Simulation-based dynamic probabilistic risk assessment of an internal flooding-initiated accident in nuclear power plant using THALES2 and RAPID

Kubo, Kotaro; Zheng, X.; Tanaka, Yoichi; Tamaki, Hitoshi; Sugiyama, Tomoyuki; Jang, S.*; Takata, Takashi*; Yamaguchi, Akira*

Proceedings of the Institution of Mechanical Engineers, Part O; Journal of Risk and Reliability, 237(5), p.947 - 957, 2023/10

https://doi.org/10.1177/1748006X221091604

Probabilistic risk assessment (PRA) is a method used to assess the risks associated with large and complex systems. However, the timing at which nuclear power plant structures, systems, and components are damaged is difficult to estimate if the risk of an external event is evaluated using conventional PRA based on event trees and fault trees. A methodology coupling thermal-hydraulic analysis with external event simulations using Risk Assessment with Plant Interactive Dynamics (RAPID) is therefore proposed to overcome this limitation. A flood propagation model based on Bernoulli's theorem was applied to represent internal flooding in the turbine building of the pressurized water reactor. Uncertainties were also taken into account, including the flow rate of the floodwater source and the failure criteria for the mitigation systems. The simulated recovery actions included the operator isolating the floodwater source and using a drainage pump; these actions were modeled using several simplifications. Overall, the results indicate that combining isolation and drainage can reduce the conditional core damage probability upon the occurrence of flooding by approximately 90%.

Composite with a glassy nonporous coordination polymer enhances gas adsorption selectivity

Zheng, X.*; Kato, Masaru*; Uemura, Yohei*; Matsumura, Daiju; Yagi, Ichizo*; Takahashi, Kiyonori*; Noro, Shinichiro*; Nakamura, Takayoshi*

Inorganic Chemistry, 62(3), p.1257 - 1263, 2023/01

https://doi.org/10.1021/acs.inorgchem.2c04068

Rediscovery of Hall-Petch strengthening in bulk ultrafine grained pure Mg at cryogenic temperature; A Combined neutron diffraction and electron microscopy study

Zheng, R.*; Gong, W.; Du, J.-P.*; Gao, S.*; Liu, M.*; Li, G.*; Kawasaki, Takuro; Harjo, S.; Ma, C.*; Ogata, Shigenobu*; et al.

Acta Materialia, 238, p.118243_1 - 118243_15, 2022/10

https://doi.org/10.1016/j.actamat.2022.118243

Dynamic probabilistic risk assessment of nuclear power plants using multi-fidelity simulations

Zheng, X.; Tamaki, Hitoshi; Sugiyama, Tomoyuki; Maruyama, Yu

Reliability Engineering & System Safety, 223, p.108503_1 - 108503_12, 2022/07

https://doi.org/10.1016/j.ress.2022.108503

Achieving excellent mechanical properties in type 316 stainless steel by tailoring grain size in homogeneously recovered or recrystallized nanostructures

Liu, M.*; Gong, W.; Zheng, R.*; Li, J.*; Zhang, Z.*; Gao, S.*; Ma, C.*; Tsuji, Nobuhiro*

Acta Materialia, 226, p.117629_1 - 117629_13, 2022/03

https://doi.org/10.1016/j.actamat.2022.117629

Phase transition and chemical reactivity of 1H-tetrazole under high pressure up to 100 GPa

Gao, D.*; Tang, X.*; Wang, X.*; Yang, X.*; Zhang, P.*; Che, G.*; Han, J.*; Hattori, Takanori; Wang, Y.*; Dong, X.*; et al.

Physical Chemistry Chemical Physics, 23(35), p.19503 - 19510, 2021/09

https://doi.org/10.1039/D1CP02913D

Pressure-induced phase transition and polymerization of nitrogen-rich molecules are widely focused due to its extreme importance for the development of green high energy density materials. Here, we present a study of the phase transition and chemical reaction of 1H-tetrazole up to 100 GPa by using Raman, IR, X-ray diffraction, neutron diffraction techniques and theoretical calculation. A phase transition above 2.6 GPa was identified and the high-pressure structure was determined with one molecule in a unit cell. The 1H-tetrazole polymerizes reversibly below 100 GPa, probably through a carbon-nitrogen bonding instead of nitrogen-nitrogen bonding. Our studies updated the structure model of the high pressure phase of 1H-tetrazole, and presented the possible intermolecular bonding route for the first time, which gives new insights to understand the phase transition and chemical reaction of nitrogen-rich compounds, and benefit for designing new high energy density materials.

Coordination number regulation of molybdenum single-atom nanozyme peroxidase-like specificity

Wang, Y.*; Jia, G.*; Cui, X.*; Zhao, X.*; Zhang, Q.*; Gu, L.*; Zheng, L.*; Li, L. H.*; Wu, Q.*; Singh, D. J.*; et al.

Chem, 7(2), p.436 - 449, 2021/02

https://doi.org/10.1016/j.chempr.2020.10.023

Pb(p,x)Bi and Bi(p,x)Bi excitation functions in the energy range of 0.04 - 2.6 GeV

Titarenko, Yu. E.*; Batyaev, V. F.*; Pavlov, K. V.*; Titarenko, A. Yu.*; Malinovskiy, S. V.*; Rogov, V. I.*; Zhivun, V. M.*; Kulevoy, T. V.*; Chauzova, M. V.*; Lushin, S. V.*; et al.

Nuclear Instruments and Methods in Physics Research A, 984, p.164635_1 - 164635_8, 2020/12

https://doi.org/10.1016/j.nima.2020.164635

The paper presents the Bi production cross-sections measured by the direct gamma-spectrometry technique in the samples of lead enriched with isotopes 208, 207 and 206, as well as in the samples of natural lead and bismuth, irradiated by protons of 11 energies in the range from 0.04 to 2.6 GeV. The obtained experimental results are compared with the previous measurements, with the TENDL-2019 data-library evaluations and the simulated data by means of the high-energy transport codes MCNP6.1 (CEM03.03), PHITS (INCL4.6/GEM), and Geant4 (INCL++/ABLA). The observed discrepancies between model predictions and experimental data are discussed.

Simulation-based Level 2 multi-unit PRA using RAVEN and a simplified thermal-hydraulic code

Zheng, X.; Mandelli, D.*; Alfonsi, A.*; Smith, C.*; Sugiyama, Tomoyuki

Proceedings of 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL 2020 and PSAM-15) (Internet), p.2176 - 2183, 2020/11

https://www.rpsonline.com.sg/proceedings/esrel2020/html/3762.xml

Case study on sampling techniques using machine learning and simplified physical model for simulation-based dynamic probabilistic risk assessment

Kubo, Kotaro; Zheng, X.; Ishikawa, Jun; Sugiyama, Tomoyuki; Jang, S.*; Takata, Takashi*; Yamaguchi, Akira*

Proceedings of Asian Symposium on Risk Assessment and Management 2020 (ASRAM 2020) (Internet), 11 Pages, 2020/11

Dynamic probabilistic risk assessment (PRA) enables a more realistic and detailed analysis than classical PRA. However, the trade-off for these improvements is the enormous computational cost associated with performing a large number of thermal-hydraulic (TH) analyses. In this study, based on machine learning (ML), we aim to reduce these costs by skipping the TH analysis. For the ML algorithm, we selected a support vector machine; we built it using a high-fidelity/high-cost detailed model and low-fidelity/low-cost simplified model. As a result, the computational costs could be reduced by approximately 80% without significantly decreasing the accuracy under the assumed conditions.