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Zhou, W.*; Miwa, Shuichiro*; Yamashita, Susumu; Okamoto, Koji*
Progress in Nuclear Energy, 177, p.105441_1 - 105441_17, 2024/12
Times Cited Count：0 Percentile：0.00(Nuclear Science & Technology)Understanding air entrainment phenomena induced by plunging water jets is critical in the fields of nuclear and hydraulic engineering. Air entrainment is one of the key safety design parameters for nuclear systems. However, most existing studies rely on empirical correlations or curve-fitting models to estimate bubble penetration depth, and no agreed-upon calculation principle exists for varying jet conditions. To address these limitations, this research developed two advanced AI approaches: an improved YOLOv5 for segmenting air entrainment and the NSGA-III-BPNN method for predicting penetration depth. The improved YOLOv5 enables real-time segmentation and extraction of air entrainment motion and dynamics under diverse conditions, demonstrating high scalability and robustness. The penetration depth estimated using the improved YOLOv5 shows greater accuracy compared to conventional empirical correlations and is more efficient than traditional image post-processing techniques for classifying shape regimes based on dynamic air entrainment patterns. To overcome the limitations of object segmentation, which typically relies on video or image data, the NSGA-III-BPNN method predicts maximum penetration depths with greater accuracy than YOLOv5, offering a more effective prediction model for air entrainment penetration depth. By leveraging advanced AI techniques, the research not only provides valuable segmentation data for refining computational fluid dynamics (CFD) modeling but also paves the way for significant advancements in both nuclear and hydraulic engineering.
FeSiSumida, Kazuki; Fujita, Yuichi*; Zhou, W.*; Masuda, Keisuke*; Kawasaki, Ikuto; Fujimori, Shinichi; Kimura, Akio*; Sakuraba, Yuya*
Physical Review B, 108(24), p.L241101_1 - L241101_6, 2023/12
Times Cited Count：2 Percentile：34.38(Materials Science, Multidisciplinary)Zhou, Z.*; Frost, W.*; Lloyd, D. C.*; Seki, Takeshi*; Kubota, Takahide*; Ramos, R.*; Saito, Eiji; Takanashi, Koki; Hirohata, Atsufumi*
Journal of Magnetism and Magnetic Materials, 571, p.170575_1 - 170575_5, 2023/04
Times Cited Count：1 Percentile：11.20(Materials Science, Multidisciplinary)Zhang, W. Q.*; Yamaguchi, Toshio*; Fang, C. H.*; Yoshida, Koji*; Zhou, Y. Q.*; Zhu, F. Y.*; Machida, Shinichi*; Hattori, Takanori; Li, W.*
Journal of Molecular Liquids, 348, p.118080_1 - 118080_11, 2022/02
Times Cited Count：3 Percentile：15.57(Chemistry, Physical)The ion hydration and association and hydrogen-bonded water structure in an aqueous 3 mol/kg RbCl solution were investigated at 298 K/0.1 MPa, 298 K/1 GPa, 523 K/1 GPa, and 523 K/4 GPa by neutron diffraction combined with EPSR methods. The second hydration layer of Rb
and Cl
becomes evident under elevated pressure and temperature conditions. The average oxygen coordination number of Rb (Cl) in the first hydration layer increases from 6.3 (5.9) ambient pressure to 8.9 (9.1) at 4 GPa, while decreasing coordination distance from 0.290 nm (0.322 nm) to 0.288 nm (0.314 nm). The orientation of the water dipole in the first solvation shell of Rb and a central water molecule is sensitive to pressure, but that in the first solvation shell of Cl does not change very much. The number of contact-ion pairs Rb-Cl decreases with elevated temperature and increases with elevated pressure. Water molecules are closely packed, and the tetrahedral hydrogen-bonded network of water molecules no longer exists in extreme conditions.
Sumida, Kazuki; Sakuraba, Yuya*; Masuda, Keisuke*; Kono, Takashi*; Kakoki, Masaaki*; Goto, Kazuki*; Zhou, W.*; Miyamoto, Koji*; Miura, Yoshio*; Okuda, Taichi*; et al.
Communications Materials (Internet), 1, p.89_1 - 89_9, 2020/11
Zhou, W.*; Seki, Takeshi*; Imamura, Hiroshi*; Ieda, Junichi; Takanashi, Koki*
Physical Review B, 100(9), p.094424_1 - 094424_5, 2019/09
Times Cited Count：7 Percentile：31.53(Materials Science, Multidisciplinary)Hou, D.*; Qiu, Z.*; Iguchi, Ryo*; Sato, Koji*; Vehstedt, E. K.*; Uchida, Kenichi*; Bauer, G. E. W.*; Saito, Eiji
Nature Communications (Internet), 7, p.12265_1 - 12265_6, 2016/07
Times Cited Count：13 Percentile：62.13(Multidisciplinary Sciences)
-implanted ZnOWang, D.*; Chen, Z. Q.*; Zhou, F.*; Lu, W.*; Maekawa, Masaki; Kawasuso, Atsuo
Applied Surface Science, 255(23), p.9371 - 9375, 2009/09
Times Cited Count：13 Percentile：48.81(Chemistry, Physical)We investigated iron-implanted ZnO by various methods. Photoluminescence measurements showed the introduction of non-radiative recombination centers. Raman scattering measurements showed the introduction of damages that distor the crystal symmetry. These defects were annealed out at 700
C. X-ray diffraction and magnetization measurements revealed the formation of iron nano-crystal and appearance of ferromagnetism accompanying the above heat treatment.
