Search Results (353)

This study proposes the SMR Smart Net-Zero City (SSNC) framework—a scalable model for achieving carbon neutrality by integrating Small Modular Reactors (SMRs), renewable energy sources, and sector coupling within a microgrid architecture. As deploying renewables alone would require economically and technically impractical energy storage systems, SMRs provide a reliable and flexible baseload power source. Sector coupling systems—such as hydrogen production and heat generation—enhance grid stability by absorbing surplus energy and supporting the decarbonization of non-electric sectors. The core contribution of this study lies in its real-time data emulation framework, which overcomes a critical limitation in the current energy landscape: the absence of operational data for future technologies such as SMRs and their coupled hydrogen production systems. As these technologies are still in the pre-commercial stage, direct physical integration and validation are not yet feasible. To address this, the researchers leveraged real-time data from an existing commercial microgrid, specifically focusing on the import of grid electricity during energy shortfalls and export during solar surpluses. These patterns were repurposed to simulate the real-time operational behavior of future SMRs (ProxySMR) and sector coupling loads. This physically grounded simulation approach enables high-fidelity approximation of unavailable technologies and introduces a novel methodology to characterize their dynamic response within operational contexts. A key element of the SSNC control logic is a day–night strategy: maximum SMR output and minimal hydrogen production at night, and minimal SMR output with maximum hydrogen production during the day—balancing supply and demand while maintaining high SMR utilization for economic efficiency. The SSNC testbed was validated through a seven-day continuous operation in Busan, demonstrating stable performance and approximately 75% SMR utilization, thereby supporting the feasibility of this proxy-based method. Importantly, to the best of our knowledge, this study represents the first publicly reported attempt to emulate the real-time dynamics of a net-zero city concept based on not-yet-commercial SMRs and sector coupling systems using live operational data. This simulation-based framework offers a forward-looking, data-driven pathway to inform the development and control of next-generation carbon-neutral energy systems. Full article

Nuclear energy can help stop climate change by generating large amounts of emission-free electricity. Nuclear reactor designs are continually being developed to be more fuel efficient, safer, easier to construct, and to produce less nuclear waste. The term advanced nuclear reactors refers either to Generation III+ and Generation IV or small modular reactors. Every reactor is associated with the nuclear fuel cycle that must be economically viable and competitive. An important matter is optimization of fissile materials used in reactor and/or reprocessing of spent fuel and reuse. Currently operating reactors use the open cycle or partially closed cycle. Generation IV reactors are intended to play a significant role in reaching a fully closed cycle. At the same time, we can observe the growing interest in development of small modular reactors worldwide. SMRs can adopt either fuel cycle; they can be flexible depending on their design and fuel type. Spent nuclear fuel management should be an integral part of the development of new reactors. The proper management methods of the radioactive waste and spent fuel should be considered at an early stage of construction. The aim of this paper is to highlight the challenges related to reprocessing of new forms of nuclear fuel. Full article

The ongoing efforts to convert High-Performance Research Reactors (HPRRs) using Highly Enriched Uranium (HEU) to Low-Enriched Uranium (LEU) fuel require reliable thermal–hydraulic assessments of modified core designs. The involute-shaped fuel plates used in several major HPRRs present unique modeling challenges due to their compact core geometries and high heat flux conditions. This study evaluates the capability of three commercial CFD tools, STAR-CCM+, COMSOL, and ANSYS CFX, to predict cladding-to-coolant heat transfer using Reynolds-Averaged Navier–Stokes (RANS) methods within the thermal–hydraulic regimes of involute-shaped plate reactors. Broad sensitivity analysis was conducted across a range of reactor-relevant parameters using two turbulence models ($k-ϵ$ and $k-\omega$ SST) and different near-wall treatment strategies. The results were benchmarked against the Sieder–Tate correlation and experimental data from historic studies. The codes produced consistent results, showing good agreement with the empirical correlation of Sieder–Tate and the experimental measurements. The findings support the use of these commercial CFD codes as effective tools for assessing the thermal–hydraulic performance of involute-shaped plate HPRRs and guide future LEU core development. Full article

