Free-surface flow simulations with floating objects using lattice Boltzmann method

Watanabe, Seiya*; Kawahara, Jun*; Aoki, Takayuki*; Sugihara, Kenta; Takase, Shinsuke*; Moriguchi, Shuji*; Hashimoto, Hirotada*

Engineering Applications of Computational Fluid Mechanics, 17(1), p.2211143_1 - 2211143_23, 2023/00

https://doi.org/10.1080/19942060.2023.2211143

In tsunami inundations or slope disasters of heavy rain, a lot of floating debris or driftwood logs are included in the flows. The damage to structures from solid body impacts is more severe than the damage from the water pressure. In order to study free-surface flows that include floating debris, developing a high-accurate simulation code of free-surface flows with high performance for large-scale computations is desired. We propose the single-phase free-surface flow model based on the cumulant lattice Boltzmann method coupled with a particle-based rigid body simulation. The discrete element method calculates the contact interaction between solids. An octree-based AMR (Adaptive Mesh Refinement) method is introduced to improve computational accuracy and time-to-solution. High-resolution grids are assigned near the free surfaces and solid boundaries. We conducted two kinds of tsunami flow experiments in the 15 and 70 m water tanks at Hachinohe Institute of Technology and Kobe University to validate the accuracy of the proposed model. The simulation results have shown good agreement with the experiments for the drifting speed, the number of trapped wood pieces, and the stacked angles.

AMR-Net: Convolutional neural networks for multi-resolution steady flow prediction

Asahi, Yuichi; Hatayama, Sora*; Shimokawabe, Takashi*; Onodera, Naoyuki; Hasegawa, Yuta; Idomura, Yasuhiro

Proceedings of 2021 IEEE International Conference on Cluster Computing (IEEE Cluster 2021) (Internet), p.686 - 691, 2021/10

https://doi.org/10.1109/Cluster48925.2021.00102

We develop a convolutional neural network model to predict the multi-resolution steady flow. Based on the state-of-the-art image-to-image translation model pix2pixHD, our model can predict the high resolution flow field from the set of patched signed distance functions. By patching the high resolution data, the memory requirements in our model is suppressed compared to pix2pixHD.

Solutions to NEANSC benchmark problems on Power Distribution within Assemblies(PDWA) using the SRAC and GMVP

Kugo, Teruhiko; Nakakawa, Masayuki;

JAERI-M 92-117, 70 Pages, 1992/08

JAERI-M-92-117.pdf:1.39MB

no abstracts in English

Plume dispersion simulation using locally mesh-refined Lattice Boltzmann Method

Onodera, Naoyuki; Idomura, Yasuhiro

no journal, , 

A real-time simulation of the environmental dynamics of radioactive substances is very important from the view-point of nuclear security. Since a lot of tall buildings and complex structures make the air flow turbulent in urban cities, large-scale CFD simulations are needed. To this end, a CFD code based on a Lattice Boltzmann Method (LBM) with a block-based Adaptive Mesh Refinement (AMR) method is developed. As the conventional LBM based on a single relaxation time collision operator often becomes numerically unstable at high Reynolds number, we apply a cumulant collision operator. The code is validated against a wind tunnel test, which was released from the National Institute of Advanced Industrial Science and Technology. The computational grids are subdivided by the AMR method, and the total number of grid points is reduced to less than 10% compared to the finest mesh. In spite of the fewer grid points, the turbulent statistics are in good agreement with the experiment data.

Tree cutting approach for reducing communication in domain partitioning of tree-based block-structured adaptive mesh refinement

Hasegawa, Yuta; Aoki, Takayuki*; Kobayashi, Hiromichi*; Idomura, Yasuhiro; Onodera, Naoyuki

no journal, , 

We developed a block-structured static adaptive mesh refinement (AMR) CFD code for the aerodynamics simulation using the lattice Boltzmann method on GPU supercomputers. The data structure of AMR was based on the forest-of-octrees, and the domain partitioning algorithm was based on space-filling curves (SFCs). To reduce the halo data communication, we introduced the tree cutting approach, which divided the global domains with a few octrees into small sub-domains with many octrees, leading to a hierarchical domain partitioning approach with the coarse structured block and the fine SFC partitioning in each block. The tree cutting improved the locality of the sub-divided domain, and reduced both the amount of communication data and the number of connections of the halo communication. In the strong scaling test on the Tesla V100 GPU supercomputer, the tree cutting approach showed 1.82 speedup at the performance of 2207 MLUPS (mega-lattice update per second) on 128 GPUs.

Examination of efficient CFD analysis mesh creation based on prediction of vortex radius by machine learning

Matsushita, Kentaro; Ezure, Toshiki; Fujisaki, Tatsuya*; Imai, Yasutomo*; Tanaka, Masaaki

no journal, , 

Development of evaluation method for cover gas entrainment (GE) by vortices generated at free surface in upper plenum of sodium-cooled fast reactor (SFR) is required. An evaluation method by predicting vortices from flow velocity distribution obtained by 3D CFD analysis is developed, and partial refinement of analysis mesh from the viewpoint of improving efficiency of 3D CFD analysis is examined. In this study, a method of predicting vortex radius that can occur in a system by machine learning and using the predicted value as a reference value for analysis mesh before applying refinement (initial mesh). CFD analysis was performed for cylinder column system where wake vortex occurs by changing parameters such as cylinder diameter and inlet flow velocity, and supervised learning was performed using the obtained variables and vortex radius was predicted. As a result, it was obtained that initial mesh size can be efficiently determined by using the predicted vortex radius as a reference.