A preliminary study on activated corrosion product source term prediction in pressurized water reactor using recurrent neural network

Performance evaluation of a surrogate model to predict effective dose under hypothesized severe accidents of pressurized water reactor based on neural network

Analysis and optimization of operating characteristics for integrated pressurized water reactor IP200

Dynamic surface control method based on backstepping for pressurized water reactor system

This article presents the novel algorithmic developments and performance analysis of the GPU-optimized REActor Physics Monte Carlo (GREAPMC) graphical processing unit (GPU)–accelerated multigroup Monte Carlo (MC) code tailored specifically for pressurized water reactor simulations. GREAPMC tackles the thread divergence issue inherent in history-based neutron tracking on GPUs by introducing two new optimization strategies. The first novel approach dynamically replaces inactive particles with new ones during the execution of the transport loop, while the second strategy enhances efficiency by capping the history length during active cycles at a predefined maximum number of interactions. Subsequently, it sorts and invokes the kernel with only the surviving neutrons. The maximum number of interactions is automatically adjusted while considering cycle time during inactive cycles. Both methods significantly accelerate computation compared to MCS, a high-fidelity MC code developed at Ulsan National Institute of Science and Technology, with the latter approach demonstrating the most substantial acceleration. GREAPMC further enhances efficiency by adopting cell-based geometry modeling. This approach eliminates cell search overhead, ensuring consistent execution times even as the number of cells increases. Overall, these algorithmic developments in GREAPMC achieve substantial computational acceleration against MCS. A single GPU card in this study demonstrates performance equivalent to approximately 570 cores from the specific CPU model used.

Metal oxides accumulate on the inner surfaces of high-temperature powerplant systems which cause a number of issues like reduced heat transfer and localized corrosion. The deposition rates of metal oxides are governed by electric double layer properties like zeta potentials and surface charge. These double layer properties inform colloid stability and aggregation tendencies which are precursors to deposition. In many cases, changes to fluid composition can strongly impact the double layer structure, providing opportunities to mitigate unwanted depositions. However, surface-ion interactions are not well-understood for many high temperature condensed phase fluids. At ambient conditions, commercial instrumentation uses electrophoretic mobility measurements to determine particle zeta potentials, but these systems are limited to temperatures below 70°C, which fall short of the elevated temperatures and pressures used in powerplant applications. While some high temperature zetameter designs have been developed to extend the range of temperatures accessible, these systems have struggled to account for background convection. To address this limitation, we have integrated particle image velocimetry (PIV) capabilities into a high temperature hydrothermal chamber design to enhance electrophoretic mobility measurements at elevated temperatures.Unlike single particle tracking methods, PIV captures the collective motion of multiple particles in an observation field, allowing for accurate separation of electrophoretic velocities from background convection. Moreover, this method enables the detection of very low velocities, facilitating data collection near the isoelectric point (IEP), a region that is challenging to study. Our study first validates this approach with zirconium dioxide (ZrO2), a well-established colloidal particle, by accurately measuring electrophoretic velocities at high temperatures. Subsequently, we apply the technique to nickel ferrite (NiFe2O4) with differing [Ni] / [Fe] ratios; a corrosion product commonly formed in pressurized water reactors which is understudied. We measured electrophoretic mobility at temperatures from 25°C to 250°C with pressures up to 50 bar. Data collected with KOH as the pH modifier revealed that IEP decreases gradually with increasing temperature. Our high-temperature measurements with two different metal oxide particles demonstrate the advantages of this approach as a novel and reliable method for measuring electrophoretic mobility at high temperatures. Overall, this technique not only enhances the speed and accuracy of measurements but also deepens our understanding of particle behavior at elevated temperatures and pressure.

High-burnup and extended enrichment fuels are of interest for extending the cycle lengths of pressurized water reactors from 18 to 24 months. Changes to the fuel design and core loading scheme have potentially significant safety implications due to power distribution effects, reduced thermal conductivity, and/or increased plenum pressures due to additional burnable poison loadings. Additionally, higher burnups result in increased material degradation and risk of fuel fragmentation, relocation, and dispersal (FFRD). A representative 24-month core design using gadolinia-doped UO2 was analyzed for performance under large-break (LB) loss-of-coolant accident (LOCA) conditions using PARCS, RELAP5-3D, and BISON. All considered acceptance criteria were met, with no cases exceeding the 1477 K maximum cladding temperature or the post-quench ductility oxidation limit of 17% equivalent cladding reacted. Full-core FFRD susceptibility was estimated to be approximately 455 kg, though high uncertainties exist with current approaches for computing susceptibility. Undoped fuel rods are more likely to be limiting due to higher linear heat rates. Relatively high burnup and linear heat rate rods located in second batch assemblies are of greatest safety significance during LB LOCA for this high-burnup core design.

The zeta (ζ)-eigenvalue equation is a formulation of the neutron transport equation that ensures criticality by scaling the density of the nuclides or materials of choice with the parameter ζ. As a consequence, the value of ζ provides quantitative information about what material density or nuclide concentration can make a system critical. This paper proposes a Monte Carlo algorithm to solve the ζ-eigenvalue equation. Additionally, it tackles practical challenges in the deterministic implementation of the method. The Monte Carlo algorithm, based on a fixed-point iteration scheme, is implemented in the code SCONE and tested on some cases of practical utility. Examples include the search of critical gadolinium concentration in a pressurized water reactor assembly and the search of critical boron in BEAVRS. The Monte Carlo implementation is successful at finding the critical densities requested and is faster than state-of-the art iterative searches based on k-eigenvalue calculations. The second part of the paper illustrates the impact of the density scaling on both the generation of multigroup constants and spatial self-shielding effects, relevant to the deterministic implementation. A computational scheme exploiting the ζ formulation to get reliable results in a deterministic framework is also suggested.

