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A multiclass classification model for predicting the thermal conductivity of uranium compounds 预测铀化合物热导率的多类分类模型
4区 工程技术 Q2 NUCLEAR SCIENCE & TECHNOLOGY Pub Date : 2023-10-20 DOI: 10.1080/00223131.2023.2269974
Y. Sun, M. Kumagai, M. Jin, E. Sato, M. Aoki, Y. Ohishi, K. Kurosaki
ABSTRACTAdvanced nuclear fuels are designed to offer improved performance and accident tol- erance, with an emphasis on achieving higher thermal conductivity. While promising fuel candidates like uranium nitrides, carbides, and silicides have been widely stud- ied, the majority of uranium compounds remain unexplored. To search for potential candidates among these unexplored uranium compounds, we incorporated machine learning to accelerate the material discovery process. In this study, we trained a multiclass classification model to predict a compound’s thermal conductivity based on 133 input features derived from element properties and temperature. The initial training data consists of over 160,000 processed thermal conductivity records from the Starrydata2 database, but a skewed data class distribution led the trained model to underestimate compound’s thermal conductivity. Consequently, we addressed the issue of class imbalance by applying Synthetic Minority Oversampling TEchnique and Random UnderSampling, improving the recall for materials with thermal con- ductivity higher than 15 W/mK from 0.64 to 0.71. Finally, our best model is used to identify 119 potential advanced fuel candidates with high thermal conductivity among 774 stable uranium compounds. Our results underscore the potential of ma- chine learning in the field of nuclear science, accelerating the discovery of advanced nuclear materials.KEYWORDS: Advanced nuclear fuelsMachine learningthermal conductivityDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of this study are openly available at https://github.com/AzarashiYifan/classification-uranium-thermal-conductivity.Additional informationFundingThis work was supported by MEXT Innovative Nuclear Research and Development Program Grant Number JPMXD0220354330 and JPMXD0222682541.
摘要先进核燃料旨在提供更好的性能和事故容忍度,重点是实现更高的导热性。虽然像氮化铀、碳化物和硅化铀等有前途的候选燃料已被广泛研究,但大多数铀化合物仍未被开发。为了在这些未开发的铀化合物中寻找潜在的候选物质,我们结合了机器学习来加速材料发现过程。在这项研究中,我们训练了一个多类别分类模型,基于133个来自元素性质和温度的输入特征来预测化合物的导热系数。最初的训练数据由来自Starrydata2数据库的超过16万条经过处理的导热性记录组成,但是数据类分布的倾斜导致训练模型低估了化合物的导热性。因此,我们通过应用合成少数过采样技术和随机欠采样来解决类别不平衡问题,将导热系数高于15 W/mK的材料的召回率从0.64提高到0.71。最后,利用我们的最佳模型在774种稳定的铀化合物中识别出119种具有高导热性的潜在先进候选燃料。我们的结果强调了机器学习在核科学领域的潜力,加速了先进核材料的发现。关键词:先进核燃料机器学习导热性免责声明作为对作者和研究人员的服务,我们提供此版本的已接受手稿(AM)。在最终出版版本记录(VoR)之前,将对该手稿进行编辑、排版和审查。在制作和印前,可能会发现可能影响内容的错误,所有适用于期刊的法律免责声明也与这些版本有关。披露声明作者未报告潜在的利益冲突。数据可用性声明支持本研究结果的数据可在https://github.com/AzarashiYifan/classification-uranium-thermal-conductivity.Additional information上公开获取。资助本工作由MEXT创新核研究与发展计划资助号JPMXD0220354330和JPMXD0222682541支持。
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引用次数: 0
Creep deformation and rupture behavior of 9Cr-ODS steel cladding tube at high temperatures from 700°C to 1000°C 9Cr-ODS钢包层管在700 ~ 1000℃高温下的蠕变变形和断裂行为
4区 工程技术 Q2 NUCLEAR SCIENCE & TECHNOLOGY Pub Date : 2023-10-19 DOI: 10.1080/00223131.2023.2269178
Yuya Imagawa, Ryuta Hashidate, Takeshi Miyazawa, Takashi Onizawa, Satoshi Ohtsuka, Yasuhide Yano, Takashi Tanno, Takeji Kaito, Masato Ohnuma, Masatoshi Mitsuhara, Takeshi Toyama
ABSTRACTThe Japan Atomic Energy Agency has been developing 9Cr-oxide dispersion strengthened (ODS) steel as a fuel cladding material for sodium-cooled fast reactors (SFRs). Previous studies have formulated the creep rupture equation for 650°C–850°C. However, little data have been obtained above 850°C, and no equation has been formulated. This study conducted creep tests to evaluate creep strength at 700°C–1000°C. Two creep test methods, the internal pressure and ring creep tests under development, were used, and the validation of the ring creep test method was conducted. The results showed that 9Cr-ODS steel undergoes almost no strength change due to the matrix’s phase transformation, and a single equation can express a creep rupture strength from 700°C to 1000°C. In validating the ring creep test method, analysis clarified the effect of stress concentration on the specimen. Plastic deformation occurs at high initial stress and may lead to early rupture. The results will be essential for future creep testing and evaluation of neutron-irradiated 9Cr-ODS steel.KEYWORDS: Oxide dispersion strengthened steelfuel cladding tube,creep strengthcreep straininternal creep testring creep testDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. AcknowledgmentsThe authors would like to express their sincere gratitude to Dr. Tomoyuki Uwaba for his valuable guidance on finite element simulation.Additional informationFundingMEXT Innovative Nuclear Research and Development Program Grant Number JPMXD0219214482, Ministry of Education Culture, Sports, Science, and Technology, supported this work.
