51 research outputs found

    High-performance tsunami modelling with modern GPU technology

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    PhD ThesisEarthquake-induced tsunamis commonly propagate in the deep ocean as long waves and develop into sharp-fronted surges moving rapidly coastward, which may be effectively simulated by hydrodynamic models solving the nonlinear shallow water equations (SWEs). Tsunamis can cause substantial economic and human losses, which could be mitigated through early warning systems given efficient and accurate modelling. Most existing tsunami models require long simulation times for real-world applications. This thesis presents a graphics processing unit (GPU) accelerated finite volume hydrodynamic model using the compute unified device architecture (CUDA) for computationally efficient tsunami simulations. Compared with a standard PC, the model is able to reduce run-time by a factor of > 40. The validated model is used to reproduce the 2011 Japan tsunami. Two source models were tested, one based on tsunami waveform inversion and another using deep-ocean tsunameters. Vertical sea surface displacement is computed by the Okada model, assuming instantaneous sea-floor deformation. Both source models can reproduce the wave propagation at offshore and nearshore gauges, but the tsunameter-based model better simulates the first wave amplitude. Effects of grid resolutions between 450-3600 m, slope limiters, and numerical accuracy are also investigated for the simulation of the 2011 Japan tsunami. Grid resolutions of 1-2 km perform well with a proper limiter; the Sweby limiter is optimal for coarser resolutions, recovers wave peaks better than minmod, and is more numerically stable than Superbee. One hour of tsunami propagation can be predicted in 50 times on a regular low-cost PC-hosted GPU, compared to a single CPU. For 450 m resolution on a larger-memory server-hosted GPU, performance increased by ~70 times. Finally, two adaptive mesh refinement (AMR) techniques including simplified dynamic adaptive grids on CPU and a static adaptive grid on GPU are introduced to provide multi-scale simulations. Both can reduce run-time by ~3 times while maintaining acceptable accuracy. The proposed computationally-efficient tsunami model is expected to provide a new practical tool for tsunami modelling for different purposes, including real-time warning, evacuation planning, risk management and city planning

    Block-structured, equal-workload, multi-grid-nesting interface for the Boussinesq wave model FUNWAVE-TVD (Total Variation Diminishing)

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    We describe the development of a block-structured, equal-CPU-load (central processing unit), multi-grid-nesting interface for the Boussinesq wave model FUNWAVE-TVD (Fully Nonlinear Boussinesq Wave Model with Total Variation Diminishing Solver). The new model framework does not interfere with the core solver, and thus the core program, FUNWAVE-TVD, is still a standalone model used for a single grid. The nesting interface manages the time sequencing and two-way nesting processes between the parent grid and child grid with grid refinement in a hierarchical manner. Workload balance in the MPI-based (message passing interface) parallelization is handled by an equal-load scheme. A strategy of shared array allocation is applied for data management that allows for a large number of nested grids without creating additional memory allocations. Four model tests are conducted to verify the nesting algorithm with assessments of model accuracy and the robustness in the application in modeling transoceanic tsunamis and coastal effects

    A comparative study of earthquake source models in high- order accurate tsunami simulations

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    The discontinuous Galerkin method is used to solve the non-linear spherical shallow water equations with Coriolis force. The numerical method is well-balanced and takes wetting/drying into account. The two fold goal of this work is a comparative study of dynamic and static tsunami generation by seabed displacement and the careful validation of these source models. The numerical results show that the impact of the choice of seabed displacement model can be significant and that using a static approach may result in inaccurate results. For the validation of the studies, we consider measurements from satellites and buoy networks for the 2011 Tohoku event and the 2004 Sumatra-Andaman tsunami. The results confirm that the method is appropriate for large-scale tsunami simulations and early warning systems

    The VOLNA code for the numerical modelling of tsunami waves: generation, propagation and inundation

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    A novel tool for tsunami wave modelling is presented. This tool has the potential of being used for operational purposes: indeed, the numerical code \VOLNA is able to handle the complete life-cycle of a tsunami (generation, propagation and run-up along the coast). The algorithm works on unstructured triangular meshes and thus can be run in arbitrary complex domains. This paper contains the detailed description of the finite volume scheme implemented in the code. The numerical treatment of the wet/dry transition is explained. This point is crucial for accurate run-up/run-down computations. Most existing tsunami codes use semi-empirical techniques at this stage, which are not always sufficient for tsunami hazard mitigation. Indeed the decision to evacuate inhabitants is based on inundation maps which are produced with this type of numerical tools. We present several realistic test cases that partially validate our algorithm. Comparisons with analytical solutions and experimental data are performed. Finally the main conclusions are outlined and the perspectives for future research presented.Comment: 47 pages, 27 figures. Other author's papers can be downloaded at http://www.lama.univ-savoie.fr/~dutykh