Zhou, W.*; Mima, Kunioki*; Nakamura, Tatsufumi; Nagatomo, Hideo*
Physics of Plasmas, 15(9), p.093107_1 - 093107_6, 2008/09
Times Cited Count：7 Percentile：27.46(Physics, Fluids & Plasmas)When a weak probe laser pulse is injected into a wakefield excited by a short high-intensity pump laser pulse, the probe pulse will be Raman scattered by the wakefield. It is possible to determine the density profile from the spectrum of this forward Raman scattered probe laser. In previous research, an analytical solution for the multiple sidebands of the forward Raman scattering of the probe laser pulse was presented. These multiple sidebands are connected with the steepening of density perturbation of the wakefield. More detailed information of the probe pulse in wakefield is studied with one-dimensional particle-in-cell simulations. The analytical solution and the results of simulation are consistent with each other and other experiments.
PtZhou, X. H.*; Xing, Y. B.*; Liu, M. L.*; Zhang, Y. H.*; Guo, Y. X.*; Ma, L.*; Lei, X. G.*; Guo, W. T.*; Oshima, Masumi; Toh, Yosuke; et al.
Physical Review C, 75(3), p.034314_1 - 034314_17, 2007/03
Times Cited Count：18 Percentile：73.15(Physics, Nuclear)High-spin states in Pt has been studied experimentally by in-beam 
-ray spectroscopy. The previously known bands based on the ![$$nu i_{13/2}, nu 7/2^{-}[503] $$](/search/images/EROG23JANFPXWMJTF2ZH0LBALRXHKIBXF2ZF24ZNPVNTKMBTLUQCI.gif)
and 
configurations have been extended to high-spin states, and new rotational bands associated with the ![$$nu 3/2^{-}[512]$$](/search/images/EROG23JAGMXTEXT1FV4VWNJRGJOSI.gif)
and ![$$nu 1/2^{-}[521]$$](/search/images/EROG23JAGEXTEXT1FV4VWNJSGFOSI.gif)
Nilsson orbits have been identified. The total Routhian surface calculations indicate that the transitional nucleus Pt is very soft with respect to 
and deformations. The band properties have been compared with the systematics observed in neiboring nuclei and have been interpreted within the framework of the cranked shell model.
bands in 
AuZhang, Y. H.*; Zhou, X. H.*; He, J. J.*; Liu, Z.*; Fang, Y. D.*; Guo, W. T.*; Lei, X. G.*; Guo, Y. X.*; Ndontchueng, M. M.*; Ma, L.*; et al.
International Journal of Modern Physics E, 15(7), p.1437 - 1445, 2006/10
Times Cited Count：3 Percentile：27.66(Physics, Nuclear)Search for low-spin signature inversion in the bands in odd-odd Au have been made through in-beam -ray spectroscopy techniques. The bands in the three nuclei have been identified and extended up to high-spin states. In particlular, the interband connection between the band and the ground-state band in 
Au have been established, leading to a firm spin-and-parity assignment for the band. The low-spin signature inversion is found in the bands in Au.
Sasao, Eiji; Ota, Kunio; Iwatsuki, Teruki; Niizato, Tadafumi; Arthur, R. C,*; Stenhouse, M. J.*; Zhou, W.*; Metcalfe, R.*; Takase, Hiroyasu; MacKenzie, A. B.*
Geochemistry; Exploration, Environment, Analysis, 6(1), p.5 - 12, 2006/02
The present natural analogue study of the Tono uranium deposit (Tono Natural Analogue Project) was started in 2001 with the main aim of studying a worst case scenario for safety assessment. The project has involved characterising the geology, hydrogeology, geochemistry and microbiology of the deposit and obtaining quantitative information about specific times in the past, as a means for developing, and building confidence in, conceptual and numerical models. Some recent results from site investigations, such as depositional age of the host sedimentary sequence and fault activity, are presented in this paper and in other papers in the series.
MnGa filmsSumida, Kazuki; Sakuraba, Yuya*; Masuda, Keisuke*; Kono, Takashi*; Kakoki, Masaaki*; Goto, Kazuki*; Zhou, W.*; Miyamoto, Koji*; Miura, Yoshio*; Okuda, Taichi*; et al.
no journal, ,
no abstracts in English
MnGa filmsSumida, Kazuki; Sakuraba, Yuya*; Masuda, Keisuke*; Kono, Takashi*; Kakoki, Masaaki*; Goto, Kazuki*; Zhou, W.*; Miyamoto, Koji*; Miura, Yoshio*; Okuda, Taichi*; et al.
no journal, ,
no abstracts in English
MnGa filmsSumida, Kazuki; Sakuraba, Yuya*; Masuda, Keisuke*; Kono, Takashi*; Kakoki, Masaaki*; Goto, Kazuki*; Zhou, W.*; Miyamoto, Koji*; Miura, Yoshio*; Okuda, Taichi*; et al.
no journal, ,
no abstracts in English
Yamashita, Susumu; Zhou, W.*; Miwa, Shuichiro*
no journal, ,
The development of data-driven plant safety assessment methods by integrating artificial intelligence (AI) technology and thermal hydraulics has been attracting attention as a way to reduce the time cost, range of applicability to actual phenomena, and accuracy variability that are problems in developing physical models in the field of thermal hydraulics. To contribute to developing a data-driven analysis method that automatically interprets the results of CFD calculations using AI technology, training images for building an AI model were generated by analyzing water jet into a pool system using JUPITER. The analysis was conducted using the several parameters (nozzle height, jet velocity, etc). In this presentation, we will present the calculation results for building the AI model, a quantitative comparison of the penetration length with the experimental results, problems in simulating the water impingement phenomenon, and a prediction of the penetration length by machine learning.