摘要日本原子能机构一直致力于开发9cr -氧化物弥散强化(ODS)钢作为钠冷快堆(SFRs)燃料包壳材料。已有研究建立了650℃- 850℃蠕变断裂方程。然而,850℃以上的数据很少,也没有公式。本研究进行了蠕变试验,以评估700°C - 1000°C的蠕变强度。采用正在开发的内压蠕变试验和环蠕变试验两种蠕变试验方法,对环蠕变试验方法进行了验证。结果表明:9Cr-ODS钢的强度几乎不受基体相变的影响,700 ~ 1000℃范围内的蠕变断裂强度可以用单一公式表示。在验证环蠕变试验方法时,分析了应力集中对试件的影响。塑性变形发生在高初始应力下,可能导致早期断裂。研究结果对今后中子辐照9Cr-ODS钢的蠕变试验和评价具有重要意义。关键词:氧化物弥散强化钢燃料包壳管,蠕变强度,蠕变应变,内部蠕变试验管柱蠕变试验免责声明作为对作者和研究人员的服务,我们提供此版本的接受稿件(AM)。在最终出版版本记录(VoR)之前,将对该手稿进行编辑、排版和审查。在制作和印前,可能会发现可能影响内容的错误,所有适用于期刊的法律免责声明也与这些版本有关。作者衷心感谢Tomoyuki Uwaba博士在有限元模拟方面的宝贵指导。文部省创新核研究与发展计划资助号:JPMXD0219214482,教育、文化、体育、科学和技术部支持这项工作。
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引用次数: 0
Benchmark simulation code for the thermal-hydraulics design tool of the accelerator-driven system: validation and benchmark simulation of flow behavior around the beam window 加速器驱动系统热工设计工具的基准仿真代码:梁窗周围流动特性的验证和基准仿真
4区 工程技术 Q2 NUCLEAR SCIENCE & TECHNOLOGY Pub Date : 2023-10-09 DOI: 10.1080/00223131.2023.2268676
Susumu Yamashita, Nao Kondo, Takanori Sugawara, Hideaki Monji, Hiroyuki Yoshida
ABSTRACTA detailed computational fluid dynamics code named JAEA Utility Program for Interdisciplinary Thermal-hydraulics Engineering and Research (JUPITER) for the thermal-hydraulics around the beam window (BW) of accelerator-driven system (ADS) was used to confirm the validity of the thermal-hydraulics design tool based on the ANSYS Fluent. The Fluent uses the Reynolds-averaged Navier – Stokes (RANS) model and can quickly calculates the turbulent flow around the BW as a BW design tool. First, the results of JUPITER were compared with the experimental results using a mock-up BW system in water to confirm the validity of JUPITER. This study confirmed that the numerical results are in good agreement with the experimental results. Thus, JUPITER could be used as a benchmark code. A benchmark simulation for the Fluent calculation was also performed using validated JUPITER to demonstrate the applicability of JUPITER as an alternative for experiments. Therefore, the mean values around the BW agreed with each other (e.g. the mean velocity profile for the stream and horizontal directions). Therefore, results confirmed that JUPITER demonstrated a good performance in validating the thermal-hydraulics design tool as a fluid dynamics solver. Moreover, Fluent has sufficient accuracy as a thermal-hydraulics design tool for the ADS.KEYWORDS: Computational fluid dynamics (CFD)thermal-hydraulicsaccelerator-driven system (ADS)beam windowlead-bismuth eutectic flowDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. AcknowledgmentsThis research was conducted using the supercomputer HPE SGI8600 at the Japan Atomic Energy Agency. The authors would like to express their gratitude to Mr. Asari (LIFULL Co., Ltd.) for fruitful discussions about the mock-up experiment.NomenclatureTableDisplay TableFigure 1. Conceptual view of LBE-cooled ADS (left) and its BW (right)Display full sizeFigure 2. Schematic diagram of the experimental apparatusDisplay full sizeFigure 3. System of the experimental analysisDisplay full sizeFigure 4. Definitions of jet part and its axes. (a) Free jet part, (b) Impinging jet part. The dotted line indicates the centerline of the jet.Display full sizeFigure 5. Radial distribution of the velocity for the y-direction in the free jet part. (a) numerical simulation and (b) experimentDisplay full sizeFigure 6. Comparison between the simulation and the experiment in the free jet partDisplay full sizeFigure 7. Centerline velocity distribution in impinging jet partDisplay full sizeFigure 8. Comparison between the simulation and the experiment for nondimensional velocity distribut
