A Perspective on Data-Driven Coarse Grid Modeling for System Level Thermal Hydraulics
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Nuclear Science & Technology↗
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Capability Enhancements for System-level thermal Hydraulic Modeling of Lead Fast Reactors
This project has focused on the use, assessment, and development of the SAS4A/SASSYS-1 (SAS) safety analysis software. Although SAS was originally intended as a safety analysis tool for Liquid Metal cooled Fast Reactors (LMFRs), which includes both Sodium Fast Reactors (SFRs) and Lead Fast Reactors (LFRs), the vast majority of its recent development and customization has been tailored to SFRs. In general, enhancements that are made to the software for SFRs are applicable to LFRs, however, the fuel composition and corrosive nature of lead requires careful consideration when performing safety analysis of an LFR. In this project an emphasis was placed on closing gaps that are associated with modeling LFRs using SAS. The principal objective of the project was to enhance the ability of SAS as a licensing tool for LFRs. This objective was to be accomplished through three tasks: 1) Enhance the ability of SAS to couple with external software; 2) Improve the underlying physics models in SAS, with priority given to models that are highly relevant to LFRs; 3) Extend the Verification and Validation (V&V) basis of the software
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
A Probabilistic Model-Based Diagnostic Framework for Nuclear Engineering Systems
A fault diagnostic framework was investigated in this study for applications in thermal–hydraulic systems of nuclear power plants. The proposed framework consists of quantitative model-based diagnosis, statistical change detection and probabilistic reasoning. The use of physics-based diagnostic models provides high detection sensitivity and allows noise and measurement uncertainty to be incorporated robustly. Performance-related parametric models for each component are constructed based on first principles. Numerical model residuals are generated using the concept of analytical redundancy. Statistical change detection methods are employed to detect non-zero residuals in the presence of uncertainty. The diagnosis task is performed using Bayesian inference to detect and localize possible faults. Application to a single-phase heat exchanger for demonstration showed that the proposed probabilistic framework can provide improved results in comparison with traditional approaches while remaining less sensitive to false alarms in the presence of measurement and modeling uncertainty.
Bayesian network↗
A digital twin approach to system-level fault detection and diagnosis for improved equipment health monitoring
Automating the task of fault detection and diagnosis is crucial in the effort to reduce the operation and maintenance cost in the nuclear industry. This paper describes a physics-based approach for system-level diagnosis in thermal-hydraulic systems in nuclear power plants. The inclusion of physics information allows for the creation of virtual sensors, which provide improved fault diagnosis capability. The physics information also serves to better constrain diagnostic solutions to the physical domain. As a demonstration, various test cases for fault diagnosis in a high-pressure feedwater system were considered. The use of virtual sensors allows constructing performance models for two first-point feedwater heaters which would not have been possible otherwise due to the limited sensor set. Real-time plant data provided by a utility partner were used to assess the diagnostic approach. The detection of an abnormal event immediate after a plant startup pointed to faulty behaviors in the two first-point feedwater heaters. Further, this double-blind fault diagnosis was subsequently confirmed by the plant operator. In addition, several simulated sensor fault events demonstrated the capability of our algorithms in detecting and discriminating sensor faults.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Benchmark Simulation of the Natural Convection Shutdown Heat Removal Test Facility Using SAM
Natural convection systems are a promising method to passively remove heat from reactor cavities during loss of forced flow accident scenarios. In this work at Argonne National Laboratory (ANL), a highly instrumented Natural Convection Shutdown Heat Removal Test Facility (NSTF) was used to demonstrate the effectiveness of air-cooled natural convection systems. In previous work, RELAP5-3D simulations were performed on this facility with favorable comparisons to experiment for mass flow rate, pressure drop, air temperature increase, and air velocity. Both experimental and simulation efforts with this facility present a useful opportunity to perform a benchmark study with the System Analysis Module (SAM). SAM is an advanced thermal-hydraulic system code currently in development at ANL for advanced non-light water reactor safety analysis.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
The 25 kWe solar thermal Stirling hydraulic engine system: Conceptual design
The conceptual design and analysis of a solar thermal free-piston Stirling hydraulic engine system designed to deliver 25 kWe when coupled to a 11 meter test bed concentrator is documented. A manufacturing cost assessment for 10,000 units per year was made. The design meets all program objectives including a 60,000 hr design life, dynamic balancing, fully automated control, more than 33.3 percent overall system efficiency, properly conditioned power, maximum utilization of annualized insolation, and projected production costs. The system incorporates a simple, rugged, reliable pool boiler reflux heat pipe to transfer heat from the solar receiver to the Stirling engine. The free-piston engine produces high pressure hydraulic flow which powers a commercial hydraulic motor that, in turn, drives a commercial rotary induction generator. The Stirling hydraulic engine uses hermetic bellows seals to separate helium working gas from hydraulic fluid which provides hydrodynamic lubrication to all moving parts. Maximum utilization of highly refined, field proven commercial components for electric power generation minimizes development cost and risk.
