Groundwater flow problems involve complex nonlinear and spatiotemporal characteristics, where traditional numerical methods (e.g., finite element, finite difference) often encounter challenges such as low computational efficiency and insufficient accuracy when dealing with complex boundary conditions and heterogeneous media. To address these issues, this study proposes a novel physics-informed Kolmogorov–Arnold network (PKAN) framework that combines the unique variable decomposition mechanism of KAN networks with physical constraints. The framework introduces three key innovations: (1) implementing KAN network’s univariate function decomposition to enhance the network’s ability to express nonlinear features; (2) designing a pre-training network mechanism to effectively handle complex boundary conditions; and (3) innovatively incorporating a distance function to achieve natural transition from boundary to interior solutions. The results demonstrate that in one-dimensional heterogeneous medium transient simulation, PKAN achieves superior prediction accuracy (R2 = 0.9966, RMSE = 0.0313) compared to traditional PINN (R2 = −0.7194, RMSE = 0.7001). In two-dimensional multi-well pumping system simulations, PKAN (R2 = 0.917, RMSE = 0.077) similarly exhibits exceptional performance (PINN: R2 = −0.3043, RMSE = 0.3067). Notably, in handling local strong gradient problems, PKAN accurately captures cone of depression characteristics and precisely reproduces inter-well interference effects, with maximum error only one-fourth that of traditional PINN. Sensitivity analysis reveals that a configuration of 50 × 50 uniform sampling points combined with four hidden layers and 64 neurons per layer achieves optimal balance between computational efficiency and simulation accuracy. These findings demonstrate PKAN’s breakthrough in groundwater numerical simulation, offering a novel approach for the efficient solution of complex hydrogeological problems.

The Zubair Formation is a transitional delta sedimentary system, with complex sedimentary microfacies and multiple sets of thin sandstone. The genesis of sand bodies is diverse, with small vertical interlayer spacing and large horizontal thickness variations. It is difficult to predict the distribution of sand bodies between wells. Therefore, it is of great significance to clarify the sedimentary characteristics of the Zubair Formation, determine the sedimentary microfacies, establish sedimentary facies models, and predict the distribution of favorable facies zones for accurate prediction of complex sand reservoirs in the next step. Based on the sedimentary development background of the Zubair Formation's transitional delta, combined with research on rock types, single well facies, logging curve characteristics, and seismic facies analysis, the sedimentary microfacies are identified and the sedimentary evolution laws of sand bodies are revealed. The sedimentary facies model is established. It is pointed out that underwater distributary channels and mouth bars are favorable facies zones, which are most prone to developing high-quality reservoirs. This research provides a geological basis for clarifying the distribution characteristics and genetic mechanisms of sandstone in the Middle East, and provides the study direction for fine seismic inversion of complex sandstone reservoirs in the later stage, and it provides further guidance for the deployment of well locations and the preparation of development plans for reservoirs.

Fire is one of the most important hazards that must be considered in advanced nuclear power plant safety assessments. The Nuclear Regulatory Commission (NRC) has developed a large collection of experimental data and associated analyses related to the study of fire safety. In fact, computational fire models are based on quantitative comparisons to those experimental data. During the modeling process, it is important to develop diagnostic health management systems to check the equipment status in fire processes. For example, a fire sensor does not directly provide accurate and complex information that nuclear power plants (NPPs) require. With the assistance of the machine learning method, NPP operators can directly get information on local, ignition, fire material of an NPP fire, instead of temperature, smoke obscuration, gas concentration, and alarm signals. In order to improve the predictive capabilities, this work demonstrates how the deep learning classification method can be used as a diagnostic tool in a specific set of fire experiments. Through a single input from a sensor, the deep learning tool can predict the location and type of fire. This tool also has the capability to provide automatic signals to potential passive fire safety systems. In this work, test data are taken from a specific set of the National Institute of Standards and Technology (NIST) fire experiments in a residential home and analyzed by using the machine learning classification models. The networks chosen for comparison and evaluation are the dense neural networks, convolutional neural networks, long short-term memory networks, and decision trees. The dense neural network and long short-term memory network produce similar levels of accuracy, but the convolutional neural network produces the highest accuracy.

