In a power plant scale fusion reactor, a huge amount of thermal power produced by the fusion reaction and external heating must be exhausted through the narrow area of the divertor targets. The targets must withstand the intense bombardment of the diverted particles where high heat fluxes are generated and erosion takes place on the surface. A considerable amount of volumetric nuclear heating power must also be exhausted. To cope with such an unprecedented power exhaust challenge, a highly efficient cooling capacity is required. Furthermore, the divertor must fulfill other critical functions such as nuclear shielding and channeling (and compression) of exhaust gas for pumping. Assuring the structural integrity of the neutron-irradiated (thus embrittled) components is a crucial prerequisite for a reliable operation over the lifetime. Safety, maintainability, availability, waste and costs are another points of consideration. In late 2020, the Pre-Conceptual Design activities to develop the divertor of the European demonstration fusion reactor were officially concluded. On this occasion, the baseline design and the key technology options were identified and verified by the project team (EUROfusion Work Package Divertor) based on seven years of R&D efforts and endorsed by Gate Review Panel. In this paper, an overview of the load specifications, brief descriptions of the design and the highlights of the technology R&D work are presented together with the further work still needed. * Corresponding author. E-mail address: you@ipp.mpg.de (J.H. You).
BAI Bing, YANG Wen, HE Xinfu, DOU Yankun, QIN Bo, CAO Jinli, YU Bintao, GAO Jin, CAO Han, YANG Wanhuan, ZHONG Weihua, WANG Rongdong, LONG Bin, ZHU Qingfu
The development of new nuclear reactors hinges on materials. For advanced reactor materials, particularly radiation-resistant materials, the research and development process faces complex extreme environments such as high-temperature long-term exposure, neutron irradiation, and environmental corrosion. This leads to lengthy development cycles and high costs. Currently, both domestically and internationally, there is limited accumulated experience, and few references are available. Therefore, there is an urgent need for a more efficient and intelligent new paradigm to accelerate research, development, and application. This paper proposes a new paradigm for the digital-intelligent development of reactor materials, which involves the cross-iterative integration of high-fidelity multiscale simulation technology, big data and artificial intelligence technology, and efficient experimental technology, all centered around material requirements, to achieve rapid material design and optimization. The realization of digital-intelligent development for reactor materials will significantly shorten the cycle and reduce the cost from material development to application. This paper focuses on the progress made by the China Institute of Atomic Energy in the aforementioned three key technologies, as well as the application effects in the development of typical reactor materials such as refractory alloys. Finally, the challenges and opportunities faced in the future deep integration of these three technologies to form a new digital-intelligent paradigm for reactor materials are summarized and prospected.
BackgroundReactor activation-burnup calculation is a crucial component of reactor analysis, involving an iterative process that combines criticality programs with point burnup programs.PurposeThis study aims to design and develop a novel lightweight, general-purpose activation-burnup program, named LightAB (Light Activation and Burnup) for activation-burnup calculation.MethodsBurnup databases on the basis of ORIGEN-2 and ORIGEN-S were utilized and the Chebyshev rational approximation (CRAM) algorithm was implemented in LightAB for accurate burnup systems. Point burnup calculations in decay mode, constant flux mode, and constant power mode were supported by LightAB with well-structured program architecture, consisting of a solver module, an I/O module, and a burnup chain module. In the meanwhile, nuclide was used as the fundamental unit of storage, and physical quantities such as burnup database path and sub-burnup step division were specified as the input module of LightAB. Thereafter, the decay of 237Np and the irradiation of Zr under fixed-flux conditions were calculated using LightAB for accuracy validation, and various reactor burnup models, including pressurized water reactor (PWR) cell, PWR assembly, and Organisation for Economic Co-operation and Development/Nuclear Energy Agency (OECD/NEA) fast reactor models, were calculated by coupling LightAB with RMC programs. Finally, LightAB was applied to the irradiation production of transplutonium isotope with comparison to RMC.ResultsResults of LightAB are consistent with that of ORIGEN 2.1 for the calculation of 237Np's decay and Zr's irradiation. Calculation results of LightAB coupling with RMS programs are consistent with RMC calculations. The errors between LightAB and RMC for production calculation of transplutonium isotope in three cases are within 5%.ConclusionsLightAB has shown promising application prospects in the irradiation production of transplutonium isotopes compared with RMC simulation calculations.
