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Numerical Study of Building Drag Dissipation Formulations in the Integral Porosity Shallow Water Model

Author(s): Ilhan Zgen; Martin Bruwier; Jiaheng Zhao; Dongfang Liang; Pierre Archambeau; Benjamin Dewals; Kenichiro Kobayashi; Satoru Oishi; Reinhard Hinkelmann

Linked Author(s): Dongfang Liang, Reinhard Hinkelmann, Satoru Oishi, Kenichiro Kobayashi, Benjamin J. Dewals

Keywords: No Keywords

Abstract: The integral porosity shallow water model is a type of porous shallow water model for urban flood modeling, that defines two types of porosity, namely a volumetric porosity inside the computational cell and a conveyance porosity at each edge. Porosity terms are determined directly from the underlying building geometry, hence buildings do not need to be discretized exactly. This enables simulations with significantly reduced CPU time on meshes with cell sizes larger than the building size. Here, the macroscopic model view leads to an additional source term at the unresolved building-fluid interface, yielding a building drag dissipation source term. In literature, several formulations for this term can be found. The integral porosity shallow water model is sensitive to the building drag dissipation, and using the drag parameters as a calibration parameter enhances the accuracy of model results. However, the ideal way to achieve this is still an open research question. In this contribution, we present a simple technique to estimate building drag dissipation that uses the conveyance porosity configuration to estimate the projected area inside the cell, which is then used in a drag force equation. The advantage of this approach is that it is computationally inexpensive, no additional parameters need to be stored, and only a single parameter has to be calibrated. The proposed approach is compared with drag dissipation formulations from existing literature in a laboratory experiment that features a dam-break against an isolated obstacle. The aim of the comparison is to evaluate present existing building drag dissipation models with regard to accuracy and computational cost.


Year: 2018

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