Small modular reactors are progressing towards greater levels of automation and intelligence, with intelligent control emerging as a pivotal trend in SMR development. When contrasted with traditional commercial nuclear power plants, SMR display substantial disparities in design parameters and the designs of safety auxiliary systems. As a result, fault diagnosis systems tailored for commercial nuclear power plants are ill-equipped for SMRs. This study utilizes the PCTRAN-SMR V1.0 software to develop an intelligent fault diagnosis system for the SMART small modular reactor based on an attention mechanism. By comparing different network models, it is demonstrated that the CNN–LSTM–Attention model with an attention mechanism significantly outperforms CNN, LSTM, and CNN–LSTM models, achieving up to a 7% improvement in prediction accuracy. These results clearly indicate that incorporating an attention mechanism can effectively enhance the performance of deep learning models in nuclear power plant fault diagnosis. Full article

The lead-cooled fast reactor (LFR), a Generation IV nuclear system candidate, presents unique neutronic characteristics distinct from pressurized water reactors. Its neutron spectrum spans wider energy ranges with fast neutron dominance, exhibiting resonance phenomena across energy regions. These features require a fine energy group structure for fuel lattice calculations, significantly increasing computational demands. To balance local heterogeneity modeling with computational efficiency, researchers across the world adopt fuel assembly equivalence methods using 1D cylindrical models through volume equivalence principles. This approach enables detailed energy group calculations in simplified geometries, followed by lattice homogenization for few-group parameter generation, effectively reducing whole-core computational loads. However, limitations emerge when handling strongly heterogeneous components like structural/control rods. This study investigates the 1D equivalence method’s accuracy in lead–bismuth fast reactors under various fuel assembly configurations. Through comprehensive analysis of material distributions and their equivalence impacts, the applicability of the one-dimensional equivalence approach to fuel assemblies of different geometries and material types is analyzed in this paper. The research further proposes corrective solutions for low-accuracy scenarios, enhancing computational method reliability. This paper is significant in its optimization of the physical calculation and analysis process of a new type of fast reactor component and has important engineering application value. Full article

As a promising nuclear technology, molten salt reactors (MSRs) have a bright future in the energy sector due to their unique advantages such as high efficiency, safety, and fuel flexibility. However, the accurate analysis of fission products in MSRs requires reliable fission yield data. Current reactor burnup analysis often uses the ORIGEN2 code, whose fission yield libraries mainly originate from the outdated 1970s ENDF/B-VI nuclear database, thus risking data obsolescence. This study evaluates ORIGEN2’s fission yield libraries (THERMAL, PWRU, PWRU50) against the modern ENDF/B-VIII.0 library. Through a comprehensive comparative analysis of Oak Ridge National Laboratory’s Molten Salt Reactor Experiment (MSRE) model, numerical simulations reveal library-dependent differences in MSR burnup characteristics. The PWRU library best matches ENDF/B-VIII.0 for U-235-fueled cases in keff results, while the PWRU50 library has minimal keff deviation in U-233-fueled setups. Moreover, in both fuel cases, the fission yield library was found to significantly affect the activity of key radionuclides, including Kr-85, Kr-85m, I-133m, Cs-136, Sn-123, Sn-125, Sn-127, Sb-124, Sb-125, Cd-115m, Te-125m, Te-129m, etc. Additionally, the fission gas decay heat power calculated via the ORIGEN2 library is over 20% lower than that from the ENDF/B-VIII.0 library tens of days after shutdown, mainly due to differences in long-lived Kr-85 production. These findings highlight the need to update traditional fission yield libraries in burnup codes. For next-generation MSR designs, this is crucial to ensure accurate safety assessments and the effective development of this promising energy technology. Full article

This article is devoted to an overview of the conducted work and the current status of decommissioning of the world’s first BN-350 industrial fast neutron reactor. The reactor was put into operation on 16 July 1973 in Aktau. In 1999, the government of Kazakhstan decided to shut down the reactor, and from that moment to the present, it has been in the decommissioning stage. All work on decommissioning the reactor facility was grouped into five stages. The first stage was completed in 2010 when the spent fuel of the BN-350 reactor was placed for long-term storage. The second stage is nearing completion. Research is currently underway to develop technologies for processing radioactive sodium. The goal of the third and fourth stages of the BN-350 reactor decommissioning is the comprehensive processing of liquid and solid radioactive waste. Now such waste is stored in special storage directly on the territory of the nuclear power plant. Full article