White, Maurice↗
Monitoring of Liquid Metal Reactor Heater Zones with Recurrent Neural Network Learning of Temperature Time Series
Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F 1 score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F 1 scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively.
gated recurrent unit↗
Machine Learning in Nuclear Thermal Hydraulics, Part 2: Opportunities and Perspectives
This paper summarizes the issues in nuclear thermal hydraulics (NTH), mainly from the three named aspects, and illustrates potential opportunities of machine learning (ML) applications in dealing with these issues. These issues are problems in simultaneously realizing computational efficiency and accuracy of system thermal hydraulic (STH) modeling and simulation, uncertainty in the scaling analysis for the best-estimate calculations of transient and accident analysis, and difficulties in optimal data production and usage for the development and assessment of evaluation models. Due to ML uncertainty, data insufficiency, and lack of cognition about how to efficiently incorporate knowledge and data, challenges of adapting ML techniques still exist. Accordingly, several perspectives are proposed to provide insights for ML applications in solving these issues.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
A multiphysics model of the versatile test reactor based on the MOOSE framework
The traditional modeling approach for sodium fast reactor cores relies on separate physics models, where the fuel performance, thermal–hydraulics, and neutronics calculations required to predict the core physics characteristics for nominal conditions are decoupled by relying on user-imposed boundary conditions. Here, this paper aims at evaluating the impact of multiphysics simulations for predicting the core characteristics of the Versatile Test Reactor, which is being designed as a 300-MWt sodium-cooled fast reactor. The purpose of the Versatile Test Reactor is to accelerate the testing of advanced nuclear materials in the United States. The proposed multiphysics model relies on the Griffin reactor physics code, the SAM thermal–hydraulic system code, the BISON fuel performance code, as well as generic Multiphysics Object-Oriented Simulation Environment capabilities implemented in the open-source tensor mechanics module. For k eff calculations, the introduction of a tight coupling between the neutronics, thermo-mechanical and thermal–hydraulics models induces a change of around 543 pcm in the eigenvalue, compared to the traditional standalone neutronics calculation where approximate temperature profiles are used. The multiphysics model is then employed for quantifying the impact of the thermal conductivity uncertainties on some of the key figures of merit, such as the fuel centerline temperature, assembly powers, and keff for nominal core conditions. As anticipated, uncertainties on fuel thermal conductivity mostly impact the fuel centerline temperature, and to a lesser extend the k eff .
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Signal Decomposition for Intrusion Detection in Reliability Assessment in Cyber Resilience (Summary Report)
The complexity of assuring cyber resilience for physical process interactions in connected systems such as energy grids increases dramatically as the coupling between processes becomes more direct and responsive. An example of this growing complexity is provided by Integrated Energy Systems (IES), in which various processes such as nuclear heat generation and commodity production are being directly coupled for increased responsiveness to highly variable signals such as market pricing or electricity demand. As such, the potential attack surface of the coupled processes is larger than the two processes independently. Securing these complex systems requires two-fold monitoring: cybersecure monitoring for potential malicious incursion, and physics monitoring for system tampering. Physics monitoring includes analyzing the behavior of the signals within the system for anomalous behavior. This analysis has been shown to be insufficient if approached by only data-driven machine learning and artificial intelligence (MLAI) techniques or only low-level model comparison. Previous efforts at Purdue University suggested combining high-fidelity models with MLAI algorithms as a basis for a software tool for detecting anomalies in physical processes. This work built on that suggestion, developing an advanced library for signal decomposition and analysis using both MLAI and high-fidelity physics algorithms for greatly improved anomaly detection, especially false data injection. This software can be used as part of a secure imbedded intelligence (SEI) system designed under Consequence-driven Cyber-informed Engineering (CCE) for complex coupled systems. This library established a foundation for online and posteriori analysis of digital signals for the purpose of detecting potential malicious tampering in digital signals representing physical processes. Demonstrations carried out throughout the development highlight the effective use of characterization algorithms to detect signal perturbations, particularly triangle attack-style perturbations, in three wide-ranging applications: seismic monitoring, nuclear thermal hydraulics system simulation, and custom manufacturing.