利用JAEA跨学科热工水力工程与研究实用程序(Utility Program for Interdisciplinary thermal-hydraulic Engineering and Research,简称JUPITER)详细计算流体力学代码,对加速器驱动系统(ADS)梁窗周围热工水力设计工具的有效性进行了验证。Fluent使用reynolds -average Navier - Stokes (RANS)模型,可以快速计算出BW周围的湍流,作为BW设计工具。首先,将JUPITER的计算结果与水中BW系统模型的实验结果进行了比较,验证了JUPITER的有效性。研究结果表明,数值计算结果与实验结果吻合较好。因此,JUPITER可以用作基准代码。还使用经过验证的JUPITER对Fluent计算进行了基准模拟,以证明JUPITER作为实验替代方案的适用性。因此,BW周围的平均值是一致的(例如,水流和水平方向的平均速度剖面)。因此,结果证实,JUPITER在验证热工设计工具作为流体动力学求解器方面表现良好。关键词:计算流体动力学(CFD)热液压加速器驱动系统(ADS)束流窗铅铋共晶流免责声明作为对作者和研究人员的服务,我们提供了这个版本的接受稿件(AM)。在最终出版版本记录(VoR)之前,将对该手稿进行编辑、排版和审查。在制作和印前,可能会发现可能影响内容的错误,所有适用于期刊的法律免责声明也与这些版本有关。本研究是在日本原子能机构的超级计算机HPE SGI8600上进行的。作者对Asari先生(LIFULL Co., Ltd)对模型实验进行的富有成果的讨论表示感谢。NomenclatureTableDisplay表lbe冷却ADS的概念图(左)和BW(右)实验装置原理图显示全尺寸图3。系统实验分析显示全尺寸图4。射流零件及其轴的定义。(a)自由射流部分,(b)冲击射流部分。虚线表示喷气机的中心线。显示完整尺寸图5自由射流部分y方向速度的径向分布。(a)数值模拟(b)实验自由射流部分的仿真与实验对比显示全尺寸图7。撞击射流部件的中心线速度分布显示全尺寸图8。几个s/D位置冲击射流零件无因次速度分布的仿真与实验比较压力测量示意图显示全尺寸图10。BW压力波动。左:数值模拟,右:实验计算系统在垂直截面C-C '为木星和FluentDisplay全尺寸图12。木星计算网格(a)和Fluent计算网格(b)体积热源的分布。左:PHITS,右:拟合功能显示全尺寸图14木星(左)和Fluent(右)的速度矢量分布显示完整尺寸图15。木星(左)和Fluent(右)的温度分布木星和Fluent在y方向上的展向平均速度剖面的比较。圆:流畅,实线:木星。速度轮廓线表示Fluent结果。显示完整尺寸图17木星和Fluent在y方向上的平均流速剖面结果的比较。圆:流畅,实线:木星。速度轮廓线表示Fluent结果。显示完整尺寸图18。木星和Fluent在y方向平均温度分布结果的比较。圆:流畅,实线:木星。温度等高线表示木星的结果。显示完整尺寸图19。木星温度分布的时间变化,网格分辨率,388 × 1632 × 20.2 m≤y≤0.6 m范围内射流与体边界处的速度大小和温度分布(a)栅格情况下s方向BW内外表面温度分布;显示全尺寸图22。(b)栅格情况下s方向BW内外表面温度分布;显示全尺寸图23。 4实际上,真空区域内是没有物质和流动的,但是数值计算不能建立一个什么都没有的情况。因此,通过给出非常小的导热系数,只再现了真空区域无热传递的特性,并将T91的性质作为其他物理性质的假设值。
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引用次数: 0
Irradiated mechanical properties predicted by a machine learning method with the Fourier-transform-based feature extension 基于傅里叶变换特征扩展的机器学习方法预测辐照力学性能
4区 工程技术 Q2 NUCLEAR SCIENCE & TECHNOLOGY Pub Date : 2023-10-09 DOI: 10.1080/00223131.2023.2267044
Yingxuan Dong, Junnan Lv, Hong Zuo, Qun Li
ABSTRACTAfter irradiation, the variation of yield strength in metallic materials is multivariate nonlinear. High-dimensional nonlinear relationships between the irradiated yield strength and its influencing factors, including material properties, doses, irradiation temperatures, and crystal structures, etc. are difficult to explicitly characterize in the absence of a comprehensive database. In this study, we developed a machine learning method with the Fourier-transform-based feature extension, successfully constructing the prediction model of irradiated yield strength by a relatively small and sparse database of irradiated material properties. The analysis suggests that the proposed feature extension method improves the training performances of machine learning with small dataset. And the present model is accurate and feasible for predicting the irradiated yielding behaviors. Furthermore, we attempt the inverse machine learning model to determine material properties and irradiation conditions according to the desired yield strength. Since the parameter combinations commensurate with a fixed strength are diverse, the optimal model is helpful in reversely calculating and optimizing material performances. The data-driven machine learning method, which can detect the implicit correlations among numerous data, exhibits great prospects in investigating irradiated mechanical properties and exploring multiscale links in the nuclear material field. This work holds the promise for optimizing the design of in-pile structural components and can be further extended to other machine learning problems with the small dataset.KEYWORDS: Yield strengthmachine learningirradiationDimensional extension method of feature vectorSupported vector machine for regressionDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. AcknowledgmentsThis work was supported by the Natural Science Foundation of China (12172270), the Fund of Science and Technology on Reactor Fuel and Materials Laboratory (6142A06190111), the Project of Nuclear Power Institute of China (No. K902023-04-FW-HT-20220003), the Youth Science and Technology Innovation Team Project of China National Nuclear Corporation (JT211), the Qin Chuangyuan “Scientists+Engineers” Team Construction Project in Shaanxi Province (2022KXJ-085), and the Innovative scientific Program of CNNC. The computation has made use of the High Performance Computing (HPC) platform of Xi’ an Jiaotong University.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe work was supported by the National Natur