This study introduces a novel optical coupling structure for scintillation detectors, which are critical tools in high-energy physics, nuclear engineering, medical physics, and radiation applications. The proposed design focuses on optimizing optical coupling efficiency, mechanical strength, stability, and maintainability between a light guide and a photomultiplier tube (PMT), particularly for large-area scintillators. Accordingly, a customized support structure was fabricated using fused deposition modeling to precisely align and secure the light guide and PMT. A fabricated structure using carbon-fiber-reinforced polyethylene terephthalate glycol filament was equipped with an interlocking mechanism enabling easy assembly and disassembly. To assess the long-term performance and stability of different optical coupling techniques, we evaluated the effects of reactor neutrino exposure and environmental conditions on optical transmittance. Specifically, ultraviolet–visible spectroscopy was performed on optical cement exposed to a reactor neutrino exposure environment for 12 years, revealing a decline in optical performance likely due to oxidation, decomposition, and the formation of color centers or defects. Additionally, the transmittance spectra of optical grease and cement cured under controlled temperature and humidity were compared. Finally, the impact of an air gap between the light guide and PMT, caused by insufficient optical grease application, on the cosmic ray background charge distribution was analyzed. In the future, this study could contribute to improving the better optical coupling structure between light guide and PMT for the development of radiation measurement technology.
Kaiqiang Zhang, Alexandros Plianos, Luca Raimondi
et al.
ABSTRACT The potential of nuclear power in tackling the global energy crisis hinges on the development of effective decommissioning strategies. While the use of human workforce is still common practice, robotics has the potential to revolutionise decommissioning tasks. However, the restricted access, crammed environments, and harsh environmental conditions of nuclear power plants are not suited for conventional robots. In this scenario, the slender design and overall length of long-reach manipulators represent a promising solution. Yet, the same features that make these manipulators a suitable candidate, also introduce significant engineering challenges in design and control. The key challenges and hazards of nuclear decommissioning are summarized using the decommissioning of the Fukushima Daiichi nuclear power plant as a representative use case, in which a manipulator over 20 m long with 18 degrees of freedom is planned to be used for fuel debris retrieval. Critical research gaps on the state-of-the-art of long-reach manipulators for nuclear decommissioning are presented to summarise the focused key research areas ongoing and foster further research engagement within the community.
Abstract Monitoring three-dimensional flux distribution in a nuclear reactor core is essential for improving safety and economics, which requires strategically placed in-core detectors. However, the deployment of these sensors is often constrained by physical, industrial, and economic limitations. This study treats optimizing the location of in-core detectors as a Markov decision process and develops a reinforcement learning (RL)–based framework to provide a solution for detector placement given a fixed number of detectors and available detector positions. The RL-based framework contains an environment consisting of a Proper Orthogonal Decomposition–based power reconstruction function paired with a novel reward function based on the power reconstruction error and a well-educated agent that updates the detector placement. Four RL algorithms including Proximal Policy Optimization, Deep Q-Network, Advantage Actor-Critic, and Monte Carlo Tree Search are investigated to optimize the detector placement and are analyzed. Genetic Algorithm (GA), a traditional optimization approach, is applied for comparison. The findings reveal that RL outperforms GA in terms of the quality of optimal solutions, demonstrating an inclination toward locating a global solution. Moreover, the flexible nature of RL enables the integration of developed novel reward functions from a specific reactor core into other reactors, considering the particular engineering requirements within the RL-based framework, thereby enhancing the optimization of in-core detector configurations.
A. Seki, Masanori Yoshikawa, Ryota Nishinomiya
et al.
