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General synthesis of single-atom catalysts for hydrogen evolution reactions and room-temperature Na-S batteries

Lai, W.-H.*; Wang, H.*; Zheng, L.*; Jiang, Q.*; Yan, Z.-C.*; Wang, L.*; 吉川 浩史*; 松村 大樹; Sun, Q.*; Wang, Y.-X.*; et al.

Angewandte Chemie; International Edition, 59(49), p.22171 - 22178, 2020/12

 被引用回数:3 パーセンタイル:27.93(Chemistry, Multidisciplinary)

Herein, we report a comprehensive strategy to synthesize a full range of single-atom metals on carbon matrix, including V, Mn, Fe, Co, Ni, Cu, Ge, Mo, Ru, Rh, Pd, Ag, In, Sn, W, Ir, Pt, Pb, and Bi. The extensive applications of various single-atom catalysts (SACs) are manifested via their ability to electro-catalyze typical hydrogen evolution reactions (HER) and conversion reactions in novel room-temperature sodium sulfur batteries (RT-Na-S). The enhanced performances for these electrochemical reactions arisen from the ability of different single active atoms on local structures to tune their electronic configuration. Significantly, the electrocatalytic behaviors of diverse SACs, assisted by density functional theory calculations, are systematically revealed by in situ synchrotron X-ray diffraction and in situ transmission electronic microscopy, providing a strategic library for the general synthesis and extensive applications of SACs in energy conversion and storage.


Dynamic PRA of flooding-initiated accident scenarios using THALES2-RAPID

久保 光太郎; Zheng, X.; 田中 洋一; 玉置 等史; 杉山 智之; Jang, S.*; 高田 孝*; 山口 彰*

Proceedings of 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL 2020 and PSAM-15) (Internet), p.2279 - 2286, 2020/11

確率論的リスク評価(PRA)は巨大かつ複雑なシステムをリスクを評価する手法の1つである。従来のPRA手法を用いて外部事象のリスクを評価する場合、構造物、系統及び機器の機能喪失時刻の取扱いが困難である。この解決策として、熱水力解析と外部事象評価シミュレーションをRAPID (Risk Assessment with Plant Interactive Dynamics)コードを用いて結合した。外部事象としてPWRプラントにおけるタービン建屋内での内部溢水を選定し、溢水進展評価にはベルヌーイ則に式を用いた。また、溢水源の流量及び緩和設備の没水基準に関する不確実さを考慮した。回復操作については、運転員による溢水源の隔離とポンプによる排水を仮定とともにモデル化した。結果として、隔離操作が排水と組み合わせることによりより有効になることが示された。


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

Zheng, X.; Mandelli, D.*; Alfonsi, A.*; Smith, C.*; 杉山 智之

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

The paper introduces a simulation-based Level 2 probabilistic risk assessment (PRA) of a multi-unit nuclear power plant. We propose the methodology by quantifying risk for a station-blackout accident scenario, initialized by a loss-of-offsite-power event. Contrary to classical PRA that applies static models such as event-tree/fault-tree, the analysis is seamlessly integrated with mechanistic simulation and PRA models, including: (1) a simplified thermal-hydraulic code for simulating system behaviors; (2) a Markovian model for the failure mechanism of decay-heat-removal systems, to investigate the interaction between mechanistic simulation and reliability analysis; and (3) classical containment event trees for evaluating containment performances and hydrogen-explosion risk under severe accident conditions. All dynamic and static models, including plant dependencies, are unified within the RAVEN computational framework, applying RAVEN components, External Model, Ensemble Model, and PRA Plugins. The study demonstrates an integrated assessment of risks by considering accident progression and inter-unit system interactions, both time dependent. Statistical data analysis is used to quantifying risk metrics, including core damage frequencies, large early release frequencies and plant damage status. The methodology pertains to modern risk-analysis methodologies such as risk-informed safety margin characterization (RISMC) and dynamic PRA.