97 MATHEMATICS AND COMPUTING↗
Non-Nuclear Test Facility Development to Support Verification and Validation Needs
This study focuses on developing a non-nuclear test facility to help meet the verification and validation (V&V) requirements of a water-cooled small modular reactor (SMR) system. The scaling analysis necessary for reactor system experimentation and assessment model V&V considers various reactor system accident scenarios such as main steam line break (MSLB), steam generator tube rupture (SGTR), and containment condensation and heat transfer. The goal is to identify key physics based phenomena of interest (POI), specific figures of merit (FOMs), and the state of knowledge (SOK) by employing phenomena identification and ranking table (PIRT) studies. Important physics phenomena such as heat transfer, fluid dynamics, structural integrity, and material selection must be addressed when scaling a thermal-hydraulics system. Alongside these considerations, modifications to the facility may be needed to align the scaled system with the available footprint (height and space) of the facility. These modifications may include infrastructure upgrades, changes to the piping layout, implementation of safety measures, and installation of new instrumentation and control systems. The test facility aims to provide a scaled-down version of prototypic facilities for comprehensive V&V activities by overcoming these challenges and making the necessary modifications. This approach minimizes scaling distortion, simulates prototypic plant conditions, and accurately models accident event progressions. Thus, the facility will be capable of adequately modeling a scaled SMR system to support the design, development and licensing needs.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
On the applicability of the Redlich-Kister framework for viscosity estimation of molten halide salt mixtures
For molten halide salt mixtures already being utilized or under consideration for carbon-free energy production systems, it is crucial that their viscosity is well understood so that system thermal hydraulics can be reliably assessed. Because of the difficulty in accurately measuring molten halide viscosity and the sheer size of the matrix of possible higher order salt mixtures that may be of interest to the energy industry, there are several gaps in the quantified understanding of molten halide viscosity across this matrix. As such, both first-principles and semi-empirical modeling techniques may be crucial for rapidly assessing this broad, complex compositional domain. Herein, the Redlich-Kister framework is applied to assess the feasibility of broadly interpolating and estimating the viscosity of several pseudobinary and pseudoternary molten halide salt systems that may be of key interest to the energy industry. The framework is based on the assumption that an ideal component and a nonideal component collectively describe the viscosity as a function of composition and temperature for a given molten halide system. Three different ideal models were considered for the ideal component, including Grunburg-Nissan, Katti-Chaudhri, and Gambill methods. Regarding the pseudobinary interpolations, the Redlich-Kister models with either the Grunburg-Nissan or Katti-Chaudhri models as the ideal component resulted in either highly (average error less than 5%) or reasonably (average error less than 15%) accurate interpolations of pseudobinary halide viscosity; BeF 2 - or UF 4 -bearing salts tended to result in reasonably accurate interpolations, whereas other pseudobinary mixtures tended to show high accuracy. Regarding the pseudoternary extrapolations, the Redlich-Kister framework shows reasonable success at estimating the extent to which a pseudoternary system may indicate deviations from ideal Grunburg-Nissan mixing, where discrepancies with comparative experimental data generally stay within 30%. Finally, the primary reasons identified for such discrepancies are (1) inaccuracy in the underlying experimental data, (2) different complexation behavior in the higher order systems compared to the pseudobinary subsystems, and (3) extrapolation into temperatures too far out of the domain, which is valid for the underlying experimental data feeding the Redlich-Kister model.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Validation of a Hybrid Domain Overlapping Coupling Between SAM and CFD Against the TALL-3D Transients
The System Thermal Hydraulics (STH) code SAM has been coupled to the Computational Fluid Dynamics (CFD) code Simcenter STAR-CCM+ utilizing a hybrid domain overlapping method with an explicit coupling in time. The coupling aims to extend the STH code’s applicability to scenarios where local momentum and energy transfers are important yet difficult for STH codes to capture, such as three-dimensional (3D) mixing. The coupling method’s numerical stability was verified in the past against two closed-loop configurations, and it was validated against a double T-junction experiment with 3D scalar mixing. In the present work, the coupling method is validated against the TALL-3D STH/CFD coupling benchmark facility. TALL-3D is a three-legged, liquid-metal facility with a large, pool-type enclosure (test section) that exhibits 3D flow effects to be modeled by a CFD code. The rest of the system exhibits approximately 1D behavior well-predicted by an STH code. First, the present STAR-CCM+ CFD model of the 3D test section is validated against experimental data. Then, the SAM-STARCCM+ coupled model is