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Improving the performance of a reduced-order mass-consistent model for urban environments and complex terrain with a higher-order geometrical representation
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  • Behnam Bozorgmehr,
  • Pete Willemsen,
  • Jeremy Gibbs,
  • Rob Stoll,
  • Jae-Jin Kim,
  • Zachary Patterson,
  • Eric R. Pardyjak
Behnam Bozorgmehr
Washington State University
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Pete Willemsen
University of Minnesota Duluth
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Jeremy Gibbs
NOAA National Severe Storms Laboratory
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Rob Stoll
University of Utah
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Jae-Jin Kim
Pukyong National University
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Zachary Patterson
University of Minnesota Duluth
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Eric R. Pardyjak
University of Utah

Corresponding Author:[email protected]

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Abstract

Solid structures (buildings and topography) act as obstacles and significantly influence the wind flow around them. Because of their importance, faithfully representing the geometry of structures in numerical predictions is critical to modeling accurate wind fields. A higher-order geometry representation called the cut-cell method is incorporated in the mass-consistent wind model, QES-Winds. To represent the differences between a stair-step and the cut-cell method, an urban case study (the Oklahoma City JU2003 experiments) and a complex terrain case (from the MATERHORN campaign) are modeled in QES-Winds. Comparison between the simulation results with the stair-step and cut-cell methods and the measured data for sensors close to walls and buildings showed that the sensitivity of the cut-cell method to changes in resolution is less than the stair-step method.
Another way to improve the effects of solid geometries on the flow is to correct the velocity gradient near the surface. QES-Winds solves for the mass-consistent flow field and does not include the momentum effects. This means that QES-Winds overestimates velocity gradients near the surface which can lead to higher rates of scalar transport and incorrect turbulence near the wall. The near-surface parameterization is designed to correct the tangential near-surface velocity component in the normal direction using the logarithmic assumption. Results, including the near-wall parameterization, are evaluated with data from the Granite Mountain case (the MATERHORN campaign), which indicates that the parameterization slightly improves the performance of the model for cells near the surface.
07 Jun 2024Submitted to ESS Open Archive
10 Jun 2024Published in ESS Open Archive