Operation Of Nuclear Reactors Research Articles

Seismic behavior analysis of control rod dropping by vector form Intrinsic Finite-Element method

Control rod drive mechanism (CDRM) play a major role in ensuring safe operation of nuclear reactor during the earthquake, under which the dropping time of control rod is crucial for safe shutdown. Under the earthquake, Rod Cluster Control Assembly (RCCA) has contact collision with the guide tube, resulting in an increase of friction and a decrease of the speed of the falling rod. In addition, in view of the slender structure of the falling rod, the flexible deformation vibration will occur under the impact excitation, which will aggravate the collision and friction. In order to solve the nonlinear problem caused by contact collision between control rod and guide tube, we proposed a dynamic behavior analysis program of rod dropping based on vector finite element method. In order to simulate contact collision force more accurately, we proposed a conformal contact law to simulate the contact force between control rod and guide tube. The vector finite element model and simulation program are validated by comparing with a rod drop experiment. Based on the developed program, the rod dropping behavior, including rod dropping time, contact force, friction force and control rod deformation under several earthquake condition were discussed in this work.

The influence of impurities on the evaporation behavior of Po from liquid Pb–Bi eutectic at high temperatures

AbstractThis study presents an in-depth analysis of the polonium evaporation from high-energy and high-intensity proton-irradiated liquid lead-bismuth eutectic. The applied experimental conditions closely mimic those encountered within an accelerator-driven nuclear reactor, particularly focusing on the interaction of other impurities with polonium. Utilizing proton-irradiated lead-bismuth eutectic with an impurity spectrum similar to that of a real reactor, this research establishes reliable data for the polonium evaporation in the presence of said impurities, employing the transpiration method. The results agree well with those of prior model experiments using pure lead-bismuth eutectic. This indicates that the other coexisting impurities have a negligible impact on the polonium evaporation. The good agreement of the experimental values with literature data emphasizes the reliability of the applied methods and the robustness of the current understanding. These findings have significant implications for the operation and safety assessment of heavy metal-cooled nuclear reactors and support the advancement of Generation IV accelerator-driven systems.

Determination of the elemental composition and changes in thermal and electrical conductivity of “Hanford” and low-ash medium-grained graphite’s grade graphites depending on the fast neutrons fluence

Studying the properties of graphite recovered from operating nuclear reactors is vital for predicting the properties and integrity of graphite as part of assessing the continued operation and life extension of nuclear reactors. The purpose of the study is to determine the electrical conductivity and thermal conductivity of low-ash medium-grained graphite’s grade graphite in the thermal column masonry of the VVR-SM research reactor in the measurement temperature range corresponding to the conditions of normal operation of the reactor to determine the service life. In this work, the change in thermal conductivity and electrical conductivity of GMZ graphite was studied, as well as for comparison of Hanford grade graphite depending on the fluence of fast neutrons and the measurement temperature. The dependence of electrical conductivity and thermal conductivity on dose and temperature has been established. It has been shown that the greater the neutron fluence, the more both the thermal conductivity and electrical conductivity of the material decreases. The service life of the thermal column has been determined.

Improvements in Transient Testing Reactor (TREAT) with a Choice of Filter

The safe and reliable operation of nuclear reactors has always been one of the topmost priorities in the nuclear industry. Transient testing allows us to understand the time-dependent behavior of the neutron population in response to either a planned change in the reactor conditions or unplanned circumstances. These unforeseen conditions might occur due to sudden reactivity insertions, feedback, power excursions, instabilities, and accidents. To study such behavior, we need transient testing, which is like car crash testing to estimate the durability and strength of a car design. In nuclear designs, such transient testing can simulate a wide range of accidents due to sudden reactivity insertions and helps study the feasibility and integrity of the fuel used in certain reactor types. This testing involves a high neutron flux environment and real-time imaging technology with advanced instrumentation with appropriate accuracy and resolution to study the fuel slumping behavior. With the aid of transient testing and adequate imaging tools, it is possible to test the safety basis for reactor and fuel designs that serves as a gateway in licensing advanced reactors in the future. To that end, it is crucial to fully understand advanced imaging techniques both analytically and via simulations. This paper presents an innovative method of supporting real-time imaging of fuel pins and other structures during transient testing. The major fuel-motion detection device that is studied in this dissertation is the Hodoscope which requires collimators. This paper provides 1) an MCNP model and simulation of a TREAT core with a central fuel element replaced by a slotted fuel element that provides an open path between test samples and a hodoscope detector, and 2) a choice of good filter to improve image resolution.