validated against six different TALL-3D steady states, including SAM standalone model results for comparison. Lastly, the SAM-STARCCM+ coupled model is validated against two TALL-3D transients, one exhibiting flow reversal in the test section and one exhibiting nonlinear, Limit Cycle Oscillations (LCO). For the first transient, the SAM-STARCCM+ coupled model properly predicts an increase in the test section’s inlet temperature during flow reversal, and this is not predicted by the SAM standalone model. Following flow reversal, the SAM-STARCCM+ coupled model better-predicts the initial flow recovery and following oscillations as the system approaches a final natural circulation state. For the second transient, no true final steady state is observed due to LCO. Neither the SAM-STARCCM+ coupled model nor the SAM standalone model can perfectly capture the experiment’s changing oscillation frequency during the transient. However, the SAM-STARCCM+ coupled model does reproduce the oscillatory feedback observed in the system. This is a significant achievement as the SAM-STARCCM+ coupled model only uses an explicit coupling in time, as opposed to a semi-implicit coupling. In comparison, previous STH/CFD coupling efforts of the TALL-3D facility required semi-implicit coupling to obtain similar results.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Machine learning from RANS and LES to inform coarse grid simulations
Nuclear system thermal hydraulic analysis has historically relied on computationally inexpensive 1D codes. However, such tools are unable to capture multiscale multidimensional effects in large nuclear reactor enclosures. On the other hand, simulations with higher fidelity can be too expensive for such purposes. One of the ways to reduce computational cost is to perform simulations on a coarse grid, which, unfortunately, introduces large discretization errors. In this paper, two high-to-low data-driven approaches are investigated: (1) a coarse grid turbulence model to predict eddy viscosity and (2) correction of errors in coarse grid velocity fields. The approaches aim to reduce grid- and turbulence model-induced errors in coarse grid Reynolds-averaged Navier–Stokes (RANS) simulations. Two sources of high-fidelity data, RANS and large eddy simulations (LES), are explored. To extract the eddy viscosity from the LES data, an inverse optimization problem is solved. However, the LES eddy viscosity is shown to be comparable to the RANS eddy viscosity in terms of error reduction. Therefore, the directly available RANS eddy viscosity was used to develop a coarse grid data-driven turbulence model. Additionally, error correction in velocity is used to reduce the remaining uncertainties and bring the results closer to reality. In conclusion, the performance of the frameworks is demonstrated for a scaled upper plenum of a gas-cooled reactor facility.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Implementation of a Drift Flux Model into SAM with Development of a Verification and Validation Test Suite for Modeling of Noncondensable Gas Mixtures
The advanced thermal-hydraulic system code, System Analysis Module (SAM), was originally developed for the modeling of single-phase flow in advanced reactors. It has since been expanded to include a four-equation drift flux model for the modeling of two-phase flows containing a noncondensable gas. The model was expanded to support the modeling of molten salt reactor (MSR) designs in which the fuel is directly dissolved in the circulating coolant. These designs have shown that circulating gas bubbles can play an important role in the management of fission products and the operational behavior of the reactor. A drift flux model was implemented to more accurately capture the localized behavior of the void in the core and its impact on the mass transfer of fission products. A thorough assessment of the new model was performed by developing a verification and validation test suite. Verification problems were designed to test all major terms in the new governing equations. The new model converged to the correct solution at the expected order of accuracy for all verification cases. The validation cases included a wide range of flow and void conditions in different pipe geometries. Although higher void experiments show a slight underprediction of void by the drift flux model, experiments that aim to reproduce Molten Salt Reactor Experiment (MSRE) experimental conditions show good agreement with the model. The gas transport model was activated for a SAM model of the MSRE to demonstrate that it can be used in a more complex model. Finally, this gas transport model will be used along with an interfacial area transport equation being implemented in SAM for the prediction of mass transport behavior in MSR conditions.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Toward a Fully Integrated Multiphysics Simulation Framework for Fusion Blanket Design