Abstract In the case of a new nuclear reactor, existing evaluation experience is limited; thus, accidents and troubles may occur as a result of such lack of experience. To deal with such situations, it is desirable to use a virtual nuclear plant to reproduce behaviors under various conditions and identify unknown anomalies from the behaviors. Then, when an abnormal situation occurs, one can quickly determine the cause of the abnormality to operate plant equipment and return the plant to a stable condition as quickly as possible. Two types of deep neural network (DNN) systems have been constructed to support the identification of unknown anomalies and the determination of their causes. One is a surrogate system that can estimate physical quantities of a nuclear power plant in a computational time of several orders less than a physical simulation model. The other is an abnormal situation identification system that can estimate the state of the disturbance causing an anomaly from physical quantities of a nuclear power plant. Both systems are trained and tested using data obtained from the analytical code for incore and plant dynamics (ACCORD), which reproduces the steady and dynamic behavior of the actual High Temperature Engineering Test Reactor (HTTR) under various scenarios. The DNN models are built by adjusting the main hyperparameters. Through these procedures, these systems are shown to be able to perform with a high degree of accuracy.
S. Prasad, Oscar L. Delgado, Alexander Tucker
et al.
Abstract A virtual reality learning module to train nuclear engineering students in reactor operations to understand reactor power excursions has been developed. The learning module was taught with an Oculus-2 headset and controllers (now called Meta Quest 2). The class was comprised of 71 undergraduate students, mostly in their fourth year of the nuclear engineering curriculum at Texas A&M University. The learning module simulation of power excursion, called pulsing the reactor, was modeled after the Texas A&M Engineering Experiment Station TRIGA reactor. First, the students visited the TRIGA reactor for pulsing and answered a technical quiz on the subject. Next, the students performed pulsing in the equivalent virtual reality module developed in this work. One of the primary learning objectives in the laboratory exercise was the role of passive and active safety mechanisms in a rapid reactivity insertion and power excursion. Data from the actual reactor visit showed that most students did not understand a key passive safety mechanism during the reactor visit. However, the students showed a notable improvement in their understanding of the safety mechanisms after the virtual reality reactor visit. When asked if the virtual reality learning module would have made the quiz at the reactor easier, 96% of the students reported that at least one of the quiz questions would be have been better answerable with the virtual reality module. Students also noted that the virtual reality module needed to expand its scope to include more details and teaching components. Although most students were reluctant to completely replace the pulsing reactor visit with its virtual reality module version available at the time of the study, they appreciated it as a learning reinforcement tool. Student opinion may change more favorably in the future with continued improvements and enhancements of the module.
Source term investigation is a critically important aspect of reactor decommissioning, particularly as the range of nuclides under consideration expands beyond the capabilities of existing analysis methods. In this study, we try to propose a methodology to indirectly determine the radioactivity of long-lived nuclides which are non-γ or low energy in various nuclear waste materials by measuring the radioactivity of 60Co. The critical point of this method is to establish relationship between some easy to measure (ETM) key nuclides, such as certain γ emitters (like 60Co), and the difficult to measure (DTM) nuclides to derive information for the DTM nuclides of interest. To begin, we calculate nuclide bulk densities of 55Fe, 60Co, 63Ni and 152Eu in nuclear waste materials. By constructing inversion matrices and analyzing the intensity matrices of characteristic γ lines emitted by 60Co, we can extract the radioactivity of non-γ radionuclides (55Fe, 63Ni, and 152Eu) present in the nuclear waste materials that are contained within a specific container. Furthermore, our methodology accounts for the influence of voids within the container, thereby ensuring the reliability and validity of the obtained results. This innovative approach offers a promising avenue for efficiently sorting nuclear waste.