摘要辐照后金属材料屈服强度的变化是多元非线性的。辐照屈服强度与其影响因素(包括材料性质、剂量、辐照温度和晶体结构等)之间的高维非线性关系在缺乏综合数据库的情况下难以明确表征。在本研究中,我们开发了一种基于傅里叶变换的特征扩展的机器学习方法,利用相对较小且稀疏的辐照材料特性数据库成功构建了辐照屈服强度的预测模型。分析表明,所提出的特征扩展方法提高了小数据集机器学习的训练性能。该模型对辐照屈服特性的预测是准确可行的。此外,我们尝试使用逆机器学习模型来根据所需的屈服强度确定材料性能和辐照条件。由于与固定强度相匹配的参数组合是多种多样的,因此最优模型有助于材料性能的反向计算和优化。数据驱动的机器学习方法能够发现大量数据之间的隐式相关性,在研究核材料辐照力学性能和探索多尺度联系方面具有广阔的应用前景。这项工作有望优化桩内结构部件的设计,并可以进一步扩展到其他小数据集的机器学习问题。关键词:屈服强度机器学习辐照特征向量维度扩展回归支持向量机免责声明作为对作者和研究人员的服务,我们提供此版本的已接受稿件(AM)。在最终出版版本记录(VoR)之前,将对该手稿进行编辑、排版和审查。在制作和印前,可能会发现可能影响内容的错误,所有适用于期刊的法律免责声明也与这些版本有关。国家自然科学基金项目(12172270);反应堆燃料与材料科学技术重点实验室项目(6142A06190111);中国核电科学研究院项目(6142A06190111);K902023-04-FW-HT-20220003)、中核集团青年科技创新团队项目(JT211)、陕西省秦创源“科学家+工程师”团队建设项目(2022KXJ-085)、中核集团科技创新计划项目。计算利用了西安交通大学的高性能计算(HPC)平台。披露声明作者未报告潜在的利益冲突。本研究得到国家自然科学基金资助[12172270];反应堆燃料与材料科学技术基金重点实验室[6142A06190111]。
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引用次数: 0
EXFOR-based simultaneous evaluation for neutron-induced fission cross section of plutonium-242 基于exfor的钚-242中子诱导裂变截面的同时评价
4区 工程技术 Q2 NUCLEAR SCIENCE & TECHNOLOGY Pub Date : 2023-10-06 DOI: 10.1080/00223131.2023.2267070
Riko Okuyama, Naohiko Otuka, Go Chiba, Osamu Iwamoto
ABSTRACTThe 242Pu neutron-induced fission cross section was evaluated from 100 keV to 200 MeV. The experimental 242Pu and 235U fission cross sections and their ratios in the EXFOR library were reviewed and analysed by the least-squares method. Additional simultaneous evaluation was performed by including the experimental database of the 233,238U and 239,240,241Pu fission cross sections and their ratios developed for JENDL-5 evaluation. The 242Pu fission cross sec- tions from our evaluation and JENDL-5 evaluation are close to each other below 1 MeV while systematically differ from each other above 10 MeV. The cross section from our evaluation is systematically lower than the JENDL-4.0 cross section in the prompt fission neutron spectrum peak region (∼5% lower around 1 MeV). The newly evaluated 242Pu fission cross section was verified against the cross section measured in the 252Cf spontaneous fission neutron field and criticalities of small-sized LANL fast systems, and demonstrated better performance than the JENDL-4.0 cross section on the same level with the JENDL-5 cross section.KEYWORDS: Plutonium-242fissionsimultaneous evaluationJENDLEXFORDisclaimerAs a service to authors and researchers we are providing this version of an accepted manuscript (AM). Copyediting, typesetting, and review of the resulting proofs will be undertaken on this manuscript before final publication of the Version of Record (VoR). During production and pre-press, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal relate to these versions also. AcknowledgementAntonio Jime´nez-Carrascosa and Oscar Cabellos (Universidad Polite´cnica de Madrid) per- formed criticality calculations by KENO to check if our criticality calculations are reasonable. We thank Melissa Denecke (IAEA) for careful reading of the manuscript. RO would like tothank the members of IAEA Nuclear Data Section for their hospitality during her internship. Her internship was financially supported by “Fundamental Nuclear Education Program by Japanese University Network for Global Nuclear