Annals of Nuclear Energy最新文献

{"title":"Dissolved oxygen concentration control and prediction modelling for liquid LBE loop: UPBEAT loop","authors":"Ruixian Liang,&nbsp;Wei Mao,&nbsp;Xiangtian Hou,&nbsp;Zulong Hao,&nbsp;Haicai Lyu,&nbsp;Hao Wu,&nbsp;Huiping Zhu,&nbsp;Fang Liu,&nbsp;Yang Liu,&nbsp;Fenglei Niu","doi":"10.1016/j.anucene.2026.112140","DOIUrl":"10.1016/j.anucene.2026.112140","url":null,"abstract":"<div><div>The application of liquid lead–bismuth eutectic (LBE) alloy coolant technology necessitates the implementation of real-time monitoring of dissolved oxygen concentration. Furthermore, in accordance with operational requirements, the dissolved oxygen concentration within the liquid LBE must be maintained within a reasonable target range. In order to enhance the provision of rapid and efficient oxygen replenishment to the liquid LBE loop, the oxygen supply behavior of the mass exchanger (MX) was modelled. The development of the oxygen control model of the MX was achieved by the collection of input (MX temperature) and output (signal of oxygen sensor) data from the solid-phase oxygen control experiments in the liquid LBE recirculation loop. The least squares method and neural network algorithm were utilised in the development of the oxygen control model, respectively. The findings demonstrate the efficacy of the oxygen control model in predicting the dissolved oxygen concentration within the LBE loop. This provides a theoretical framework for the subsequent optimisation of the solid-phase oxygen control strategy within the LBE system.</div></div>","PeriodicalId":8006,"journal":{"name":"Annals of Nuclear Energy","volume":"230 ","pages":"Article 112140"},"PeriodicalIF":2.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and improvement of the quadratic depletion method","authors":"Runze Liu,&nbsp;Zhouyu Liu,&nbsp;Xiaoqing Liang,&nbsp;Liangzhi Cao,&nbsp;Hongchun Wu","doi":"10.1016/j.anucene.2026.112179","DOIUrl":"10.1016/j.anucene.2026.112179","url":null,"abstract":"<div><div>In nuclear reactor depletion calculations, the conventional predictor–corrector (PC) method requires two neutron transport calculations per burnup step. However, in high-fidelity calculations, these two transport calculations significantly reduce computational efficiency, especially for reactors reloaded with gadolinium (Gd)-bearing fuels, which require much finer depletion steps. The quadratic depletion (QD) method skips the corrector-step transport calculation and employs quadratic interpolation combined with a post-correction method to preserve accuracy. Nevertheless, the traditional QD method requires an excessive number of substeps to maintain precision for Gd-bearing systems, leading to high computational costs. To address this issue, a novel two-level predictor–corrector QD (TL-PC-QD) method is proposed to reduce the number of substeps. Comparative applications to Gd-bearing assembly and whole-core cases show that the TL-PC-QD method significantly reduces the number of substeps of the traditional QD method.</div></div>","PeriodicalId":8006,"journal":{"name":"Annals of Nuclear Energy","volume":"230 ","pages":"Article 112179"},"PeriodicalIF":2.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146185042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinvestigating the off-grid project priorities of small-scale nuclear reactors using an enhanced integrated fuzzy decision support system","authors":"Serhat Yüksel ,&nbsp;Hasan Dinçer ,&nbsp;Merve Acar ,&nbsp;Edanur Ergün ,&nbsp;Serkan Eti","doi":"10.1016/j.anucene.2026.112160","DOIUrl":"10.1016/j.anucene.2026.112160","url":null,"abstract":"<div><div>Small-scale nuclear reactors represent a promising option for providing reliable and continuous energy in off-grid and isolated regions; however, limited investment budgets necessitate a clear prioritization of project objectives. This study addresses the need to identify the most critical project priorities and the most suitable off-grid energy applications for small-scale nuclear reactor deployments. To this end, a novel decision-making framework is developed by integrating artificial intelligence-based expert weighting with advanced fuzzy modeling techniques to effectively manage uncertainty and incomplete evaluations. The proposed approach enables a systematic assessment of strategic priorities without being tied to a specific reactor technology. The results indicate that security supported by passive safety systems is the most influential project priority, followed by cost effectiveness and operational flexibility. When alternative off-grid applications are evaluated, steady energy supply for rural industry fields emerges as the most appropriate option due to its strong and balanced performance across safety, economic, and operational dimensions. These findings highlight the interdependence between technical design considerations and application-level decisions. Overall, the study provides practical insights for policymakers and project managers by identifying strategic priorities that can enhance the effectiveness, feasibility, and long-term viability of small-scale nuclear energy investments in off-grid contexts.