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Kawamura, Takuma; Shimomura, Kazuya; Idomura, Yasuhiro
Journal of Advanced Simulation in Science and Engineering (Internet), 13(1), p.29 - 43, 2026/04
This paper proposes a virtual reality (VR)-based in-situ control framework called VR IS-PBVR for simulations in computational fluid dynamics. This framework enables users to observe simulation results in an immersive VR environment while the simulation runs on a supercomputer and interactively adjust simulation conditions based on the visualization results. VR IS-PBVR enables in-situ control without disrupting simulation execution by utilizing compressed visualization particles and file-based communication. Furthermore, by extending the KVS visualization library, which forms the basis of VR IS-PBVR, with OpenXR, we achieved an implementation compatible with general-purpose head-mounted displays. The effectiveness of this framework is demonstrated through an urban wind simulation using OpenFOAM. This example shows that combining immersive VR visualization with interactive steering enables more intuitive understanding and analysis of behavior during simulation execution.
Kawamura, Takuma; Shimomura, Kazuya; Osaki, Tsukasa*; Idomura, Yasuhiro
EPJ Web of Conferences, 302, p.11002_1 - 11002_8, 2024/10
Times Cited Count：1 Percentile：85.71(Computer Science, Interdisciplinary Applications)In the field of nuclear engineering, complex simulations on exa-scale supercomputers generate large-scale data. To facilitate efficient analysis of such simulation data, one needs to share them among scientists at remote locations. However, data I/O and data transfer for such large-scale data are quite costly. To resolve these issues, we developed a remote in-situ visualization system IS-PBVR based on the particle-based volume rendering (PBVR), which is suitable for parallel processing on modern supercomputers. In this study, we extend IS-PBVR for VR visualization on multiple client PCs, thus developing a multi-point remote VR visualization. We apply this technique to fluid simulations on GPU-based supercomputers and verify its utility by sharing in-situ VR visualization between multiple client PCs.
collisions at 
= 200 and 62.4 GeVAdare, A.*; Afanasiev, S.*; Aidala, C.*; Ajitanand, N. N.*; Akiba, Yasuyuki*; Al-Bataineh, H.*; Alexander, J.*; Aoki, Kazuya*; Aphecetche, L.*; Armendariz, R.*; et al.
Physical Review C, 83(6), p.064903_1 - 064903_29, 2011/06
Times Cited Count：200 Percentile：99.33(Physics, Nuclear)Transverse momentum distributions and yields for 
, and 
in collisions at = 200 and 62.4 GeV at midrapidity are measured by the PHENIX experiment at the RHIC. We present the inverse slope parameter, mean transverse momentum, and yield per unit rapidity at each energy, and compare them to other measurements at different collisions. We also present the scaling properties such as 
and 
scaling and discuss the mechanism of the particle production in collisions. The measured spectra are compared to next-to-leading order perturbative QCD calculations.
and Au+Au collisions at 
= 200 GeVAdare, A.*; Afanasiev, S.*; Aidala, C.*; Ajitanand, N. N.*; Akiba, Yasuyuki*; Al-Bataineh, H.*; Alexander, J.*; Aoki, Kazuya*; Aphecetche, L.*; Aramaki, Y.*; et al.
Physical Review C, 83(4), p.044912_1 - 044912_16, 2011/04
Times Cited Count：10 Percentile：53.47(Physics, Nuclear)Measurements of electrons from the decay of open-heavy-flavor mesons have shown that the yields are suppressed in Au+Au collisions compared to expectations from binary-scaled collisions. Here we extend these studies to two particle correlations where one particle is an electron from the decay of a heavy flavor meson and the other is a charged hadron from either the decay of the heavy meson or from jet fragmentation. These measurements provide more detailed information about the interaction between heavy quarks and the quark-gluon matter. We find the away-side-jet shape and yield to be modified in Au+Au collisions compared to collisions.