Deep heterogeneous joint architecture: A temporal frequency surrogate model for fuel performance codes

Fuel performance codes, such as Transuranus, predict fuel behavior and are used to ensure the safe operation of nuclear reactors. These codes are moderately time-consuming and affordable in many applications but may be limited in others, primarily when many fuel rods must be evaluated simultaneously. This work presents how the temporal neural network techniques, Temporal Convolutional Networks, and a Fourier Neural Operator can be combined to form a deep heterogeneous joint architecture as a surrogate model for fuel performance modeling in time-critical situations. We train the model using realistic power histories and corresponding outputs generated using the fuel performance code Transuranus. The ultimate result is a surrogate model for use in time-critical situations that take milliseconds to evaluate for thousands of fuel rods and have a mean test error of unseen data around a few percent.

Lattice Boltzmann modeling of natural circulation loop with emphasis on non-Boussinesq mechanism

The natural circulation loop is crucial for the safe and stable operation of nuclear reactors and other applications. Traditional numerical algorithms, based on the Boussinesq approximation, have limitations when dealing with large temperature differences and density disparity, and they do not fully address fluid compressibility. This paper adopts the decoupled and stabilized lattice Boltzmann method (DSLBM) with a non-Boussinesq algorithm to study the natural circulation loop. The DSLBM provides a detailed flow description under large temperature and density differences, incorporating the pseudopotential multiphase model, temperature equation, and state equation, without relying on assumptions. The study examines the loop's performance under various temperature differences, central height differences, and heating source lengths, focusing on mass flow rate, driving head, and heating power. It reveals the energy performance, flow characteristics, and heat transfer properties of the loop, highlighting the physical mechanisms involved. Comparison with the empirical formulation of the incompressible equation from the theoretical aspect shows that when the temperature difference coefficient is lower than 0.15, the two methods are not much different from each other. When the temperature difference coefficient reaches 0.2, 0.3, and 0.4, the difference between the two methods is 9.47%, 19.11%, and 42.64%, respectively. Consequently, the Boussinesq approximation can be compensated by DSLBM, which proves the value of the application of the algorithm in exploiting the compressibility of fluids. The dimensionless fitting correlation with greater universality is obtained, which helps to predict the properties of the natural circulation loop with varying temperature differences, friction coefficients, and geometric structures. The research in this paper will lay the foundation for optimizing the system design of the natural circulation loop and improving energy utilization efficiency.

Integrating 3DVAR and ANN for inversion optimization of physical models of fuel-assembly bowing

This study introduces a novel inversion optimization method for the core physical model of fuel-assembly bowing, integrating measurement data based on three-dimensional variational (3DVAR) data assimilation and artificial neural network (ANN) techniques. Recognizing the deviation between theoretical physical models and actual core conditions due to factors like uneven coolant distribution and fuel-assembly bowing, this work aims to enhance model reliability and simulation accuracy. By generating a dataset through random perturbation and employing ANN for training, the method establishes a relationship between the core physical model and model-simulated values corresponding to measurement data. The construction of the cost function, essential for optimizing the inversion process, is executed using 3DVAR. To verify its validity, the method is implemented, and an inversion optimization process is developed using our self-developed Bamboo-C code system. The optimization process, validated through application in a commercial pressurized water reactor (PWR) in a nuclear power plant (NPP) in China, demonstrates a significant reduction in the relative errors of power distribution, thus affirming the method’s efficacy in achieving a more realistic model of fuel-assembly bowing. The integration of measurement data into the optimization process addresses the critical need for accurate core physical models in nuclear engineering, offering a promising avenue for enhancing the safety and efficiency of nuclear reactor operations.