</div></div>","PeriodicalId":8006,"journal":{"name":"Annals of Nuclear Energy","volume":"230 ","pages":"Article 112160"},"PeriodicalIF":2.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial neural network approach for optimizing X-ray shielding by leveraging the photoelectric absorption edge","authors":"M.R. Alipoor,&nbsp;M. Eshghi","doi":"10.1016/j.anucene.2026.112142","DOIUrl":"10.1016/j.anucene.2026.112142","url":null,"abstract":"<div><div>The growing use of ionizing radiation necessitates efficient, non-toxic, and lightweight shielding materials. This research employs an Artificial Neural Network refined with Stochastic Gradient Descent to predict the X-ray attenuation of lead-free composites. The model estimates the linear attenuation coefficient based on material density, photon energy, and absorption edge energy. Validation against Geant4 simulations showed excellent accuracy with an average difference of ±0.4 and achieved a nearly perfect regression fit (R² = 0.999). The optimized composites developed using this model achieved superior attenuation, surpassing 99.9% at 40 keV and 92.7% at 120 keV with a mere 1 mm thickness. This study confirms that a physics-informed machine learning approach can rapidly develop high-performance, lead-free shielding for medical and industrial applications.</div></div>","PeriodicalId":8006,"journal":{"name":"Annals of Nuclear Energy","volume":"230 ","pages":"Article 112142"},"PeriodicalIF":2.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and simulation of heavy reflector PWR-Core using the NECP-Bamboo software","authors":"Junwei Qin,&nbsp;Yunzhao Li,&nbsp;Liangzhi Cao,&nbsp;Hongchun Wu","doi":"10.1016/j.anucene.2026.112178","DOIUrl":"10.1016/j.anucene.2026.112178","url":null,"abstract":"<div><div>At present, the two-step method based on homogenization theory still remains the main force for reactor core physics analysis in PWRs. Among those, the heavy reflectors differ significantly from traditional baffle reflectors in terms of physical properties, primarily due to the large number of materials containing intermediate-mass nuclides in the external structure. This study conducts simulation research on heavy reflector PWRs by using NECP-Bamboo, a PWR-core fuel management code system developed by the NECP Laboratory of Xi’an Jiaotong University. It incorporates multiple calculation schemes for neutronics calculation: the coarse-mesh diffusion two-step method, the pin-by-pin SP₃ two-step method, and more recently, the newly proposed pin-by-pin P₁ two-step method. It has undergone extensive verification against measured data from in-service commercial PWRs with baffle reflectors, which demonstrates its accuracy and reliability. Numerical results indicate improved accuracy when using the pin-by-pin two-step method compared to conventional approaches. For the three-dimensional PWR problem with heavy reflectors, it reduces the eigenvalue bias from approximately 300 pcm to around 150 pcm, and lowers the maximum bias of the assembly power distribution from roughly 6% to about 4%. These improvements further demonstrate the accuracy advantages of the pin-by-pin two-step method. Additionally, all three calculation methods can meet the requirements of engineering limits, which confirms that NECP-Bamboo is applicable to the core physics analysis of PWRs with heavy reflector.</div></div>","PeriodicalId":8006,"journal":{"name":"Annals of Nuclear Energy","volume":"231 ","pages":"Article 112178"},"PeriodicalIF":2.