Onodera, Naoyuki; Hasegawa, Yuta; Idomura, Yasuhiro; Asahi, Yuichi; Kawamura, Takuma; Ina, Takuya; Shimomura, Kazuya; Inagaki, Atsushi*; Suzuki, Shinichi*; Hirano, Kohin*; et al.
no journal, ,
Wind prediction based on digital twin is a promising technology that can contribute to the construction of new social infrastructures, including applications to smart city design and operation. In this poster presentation, we will introduce wind simulations based on data assimilation with observations and mesoscale meteorological data for the realization of a digital twin of wind conditions in urban areas.
Yano, Midori; Kawamura, Takuma; Shimomura, Kazuya; Sugihara, Kenta; Idomura, Yasuhiro
no journal, ,
In the field of computational fluid dynamics (CFD), ensemble simulations, which are initialized including random initial errors, are important for evaluating uncertainties of the simulation results. Recently, many in-situ visualization techniques that visualize simulations at runtime have been developed because of limitations on the performance of data I/O, data transfer, and visualization for a large amount of ensemble data generated from extreme-scale CFD simulations. This study proposes an in-situ ensemble visualization, applies it to ensemble data of a real-time plume dispersion simulation, and analyzes pollutant concentrations statistically.
Shimomura, Kazuya; Kawamura, Takuma; Idomura, Yasuhiro
no journal, ,
In-situ simulation steering control has been gaining attention for parameter optimization and inverse problem analysis in large-scale CFD simulations in supercomputers. At the same time, the increasing resolution of simulations has enhanced realism, further emphasizing the importance of virtual reality (VR) technology. To address these needs, we propose in-situ steering simulation in a VR environment using the visualization application PBVR. PBVR is a remote visualization application that employs a particle-based volume rendering method. Independent versions of PBVR have been developed with in-situ visualization functionality, enabling interactive data visualization during simulation execution, VR visualization functionality for projecting visualization results into a VR space, and interactive simulation steering control functionality. In this study, we integrated these capabilities to achieve in-situ simulation control for large-scale CFD simulations and validated its effectiveness using an OpenFOAM-based wind condition analysis code.
Shimomura, Ryotaro*; Suzuki, Tomoya*; Motokawa, Ryuhei; Ueda, Yuki; Shiwaku, Hideaki; Koyama, Kazuya*; Narita, Hirokazu*
no journal, ,
no abstracts in English
Onodera, Naoyuki; Shimokawabe, Takashi*; Idomura, Yasuhiro; Kawamura, Takuma; Asahi, Yuichi; Hasegawa, Yuta; Ina, Takuya; Shimomura, Kazuya; Inagaki, Atsushi*; Hirano, Kohin*; et al.
no journal, ,
The project goal is to realize real-time wind prediction in urban areas by assimilating observed data into real-time wind simulations on GPU supercomputers. In FY2022, the first year of the project, we developed a dynamic optimization method for model variables by applying a particle filter (PF) based data assimilation method to reproduce wind conditions in the atmospheric boundary layer with high accuracy. The numerical simulations for the field experiment in Oklahoma City showed improvements of about 10 % for the standard deviation error of the all-day velocity compared to the results without the application of PF. In addition, a multi-scale analysis based on boundary conditions given by a geographic information system (GIS) and a cloud-resolving numerical model (CReSS) was realized for the Tokyo metropolitan area.
Shimomura, Kazuya; Kawamura, Takuma; Idomura, Yasuhiro
no journal, ,
In data assimilation for computational fluid dynamics (CFD), ensemble-based methods are widely used to improve prediction accuracy, and statistical analysis of ensemble data is essential for validating assimilation schemes and quantifying uncertainty. In large-scale simulations such as nuclear safety analysis and weather prediction, it is necessary to maintain a large number of time-evolving three-dimensional fields, resulting in datasets that can reach several tens of terabytes. However, aggregating large ensemble fields consisting of dozens or hundreds of cases to compute statistics such as mean and variance suffers from bottlenecks due to intensive global communication and increased memory consumption, making interactive visualization of statistical quantities difficult. In this study, we develop an ensemble particle-based volume rendering (PBVR) method in which CFD solutions are compressed and stored as visualization particles, and statistical quantities are computed and visualized directly from the particle ensembles. We propose visualization algorithms for mean, variance, and skewness fields, and evaluate memory reduction and visualization quality using test datasets.