Human Resource Development” entrusted to Tokyo Institute of Technology by Ministry of Education, Culture, Sports, Science and Technology (MEXT).Figure 1 242Pu/235U fission cross section ratios below 1 MeV from evaluations along with the experimental ones used in the present evaluationCitation13, Citation48, Citation52, Citation53, Citation56, Citation57.Display full sizeFigure 2 242Pu/235U fission cross section ratios above 1 MeV from evaluations along with the experimental ones used in the present evaluationCitation13, Citation48, Citation52–57.Display full sizeFigure 3 242Pu fission cross sections from evaluations along with the experimen- tal ones used in the present evaluationCitation12, Citation14, Citation15, Citation50, Citation51. Three datasets excluded from the present evaluationCitation11, Citation16, Citation27 are also plotted by grey symbols.Display full sizeF
摘要在100 keV到200 MeV范围内对242Pu中子诱导裂变截面进行了计算。用最小二乘法对EXFOR库中242Pu和235U的裂变截面及其比值进行了回顾和分析。另外,通过将为JENDL-5评估开发的233,238U和239,240,241Pu裂变截面及其比值的实验数据库纳入其中,进行了额外的同步评估。我们的评价结果与JENDL-5评价结果在1 MeV以下的242Pu裂变截面接近,而在10 MeV以上的242Pu裂变截面存在系统差异。我们评估的截面在快速裂变中子谱峰区比JENDL-4.0的截面低(在1 MeV左右低约5%)。新计算的242Pu裂变截面与252Cf自发裂变中子场测量的截面和小型LANL快系统的临界值进行了验证,在与JENDL-5截面相同的水平上,其性能优于JENDL-4.0截面。作为对作者和研究人员的服务,我们提供此版本的已接受稿件(AM)。在最终出版版本记录(VoR)之前,将对该手稿进行编辑、排版和审查。在制作和印前,可能会发现可能影响内容的错误,所有适用于期刊的法律免责声明也与这些版本有关。感谢antonio Jime ' nez-Carrascosa和Oscar Cabellos (universsidad Polite ' cnica de Madrid)使用KENO的临界计算来检查我们的临界计算是否合理。我们感谢梅丽莎·德内克(国际原子能机构)仔细阅读了手稿。总干事要感谢原子能机构核数据科成员在她实习期间的盛情款待。实习项目由日本文部科学省委托东京工业大学实施的“日本全球核人力资源开发大学网络核基础教育计划”资助。图1 242Pu/235U在1mev以下的裂变截面比来自于评估和本评估中使用的实验数据citation13, Citation48, Citation52, Citation53, Citation56, Citation57。图2242pu /235U在1mev以上的裂变截面比来自于评估和本评估中使用的实验数据citation13, Citation48, Citation52-57。图3242pu裂变截面来自于评估以及本评估中使用的实验截面citation12, Citation14, Citation15, Citation50, Citation51本评估中排除的三个数据集citation11, Citation16, Citation27也用灰色符号绘制。图4235u裂变截面来自评估以及本评估中使用的实验截面引文62 - 78。为便于阅读,省略实验数据点误差条。图5评价结果之间242Pu裂变截面的差异。图6目前评估的252Cf自发裂变中子谱平均截面的比率,评估数据库citation17, Citation19, Citation40-42和Mannhart的推荐citation45与AdamovCitation38测量的比率。ENDF / B-VIII。citation43和TENDL-2021Citation44库采用JENDL-4.0评价,未示出。图7 LANL小型快速系统(规格- met - fast -004-1, 2和3)临界度的C/E值由ACE-FRENDY-CBZ序列计算,JENDL-4.0库更新了当前的242Pu评估。本研究得到了国家教育、文化、体育、科学和技术部的支持。
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引用次数: 0
Development of extremely high-temperature X-ray absorption fine structure measurement method for oxide samples 氧化物样品极高温x射线吸收精细结构测量方法的建立
4区 工程技术 Q2 NUCLEAR SCIENCE & TECHNOLOGY Pub Date : 2023-10-06 DOI: 10.1080/00223131.2023.2267560
Keisuke Niino, Yuji Arita, Kenji Konashi, Hiromichi Watanabe, Tsuyoshi Yaita, Hajime Tanida, Tohru Kobayashi, Kyoichi Morimoto, Masashi Watanabe, Yusuke Miura
Click to increase image sizeClick to decrease image size AcknowledgementsThis research was supported by the Japan MEXT National Problem-Making R&D Promotion Project “Acceleration of Nuclear Fuel Development Research Incorporating Artificial Intelligence (AI) Technology” for the Nuclear Energy System Research and Development Project. The synchrotron radiation experiments were performed at BL22XU in SPring-8 with the approval of the Japan Atomic Energy Agency (Proposals No. 2022A3744, 2022B3714, and 2023A3714).Figure 1. Phase diagram of ZrO2–Y2O3 system [Citation7].Display full size Figure 2. Equipment for high-temperature measurement.