An Efficient GPU Implementation of a Coupled Overland-Sewer Hydraulic Model with Pollutant Transport

Abstract

Numerical simulation of flows that consider interaction between overland and drainage networks has become a practical tool to prevent and mitigate flood situations in urban environments, especially when dealing with intense storm events, where the limited capacity of the sewer systems can be a trigger for flooding. Additionally, in order to prevent any kind of pollutant dispersion through the drainage network, it is very interesting to have a certain monitorization or control over the quality of the water that flows in both domains. In this sense, the addition of a pollutant transport component to both surface and sewer hydraulic models would benefit the global analysis of the combined water flow. On the other hand, when considering a realistic large domain with complex topography or streets structure, a fine spatial discretization is mandatory. Hence the number of grid cells is usually very large and, therefore, it is necessary to use parallelization techniques for the calculation, the use of Graphic Processing Units (GPU) being one of the most efficient due to the leveraging of thousands of processors within a single device. In this work, an efficient GPU-based 2D shallow water flow solver (RiverFlow2D-GPU) is fully coupled with EPA’s Storm Water Management Model (SWMM). Both models are able to develop a transient water quality analysis taking into account several pollutants. The coupled model, referred to as RiverFlow2D-GPU UD (Urban Drainge) is applied to three real-world cases, covering the most common hydraulic situations in urban hydrology/hydraulics. A UK Environmental Agency test case is used as model validation, showing a good agreement between RiverFlow2D-GPU UD and the rest of the numerical models considered. The efficiency of the model is proven in two more complex domains, leading to a >100x faster simulations compared with the traditional CPU computation.

Flood Risk in Urban Areas: Modelling, Management and Adaptation to Climate Change. A Review

“…Several examples of these approaches have been seen in recent years [83][84][85][86][87][88][89][90], including 1D-1D dual-drainage models [91,92], in which both the overland and sewer flows are computed with 1D equations, and 1D-2D dual-drainage models that consider the full interaction between the 2D overland flow and the 1D sewer network through gullies and manholes [83,86,[93][94][95]. Along the same lines as the latter, several publications in recent years have presented dual-drainage models that couple a 2D inundation-flow model with the well-established Storm-Water-Management Model (SWMM) [93,94,[96][97][98][99][100][101]. Other, simpler approaches that only account for the 2D overland flow and that do not resolve the flow within the sewer network have also been used to model pluvial flooding [101][102][103].…”

“…The development of CUDA, a parallel-computing platform and programming model developed by NVIDIA for general computing on graphical processing units, has enabled modellers to develop GPU parallelizations of shallow-water codes applied to flood modelling that yield increases in the computational time of up to two orders of magnitude with respect to the standard sequential implementation on CPUs [104]. Some of these studies implement shared-memory GPU parallelization algorithms [39,98,[105][106][107][108][109][110] while others go one step beyond with the implementation of multi-GPU parallelization schemes [111,112]. Although implementations on multiple CPUs are less common nowadays, several 2D flood-inundation models have also been parallelized using sharedmemory (OpenMP) as well as distributed-memory (Message Passing Interface, MPI) standards [39,59,73,[113][114][115].…”

Comparison of an Explicit and Implicit Time Integration Method on GPUs for Shallow Water Flows on Structured Grids

“…A large number of studies appeared on shallow water modelling on GPUs in the last decade. In most of these studies, an explicit time integration method was used; see for example [5][6][7][8][9][10][11][12][13][14]. Very often, these GPU codes were developed for flood inundation.…”

A 2D Hydraulic Simulation Model Including Dynamic Piping and Overtopping Dambreach

“…As the computational efficiency of the numerical method used is limited by the stability restriction, a CUDA-based implementation on GPU is used. This is the core tool of the RiverFlow2D-GPU software developed by the authors and distributed by Hydronia LLC [10][11][12][13]. It is able to develop a transient flow analysis taking into account several scenarios.…”