Micro and Small Modular Reactor (MSMR) is an emerging energy technology that meets the requirements of market demand, safety, efficiency, and sustainability. This paper summarizes the advantages, application scenarios, and advanced technologies to support MSMR. Now that the energy market is more flexible and the requirements are more complex, while MSMR can meet the market demand and has a lower cost compared with other clean energies such as wind and solar photovoltaic. The United States is vigorously developing MSMRs into residential energy markets. The MSMR developed around the world has more than three generations of safety characteristics that have adopted passive safety features. MSMR can be manufactured in the factory which reduces construction schedule, cost, and waste. The nuclear fuel supply chain for MSMR is complete and perfect, including the front end and back end. An increasing number of advanced technologies support the development of MSMR, including advanced materials (TRISO fuel and accident-tolerance fuel), advanced control knowledges (DI&C, cybersecurity, and AI), and an advanced computational platform (MOOSE framework).

EDITORIAL article Front. Energy Res., 23 September 2022Sec. Nuclear Energy https://doi.org/10.3389/fenrg.2022.1028698

EDITORIAL article Front. Energy Res., 06 June 2022Sec. Nuclear Energy Volume 10 - 2022 | https://doi.org/10.3389/fenrg.2022.928836

EDITORIAL article Front. Energy Res., 08 July 2022Sec. Nuclear Energy https://doi.org/10.3389/fenrg.2022.965581

In a core meltdown accident in light water reactors, molten corium may drop into the lower plenum of the pressure vessel and interact with water, which is called fuel–coolant interaction (FCI). The behavior of the corium jet breakup in water during FCIs is important for the in-vessel retention strategy and has been extensively studied. While in previous studies, the jet cross-section shapes are naturally assumed to be circular, which is actually not always the case, in this study, the breakup processes of the corium jets with four different elliptical cross-section shapes and three different penetration velocities are simulated with color-gradient lattice Boltzmann method. The effect of the cross-section shape on the hydrodynamic breakup behavior of the corium jet is analyzed in detail. It is found that the effect of the cross-section shape on the jet penetration depth is very limited. With the increase in the aspect ratio under the same penetration velocity, the jet breakup length decreases gradually. In general, the dimensionless corium surface area increases with the increase in the aspect ratio for the jets under the same penetration velocity.

The fire probabilistic risk assessment framework for nuclear power plants relies on experimental data to determine expected fire behavior or to validate models to predict fire conditions in the plant. To support reducing the uncertainty in this experimental data, a research effort was conducted to identify the most frequent and challenging fire scenarios using historic fire data from nuclear power plants in the United States. To support this effort, an electronic version of the publicly available Updated Fire Event Database developed by Electric Power Research Institute was produced resulting in data on 2111 fire events, 540 events were labelled as being challenging fires with 74.2% of these challenging fire events being due to eleven selected fire types. Of these fire types, electrical and electronic equipment, transient combustibles, and liquid fires were the most frequent of the challenging fires. The fire scenario specifics were characterized for each of the eleven selected types and then related to existing fire experiments.

Studies about using nanofluids to enhance the Critical Heat Flux (CHF) of In-vessel Retention (IVR) strategy in the third-generation reactor have been conducted extensively and show a significant CHF enhancement effect. However, low carbon steel SA508 used in the reactor vessel is easy to oxidize and the oxidation can lead to changes in the surface properties which may affect the CHF enhancement effect of nanofluids. In this study, pool boiling CHF experiments with low carbon steel SA508 surfaces were conducted in distilled water and nanofluids under different boiling time to investigate the CHF enhancement effect of nanofluids under low carbon steel surface oxidization condition. CHF in distilled water increases rapidly with boiling time due to the rapid surface oxidation during the boiling and the increase ratio can be nearly 2 due to the surface oxidation. CHF in nanofluids is stable and independent of boiling time. The difference between CHF in nanofluids and CHF in distilled water decrease to 17% under the longest boiling time conditions due to the surface oxidation. The deposition layer of nanoparticles on the surface leads to the capillary wicking and decrease in the nucleation site and thus, CHF is enhanced. This study is of great significance for exploring the actual effect of nanofluids on the CHF enhancement of IVR strategy.