Experimentally Validated Potential Theory Approach for Investigation of the Flow Field Structure of an Open Pool of a Nuclear Reactor

Detailed information on the flow field structure is often important in numerous industrial applications. Although commercial computational fluid dynamics packages are often capable of providing the required data, they are costly and not universally available. This study was motivated by the operation of an open-pool nuclear research reactor where low radiation levels can be maintained by the installation of a stable purified hot water layer in the upper part of the pool. Maintaining a stable stratification requires a detailed description of the structure of the velocity field. Due to the inherent complications and restrictions of performing accurate measurements in a pool of a real-size operating reactor, either smaller-scale models or oversimplified fluid dynamics computational schemes are routinely used. These methods cannot be validated, and therefore do not necessarily capture the large-scale behavior correctly. We present an alternative approach to evaluate the velocity components in the pool that is based on the potential flow theory. The model results are validated by measurements using particle image velocimetry. The presented potential theory allows for the quick and easy assessment of the global properties of the fluid velocity distribution within the pool, and in particular, close to its surface. The suggested computational models are flexible and allow for easily varying the spatial dimensions of the flow field. The technique thus can be upscaled, and enables the validation of numerical computations in various fluid mechanical installations where the flow field cannot be resolved.

Study on non-uniform circumferential temperature distribution of annular fuel in the tight lattice configuration of a lead-bismuth cooled fast reactor

To guarantee the safe operation of nuclear reactors and protect fuel elements from being damaged under long-term thermal stress, analyzing the circumferential temperature distribution of the outer cladding of nuclear fuel rods in reactors with tight lattice structures is crucial. In this work, a mathematical and physical model was established through theoretical analysis to calculate the circumferential temperature distribution of the outer cladding of an annular fuel rod. The proposed model provides an analytical expression for the circumferential temperature distribution, and the key factors influencing this distribution are determined. The results of the mathematical-physical model and numerical simulation are then combined through numerical fitting. Consequently, a numerical fitting equation is obtained under the steady-state condition of the lead-bismuth reactor using annular fuel and compared with the numerical simulation data acquired via computational fluid dynamics. The results demonstrate that the proposed numerical fitting equation has high accuracy and can serve as a valuable computational tool for analyzing the circumferential temperature of fuel claddings during the operation of lead-bismuth reactors with annular fuels.

Investigation of the Relationship between Molten Fluoride Salt Structure and Fluoroacidity Using Chromium As a Probe Redox Ion

Molten fluoride salts have been studied for decades due to their applications in the production of aluminum as well as in the operation of advanced nuclear reactors. Advanced fission and fusion nuclear reactor designs may include molten fluoride salts as coolants, fuel carriers (fission), or fuel breeding blankets (fusion). Relevant to their nuclear applications are the thermophysical and thermochemical properties of the salt. These include viscosity and thermal conductivity, as well as density, vapor pressure, and heat capacity. Underlying these properties are the interactions of the ions constituting the liquid, characterized by their coordination number, correlation times, and the possible formation of polymeric networks in the melt. The composition and structure of a melt, that is, what ions are present and how they interact, dictates the macroscopic properties relevant to reactor operation. Fluoroacidity, or the activity of dissociated fluoride anions, is one metric that may be used to describe the relationship between salt chemistry and structure. Overall, understanding the relationship between molten fluoride salt chemistry and structure enables deeper understanding and, thus, prediction of property changes that may take place throughout a reactor’s lifetime.In this poster, we present cyclic and square wave voltammetry studies of chromium speciation and diffusivity in various molten fluoride salts, connecting results with current understanding of the structure of the melts. Chromium is a thermodynamically favored corrosion product in molten salt systems, and in this study, it serves as a probe ion in different fluoride melts. Chromium trifluoride is added to various fluoride solvents and the diffusion coefficients of its various oxidation states are calculated and interpreted in light of salt structure. Experimental apparatus design and know-how for high-temperature molten salt electrochemistry are also highlighted.