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146186143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new visual method for measuring the melting point of uranium dioxide","authors":"Juan Xu ,&nbsp;Junhua Shen ,&nbsp;Hong Wang ,&nbsp;Ying Meng ,&nbsp;Yiming Gao","doi":"10.1016/j.anucene.2026.112146","DOIUrl":"10.1016/j.anucene.2026.112146","url":null,"abstract":"<div><div>In order to accurately measure the melting point of uranium dioxide, a novel visual method was developed. Uranium dioxide was heated using a fiber laser, with temperature monitored by a coaxially aligned 2-color pyrometer-Ⅱ. The temperature and melting process of uranium dioxide were observed using a 2-color pyrometer-Ⅰ equipped with a CCD camera. The melting point was determined based on the abrupt changes in the temperature curve of uranium dioxide, as well as the visual observations of the melting process captured by the CCD camera. The melting point of uranium dioxide was measured at 2855.6 ℃, while the melting point of molybdenum (Mo) was recorded at 2609.2 ℃. These values closely align with those reported in the literature.</div></div>","PeriodicalId":8006,"journal":{"name":"Annals of Nuclear Energy","volume":"230 ","pages":"Article 112146"},"PeriodicalIF":2.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aerosol generation characteristics during laser cutting of carbon and stainless steel surfaces for nuclear power plant decommissioning","authors":"Avadhesh Kumar Sharma ,&nbsp;Ruicong Xu ,&nbsp;Zeeshan Ahmed ,&nbsp;Ravinder Kumar ,&nbsp;Shuichiro Miwa ,&nbsp;Shunichi Suzuki","doi":"10.1016/j.anucene.2026.112156","DOIUrl":"10.1016/j.anucene.2026.112156","url":null,"abstract":"<div><div>The decommissioning of the Fukushima Daiichi Nuclear Power Plant presents significant challenges due to high radiation levels and the need for safe and efficient fuel debris retrieval. Laser cutting is a promising technique for decontamination and dismantling, but it generates submicron-sized radioactive aerosols, necessitating precise aerosol management strategies. This study investigates aerosol generation during laser cutting of carbon steel (CS) and stainless steel (SS) surfaces under varying power levels and surface coatings at the Mitsubishi Heavy Industries Ltd. (MHI) Research &amp; Innovation Center utilizing class-4 laser. Experimental results indicate that increasing laser power leads to higher aerosol concentrations, particularly for larger aerosols, while smaller aerosol concentrations decline. This effect is more pronounced in CS surfaces than in SS. Coated surfaces, especially with zirconium dioxide (ZrO₂), exhibit higher aerosol generation at elevated power levels, suggesting an intensified laser-material interaction. The experimental results highlight the role of coating composition in aerosol generation and importance of dispersion control methods during decommissioning. The analysis of aerosol dispersion results can give insight to enhance radiation worker safety, protect sensitive electronics, and improve the effectiveness of remote laser-based decontamination in high-dose environments.</div></div>","PeriodicalId":8006,"journal":{"name":"Annals of Nuclear Energy","volume":"230 ","pages":"Article 112156"},"PeriodicalIF":2.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of tantalum–tungsten–oxygen compounds as lead-free radiation shielding materials","authors":"H.C. Manjunatha ,&nbsp;B.M. Sankarshan ,&nbsp;P.S. Damodara Gupta ,&nbsp;L. Seenappa ,&nbsp;K.N. Sridhar ,&nbsp;R. Munirathnam","doi":"10.1016/j.anucene.2026.112141","DOIUrl":"10.1016/j.anucene.2026.112141","url":null,"abstract":"<div><div>This study investigates alternative materials to lead for radiation shielding, addressing the need for safer and more effective options. Traditional materials like lead, although effective due to their high atomic number, are toxic and pose environmental risks. The study explores a set of tantalum–tungsten–oxygen (Ta–W–O) compounds, including TaW<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>, TaW<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O<span><math><msub><mrow></mrow><mrow><mn>4</mn></mrow></msub></math></span>, Ta<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>W<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O<span><math><msub><mrow></mrow><mrow><mn>5</mn></mrow></msub></math></span>, TaWO<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>, and others. These compounds offer promising shielding properties due to their high density, atomic number, and stability. Key shielding parameters such as mass attenuation coefficient (MAC), linear attenuation coefficient (LAC), half-value layer (HVL), and effective atomic number (Z<span><math><msub><mrow></mrow><mrow><mtext>eff</mtext></mrow></msub></math></span>) were calculated and compared to lead. Among all the studied Ta–W–O compounds, TaW<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O₃ was identified as the most efficient and thermodynamically stable lead-free shielding material, exhibiting the highest photon attenuation performance across low- and intermediate-energy ranges. Across various energy ranges, these compounds demonstrate superior radiation protection efficiency (RPE) and electron density, essential for shielding in healthcare, nuclear, and aerospace applications. The findings suggest that tantalum–tungsten compounds could serve as viable lead-free shielding materials, offering a safer and more sustainable alternative for radiation protection.