STT55-P01

Japan Geoscience Union Meeting 2016

Development of large-scale particle simulations for fluid and granular dynamics*Daisuke Nishiura1, Mikito Furuichi1, Satori Tsuzuki1, Takayuki Aoki2, Hide Sakaguchi1
1.Japan Agency for Marine-Earth Science and Technology, 2.Tokyo Institute of Technology
Large-scale parallel computing is important for numerically reproducing actual measurement results
and dynamics of phenomena in various science and engineering areas, such as civil engineering,bioengineering, and earth sciences. The computational performance of parallelized software tools
plays a critical role in such simulation studies, as these improve the computational accuracy
relative to the simulation resolution within a limited computation time. Recent massively parallel
computer systems based on shared- and distributed-memory architectures employ various types ofarithmetic processors. Current processor designs are known to exhibit totally different
computational performance depending on the numerical algorithms and implementation methods
employed. Currently, parallel computing generally uses either a multi-core CPU, graphics processing
unit (GPU), or many integrated core (MIC) processor. Multi-core CPUs have traditionally been usedin high-performance computing, whereas GPUs were originally designed for computer graphics withmany arithmetic cores. The common progress of current processor designs is the increase in thenumber of cores using vector operations such as single-instruction–multiple-data (SIMD). In such asituation, the shared-memory parallelization plays a basic but critical role in dealing with theincreasing number of arithmetic cores in an efficient manner.
Particle simulation method (PSM) has a benefit of being mesh-free, allowing the computation oflarge-scale deformations and fractures of a continuum body without expensive remeshing tasks. As aPSM, smoothed particle hydrodynamics (SPH) is often used for tsunami disaster simulations becauseof its robustness in free-surface fluid dynamics. The discrete element method (DEM) is one popular
PSM for granular dynamics in which geometrical size and shape attributes are provided for eachparticle. Therefore, the DEM is attractive to simulate granular materials such as sand, pebbles,and other grains.
An efficient parallel implementation of SPH and DEM methods is however known to be difficult
especially for the distributed-memory architecture. Particle methods inherently have workload
imbalance problem for parallelization with the fixed domain in space, because particles move aroundand change workloads during the simulation run. Therefore, dynamic load balance is key technique toperform the large scale SPH or DEM simulation. In this presentation, we introduce the several
techniques of parallel implementation utilizing dynamic load balancing algorithms toward the highresolution simulation over large domain using the massively parallel super computer system. We will
also introduce the applications of large-scale particle simulations such as Tsunami disaster
simulation in consideration of structures–soil–fluid interactions and sandbox simulation for thrust
dynamics of an accretion prism that require a high performance computing resources.
Keywords: DEM, SPH, Parallel computing, Tsunami, Sandbox, Accretion prism©2016. Japan Geoscience Union. All Right Reserved.

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Japan Geoscience Union Meeting 2016

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Japan Geoscience Union Meeting 2016

Development of tsunami Green’s function database based on linear dispersive-wave theoryand its application to real-time tsunami forecasting*Hiroaki Tsushima1, Yutaka Hayashi1, Toshitaka Baba2, Kazuto Ando3, Toshihiro Kato4
1.Meteorological Research Institute, Japan Meteorological Agency, 2.Tokushima University,
3.JAMSTEC, 4.NEC Corporation
Real-time tsunami forecasting based on source inversion of offshore tsunami data is effective for
update of tsunami early warnings. We developed tsunami forecasting method based on inversion ofoffshore tsunami data for initial sea-surface height distribution, named tFISH (Tsushima et al.,2009). The purpose in this study is improvement of tsunami-waveform Green’s function database (DB)that is one of key elements of tFISH algorithm. In tFISH algorithm, tsunami forecasting calculation
is based on linear superposition of Green functions. Therefore, error in the functions directly
affects tsunami-forecasting error. The present DB is prepared by using tsunami simulations based onlinear long wave (LLW) theory. Generally, tsunamis have lone-wave length, and thus thisapproximation is valid. When a tsunami source is abundant in short-wave length component, however,
the resultant tsunami waveforms become dispersive even at the near-field observing point (Saito etal., 2009). In such situation, use of LLW Green function degrades source-estimation accuracy (Saitoet al., 2010). To overcome this problem, we have to prepare Green’s function DB based on linear
dispersive-wave (DSP) theory, instead of LLW. However, the cost of the DSP calculation is muchhigher than that of LLW simulation. In addition, we have to conduct DSP simulations for more than3000 cases to construct DB, and thus total computation cost is extremely huge. To overcome thisdifficulty, we implemented functions to accomplish these huge calculations in realistic calculation
time by using K computer and then constructed DSP DB for Nankai-trough region (Tsushima et al.,2015, JpGU). In this presentation, we introduce construction of DSP DB for Kuril-Japan trench
region and results of numerical simulations to show effectiveness of DSP Green functions inreal-time tsunami forecasting.
In numerical simulations of tsunami forecasting, we assumed the 1933 Sanriku earthquake (magnitude8.4) as a target event. This earthquake is an outer-rise normal-faulting event. Since the seafloor
deformation is abundant in short wavelength and water depth in the source is great, the resultant
tsunami waveforms are dispersive. To produce synthetic observation, we assumed earthquake faulting
model proposed by Kanamori (1971) and then calculate the tsunami propagation based on nonlinear
dispersive-wave theory. Then, we estimated initial tsunami height distribution using the synthetic
data at offshore stations to forecast coastal tsunami waveforms. In tFISH inversion with LLW DB,
significant source artifact appeared, while the artifact disappeared by applying our DSP DB. At
coastal points around which offshore tsunami stations are few, better forecasting results wereobtained with DSP Green functions than with LLW ones. This indicates that use of DSP Greenfunctions is important to improve tsunami source estimation and tsunami prediction for dispersive
event. Next, we assumed one of the huge Nankai-trough earthquakes proposed by the Cabinet Office:an earthquake with huge slip off Kochi Prefecture. To simplify situation, we neglect finiteness ofrupture velocity in production of synthetic observations. It is noteworthy that the resulting
synthetic data are less dispersive. Then, we compared the predicted tsunami waveforms at coastal
points based on DSP DB and those based on LLW DB. As a result, these show good agreement. This
result indicates the possibility that DSP DB works well for both dispersive and non-dispersive
events. To clarify this point, we will perform more performance tests in future.