Comparative criticality assessment of a hypothetical fuel element with 316 stainless steel-clad rods doped with niobium through computational simulation

Several applications were explored, including fuel rod coatings, structural alloys, and cooling materials in nuclear reactors, with a focus on performance, safety, and durability improvements. The results revealed that niobium doping provided a range of benefits, such as increased corrosion resistance, greater stability in high-temperature and radiation environments, and contributed to reducing criticality in nuclear fuel elements. These findings underscore the potential of niobium as a strategic material in the nuclear industry, offering promising solutions to the challenges faced in the safe and efficient operation of nuclear reactors, including small modular reactors (SMRs).

Japan's nuclear water disaster on international law

With the development of human science and technology, the ecological environment is also destroyed. One particularly big source of contamination recently is the release of contaminated water from Japan's nuclear reactors. The nuclear waste generated in the operation of nuclear facilities and reactors is radioactive and inevitably causes environmental pollution. If there are mistakes in the operation process and operation procedures, it will lead to nuclear accidents, cause huge environmental disasters, damage human health and cause difficult to estimate property losses. After the cabinet meeting, the Japanese government announced that it will start to discharge contaminated water from the reactors of the Fukushima nuclear power plant from the 24th. According to this idea, the Fukushima nuclear power plant will continue to discharge contaminated water for at least the next 30 years. According to this incident, many media and experts are judging the consequences of this incident and what to do in the future. So, how should we deal with and view this incident? So the first thing to look at is how this is regulated or violated in international law. In the future, more international solutions and actions will be needed to maintain world peace and health.

Nuclear Reactor Operations Education and Training with Virtual Reality and an Immersive Desktop Application

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.

Investigating the possibility of using a mixture of thorium with different fissile materials as a fuel in TRISO particles for the PBMR-400 reactor

The increasing demand for nuclear reactors as a source of clean energy forced nuclear researchers to search for ways to enhance the safety operation of nuclear reactors. This paper discusses the viability of using different thorium compounds ((233U, Th)O2, (235U, Th)O2, (RGPu, Th)O2) as an accident tolerant fuel/advanced technology fuel (ATF) for Pebble Bed Modular Reactor 400 (PBMR-400). A comprehensive analysis of thorium-based fuel performance was carried out and compared with UO2 to develop a novel nuclear fuel with the capability to tolerate severe accident conditions. The neutronic characteristics such as the infinity multiplication factor, initial heavy metal concentration, RGPu concentration and fission products concentration were analyzed. In addition to, the main safety parameters have been calculated to distinguish the effect of the proposed fuels on them. The thermal spectrum and thermal output power have been visualized to give a good insight view inside the core about the distribution of the heat. In general, the neutronic analysis demonstrated good behavior of the examined thorium-based fuel in the PBMR-400 and especially in the case of (RGPu, Th)O2. PBMR-400 showed excellent performance in incinerating a large amount of reactor-grade plutonium when it was used in a kernel fuel bed.