</div></div>","PeriodicalId":8006,"journal":{"name":"Annals of Nuclear Energy","volume":"230 ","pages":"Article 112141"},"PeriodicalIF":2.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the nuclear fuel cycle under global capacity expansion scenarios toward 2050","authors":"Iván Merino Rodríguez ,&nbsp;Bairon Faundes ,&nbsp;Yerko Véliz ,&nbsp;Pablo Romojaro ,&nbsp;Victor J. Casas-Molina ,&nbsp;Francisco Álvarez-Velarde","doi":"10.1016/j.anucene.2026.112175","DOIUrl":"10.1016/j.anucene.2026.112175","url":null,"abstract":"<div><div>This study examines the implications of tripling global nuclear capacity by 2050 on the nuclear fuel cycle, based on national projections and COP28 climate commitments. Regionally disaggregated electricity scenarios were generated and used as inputs for the ANICCA simulation code, applying Monte Carlo methods to assess uncertainty in fuel cycle metrics. Three strategies were analyzed: open cycle, partially closed cycle (Pu mono-recycling in LWRs), and advanced closed cycle (Pu and MA multi-recycling in LFRs).</div><div>Results show that the open cycle could require about 15 million tons of natural uranium by 2100, surpassing identified reserves. Pu mono-recycling reduces uranium and enrichment needs by ∼9% and achieves Pu balance post-2050. The advanced cycle cuts minor actinide accumulation by ∼50%, easing long-term repository burdens.</div><div>These results highlight the need to explore advanced fuel cycles and expand infrastructure for reprocessing, MOX fabrication, and waste management to meet sustainability goals under high nuclear deployment scenarios.</div></div>","PeriodicalId":8006,"journal":{"name":"Annals of Nuclear Energy","volume":"230 ","pages":"Article 112175"},"PeriodicalIF":2.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian-optimized, feature-augmented deep ensemble for physics-guided critical heat-flux prediction with uncertainty quantification","authors":"Zaid Abulawi,&nbsp;Doyeong Lim,&nbsp;Abhiram Garimidi,&nbsp;Yang Liu","doi":"10.1016/j.anucene.2026.112139","DOIUrl":"10.1016/j.anucene.2026.112139","url":null,"abstract":"<div><div>Accurate prediction of the critical heat flux (CHF) is a crucial design and safety consideration for a wide range of high-performance thermal systems, including water-cooled nuclear reactors. Traditional predictive tools, such as empirical correlations and look-up tables, often lack accuracy when extrapolated or at different interpolation regions. To overcome these limitations, this work introduces a novel physics-guided, optimized deep-ensemble framework for robust CHF prediction with comprehensive uncertainty quantification. Our approach first expands the model’s inputs by augmenting base thermal-hydraulic parameters with physics-based features derived from established correlations. This feature engineering injects domain knowledge, constraining the solution space and promoting convergence to physically plausible solutions. Furthermore, we employ a sophisticated hyperparameter optimization strategy, combining a Sobol sequence with Bayesian optimization, to systematically select a diverse and high-performing set of neural networks for the ensemble. The resulting physics-guided ensemble demonstrates superior performance across all metrics compared to a baseline ensemble, a standard look-up table, and a benchmark neural network. The model produces smoother, more physically consistent predictive trends and provides reliable uncertainty estimates. This framework offers a powerful and broadly applicable tool for CHF prediction, enabling higher-fidelity safety margins and the design of more efficient and reliable thermal management systems.</div></div>","PeriodicalId":8006,"journal":{"name":"Annals of Nuclear Energy","volume":"230 ","pages":"Article 112139"},"PeriodicalIF":2.3,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}