Fusion is an attractive clean-energy solution, thanks to its various advantages, such as reduced radioactivity, little high-level nuclear waste, ample fuel supplies, and increased safety. However, the harsh operating environment introduced by a complex fusion plasma system makes design and integration of fusion blankets incredibly challenging and time-consuming. This work focuses on developing a fully integrated multiphysics simulation framework based on an advanced open-source platform—the Multiphysics Object-Oriented Simulation Environment (MOOSE)—to alleviate the difficulties in fusion blanket design and integration. MOOSE is a massively parallel finite element/volume multiphysics simulation platform that has been widely adopted within the nuclear fission community. Even though fission and fusion are fundamentally different, they involve similar multiphysics phenomena. A fully integrated open-source multiphysics simulation framework tailored for the fusion blanket design will be implemented by leveraging the well-established multiphysics capabilities in MOOSE. Once successfully developed, this fully integrated framework will rapidly evaluate a blanket design concept and offer insights for subsequent iterations. As the first step, we will mainly aim to integrate neutronics analysis, system thermal hydraulics simulation, and full 3-D heat transfer calculations. The efficacy of the integrated framework will be verified using an innovative solid ceramic blanket design. While the project’s final goal is to enable a fully integrated multiphysics simulation platform for various fusion blanket concepts, here this work, as a preliminary step, will mainly focus on a solid ceramic breeder helium-cooled blanket.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Semi-Annual Report for Horizontal Compact High Temperature Gas Reactor (HC-HTGR) Development during Performance Period April 2023 – September 2023
Horizontal Compact High Temperature Gas Reactor (HC-HTGR) is being designed by a multi-disciplinary team of nuclear, mechanical, and structural engineers under the support of a DOE-NE Advanced Reactor Demonstration Program’s Advanced Reactor Concepts-20 (ARC-20) award. The objective of this ARC-20 project is to deliver a conceptual design for the proposed HC-HTGR in 3 years and support its commercialization as a safe, low-cost HTGR. Argonne National Laboratory (Argonne) is responsible for the design and analysis of the reactor cavity cooling system (RCCS) as a safety system for passive decay heat removal of the reactor concept. Additionally, Argonne is providing analysis of the primary coolant system to ensure temperatures within the core remain below safety margins during steady-state and potential accident scenarios. This fourth semi-annual report summarized the progress made at Argonne on the two tasks during the second half of FY23. As a part of the RCCS design task, recent efforts have been made to complete a conceptual design of the RCCS for the HC-HTGR, including the design update of the water panel and system configuration favorable in point of view of fabrication and system operation. Design calculations were conducted under various heat load conditions to validate the system design. Transient simulations using RELAP5-3D were conducted to investigate system dynamics under transients of interest and to evaluate the system performance in the design condition. The results demonstrated the overall system feasibility that the RCCS design maintains structures temperatures lower than maximum allowable temperature with sufficient system inventory without any active heat sink. In the primary system thermal hydraulics task, the preliminary analysis was performed for a long term pressurized conduction cooldown (PCC) transient. This analysis used a combination of a fully resolved and coarse homogenized mesh to predict the temperature distribution for the steady-state initial condition and the PCC transient. The steady-state initial condition was determined using a fully resolved full core model with 3D solid to 1D fluid coupling. The fully resolved mesh was also used to model the first 20 seconds of the PCC. The temperature difference between fuel pins and the graphite matrix becomes minimal and the dominant heat transfer shifts to a larger scale radially towards the RCCS. After 20 seconds, a homogenized coarse mesh is used, greatly reducing the computational costs of the model. These results demonstrate that the core is designed to passively remove enough decay heat in a protected loss of primary coolant flow to prevent an unsafe rise of core temperatures.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Design of a SMART Valve Testbed for Nuclear Thermal Dispatch
By the year 2050, the United States aims to achieve net-zero carbon emissions. To achieve this target, the licensing of the Light Water Reactor (LWR) fleet has been extended for 20 more years. To stay economically competitive with other power sources such as renewable and fossil-fuel power plants, the U.S. Department of Energy has introduced a plan to modernize the existing LWR fleet and diversify the revenue stream. One of the plans is to dispatch thermal energy to endothermic industrial processes. SMART valves will play an important role in this initiative by efficiently balancing the load by regulating valves in a coordinated manner while monitoring the thermal-hydraulic systems to enhance safety and maintain the integrity of the power plant. This research aims to develop a facility to test the coordinated control algorithm and produce various test results for training the monitoring system. The constructed facility is capable of simulating various operational and accidental scenarios by coordinating all the valves (positions) and pump (flowrate). The facility is developed with an Internet of Things (IoT)-based custom system and a python-based valve position control and coordination mechanism. It has achieved stable sensor outputs, pump control, and coordinated valve regulation in all three valves with minimum obstruction in the system.
22 - GENERAL STUDIES OF NUCLEAR REACTORS↗