The Volumetric Neutron Source (VNS) is a pivotal facility proposed for advancing fusion nuclear technology, particularly for the qualification of breeding blanket systems, a key component of DEMO and future fusion reactors. This study focuses on the design and optimisation of the VNS Thermal Shield, adopting a multidisciplinary approach to address its thermal and structural behaviours. The Thermal Shield plays a crucial role in protecting superconducting magnets and other cryogenic components by limiting heat transfer from higher-temperature regions of the tokamak to the cryostat, which operates at temperatures between 4 K and 20 K. To ensure both thermal insulation and structural integrity, multiple design iterations were conducted. These iterations aimed to reduce electromagnetic (EM) forces induced during magnet charge and discharge cycles by introducing strategic cuts and reinforcements in the shield design. The optimisation process included the evaluation of various aluminium alloys and composite materials to achieve a balance between rigidity and weight while maintaining structural integrity under EM and mechanical loads. Additionally, an integrated thermal study was performed to ensure effective temperature management, maintaining the shield at an operational temperature of around 80 K. Cooling channels were incorporated to homogenise temperature distribution, improving thermal stability and reducing thermal gradients. This comprehensive approach demonstrates the viability of advanced material solutions and design strategies for thermal and structural optimisation. The findings reinforce the importance of the VNS as a dedicated platform for testing and validating critical fusion technologies under operationally relevant conditions. Full article

This computational study using MCNP5 evaluated the feasibility of replacing 6061-T6 aluminum with 316L stainless steel (SS-316L) for the tubes hosting the uranium slugs in the subcritical nuclear reactor Nuclear Chicago model 9000, thereby contributing to its preservation as a key resource for nuclear research and education in Mexico. Simulations and dosimetric analyses (ICRP/ICRU) confirmed subcriticality in both configurations. Notably, SS-316L demonstrated an effective attenuation of peripheral gamma radiation and a reduction in the ambient neutron dose, indicating a considerable improvement in radiological safety. Although a reduction in thermal and epithermal neutron fluence was observed, the similarity in the gamma spectrum suggests no significant alteration for gamma spectroscopic experiments. In conclusion, SS-316L presents a promising alternative that enhances radiological safety and reactor longevity, making it a worthy consideration as a replacement material. Further experimental investigation is recommended to assess material activation and the gamma dose in the vicinity of the fuel. Full article

Overload detection is crucial in nuclear fusion experiments to prevent damage to plasma-facing components (PFCs) and ensure the safe operation of the reactor. At Wendelstein 7-X (W7-X), real-time monitoring and prediction of thermal events are essential for maintaining the integrity of PFCs. This paper proposes a machine learning approach for developing a real-time overload detector, trained and tested on OP1.2a experimental data. The analysis showed that Self-Organizing Maps (SOMs) are efficient in detecting the overload risk starting from a set of plasma parameters that describe the magnetic configuration, the energy behavior, and the power balance. This study aims to thoroughly evaluate the capabilities of the SOM in recognizing overload risk levels, defined by quantizing the maximum criticality across different IR cameras. The goal is to enable detailed monitoring for overload prevention while maintaining high-performance plasmas and sustaining long pulse operations. The SOM proves to be a highly effective overload risk detector. It correctly identifies the assigned overload risk level in 87.52% of the samples. The most frequent error in the test set, occurring in 10.46% of cases, involves assigning a risk level to each sample adjacent to the target one. The analysis of the results highlights the advantages and drawbacks of criticality discretization and opens new solutions to improve the SOM potential in this field. Full article

In support of global efforts to strengthen the nuclear non-proliferation regime, the IVG.1M research water-cooled thermal reactor at the National Nuclear Center of the Republic of Kazakhstan was successfully converted to low-enriched uranium (LEU, 19.75% ²³⁵U) fuel in 2023. The reactor’s operability with innovative bimetallic, fiber-type, dual-blade LEU fuel rods was experimentally verified during power start-up experiments. The test program included investigations of power distribution in the core, evaluation of temperature, power, and hydrodynamic reactivity effects, and the measurement of fission product release to the coolant. The results were in good agreement with safety calculations, confirming that the enrichment reduction did not degrade reactor performance characteristics. It was shown that the power reactivity effect increased by more than 1.5 times at a power level of 9 MW. The measured temperature reactivity coefficient (≈0.021 β_eff/°C) and the level of fission product release remained within acceptable and expected limits. Full article