The periodic shedding of cloud cavitation in a nozzle orifice has a significant influence on the flow field and may have destructive effects. Most of the existing research on the shedding of cloud cavitation in an orifice is based on experimental visualization with a focus on the two-dimensional (2D) motion of the re-entrant jet and the shedding mechanism. However, the actual cloud cavitation shedding in an orifice is a complex three-dimensional (3D) process. Some limited signs of three-dimensionality and asymmetry in cylindrical orifices have been detected recently, but the 3D shedding characteristics remain unclear. In this paper, the cavitation regimes and periodic shedding process in the scaled-up nozzle orifice used by the Stanley experiment were simulated with large eddy simulation (LES). The re-entrant jet and periodic shedding mechanism, as well as, the shedding frequency, were analyzed from 2D and 3D perspectives. The main results show that the simulated cavitation regimes and the 2D periodic shedding mechanism agree fairly well with the experimental observations, but more 3D features are revealed. By analyzing the 3D shedding process and the three-dimensionality caused by the inclination of the closure line, the three-dimensional asymmetric shedding mode with phase difference π is revealed. Based upon this finding, the shedding frequency, and Strouhal number are calculated. The corresponding relationships between shedding frequencies and the frequency peaks of the power spectrum density (PSD) for pressure fluctuations are also confirmed. These results extend the understanding of the unsteady cavitating flow within nozzle orifices from 2D to 3D patterns.

Since the accident at Fukushima, one major goal of reactor safety research has been the development of more accident tolerant technologies that can mitigate or delay fuel degradation during a Beyond Design Basis Accident (BDBA). One major effort has been focused on increasing the capability of the fuel to be more tolerant of damage during an accident, i.e., Accident Tolerant Fuel (ATF) materials. In this work, we present the development of a generic BWR plant model, the modification of MELCOR to model ATF materials and the use of ATF materials (specifically FeCrAl alloy) as a coating on Zircaloy cladding or as a substitute material for cladding and fuel assembly canister material and its effect on severe accident progression, specifically, a Station Blackout accident. The analysis indicates that significant fuel degradation via fuel heat-up, clad oxidation, and hydrogen generation was delayed up to an hour if FeCrAl alloy was used as a clad and canister material. And, combined with the passive safety systems (i.e., the Reactor Core Isolation Cooling system, RCIC), the extended operation of these systems delayed fuel degradation further. However, an adverse effect should be emphasized for the monolithic FeCrAl design-it generated more hydrogen than the designs based on the Zircaloy due to the high reaction rate at a high temperature of FeCrAl. The design of the FeCrAl-coated-Zircaloy avoids this defect. Therefore, it is a promising choice to combine some of the beneficial traits of both materials.

FRAPCON and FRAPTRAN are two 1.5-D performance analysis codes that are developed by Pacific Northwest National Laboratory for nuclear regulatory commission to evaluate in-pile behaviors of fuel rods in pressurized water reactors or boiling water reactors under normal operation conditions. This chapter reviewed these two famous and widely used codes in three sections. In the first section, objectives, relations, limitations, and development histories of the two codes were introduced. The steady-state codes, FRAPCON-4.0 and its transient companion code, FRAPTRAN-2.0, were fully restructured and modularized in the latest version in 2016 to be more readable. In the next section the main models applied in the two codes, including thermohydraulic model, mechanical model, fission gas release model, internal gas pressure model, and balloon model for FRAPTRAN, were briefly introduced. These models are fully coupled via gap condition between cladding and pellet. In the last section, relevant validations on operational condition, loss of coolant accident and reactivity initiated accident, were also introduced and proofed that these two codes have high accuracy in a certain range. The calculation of these two codes is fast, highly robust, and well assessed, and they are great tools for evaluating the steady-state or transient performance of the fuel. However, due to the fundamental assumptions of the two codes, the axial and hoop heat transfer are ignored. Besides, the radial cladding stresses are also ignored due to the plane strain hypothesis. These disadvantages limit their further application compared with 3D codes, such as BISON. Further modifications are still needed before the codes are applicated in other reactors with different geometry fuels or other forward position fields, such as plate-type fuel and accident tolerant fuel.

In recent years, micro-reactor concepts have attracted increasing attention in the nuclear industry due to the market demand for flexible, reliable, and sustainable power and heat on-site for industrial or federal installations or remote communities. To help demonstrate and validate these innovative reactor concepts, the Micro-reactor AGile Non-nuclear Experimental Test-bed (MAGNET) is being constructed at Idaho National Laboratory (INL) with an initial focus on the thermal and structural performance of heat pipe cooled micro-reactors. At this time, the preliminary design parameters of the MAGNET facility have been specified. In this work, a simulation using the System Analysis Module (SAM) code is performed for the prototypical 37-heat-pipes test article to predict its experimental facility performance and associated uncertainties. We first carry out a benchmark demonstration of our modeling method with an example provided by Argonne National Laboratory (ANL). Then, we predict the thermal performance of the MAGNET facility under steady-state operation. Moreover, several sensitivity parameters are analyzed to investigate their impact on facility thermal performance. This MAGNET experiment simulation provides valuable information for researchers to validate the facility's initial design and steady-state operation.