In this study deuteron induced nuclear reaction on 20Ne target was studied to get nuclear information about 18F and 21Na radioisotopes which are widely used in medical and nuclear reactor technologies respectively. This nuclear reaction is important to get nuclear data since the produced radioisotope is widely used in nuclear medicine due to its appropriate short half-life and positron emitter. The main objective of the study was to analyse and interpret the behaviour of the resulting reaction cross section. It also aimed to compare the computed theoretical result with the experimental data retrieved from IAEA EXFOR for its validity. From such nuclear reaction, the reaction cross sections of 20Ne(d, α)18F, 20Ne(d, x)18F, and 20Ne(d, n)21Na reaction channels were computed using a nuclear computational code called COMPLET. The computed reaction cross sections for each channel were found in good agreement with the experimental data within the specified energy range and show a strong correlation as assessed by Pearson’s correlation coefficient. The analysis of the result shows that compound nucleus reaction is found the dominant reaction mechanism at the lower energies of the projectile.
SUN Haohan;GUO Gang*;LIU Jiancheng;ZHANG Zheng;ZHANG Fuqiang;ZHAO Yongle;YANG Zhi
As a key technology to study the physical mechanism of single event effects (SEEs) and to identify the sensitive area of microelectronic devices, heavy ion micro-beam irradiation has been paid more and more attention by researchers. Single ion hit (SIH) is an important part of heavy ion micro-beam irradiation, which could reduce the number of incident ions to just one through a series of monitoring and control methods. Owing to this background, the single ion hit (SIH) system was established based on the pinhole heavy ion micro-beam facility in Beijing HI-13 tandem accelerator. To realize single ion hit, it is necessary to cut off the beam current immediately after monitoring the first ion incidence, so as to avoid the next ion passing through the beam switch. Therefore, the beam monitoring device is important to SIH system. The beam monitoring device consists of a 7 nm carbon film, a secondary electron detector and a beam monitoring computer. There is a fixed coefficient K between the number of ions incident on the device and the number of secondary electrons collected by the secondary electron detector. According to the value of K, the number of incident ions can be controlled by the number of secondary electrons. Through the analysis of the potential factors affecting the SIH performance, the theoretical reference formula of factors that affect the accuracy of SIH was given. The three main factors, K, beam intensity and shutter time were experimentally. The results show that the accuracy of this SIH system can reach 90% when the shutter time is 60 ms and the beam intensity is low. At the same time, lower beam intensity and shorter shutter tim are conducive to continuously improving the SIH performance. However, due to the technology limitation of the accelerator, the intensity and uniformity of the beam cannot be achieved at the same time. If the current intensity is too low, the uniformity will become much worse. Therefore, the single ion hit experiment should be carried out when the beam is relatively uniform and the K is stable. At last, the SIH system was applied to 28 nm SRAM device irradiation to reduce the interference of multiple ion incidence. Different patterns and the distribution of multiple cell upsets (MCUs) induced by the single ion were obtained, which verified the availability of the system in the research of radiation effect mechanism of nano devices. Based on the SIH system, it can be determined that all the MCUs are real events without obtaining chip layout information or post processing of data, which could improve data analysis efficiency and provide more real-time information for the experimental personnel.
High-level radioactive waste produced by nuclear power plants are disposed subterraneously utilizing an engineered barrier system (EBS). A gap inevitably exists between the disposal canisters and buffer materials, which may have a negative effect on the thermal transfer and water-blocking efficiency of the system. As few previous experimental works have quantified this effect, this study aimed to create an experimental model for investigating differences in the temperature changes of bentonite buffer in the presence and absence of air gaps between it and a surrounding stainless steel cell. Three test scenarios comprised an empty cell and cells partially or completely filled with bentonite. The temperature was measured inside the buffers and on the inner surface of their surrounding cells, which were artificially heated. The time required for the entire system to reach 100 °C was approximately 40% faster with no gap between the inner cell surface and the bentonite. This suggests that rock–buffer spaces should be filled in practice to ensure the rapid dissipation of heat from the buffer materials to their surroundings. However, it can be advantageous to retain buffer–canister gaps to lower the peak buffer temperature.