Effectively monitoring the operational status of reactor coolant pumps (RCPs) is crucial for enhancing the safety and stability of nuclear power operations. To address the challenges of limited interpretability and suboptimal detection performance in existing methods for detecting abnormal operating states of RCPs, this paper proposes an interpretable, unsupervised anomaly detection approach. This innovative method designs a framework that combines Kernel Self-Organizing Map (Kernel SOM) clustering with Bayesian Posterior Inference. Specifically, the proposed method uses Kernel SOM to extract typical patterns from normal operation data. Subsequently, a distance probability distribution model reflecting the data distribution structure within each cluster is constructed, providing a robust tool for data distribution analysis for anomaly detection. Finally, based on prior knowledge, such as distance probability distribution, the Bayesian Posterior Inference is employed to infer the probability of the equipment being in a normal state. By constructing distribution models that reflect data distribution structures and combining them with posterior inference, this approach realizes the traceability and interpretability of the anomaly detection process, improving the transparency of anomaly detection and enabling operators to understand the decision logic and the analysis of the causes of anomalous occurrences. Verification via real-world operational data demonstrates the method’s superior effectiveness. This work offers a highly interpretable solution for RCP anomaly detection, with significant implications for safety-critical applications in the nuclear energy sector. Full article

The advantages and recent advancements of the Discrete-Ordinates (S_N) Method have established its widespread adoption in particle transport calculations for nuclear energy systems. The mathematical foundations and diverse applications of the S_N method are comprehensively summarized in this review. Recent advances are critically evaluated, with particular emphasis placed on advanced discretization techniques, high-performance computing implementations, and hybrid coupling strategies with MC, MOC method, and so on. Despite these developments, challenges remain, including the need for high-fidelity simulations, optimization of computational performance, and the complexity introduced by temporal dependencies in dynamic radiation field calculations, which necessitates innovative numerical methods. Future developments of the S_N method are anticipated to address these challenges through enhanced high-fidelity numerical simulation, robust high-performance computing frameworks, multi-physics field coupling, and AI integration. These developments advance the industrial-scale implementation of the S_N method in nuclear energy applications, enabling efficient and accurate analyses of complex reactor systems. Full article

A negative value of the void reactivity coefficient (α_V) is one of the most important passive safety properties for the operation of nuclear reactor. Herein, are presented calculated values of the void reactivity coefficient for different geometries of reactors cooled by liquid lead (LFR) and sodium (SFR) with U-238-Pu-239 and Th-232-U-233 fuels. The calculations were carried out for the reactors filled with either one or two types of fuel assemblies. The most interesting results are obtained for reactor filled with two different types of fuel assemblies (hybrid reactor). Hybrid reactors consist of central and peripheral types of fuel assemblies using low enrichment fuel and high enrichment fuel, respectively. Both hybrid reactors based on the uranium cycle (U-cycle) and the thorium cycle (Th-cycle) can maintain a negative void reactivity coefficient value for wide range of reactor parameters. The calculation results of the hybrid reactor matched those from FBR-IME reactor. Full article

Nuclear energy is a cornerstone of the global energy mix, delivering reliable, low-carbon power essential for sustainable energy systems. However, the safety of nuclear reactors is critical to maintaining operational reliability and public trust, particularly during accidents like a Loss of Coolant Accident (LOCA) or a Steam Line Break Inside Containment (SLBIC). This study introduces a Bayesian Network (BN) framework used to enhance nuclear energy safety by predicting accident severity and identifying key factors that ensure energy production stability. With the integration of simulation data and physical knowledge, the BN enables dynamic inference and remains robust under missing-data conditions—common in real-time energy monitoring. Its hierarchical structure organizes variables across layers, capturing initial conditions, intermediate dynamics, and system responses vital to energy safety management. Conditional Probability Tables (CPTs), trained via Maximum Likelihood Estimation, ensure accurate modeling of relationships. The model’s resilience to missing data, achieved through marginalization, sustains predictive reliability when critical energy system variables are unavailable. Achieving R² values of 0.98 and 0.96 for the LOCA and SLBIC, respectively, the BN demonstrates high accuracy, directly supporting safer nuclear energy production. Sensitivity analysis using mutual information pinpointed critical variables—such as high-pressure injection flow (WHPI) and pressurizer level (LVPZ)—that influence accident outcomes and energy system resilience. These findings offer actionable insights for the optimization of monitoring and intervention in nuclear power plants. This study positions Bayesian Networks as a robust tool for real-time energy safety assessment, advancing the reliability and sustainability of nuclear energy production. Full article