(a). Photograph of heating chamber. (b). Schematic diagram of heating system.Display full size Figure 3. Newly conceived sample holder with slit for high-temperature X-ray absorption fine structure measurement.Display full size Figure 4. Current density distribution (element vector) near the slit of a heater by finite-element analysis (at current = 120 A).Display full size Figure 5. Temperature distribution using finite-element analysis of a slit heater (at current = 120 A)Display full size Figure 6. Temperature relative to current value estimated using finite-element method analysis.Display full size Figure 7. High-temperature X-ray absorption fine structure spectra obtained from room temperature (RT) to 3427 K.Display full size Figure 8. X-ray absorption fine structure spectra of 10A (solid phase) and 180A (liquid phase) and absorption change α(×10).Display full size Figure 9. Plotting Δα versus current for phase transformation and melting analysis. (Error bars on the X-axis are control variations in current values, which are 0.1A. Because they are extremely small, the error bars look like crosses. Error bars on the Y-axis are evaluated based on the statistical variation of the measured data.)Display full size Figure 10. Scanning electron microscopy images of yttria-stabilized zirconia (YSZ) sample before and after measurement. (a)Sample holder filled with YSZ powder (before XAFS measurement). (b)After XAFS measurement.Display full size Figure 11 Comparison of temperature calibration results and finite-element method analysis results.Display full sizeAdditional informationFundingThe work was supported by the The Japan MEXT National Problem-Making R&D Promotion Project ”Acceleration of Nuclear Fuel Development Research Incorporating Artificial Intelligence (AI) Technology” for the Nuclear Energy System Research and Development Project. .
本研究得到了日本省文部省国家问题制造研发促进项目“核能系统研究与开发项目”“结合人工智能(AI)技术加速核燃料开发研究”的支持。经日本原子能机构批准(提案号2022A3744、2022B3714和2023A3714),同步辐射实验于春季-8在BL22XU进行。图1所示。ZrO2-Y2O3体系相图[Citation7]。显示全尺寸图2。(a)高温测量设备。加热室照片。(b)加热系统示意图。显示全尺寸图3。新设计的用于高温x射线吸收精细结构测量的带狭缝样品架。显示全尺寸图4。通过有限元分析加热器狭缝附近的电流密度分布(元件矢量)(电流= 120a时)。显示全尺寸图5。使用有限元分析狭缝加热器的温度分布(电流= 120a)用有限元法分析估计温度相对于电流值。显示全尺寸图7。从室温(RT)到3427 K的高温x射线吸收精细结构光谱。显示完整尺寸的图8。10A(固相)和180A(液相)的x射线吸收精细结构光谱及吸收变化α(×10)。显示完整尺寸的图9。绘制Δα与电流的关系,用于相变和熔化分析。(x轴上的误差条是电流值的控制变化,为0.1A。因为它们非常小,所以误差条看起来像十字架。y轴上的误差条是根据测量数据的统计变化来评估的。)显示全尺寸图10。测量前后氧化钇稳定氧化锆(YSZ)样品的扫描电镜图像。(a)装有YSZ粉末的样品架(XAFS测量前)。(b) XAFS测量后。图11温度校准结果与有限元法分析结果对比本研究由日本文部科学省国家问题解决研发促进项目“核能系统研究与开发项目“加速纳入人工智能(AI)技术的核燃料开发研究”提供支持。
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引用次数: 0
Basic study on tritium monitor using plastic scintillator for treated water discharge at Fukushima Daiichi Nuclear Power plant 用塑料闪烁体监测福岛第一核电站污水排放氚的基础研究
4区 工程技术 Q2 NUCLEAR SCIENCE & TECHNOLOGY Pub Date : 2023-09-24 DOI: 10.1080/00223131.2023.2258880
Yukihisa Sanada, Tomohisa Abe, Miyuki Sasaki, Marina Kanno, Tsutomu Yamada, Takamasa Nakasone, Nobuyuki Miyazaki, Keisuke Oshikiri, Hiroshi Watabe
In response to the Fukushima Daiichi Nuclear Power Station (FDNPS) accident, the treated water from which the primary radioactive substances were removed contained tritium, and the Japanese government discussed how to treat this water. As the storage capacity of the treated water reached its limit, the Japanese government decided on a method to discharge the treated water into the sea in 2023. Herein, a basic study was conducted to develop a tritiated monitor to directly measure tritium in wastewater for preparing the release of tritiated water from the FDNPS. Two plastic scintillators with different shapes (sheet and pellet types) were compared as detectors. The pellet-type detector was found to be more sensitive to tritiated water than the sheet-type, with an efficiency of 2.95 × 10−5 cps Bq−1 L in the test configuration. In the future, optimizing the design for background reduction should achieve a minimum detectable radioactivity of 1,500 Bq L−1, the emission standard set by nuclear power plant operators. Through this study, we could obtain basic data for developing such a practical tritiated monitor.