©2016. Japan Geoscience Union. All Right Reserved.

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Japan Geoscience Union Meeting 2016

This research used computational resources of the K computer provided by the RIKEN Advanced
Institute for Computational Science through the HPCI System Research project (Project ID:hp150216).
Keywords: K computer, Real-time tsunami forecasting, Database, Linear dispersive-wave theory,
Disaster mitigation©2016. Japan Geoscience Union. All Right Reserved.

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Japan Geoscience Union Meeting 2016

A proposal of monitoring and forecasting system for crustal activity in and around Japan
using a large-scale high-fidelity finite element simulation codes*Takane Hori1, tsuyoshi ichimura2, Narumi Takahashi3,1, Katsuhiko Shiomi3, Yoshiyuki Kaneda1,4
1.Japan Agency for Marine-Earth Science and Technology, 2.Earthquake Research Institute, Universityof Tokyo, 3.National Research Institute for Earth Science and Disaster Prevention, 4.Kagawa
University
Here we propose a system for monitoring and forecasting of crustal activity, especially greatinterplate earthquake generation and its preparation processes in subduction zone. Basically, we
model great earthquake generation as frictional instability on the subjecting plate boundary. So,
spatio-temporal variation in slip velocity on the plate interface should be monitored andforecasted. Although, we can obtain continuous dense surface deformation data on land and partly at
the sea bottom, the data obtained are not fully utilized for monitoring and forecasting. It isnecessary to develop a physics-based data analysis system including (1) a structural model with the
3D geometry of the plate interface and the material property such as elasticity and viscosity, (2)calculation code for crustal deformation and seismic wave propagation using (1), (3) inverse
analysis or data assimilation code both for structure and fault slip using (1)&(2). To accomplish
this, it is at least necessary to develop highly reliable large-scale simulation code to calculate
crustal deformation and seismic wave propagation for 3D heterogeneous structure. Actually, Ichimuraet al. (2014, SC14) has developed unstructured FE non-linear seismic wave simulation code, which
achieved physics-based urban earthquake simulation enhanced by 10.7 BlnDOF x 30 K time-step.
Ichimura et al. (2013, GJI) has developed high fidelity FEM simulation code with mesh generator tocalculate crustal deformation in and around Japan with complicated surface topography andsubducting plate geometry for 1km mesh. Further, for inverse analyses, Errol et al. (2012, BSSA)
has developed waveform inversion code for modeling 3D crustal structure, and Agata et al. (2015,this meeting) has improved the high fidelity FEM code to apply an adjoint method for estimating
fault slip and asthenosphere viscosity. Hence, we have large-scale simulation and analysis tools
for monitoring. Furthermore, we are developing the methods for forecasting the slip velocity
variation on the plate interface. Basic concept is given in Hori et al. (2014, Oceanography)introducing ensemble based sequential data assimilation procedure. Although the prototype described
there is for elastic half space model, we will apply it for 3D heterogeneous structure with thehigh fidelity FE model.

©2016. Japan Geoscience Union. All Right Reserved.

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