Enhancement of the treatment of system interactions in a dynamic PRA tool

田中 洋一; 玉置 等史; Zheng, X.; 杉山 智之

Proceedings of 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL 2020 and PSAM-15) (Internet), p.2195 - 2201, 2020/11

One advantage of dynamic probabilistic risk assessment (PRA) is that it can take into account the timing and ordering of event occurrences based on more explicit simulation of system dynamics. It is expected that dynamic PRA can lead us into a more realistic risk assessment, overcoming some limitations of conventional PRA. Multiple dynamic PRA tools have been developed worldwide, and applied to risk assessment of large industrial facilities such as nuclear power plants and crewed spacecrafts. Japan Atomic Energy Agency has developed the dynamic PRA tool, RAPID (Risk Assessment with Plant Interactive Dynamics), considering the interaction between accident simulation and dysfunctional models of safety-related systems. This paper introduces a recent enhancement of RAPID to treat more complicated simulation interactions from the outside of severe accident codes. It is designed to feed back and forth plant information from simulators to the accident sequence generator. It discusses how the enhancement affects the results of risk assessment, with an example analyzing thermal failure of a safety relief valve in a station blackout accident occurred at a boiling water reactor plant.


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

久保 光太郎; Zheng, X.; 石川 淳; 杉山 智之; Jang, S.*; 高田 孝*; 山口 彰*

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



Distance-selected topochemical dehydro-diels-alder reaction of 1,4-Diphenylbutadiyne toward crystalline graphitic nanoribbons

Zhang, P.*; Tang, X.*; Wang, Y.*; Wang, X.*; Gao, D.*; Li, Y.*; Zheng, H.*; Wang, Y.*; Wang, X.*; Fu, R.*; et al.

Journal of the American Chemical Society, 142(41), p.17662 - 17669, 2020/10

 被引用回数:0 パーセンタイル:100(Chemistry, Multidisciplinary)



A Comparative study of sampling techniques for dynamic probabilistic risk assessment of nuclear power plants

久保 光太郎; Zheng, X.; 田中 洋一; 玉置 等史; 杉山 智之; Jang, S.*; 高田 孝*; 山口 彰*

Proceedings of Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo 2020 (SNA + MC 2020), p.308 - 315, 2020/10



Ultra-fine CeO$$_{2}$$ particles triggered strong interaction with LaFeO$$_{3}$$ framework for total and preferential CO oxidation

Zheng, Y.*; Xiao, H.*; Li, K.*; Wang, Y.*; Li, Y.*; Wei, Y.*; Zhu, X.*; Li, H.-W.*; 松村 大樹; Guo, B.*; et al.

ACS Applied Materials & Interfaces, 12(37), p.42274 - 42284, 2020/09

 被引用回数:1 パーセンタイル:100(Nanoscience & Nanotechnology)

Interactions between the active components with the support are one of the fundamentally factors in determining the catalytic performance of a catalyst. In this study, we investigated the interaction between CeO$$_{2}$$ and LaFeO$$_{3}$$, the two important oxygen storage materials in catalysis area, by tuning the sizes of CeO$$_{2}$$ particles and highlight a two-fold effect of the strong oxide-oxide interaction in determining the catalytic activity and selectivity for preferential CO oxidation in hydrogen feeds. It is found that the anchoring of ultra-fine CeO$$_{2}$$ particles at the framework of three-dimensional-ordered macroporous LaFeO$$_{3}$$ surface results in a strong interaction between the two oxides that induces the formation of abundant uncoordinated cations and oxygen vacancy at the interface. This discovery demonstrates that in hybrid oxide-based catalysts, tuning the interaction among different components is essential for balancing the catalytic activity and selectivity.


Pressure-induced Diels-Alder reactions in C$$_{6}$$H$$_{6}$$ - C$$_{6}$$F$$_{6}$$ cocrystal towards graphane structure

Wang, Y.*; Dong, X.*; Tang, X.*; Zheng, H.*; Li, K.*; Lin, X.*; Fang, L.*; Sun, G.*; Chen, X.*; Xie, L.*; et al.