作为对福岛第一核电站(FDNPS)事故的回应,去除主要放射性物质的处理水含有氚,日本政府讨论了如何处理这些水。由于处理后的水的储存能力已经达到极限,日本政府决定在2023年将处理后的水排放到大海。为此,为了准备从FDNPS中释放氚化水,进行了一项基础研究,以开发氚化监测仪直接测量废水中的氚。比较了两种不同形状的塑料闪烁体(片状和粒状)作为探测器。颗粒型探测器比片状探测器对氚化水更敏感,在测试配置下的效率为2.95 × 10−5 cps Bq−1 L。在未来,优化设计以减少本底,应达到最低可探测放射性1,500 Bq L−1,这是核电站运营商设定的排放标准。通过本研究,为研制这种实用的氚化监测仪提供了基础数据。
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引用次数: 0
Two-dimensional elemental mapping of simulated fuel debris using laser-induced breakdown spectroscopy 利用激光诱导击穿光谱对模拟燃料碎片进行二维元素映射
4区 工程技术 Q2 NUCLEAR SCIENCE & TECHNOLOGY Pub Date : 2023-09-21 DOI: 10.1080/00223131.2023.2255186
Munkhbat Batsaikhan, Katsuaki Akaoka, Morihisa Saeki, Takahiro Karino, Hironori Ohba, Ikuo Wakaida
ABSTRACTIn this experimental study, two-dimensional elemental mapping of simulated fuel debris was conducted by laser-induced breakdown spectroscopy (LIBS). Since the real fuel debris was unavailable as a sample, simulated fuel debris was prepared from predicted materials including compounds and metals. An Nd:YAG laser at the second harmonic was used to generate plasma on the sample surface, and the optical emission from plasma was detected using an echelle spectrometer in the visible wavelength range from 435 to 650 nm. Due to the size and complexity of the collected dataset, the conventional data analysis method was ineffective; consequently, there arose a need to design a new data analysis method for study purposes. Therefore, in the present study, a method that is based on label-free chemometric methods, such as Principal Component Analysis and Multivariate Curve Resolution-Alternative Least Square methods, were implemented to obtain the spatial and spectral information regarding each constituent within the simulated sample. The study results demonstrated that the integration of LIBS and chemometric methods is a highly effective tool to obtain qualitative information regarding samples (e.g. fuel debris) with little or no prior knowledge.KEYWORDS: Laser-induced breakdown spectroscopychemometricsFukushima Daiichi nuclear power stationnuclear fuel debris Disclosure statementNo potential conflict of interest was reported by the author(s).
摘要采用激光诱导击穿光谱(LIBS)技术对模拟燃料碎片进行二维元素映射。由于无法获得真实的燃料碎片作为样本,因此用化合物和金属等预测材料制备模拟燃料碎片。利用二次谐波Nd:YAG激光器在样品表面产生等离子体,利用梯形光谱仪在435 ~ 650 nm可见波长范围内检测等离子体的光发射。由于采集数据的规模和复杂性,传统的数据分析方法是无效的;因此,有必要为研究目的设计一种新的数据分析方法。因此,在本研究中,基于无标记化学计量学方法,如主成分分析和多元曲线分辨率-可选最小二乘法,实现了一种方法,以获得模拟样品中每个成分的空间和光谱信息。研究结果表明,LIBS和化学计量学方法的集成是一种非常有效的工具,可以在很少或没有先验知识的情况下获得有关样品(例如燃料碎屑)的定性信息。关键词:激光诱导击穿光谱化学计量福岛第一核电站核燃料碎片披露声明作者未报告潜在利益冲突。
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引用次数: 0
Development of nuclear de-excitation model EBITEM Ver.2 核去激励模型EBITEM Ver.2的建立
4区 工程技术 Q2 NUCLEAR SCIENCE & TECHNOLOGY Pub Date : 2023-09-21 DOI: 10.1080/00223131.2023.2261932
T. Ogawa, S. Hashimoto, T. Sato
ABSTRACTThe gamma de-excitation model of the general-purpose radiation transport code Particle and Heavy Ion Transport code System, called the Evaluated Nuclear Structure Data File (ENSDF)-Based Isomeric Transition and isomEr production Model (EBITEM) has been upgraded with a focus on precise neutron-capture reaction simulation. The first de-excitation subsequent to neutron capture of numerous nuclei, which was formerly simulated by a model based on the single particle model, is calculated using the Evaluated Gamma Activation File. The database used for further de-excitation, ENSDF, retrieved in 2013, was replaced with Reference Input Parameter Library 3 to consider internal conversion. The internal conversion process was interfaced with the atomic de-excitation model to assess the emission of Auger electrons and fluorescent X-rays. The spectra of gamma-rays from neutron-capture reactions calculated by the upgraded EBITEM correlate better with the evaluated cross-section data than those of the previous version.KEYWORDS: Nuclear de-excitationprompt-gamma raysinternal conversion electronsfluorescent X-raysAuger electronsneutron captureEGAFRIPL-3EBITEMPHITS AcknowledgmentsT.O. would like to appreciate Dr. Camilo Cordero Ramirez and Dr. Cdric Jouanne of CEA (French Alternative Energies and Atomic Energy Commission) for useful discussions on the gamma de-excitation simulation algorithms and relevant databases. Also, T.O. would like to thank Dr. Koji Niita of RIST for his useful suggestions on the model development.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was partly supported by JSPS KAKENHI grant number [18K14159].
