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Journal Articles

CityTransformer; A Transformer-based model for contaminant dispersion prediction in a realistic urban area

Asahi, Yuichi; Onodera, Naoyuki; Hasegawa, Yuta; Shimokawabe, Takashi*; Shiba, Hayato*; Idomura, Yasuhiro

Boundary-Layer Meteorology, 186(3), p.659 - 692, 2023/03

 Times Cited Count:0 Percentile:0.01(Meteorology & Atmospheric Sciences)

We develop a Transformer-based deep learning model to predict the plume concentrations in the urban area under uniform flow conditions. Our model has two distinct input layers: Transformer layers for sequential data and convolutional layers in convolutional neural networks (CNNs) for image-like data. Our model can predict the plume concentration from realistically available data such as the time series monitoring data at a few observation stations and the building shapes and the source location. It is shown that the model can give reasonably accurate prediction with orders of magnitude faster than CFD simulations. It is also shown that the exactly same model can be applied to predict the source location, which also gives reasonable prediction accuracy.

Journal Articles

Performance measurement of an urban wind simulation code with the Locally Mesh-Refined Lattice Boltzmann Method over NVIDIA and AMD GPUs

Asahi, Yuichi; Onodera, Naoyuki; Hasegawa, Yuta; Shimokawabe, Takashi*; Shiba, Hayato*; Idomura, Yasuhiro

Keisan Kogaku Koenkai Rombunshu (CD-ROM), 27, 5 Pages, 2022/06

We have ported the GPU accelerated Lattice Boltzmann Method code "CityLBM" to AMD MI100 GPU. We present the performance of CityLBM achieved on NVIDIA P100, V100, A100 GPUs and AMDMI100 GPU. Using the host to host MPI communications, the performance on MI100 GPU is around 20% better than on V100 GPU. It has turned out that most of the kernels are successfully accelerated except for interpolation kernels for Adaptive Mesh Refinement (AMR) method.

JAEA Reports

Report of tests for radiation control executed in the JOYO MK-III performance tests

Hoshiba, Hideaki; Hashimoto, Makoto; Irokawa, H.; Usui, Toshihide; Sato, Hayato; Emori, Shuichi

JNC TN9410 2004-017, 170 Pages, 2004/08

JNC-TN9410-2004-017.pdf:18.93MB

While the JOYO MK-III Project, after the modification of primary coolant system started in oct.2000 and the integrated function tests, from Jun.2003, the performance test was executed for the purpose of verification of designed performance and confirmation of basic characteristics as an irradiation reactor. While the JOYO MK-III performance test, 28 tests were executed. Radiation control section took charge of 3 of them, "Dose Rate Distribution", "Radiation Control" and "Gaseous Waste Radioactive Concentration Measurement". The performance tests in charge of radiation control section was started on Jun.27, 2003, that is before the start-up of reactor, and were carried out when the thermal output of reactor was 40MWt, 70MWt, 105MWt and effective full power, 140MWt. The pre-operation tests in charge of radiation control section are "Test of dose rate measurement in operation and after shutdown". "Test of radioactive concentration measurement of air", and "Test of gaseous waste processing performance". The final test was "Test of dose rate measurement after shutdown", which was executed on Nov.27 2003. JOYO passed the inspection and the performance test was finished. The representative results in these performance tests are; 1.Every result is under the criterion 2.Dose rate and monitoring data are totally less than the data in MK-II operation. Though it confirmed that all the data are under the criterion, it is considered that these tests should be performed at proper intervals because the circumstances may change.

Oral presentation

Generating observation guided ensembles for data assimilation with denosing diffusion probabilistic model

Asahi, Yuichi; Hasegawa, Yuta; Onodera, Naoyuki; Shimokawabe, Takashi*; Shiba, Hayato*; Idomura, Yasuhiro

no journal, , 

This paper presents a data assimilation (DA) method using the pseudo ensembles generated by denoising diffusion probabilistic model. Since the model is trained against noisy and sparse observation data, this method can produce reasonable ensembles consistent with observations. This method displays better performance than well-established DA method when the simulation model is imperfect.

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