Angewandte Chemie; International Edition, 58(5), p.1468 - 1473, 2019/01

 被引用回数:11 パーセンタイル:25.62(Chemistry, Multidisciplinary)

芳香族の圧力誘起重合反応(PIP)は、sp$$^{3}$$炭素骨格を構築するための新しい方法であり、ベンゼンとその誘導体を圧縮することによってダイヤモンド様構造を有するナノスレッドを合成した。ここで、ベンゼン-ヘキサフルオロベンゼン共結晶(CHCF)を圧縮することにより、PIP生成物中に層状構造を有するH-F置換グラフェンを同定した。その場中性子回折から決定された結晶構造およびガスクロマトグラフィー質量スペクトルによって同定された中間生成物に基づいて、20GPaでは、CHCFがベンゼンおよびヘキサフルオロベンゼンを交互に積み重ねた傾斜カラムを形成し、それらが[4+2]重合体に転化し、次いで、短距離秩序を持つ水素化フッ素化グラフェンに変化する。反応プロセスは[4+2]ディールス-アルダー, レトロディールス-アルダー、および1-1'カップリング反応を含み、前者はPIPの重要な反応である。われわれの研究は、CHCFの素反応を初めて確認した。これは、芳香族化合物のPIPについての新しい見方を提供する。


Evaluation of chemical speciation of iodine and cesium considering fission product chemistry in reactor coolant system

石川 淳; Zheng, X.; 塩津 弘之; 杉山 智之; 丸山 結

Proceedings of Asian Symposium on Risk Assessment and Management 2018 (ASRAM 2018) (USB Flash Drive), 6 Pages, 2018/10

Japan Atomic Energy Agency is pursuing the development and application of the methodologies on fission product (FP) chemistry for source term analysis by using integrated severe accident analysis code THALES2/KICHE. Generally, specific chemical forms of iodine and cesium such as cesium iodide (CsI) and cesium hydroxide (CsOH) were assumed in the source term analysis for light water reactors using an integrated severe accident analysis code. The accident at the Fukushima Dai-ichi Nuclear Power Station leads possible chemical effects of B$$_{4}$$C control materials and atmosphere on chemical speciation of iodine and cesium such as cesium metaborate (CsBO$$_{2}$$) and hydrogen iodide (HI). The difference of chemical speciation affects not only the FP behavior in the reactor coolant system (RCS) and transport to containment but also pH value of the suppression pool water in the containment. The pH value is one of the influential factors on the release of gaseous iodine (I$$_{2}$$ and organic iodine) from containment liquid phase. In the present study, the improvement of the THALES2/KICHE code in terms of FP chemistry in RCS was performed and applied to source term analysis for severe accidents at a boil water reactor with Mark-I containment vessel. This paper discusses the chemical speciation of iodine and cesium, and FP behavior and transport to containment.


Severe accident scenario uncertainty analysis using the dynamic event tree method

Zheng, X.; 玉置 等史; 石川 淳; 杉山 智之; 丸山 結

Proceedings of 14th International Conference on Probabilistic Safety Assessment and Management (PSAM-14) (USB Flash Drive), 10 Pages, 2018/09

Several types of uncertainties exist during the simulation of a severe accident. These may result from incomplete knowledge about the plant systems, accident progression and oversimplified numerical models. Among them, parameter uncertainty can be treated via Monte-Carlo-sampling-based methods. To tackle the severe accident scenario uncertainty, we must resort to advanced dynamic probabilistic risk assessment (PRA) methods. In this paper, authors reviewed the previous dynamic PRA methods and tools, and then performed a preliminary scenario uncertainty analysis, by using an integrated SA code (THALES2) and a scenario generator (RAPID, risk assessment with plant interactive dynamics), both being developed at JAEA. THALES2 is a fast-running severe accident code for the simulation of severe accident progression and source term in light water reactors. Typical scenarios of station-blackout (SBO)-initiated accidents in boiling water reactors are generated and simulated. The dynamic event tree (DET) method is applied to consider the stochastic uncertainties during the scenario progression. Major groups of SBO sequences with the similar accident characteristics can be found. To provide a reference value for risk, a conditional core damage frequency is calculated accordingly. This is a preliminary analysis for severe accident scenario uncertainty quantification at JAEA, and further DPRA researches are in progress.