摘要对通用辐射输运码粒子和重离子输运码系统的伽马去激发模型,即基于核结构数据文件(ENSDF)的异构体跃迁和异构体产生模型(EBITEM)进行了升级,重点是精确的中子捕获反应模拟。许多原子核捕获中子后的第一次去激发,以前是由基于单粒子模型的模型模拟的,现在使用评估伽马激活文件计算。为了考虑内部转换,将2013年检索到的用于进一步去激励的数据库ENSDF替换为参考输入参数库3。内部转换过程与原子去激发模型相结合,以评估俄歇电子和荧光x射线的发射。改进后的EBITEM计算的中子捕获反应的伽马射线谱与计算截面数据的相关性较好。关键词:核去激发,伽马射线,内转换电子,荧光x射线,激发态电子,中子捕获,egafripl - 3ebitps感谢CEA(法国替代能源和原子能委员会)的Camilo Cordero Ramirez博士和Cdric Jouanne博士就伽马去激励模拟算法和相关数据库进行了有益的讨论。同时,T.O.要感谢ist的Koji Niita博士对模型开发提出的有用建议。披露声明作者未报告潜在的利益冲突。本研究得到了JSPS KAKENHI基金号[18K14159]的部分支持。
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引用次数: 0
Isoplethal study of phase formation and morphology in uranium-304L steel via scanning electron microscopy 扫描电镜对铀- 304l钢相形成和形貌的等密度研究
4区 工程技术 Q2 NUCLEAR SCIENCE & TECHNOLOGY Pub Date : 2023-09-19 DOI: 10.1080/00223131.2023.2252823
Andrew Miskowiec, Zachary E. Brubaker, Jenn Neu, J. L. Niedziela, Liam Collins, Alexander Braatz
ABSTRACTUnderstanding the formation of uranium alloys with steel is important to advance nuclear technologies involving U metal fuels and machining U metal, and for nuclear forensics applications. No known phase diagram for the quaternary U-(M = Fe, Ni, Cr) system exists. We synthesize samples of U-304 L steel (nominal composition 70.1:18.3:10.4 at% Fe:Cr:Ni) across the U composition range 4.45—63.35 at%U by arc melting under inert conditions. Using the binary UFe phase diagram as a reference, we identify four U-steel alloy phases. We find the known U-Fe analogue phases UM2 and U6M, and two low-U composition phases with nominal compositions UM10 and U2M7. We apply a correlation length analysis to backscatter scanning electron microscopy images of sectioned and polished cross sections to quantify the domain formation length scale. We demonstrate that these depend heavily on the initial composition and range from 30 nm to 1.5 µm. This result, in particular, could be applicable to theoretical predictions of transport properties. Furthering our understanding of U alloy phase formation with important structural elements such as steel primaries is foundational in developing future nuclear technology.Footnote1KEYWORDS: Uraniumsteelalloyselectron microscopyphase morphology Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. This manuscript has been authored by UT-Battelle LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).2. Fs, or “fissium”, was a combination of noble metal fission products.Additional informationFundingThe work was supported by the National Nuclear Security Administration.
摘要了解铀合金与钢的形成对推进铀金属燃料和加工铀金属的核技术以及核法医应用具有重要意义。尚没有已知的U-(M = Fe, Ni, Cr)系相图。在惰性条件下,通过电弧熔炼合成了U-304 L钢样品(标称成分为70.1:18:10.4 at% Fe:Cr:Ni), U成分范围为4.45-63.35 at%U。以二元UFe相图为参考,确定了u -钢合金的四种相。我们发现了已知的U-Fe模拟相UM2和U6M,以及两个标称成分UM10和U2M7的低u组成相。我们应用相关长度分析的后向散射扫描电子显微镜图像的切片和抛光截面量化域形成长度尺度。我们证明,这些在很大程度上取决于初始成分,范围从30 nm到1.5µm。这一结果尤其适用于输运性质的理论预测。进一步了解含有重要结构元素(如钢初等)的U合金相形成是发展未来核技术的基础。脚注1关键词:铀、钢、合金、选择、电镜、相形貌披露声明作者未发现潜在的利益冲突。本手稿由UT-Battelle LLC根据合同编号。DE-AC05-00OR22725与美国能源部(DOE)。美国政府保留和出版商,通过接受文章的出版,承认美国政府保留非排他性的,付费的,不可撤销的,全球许可,以出版或复制该手稿的出版形式,或允许其他人这样做,为美国政府的目的。美国能源部将根据美国能源部公共访问计划(http://energy.gov/downloads/doe-public-access-plan).2)向公众提供这些由联邦政府资助的研究成果。f,或“裂变”,是贵金属裂变产物的组合。本研究得到了国家核安全局的支持。
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引用次数: 0
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Journal of Nuclear Science and Technology
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