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GPU‐Accelerated Urban Flood Modeling Using a Nonuniform Structured Grid and a Super Grid Scale River Channel
by
Rong, Youtong
, Bates, Paul
, Neal, Jeffrey
in
adaptive mesh
/ Bathymetry
/ Catchments
/ Channel flow
/ Channels
/ Computational efficiency
/ Computer applications
/ Computing costs
/ domain
/ Flooding
/ Floodplains
/ Floods
/ Fluid flow
/ GPU
/ Hydraulics
/ Hydrodynamic models
/ Hydrodynamics
/ hydrologic models
/ landscapes
/ Remote sensing
/ riparian areas
/ River channels
/ Rivers
/ Spatial data
/ Structured grids (mathematics)
/ super grid scale river channel
/ Urban catchments
/ Urban environments
/ urban flood
/ water
/ Watersheds
2024
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GPU‐Accelerated Urban Flood Modeling Using a Nonuniform Structured Grid and a Super Grid Scale River Channel
by
Rong, Youtong
, Bates, Paul
, Neal, Jeffrey
in
adaptive mesh
/ Bathymetry
/ Catchments
/ Channel flow
/ Channels
/ Computational efficiency
/ Computer applications
/ Computing costs
/ domain
/ Flooding
/ Floodplains
/ Floods
/ Fluid flow
/ GPU
/ Hydraulics
/ Hydrodynamic models
/ Hydrodynamics
/ hydrologic models
/ landscapes
/ Remote sensing
/ riparian areas
/ River channels
/ Rivers
/ Spatial data
/ Structured grids (mathematics)
/ super grid scale river channel
/ Urban catchments
/ Urban environments
/ urban flood
/ water
/ Watersheds
2024
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GPU‐Accelerated Urban Flood Modeling Using a Nonuniform Structured Grid and a Super Grid Scale River Channel
by
Rong, Youtong
, Bates, Paul
, Neal, Jeffrey
in
adaptive mesh
/ Bathymetry
/ Catchments
/ Channel flow
/ Channels
/ Computational efficiency
/ Computer applications
/ Computing costs
/ domain
/ Flooding
/ Floodplains
/ Floods
/ Fluid flow
/ GPU
/ Hydraulics
/ Hydrodynamic models
/ Hydrodynamics
/ hydrologic models
/ landscapes
/ Remote sensing
/ riparian areas
/ River channels
/ Rivers
/ Spatial data
/ Structured grids (mathematics)
/ super grid scale river channel
/ Urban catchments
/ Urban environments
/ urban flood
/ water
/ Watersheds
2024
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GPU‐Accelerated Urban Flood Modeling Using a Nonuniform Structured Grid and a Super Grid Scale River Channel
Journal Article
GPU‐Accelerated Urban Flood Modeling Using a Nonuniform Structured Grid and a Super Grid Scale River Channel
2024
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Overview
New remote sensing technologies, and the meter‐scale geospatial data they create, now allow for detailed urban landscape characterization, thereby advancing grid‐based hydrodynamic models. However, using a uniform fine grid over urban catchments generally result in dense grids and can lead to prohibitive computational costs. Moreover, an inability to see below the water surface and measure river bathymetry in most terrain remote sensing can severely impact local‐scale river hydraulics calculations given the significant volume of water conveyed by the channel. This paper introduces a super grid channel model which allow river channels with any width above that of the grid resolution to be simulated in 1D manner. As an extension of a previous subgrid model, this integration facilitates a seamless transition between subgrid and super grid channels, accommodating situations where channel width may surpass or fall below the grid resolution. The key contribution is the integration of the novel 1D channel representation with a nonuniform structured 2D floodplain hydrodynamic model and then coding this for application on GPU. Compared with the previous pure 2D nonuniform structured approaches, the new model presents an efficient compromise for riverine urban flooding where we are less concerned about fine‐scale details of in‐channel flow. Three tests reveal that the proposed model maintains accuracy but with significantly reduced computational cost. By leveraging GPU architectures, a ∼10× speedup compared to CPU computations is achieved, and a typical 6‐day urban flooding problem (domain size 1.42 km2) at 1 m resolution can be achieved within 10 hr on a single 8 GB GPU. Key Points A nonuniform structured grid with a super grid river channel model is implemented on GPU for efficient meter‐scale urban flood modeling As an extension of the subgrid model approach, this integration allows a seamless transition between subgrid and super grid channels A ∼10× speedup compared to CPU computations is achieved by leveraging GPU architectures
Publisher
John Wiley & Sons, Inc,Wiley
Subject
/ Channels
/ domain
/ Flooding
/ Floods
/ GPU
/ Rivers
/ Structured grids (mathematics)
/ super grid scale river channel
/ water
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