Application of Bayesian approaches to nuclear reactor severe accident analysis

Zheng, X.; 玉置 等史; 塩津 弘之; 杉山 智之; 丸山 結

Proceedings of Asian Symposium on Risk Assessment and Management 2017 (ASRAM 2017) (USB Flash Drive), 11 Pages, 2017/11

Nuclear reactor severe accident simulation involves uncertainties, which may result from incompleteness of modeling of accident scenarios, selection of alternative models and unrealistic setting of parameters during the numerical simulation, etc. Both deterministic and probabilistic methods are required to reach reasonable estimation of risk for severe accidents. Computational codes are widely used for the deterministic accident simulations. Bayesian approaches, including both parametric and nonparametric, are applied to the simulation-based severe accident researches at Japan Atomic Energy Agency (JAEA). In the paper, an overview of these research activities is introduced: (1) Dirichlet process models, a nonparametric Bayesian approach, are applied to source term uncertainty and sensitivity analyses; (2) Gaussian process models are applied to the optimization for operations of severe accident countermeasures; (3) Nonparametric models, include models based on Dirichlet process and K-nearest neighbors algorithm, are built to predict the chemical forms of fission products. Simplified models are integrated into the integral severe accident code, THALES2/KICHE; (4) We have also launched the research of dynamic probabilistic risk assessment (DPRA), and because a great number of accident scenarios will be generated during DPRA, Bayesian approaches would be useful for the boosting of computational efficiency.


Bayesian optimization analysis of containment-venting operation in a Boiling Water Reactor severe accident

Zheng, X.; 石川 淳; 杉山 智之; 丸山 結

Nuclear Engineering and Technology, 49(2), p.434 - 441, 2017/03

 被引用回数:1 パーセンタイル:82.18(Nuclear Science & Technology)

Containment venting is one of essential measures to protect the integrity of the final barrier of a nuclear reactor during severe accidents, by which the uncontrollable release of fission products can be avoided. The authors seek to develop an optimization approach, from a simulation-based perspective, to the venting operations by using an integrated severe accident code, THALES2/KICHE. The effectiveness of containment venting strategies needs to be verified via numerical simulations based on various settings of venting conditions. The number of iterations, however, needs to be controlled for cumbersome computational burden of integrated codes. Bayesian optimization is an efficient global optimization approach. By using Gaussian process regression, a surrogate model of the "black-box" code is constructed. It can be updated simultaneously whenever new simulation results are acquired. With predictions via the surrogate model, upcoming locations of the most probable optimum can be revealed. The sampling procedure is adaptive. The number of code queries is largely reduced for the optimum finding, compared with pure random searches. One typical severe accident scenario of a boiling water reactor is chosen as an example. The research demonstrates the applicability of the Bayesian optimization approach to the design and establishment of containment-venting strategies during severe accidents.


Bayesian optimization analysis of containment venting operation in a BWR severe accident

Zheng, X.; 石川 淳; 杉山 智之; 丸山 結

Proceedings of 13th Probabilistic Safety Assessment and Management Conference (PSAM-13) (USB Flash Drive), 10 Pages, 2016/10

Containment venting is one of essential measures to protect the integrity of the final barrier of a nuclear reactor, by which the uncontrollable release of fission products can be avoided. The authors seek to develop an optimization approach to the planning of containment-venting operations by using THALES2/KICHE. Factors that control the activation of the venting system, for example, containment pressure, amount of fission products within the containment and pH value in the suppression chamber water pool, will affect radiological consequences. The effectiveness of containment venting strategies needs to be confirmed through numerical simulations. The number of iterations, however, needs to be controlled for cumbersome computational burden of severe accident codes. Bayesian optimization is a computationally efficient global optimization approach to find desired solutions. With the use of Gaussian process regression, a surrogate model of the "black-box" code is constructed. According to the predictions through the surrogate model, the upcoming location of the most probable optimum can be revealed. The number of code queries is largely reduced for the optimum finding, compared with simpler methods such as pure random search. The research demonstrates the applicability of the Bayesian optimization approach to the design and establishment of containment-venting strategies under BWR severe accident conditions.


