measurement data and calculate the corresponding means and 95% confidence intervals for comparison with the field measurements at 35 locations in downtown Oklahoma City. (C) 2017 Elsevier Ltd. All rights reserved. |
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Place of Publication |
Oxford |
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Wos |
000403515900025 |
Publication Date |
2017-04-19 |
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ISSN |
1352-2310 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
3.629 |
Times cited |
17 |
Open Access |
OpenAccess |
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Notes |
; The first author's contribution to this work was supported by the doctoral (PhD) grant number 131423 for strategic basic research from the Agency for Innovation by Science and Technology in Flanders (IWT). This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number CTS160009 (Towns et al., 2014). ; |
Approved |
Most recent IF: 3.629 |
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Call Number |
UA @ lucian @ c:irua:145761 |
Serial |
4749 |
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