Human factors engineering simulated analysis in administrative, operational and maintenance loops of nuclear reactor control unit using artificial intelligence and machine learning techniques

The nuclear reactor control unit employs human factor engineering to ensure efficient operations and prevent any catastrophic incidents. This sector is of utmost importance for public safety. This study focuses on simulated analysis of specific areas of nuclear reactor control, specifically administration, operation, and maintenance, using artificial intelligence software. The investigation yields effective artificial intelligence algorithms that capture the essential and non-essential components of numerous parameters to be monitored in nuclear reactor control. The investigation further examines the interdependencies between various parameters and validates the statistical outputs of the model through attribution analysis. Furthermore, a Multivariant ANOVA analysis is conducted to identify the interactive plots and mean plots of crucial parameters interactions. The artificial intelligence algorithms demonstrate the correlation between the number of vacant staff jobs and both the frequency of license event reports each year and the ratio of contract employees to regular employees in the administrative domain. An AI method uncovers the relationships between the operator failing rate (OFR), operator processed errors (OEE), and operations at limited time frames (OLC). The AI algorithm reveals the interdependence between equipment in the out of service (EOS), progressive maintenance schedule (PRMR), and preventive maintenance schedules (PMRC). Effective machine learning neural network models are derived from generative adversarial network (GAN) algorithms and proposed for administrative, operational and maintenance loops of nuclear reactor control unit.

Mass transfer enhancement and flow field simulations for a Venturi bubble generator with multiple inlet tubes

The Venturi device plays a crucial role in the Filtered Containment Venting System (FCVS), effectively preventing the release of radioactive pollutants from nuclear reactor containment during overpressure events. Various configurations of the Venturi device yield different outcomes, with mass transfer efficiency being contingent on bubble size and overall gas holdup. This study examines the impact of varying numbers of inlet tubes on the Venturi device, while keeping the inlet area constant. Specifically looking at the gas–liquid flow field, bubble behavior, and mass transfer capacity in the diverging section. By comparing Single Inlet Tube Venturi Device (SITVD) and Double Inlet Tubes Venturi Device (DITVD), we have reached the following conclusions: (1) In SITVD, the liquid flow field is biased to one side, whereas in DITVD, the liquid flows along the axial direction, resulting in bubbles experiencing stronger shear forces. (2) DITVD exhibits a higher overall gas holdup compared to SITVD, with an average axial gas holdup of 0.0317, which is greater than SITVD's 0.0247. However, SITVD's gas holdup is more evenly distributed. (3) SITVD exhibits a higher turbulence dissipation rate of 545 m2/s3, while the bubble residence time in DITVD is comparatively longer. (4) The average bubble diameter of DITVD is 1.116 mm, which is higher than the 1.011 mm of SITVD. (5) DITVD has better mass transfer than SITVD. The proposed structure of the new Venturi scrubber in this study demonstrates a significant enhancement in mass transfer capacity and filtration efficiency. This discovery provides a solid basis for the design and optimization of FCVS, thereby ensuring the safe operation of nuclear reactors.

A simulation study of the ability to detect power distribution perturbations in the texas A&M TRIGA reactor with self-powered neutron detectors

Given the variety of ways that nuclear reactor core power may be perturbed, reactor operators and developers are keen on understanding the accuracy and convergence time during which perturbations in reactor power distribution may be synthesized (i.e., inferred) from an array of in-core radiation detectors. A simulation study was conducted as described herein using a highly detailed model of the Texas A&M Training, Research, Isotopes, General Atomics Reactor, in which an array of self-powered neutron detectors (SPNDs) was considered for input to the power synthesis methodology. The core power synthesis is conducted using a point-based iterative method with an iterative loop built in to ensure working equation consistency. The forward problem of SPND response to simulated perturbations in reactor power was solved for Gaussian peak-type perturbations in the reactor power distribution. These perturbations varied in variance, amplitude, and core location to assess their impact on synthesis error and to determine the number of iterations required for convergence. A relation between the unique resolvability limit and perturbation width was identified such that the maximum synthesis error increased rapidly when the peak width went beneath this limit (a width approximating half the reactor’s fuel pin-to-pin pitch); this resolvability limit is specific to the SPND configuration and fuel segmentation considered herein. The synthesis error increased linearly with perturbation peak amplitude, whereas the convergence time increased nonlinearly. Perturbations located closer to the center of the core were synthesized more accurately, albeit with a higher number of required iterations. These findings provide a qualitative and quantitative understanding of the accuracy and speed at which different types of spatial power perturbations can be resolved in light-water reactors.