Global continuous optimization with error bound and fast convergence

川口 賢司*; 丸山 結; Zheng, X.

Journal of Artificial Intelligence Research, 56, p.153 - 195, 2016/06

 被引用回数:7 パーセンタイル:48.15(Computer Science, Artificial Intelligence)

This paper considers global optimization with a black-box unknown objective function that can be non-convex and partly non-smooth. Such a difficult optimization problem arises in many real-world applications, such as parameter tuning in machine learning, engineering design problem, and planning with a complex physics simulator. This paper proposes a new global optimization algorithm, called Locally Oriented Global Optimization (LOGO), to achieve both fast convergence in practice and finite-time error bound in theory. The advantage and usage of the new algorithm are illustrated via theoretical analysis and an experiment conducted with 10 bench-mark test functions. Further, we modify the LOGO algorithm to specifically solve a planning problem with continuous state/action space and long time horizon while maintaining its finite-time error bound. We apply the proposed planning method to severe accident management of a nuclear power plant. The result of the application study demonstrates the practical utility of our method.


An Integrated approach to source term uncertainty and sensitivity analysis for nuclear reactor severe accidents

Zheng, X.; 伊藤 裕人; 玉置 等史; 丸山 結

Journal of Nuclear Science and Technology, 53(3), p.333 - 344, 2016/03


 被引用回数:7 パーセンタイル:28.59(Nuclear Science & Technology)

Large-scale computer programs simulate severe accident phenomena and often have a moderate-to-large number of models and input variables. Analytical solutions to uncertainty distributions of interested source terms are impractical, and influential inputs on outputs are hard to discover. Additionally, runs of such computer programs, or integral codes, are time-consuming and hence expensive. This article presents an integrated approach to the uncertainty and sensitivity analysis for nuclear reactor severe accident source terms, with an example which simulates an accident sequence similar to that occurred at Unit 2 of the Fukushima Daiichi Nuclear Power Plant using an integral code, MELCOR. Monte Carlo based uncertainty analysis has been elaborated to investigate released fractions of representative radionuclides, Cs and CsI. In order to estimate sensitivity of inputs, which have a substantial influence on the core melt progression and the transportation process of radionuclides, a variance decomposition method is applied. Stochastic process, specifically a Dirichlet process, is applied to construct a surrogate model in sensitivity analysis as a substitute of the code. The surrogate model is cross-validated by comparing with corresponding results of MELCOR. The analysis with the simpler model avoids laborious computational cost so that importance measures for input factors are obtained successfully.


Source term uncertainty analysis; Probabilistic approaches and applications to a BWR severe accident

Zheng, X.; 伊藤 裕人; 玉置 等史; 丸山 結

Mechanical Engineering Journal (Internet), 2(5), p.15-00032_1 - 15-00032_14, 2015/10

A suite of methods has been established to quantitatively estimate uncertainties in source term analysis during a nuclear reactor severe accident. The accident sequence occurred at Unit 2 of the Fukushima Daiichi Nuclear Power Plant is taken as an example. The approach mainly consists of four steps: screening analysis, random sampling, numerical computation and verification of uncertainty distributions. First, by using an individually randomized one-factor-at-a-time screening method, a group of variables are preliminarily determined as uncertain inputs. Second, appropriate probability distributions are assigned to input variables. Random samples are generated using Latin Hypercube sampling with the consideration of rank correlation. Third, random samples of variables are inputted into MELCOR 1.8.5. Numerical simulation with multiple code runs is implemented. Finally, uncertainty distributions for representative source terms are obtained and verified. The technique of Bayesian nonparametric density estimation is applied to obtain probability density functions of source terms. The difference of probability density functions is evaluated through the comparison based on the Kullback-Leibler (KL) divergence. With the subjective judgment of small enough KL divergence, after a certain number of numerical computations, the uncertainty distributions of representative source terms are considered as stable enough as reliable results.