Multi-physics simulation of a load rejection transient in a Pressurized water reactor. Comparison against experimental data, sensitivity and uncertainty study

Sensitivity analysis of a flow redistribution model for a multidimensional and multifidelity simulation of fuel assembly bow in a pressurized water reactor

Proof of concept for the surveillance of a pressurized water reactor using muography: From nominal operation to accident conditions

A control-oriented hybrid modeling method for small pressurized water reactors based on linear models and neural networks

Formation mechanisms of multilayered cobalt deposits on 304 stainless steel in primary cooling systems of pressurized water reactor nuclear power plants

Reactor power regulation of a small pressurized water reactor based on linear active disturbance rejection control with gain scheduling

Assessment of irradiation embrittlement effect on fatigue life of a pressurized-water reactor pressure vessel using the fracture toughness master curve approach

This study proposes to quantify the uncertainty in a CPU time costly computational fluid dynamics (CFD) model used to evaluate the local temperature field in the situation of blocked fuel assembly in a pressurized water reactor (PWR) transfer tube. Several uncertain parameters are identified and a first uncertainty propagation study is conducted on a low-fidelity (poorly refined) mesh for CPU cost issues. Then, using the concept of “support points,” an algorithm is employed to reduce the size of the initial design of experiments. A high-fidelity model (finer mesh, more CPU time expensive) is then run on this small-size design of experiments. A metamodel was finally built on those high-fidelity results to propagate uncertainties and finely analyze the results. The successful results that are obtained show that metamodeling has the potential to overcome the issue of costly CPU time CFD models in the near future. Despite good quantitative results, the main purpose of the present study remains the novel methodology that was set up for uncertainty propagation in CFD.

Abstract Neural networks (NNs) and deep learning have revolutionized several fields, and nuclear safety analysis is no exception. The proper operation of nuclear reactor safety systems is crucial and is designed to meet strict safety requirements. Such systems are expected to withstand certain postulated accidents known as design basis accidents (DBAs), which include the loss of coolant accident (LOCA). As the LOCA involves complex fluid mechanics and heat transfer, the pattern recognition abilities of NNs allow for excellent prediction capabilities and the bypassing of otherwise tedious conventional analysis methods. This work investigates the deep learning techniques through long short-term memory (LSTM) architecture, for its ability to deal with time-series problems which include LOCAs. The utilized model is taught to estimate the size of pipe breaks within the cooling system based off of the corresponding pressure drops. A range of 0.5 %–100 % break-size-time-variant parameters were collected using the WSC Inc. 1,400 MWe generic pressurized water reactor (GPWR) simulator, using two circulation loops. Neural networks were trained on parameters such as loop temperature, pressure, containment pressure and Boron concentration. The performance of the LSTM model showed a mean absolute error (MAE) of 5.185, mean squared error (MSE) of 76.50, root mean squared error (RMSE) of 7.953, R 2 of 0.888 and Accuracy of 80.684 % within a tolerance of 15 % across 100 runs.

Nuclear energy plays a critical role in global decarbonisation, but its expansion raises concerns about the proliferation risks associated with conventional fuel cycles. This study addresses this challenge by evaluating Am-241 doping as a method to enhance the intrinsic proliferation resistance of nuclear fuel. Using full-core simulations across Pressurised Water Reactors (PWRs), Boiling Water Reactors (BWRs), and Molten Salt Reactors (MSRs), the research assesses the impact of Am-241 on isotopic composition, reactor performance, and safety. The results show that Am-241 reliably increases the Pu-238 fraction in spent fuel above the 6% threshold, which significantly complicates its use in nuclear weapons. Additionally, Am-241 serves as a burnable poison, reducing the need for conventional absorbers without compromising operational margins. Economic modelling indicates that the levelised cost of electricity (LCOE) increases modestly, with the most notable impact observed in MSRs due to continuous doping requirements. The project concludes that Am-241 doping offers a passive, fuel-intrinsic safeguard that complements existing verification regimes. Adoption of this approach may require adjustments to regulatory frameworks, particularly in fuel licencing and fabrication standards, but could ultimately support the secure expansion of nuclear energy in regions with heightened proliferation concerns.

Research of the spatial effect of plate-type fuel with dispersed particles in pressurized water reactors

The application of SiC fiber-reinforced SiC/SiC composites as pressurized water reactor (PWR) fuel cladding remains under investigation. In order to measure composite hermeticity under conditions more representative of PWRs, an in-situ internal pressure loading hermeticity test system was developed and relevant test configurations were identified. A duplex-layer structured SiC/SiC composite cladding was studied to investigate the influence of the outer chemical vapor deposition (CVD) SiC coating thickness on its hermeticity, and hermeticity testing of composite cladding tubes was conducted under internal pressure loading conditions. The results indicate that a CVD-SiC coating thickness of ≥130 μm is necessary to achieve an SiC/SiC composite cladding tube that meets the design leakage rate requirement, and the current SiC/SiC composite cladding tubes with 200μm CVD-SiC coating meet the hermeticity allocation at pressures up to 15 MPa. The in-situ internal pressure loading hermeticity test technique effectively measures hermeticity changes in composite cladding materials under applied stresses.