    Statistical emulation of landslide-induced tsunamis at the Rockall Bank, NE Atlantic

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    Statistical methods constitute a useful approach to understand and quantify the uncertainty that governs complex tsunami mechanisms. Numerical experiments may often have a high computational cost. This forms a limiting factor for performing uncertainty and sensitivity analyses, where numerous simulations are required. Statistical emulators, as surrogates of these simulators, can provide predictions of the physical process in a much faster and computationally inexpensive way. They can form a prominent solution to explore thousands of scenarios that would be otherwise numerically expensive and difficult to achieve. In this work, we build a statistical emulator of the deterministic codes used to simulate submarine sliding and tsunami generation at the Rockall Bank, NE Atlantic Ocean, in two stages. First we calibrate, against observations of the landslide deposits, the parameters used in the landslide simulations. This calibration is performed under a Bayesian framework using Gaussian Process (GP) emulators to approximate the landslide model, and the discrepancy function between model and observations. Distributions of the calibrated input parameters are obtained as a result of the calibration. In a second step, a GP emulator is built to mimic the coupled landslide-tsunami numerical process. The emulator propagates the uncertainties in the distributions of the calibrated input parameters inferred from the first step to the outputs. As a result, a quantification of the uncertainty of the maximum free surface elevation at specified locations is obtained

    GPU-Native Adaptive Mesh Refinement with Application to Lattice Boltzmann Simulations

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    The Lattice Boltzmann Method (LBM) has garnered significant interest in General-Purpose Graphics Processing Unit (GPGPU) programming for computational fluid dynamics due to its straightforward GPU parallelization and could benefit greatly from Adaptive Mesh Refinement (AMR). AMR can assist in efficiently resolving flows with regions of interest requiring a high degree of resolution. An AMR scheme that could manage a computational mesh entirely on the GPU without intermediate data transfers to/from the host device would provide a substantial speedup to GPU-accelerated solvers, however, implementations commonly employ CPU/hybrid frameworks instead, due to lack of a recursive data structure. A block-based GPU-native algorithm will be presented for AMR in the context of GPGPU programming and implemented in an open-source C++ code. The meshing code is equipped with a Lattice Boltzmann solver for assessing performance. Different AMR approaches and consequences in implementation are considered before careful selection of data structures enabling efficient refinement and coarsening compatible with single instruction multiple data architecture is detailed. Inter-level communication is achieved by tricubic interpolation and standard spatial averaging. Although the present open-source implementation is tailored for LBM simulations, the outlined grid refinement procedure is compatible with solvers for cell-centered block-structured grids. Link to repository: https://github.com/KhodrJ/AGALComment: 30 pages, 16 figure

    GPU-parallelisation of Haar wavelet-based grid resolution adaptation for fast finite volume modelling: application to shallow water flows

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    Wavelet-based grid resolution adaptation driven by the ‘multiresolution analysis’ (MRA) of the Haar wavelet (HW) allows to devise an adaptive first-order finite volume (FV1) model (HWFV1) that can readily preserve the modelling fidelity of its reference uniform-grid FV1 counterpart. However, the MRA entails an enormous computational effort as it involves ‘encoding’ (coarsening), ‘decoding’ (refining), analysing and traversing modelled data across a deep hierarchy of nested, uniform grids. GPU-parallelisation of the MRA is needed to handle its computational effort, but its algorithmic structure (1) hinders coalesced memory access on the GPU and (2) involves an inherently sequential tree traversal problem. This work redesigns the algorithmic structure of the MRA in order to parallelise it on the GPU, addressing (1) by applying Z-order space-filling curves and (2) by adopting a parallel tree traversal algorithm. This results in a GPU-parallelised HWFV1 model (GPU-HWFV1). GPU-HWFV1 is verified against its CPU predecessor (CPU-HWFV1) and its GPU-parallelised reference uniform-grid counterpart (GPU-FV1) over five shallow water flow test cases. GPU-HWFV1 preserves the modelling fidelity of GPU-FV1 while being up to 30 times faster. Compared to CPU-HWFV1, it is up to 200 times faster, suggesting that the GPU-parallelised MRA could be used to speed up other FV1 models
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