Application of Bayesian nonparametric models to the uncertainty and sensitivity analysis of source term in a BWR severe accident

Zheng, X.; 伊藤 裕人; 川口 賢司; 玉置 等史; 丸山 結

Reliability Engineering & System Safety, 138, p.253 - 262, 2015/06

 被引用回数:6 パーセンタイル:60.13(Engineering, Industrial)

An important issue for nuclear severe accident is the source tern uncertainty and sensitivity analysis. Generally, thousands of cases are needed to reach a stable result of sensitivity analysis. Based on the limited data obtained by MELCOR analysis, in which the accident at Unit 2 of the Fukushima Daiichi Nuclear Power Plant is used as an example, an approximate stochastic model has been constructed via Bayesian nonparametrics, specifically, the Dirichlet process. The advantage of a nonparametric model is that any deterministic function between explanatory and response variables is not necessary to be determined. The complexity of model will grow automatically as more actual data is observed. The approximate model saves the computational cost and makes it possible to complement thousands of Monte Carlo computation for uncertainty and sensitivity analysis. Probability density functions of uncertainty analysis by MELCOR and the approximate model are obtained and compared. Two densities show great accordance that proves the good predictive ability of the stochastic model. The appropriateness of the approximate model is further validated by the cross-validation through the comparison with actual MELCOR results. Global sensitivity analysis by Sobol' sensitivity index has been performed with the approximate model. Three input parameters are ranked according to their respective influences on the output uncertainty based on first-order and total effect.


Overview of recent methods for the modeling of the uncertainties on the calculations of consequences of a nuclear power plant severe accident

Chevalier-Jabet, K.*; Zheng, X.; Mabrouk, A.*; 丸山 結; Baccou, J.*

Proceedings of 2015 International Congress on Advances in Nuclear Power Plants (ICAPP 2015) (CD-ROM), 13 Pages, 2015/05

Severe accident phenomenology in light water nuclear power plants is complex. For the past decades, extensive experimental programs have been conducted to gain knowledge and computational tools have been built to predict accident progressions and consequences. Nevertheless remained uncertainties directly affect the predictability of severe accidents consequences. Monte-Carlo techniques are widely used in previous uncertainty analysis and the shortcomings are addressed by JAEA and IRSN. The first part of the article deals with uncertainty propagation. Possibilist formalisms are presented with an example. In the second part, JAEA has developed a method for source term assessment using a Dirichlet process. The implementation of the method is described from the Bayesian nonparametric model to the cross-validation process. As results, corresponding computational cost and importance measure of inputs are addressed. The third part describes the current research at IRSN. The combination of Bayesian formalism and graph theory is applied to modeling severe accident uncertainties. The method allows the information to propagate in any direction of the graph, making inference easy to perform. Bayesian networks allow the representation of a complex model in an integrated environment.


Estimation of source term uncertainty in a severe accident with correlated variables

Zheng, X.; 伊藤 裕人; 玉置 等史; 丸山 結

Proceedings of 22nd International Conference on Nuclear Engineering (ICONE-22) (DVD-ROM), 10 Pages, 2014/07

BWRのシビアアクシデント時におけるソースタームの評価を目的として、福島第一原子力発電所2号機の事故を例にとり、シビアアクシデント総合解析コードMELCOR (Ver.1.8.5)を用いた不確かさ解析を実施した。最初のステップとして、炉内溶融進展挙動及び放射性物質移行挙動モデルに係わる主要なパラメータを抽出し、Morris法によりパラメータの絞り込みを行なった。得られた各パラメータの不確かさ分布及びパラメータ間の相関を設定した後、Iman-Conover法による順位相関を考慮したラテン超方格サンプリング(LHS)法を用いて入力データセット作成し、Cs, CsI等の格納容器外放出量について不確かさを評価した。合わせて、相関係数に基づいてソースタームに大きく寄与するパラメータを検討し、炉心コンポーネントや構造物の破損、エアロゾルのプールスクラビングに係わるモデル等がソースタームの不確かさに大きな影響を及ぼすことを明らかにした。

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