Design of a novel gamma camera with large field of view for 16N diagnosis in the primary loop of nuclear reactor.

The assessment of the concentration and distribution of l6N, derived from 16O in the cooling water exposed to neutron irradiation, is essential for ensuring radiation safety during nuclear reactor operation. The imaging method allows for the visualization of the intensity distribution of these l6N by capturing gamma-rays emitted during their decay process. However, the existing gamma camera is exclusively compatible with gamma-rays below 2MeV. In this paper, a novel gamma camera featuring a thick double-conical penumbra aperture, a pixelated Lu1.8Y0.2SiO5:Ce scintillator array, and a position-sensitive photomultiplier tube is proposed to address this limitation. This innovative design offers a large field of view (FOV) and is suitable for high energy extended gamma source imaging. The optimization of key parameters of the camera was conducted, and a FOV of 60° and an angular resolution of up to 4.57° were achieved. Imaging simulations, including a simplified model of the primary loop of the pressurized-water reactor by GEANT4 code and image reconstruction using the expectation maximum algorithm, demonstrated that the proposed gamma camera could obtain a satisfactory spatial resolution for diagnosing the distribution of 16N in the primary loop of a nuclear reactor.

Integrating core physics and machine learning for improved parameter prediction in boiling water reactor operations

This study introduces a novel method for enhancing Boiling Water Reactor (BWR) operation simulations by integrating machine learning (ML) models with conventional simulation techniques. The ML model is trained to identify and correct errors in low-fidelity simulation outputs, traditionally derived from core physics computations. These corrections aim to align the low-fidelity results closely with high-fidelity data. Precise predictions of nuclear reactor parameters like core eigenvalue and power distribution are crucial for efficient fuel management and adherence to technical specifications. Current high-fidelity transport calculations, while accurate, are impractical for real-time predictions due to extensive computational demands. Our approach, therefore, utilizes the standard two-step simulation process-assembly-level lattice physics calculations followed by whole-core nodal diffusion computations-to generate initial results, which are then refined using the ML-based error correction model. The methodology focuses on improving simulation accuracy in regular BWR operations rather than developing a universal ML predictor for reactor physics. By training an advanced neural network model on the difference in high-fidelity and low-fidelity simulations, the model can reduce the nodal power error from low-fidelity simulations to around 1% on average and the core eigenvalue down to under 100 pcm. This result is under the condition of the normal variations of control rod pattern and core flow rate changes in standard BWR operations used in the training and evaluation of the machine learning model. This work suggests a promising approach for achieving more accurate, computationally feasible simulation solutions in nuclear reactor operation and management.

Thermal creep analysis and correlation development for manufactured HT9 cladding

HT9 cladding was manufactured through successive cold working and heat treatments, and its creep strains were obtained at various stresses and temperatures in the range of 10–120 MPa and 843–953 K, respectively, for up to 20,000 h. Primary and secondary creep regimes were observed, whereas only a few specimens at extreme conditions experienced a tertiary creep regime and creep rupture. The reference creep models, including the theta projection and modified Garofalo equation for HT9 cladding, did not fit the experimentally obtained creep strain curves; the manufactured HT9 cladding showed higher creep resistance than the reference models. Hence, a new correlation was developed by deriving various constants, such as the activation energy, stress exponent, and material constants, from the measured creep strains. The creep correlation successfully predicted the creep behavior of HT9 cladding manufactured. Obtained extensive creep strains over a range of stresses and temperatures, closely approximate actual operating conditions, and developed correlation in this study are valuable in various applications using HT9, providing essential data for analyzing and estimating creep resistances of cladding during the operation of nuclear reactors.