Digital instrumentation and control (DI&C) systems at nuclear power plants (NPPs) have many advantages over analog systems. They are proven to be more reliable, cheaper, and easier to maintain given obsolescence of analog components. However, they also pose new engineering and technical challenges, such as possibility of common cause failures (CCFs) unique to digital systems. This paper proposes a Platform for Risk Assessment of DI&C (PRADIC) that is developed by Idaho National Laboratory (INL). A methodology for evaluation of software CCFs in high safety-significant safety-related DI&C systems of NPPs was developed as part of the framework. The framework integrates three stages of a typical risk assessment—qualitative hazard analysis and quantitative reliability and consequence analyses. The quantified risks compared with respective acceptance criteria provide valuable insights for system architecture alternatives allowing design optimization in terms of risk reduction and cost savings. A comprehensive case study performed to demonstrate the framework’s capabilities is documented in this paper. Results show that the PRADIC is a powerful tool capable to identify potential digital-based CCFs, estimate their probabilities, and evaluate their impacts on system and plant safety. FT was quantified with SAPHIRE using a truncation level of 1E-12; RTS failure probability is 4.288E-6 with five cut sets. Results indicate hardware CCFs are the main concerns for the failure analog safety-related redundant I&C systems. Compared with the original RTS-FT, the total failure probability of integrated four-division RTS-FT is reduced about 50%.
The pipeline layout design of nuclear power plant is to find the optimal route to meet the objectives and constraints in the 3D routing space. However, due to the intensive equipment and complex structure of the nuclear power plant, various types of pipeline systems, complex layout constraints, and a large number of pipelines, even for experienced designers, pipeline layout is a difficult and time-consuming task. In order to solve the problem of the automatic layout of pipeline in 3D routing space of nuclear power plant, a pipeline automatic routing method combining Dijkstra algorithm in large space and improved A ∗ algorithm in local space is proposed in this paper. Firstly, the method identifies the key vertices of each room in the nuclear power plant, constructs the topological routing map, and determines the preliminary passage area of the pipeline through the traditional Dijkstra algorithm. Secondly, the space of the layout area is divided into 3D grids, and then the items in the area are identified and preprocessed. Finally, the 3D pipeline routing environment is established through AABB-OBB hybrid collision detection technology. On this basis, the improved method of A ∗ evaluation function is given to satisfy the pipeline layout constraints and improve the search efficiency. Through experiments, the effectiveness of this method is proved. This method can quickly and automatically route the nuclear power pipelines that meet the requirements of the engineering, which greatly improves the efficiency of 3D pipeline layout design for the nuclear power plant.
: The amount of nuclear power plant data has grown exponentially and has become an important asset as a result of the digital and intelligent transformation of the nuclear industry. However, there is a lack of lifecycle data management systems for nuclear power plants. This paper summarizes the status of research on nuclear power plant lifecycle data management in China; analyzes the demands and challenges of nuclear power plant lifecycle data management; and constructs a data management framework around the lifecycle nodes and business flows of nuclear power plants. The major tasks in establishing a nuclear power plant lifecycle data management system include (1) promoting the comprehensive digitization of the nuclear power industry and unifying the nuclear power plant lifecycle data standards; (2) classifying lifecycle data according to data types and designing data models; and (3) formulating scientific data quality control policies and data quality measurement standards to ensure the security of the lifecycle data of nuclear power plants. Furthermore, the following recommendations are made. (1) A data integration platform should be established to catalog the items, business data, and three-dimensional simulation data of nuclear power plants to promote the interconnection of data across business systems. (2) Blockchain technology should be applied to nuclear power plant lifecycle data management to ensure the authenticity and traceability of the data. (3) Digital twins and model-based system engineering should be applied in nuclear power plant lifecycle data management to improve the level of intelligent development. (4) A green policy should be established for nuclear power plant data management to realize the low-carbon, efficient, and sustainable development of data centers.
In order to ensure the safety of nuclear power plant, the engineering facilities closely related to safety are often redundant systems. However, through the probabilistic safety assessment (PSA) analysis, it is found that common cause failure is the main cause of system failure and has a significant contribution to the unavailability of safety system. Therefore, common cause failure analysis and data collection of component are very important. This paper introduces the collection method and process of component common cause failure data based on PSA model of nuclear power plant, discusses the problems encountered in the data collection process, and puts forward suggestions for the follow-up data collection work.