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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Development and assessment of a reactor system prognosis model with physics-guided machine learning

Autonomous control systems provide recommendations to help operators in decision-making during plant operations ranging from normal operation to accident management. An important step of autonomous control is prognosis. In nuclear engineering domain, prognosis is the process of predicting future conditions of a system or equipment based on present signs and symptoms of a fault. The prognosis model allows predicting future reactor states for possible candidate control strategies so that the outcomes can be evaluated to determine the best control strategy. The prognosis model requires representing direct relationships between the symptoms and the predictions. In nuclear engineering, computational simulations are approximate representations of the operation of the real system. However, prognosis with computational simulations requires high computation power and time due to possible large number of scenarios. Necessary computation resources can be reduced with machine learning (ML) approach for fast predictions by building a surrogate function using the simulation data. A critical issue is, ML models are ignorant of physical knowledge, and these models approximate statistical relationships between the system variables. This ignorance can produce results that are inconsistent with physical laws, even if an optimal result is achieved from a mathematical point of view. Physics-guided machine learning (PGML) is an approach to tackle this issue. Here, this work formulates and illustrates a framework to guide development and assessment of the ML-based prognosis model for autonomous control systems. The development of the prognosis model considers the training of a ML model which consists of optimizing many aspects of the ML approach. The assessment of the prognosis model considers training data limitations and uncertainties of the ML approach. Prognosis models with standalone ML and PGML are developed and assessed on the loss-of-flow scenario of Experimental Breeder Reactor II. The results indicate that PGML based prognosis model has the best performance compared to other prognosis models.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Roadmap for thermal property measurements of Molten Salt Reactor systems

Thermal properties of molten salts, both with and without fissile and fertile material, are fundamental to modeling and simulation that supports design, construction, operation, and accident progression evaluation of molten salt reactors. This roadmap provides guidance to the research community on the types and amounts of required data for base, or carrier, salt systems, recommends simulated burnup fuel salt compositions to evaluate, and provides a technical foundation for a focused salt property evaluation matrix. The property values generated from both computation and experimental methods are used to develop models that constitute the Molten Salt Thermal Properties Database (MSTDB). While the building blocks of MSTDB are fundamental in nature, its data facilitates constructing engineering models that allow stakeholders to investigate the behavior of specific compositions of interest and supports broader modeling and simulation of MSRs through coupling to multi-physics, multi-scale mass accountancy tools.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Reactor systems for recovering metals, and related methods

A method of recovering metals from electronic waste comprises providing a powder comprising electronic waste in at least a first reactor and a second reactor and providing an electrolyte comprising at least ferric ions in an electrochemical cell in fluid communication with the first reactor and the second reactor. The method further includes contacting the powders within the first reactor and the second reactor with the electrolyte to dissolve at least one base metal from each reactor into the electrolyte and reduce at least some of the ferric ions to ferrous ions. The ferrous ions are oxidized at an anode of the electrochemical cell to regenerate the ferric ions. The powder within the second reactor comprises a higher weight percent of the at least one base metal than the powder in the first reactor. Additional methods of recovering metals from electronic waste are also described, as well as an apparatus of recovering metals from electronic waste.

Lister, Tedd E.↗
DOE OSTI2020

Sensor Drift Estimation for Reactor Systems by Fusing Multiple Sensor Measurements

A nuclear power plant is instrumented with a variety of sensors that monitor its variables to estimate the state and initiate safety actions, if needed. We address the problem of estimating drifts or errors in sensor measurements due to factors such as calibration changes. We propose an information fusion method that uses measurements from other sensors to generate an estimate of a sensor measurement, and its difference from an actual measurement provides an error estimate. We present two fusers based on the ensemble of trees and support vector machine that are trained using sensor measurements collected at an emulated test loop of a pressurized water reactor under no-drift conditions. We present error estimates for a differential pressure sensor of the heat exchanger of the primary coolant system, under twenty controlled scenarios using the test loop. Both positive and negative errors are captured by both methods in scenarios involving calibration drifts, blocking, air gap and electromagnetic interference. The root mean square errors of estimated drifts are typically within 2% percent of the maximum.

Rao, Nageswara↗
DOE OSTI2021

Detailed analyses of a TRISO-fueled microreactor: Modeling of a Micro-Reactor System using NEAMS Tools

Our analysis provides preliminary confirmation of the feasibility of applying the NEAMS codes for multiphysics simulations to model load following transients and accidental scenarios. Detailed demonstration was performed on a simple unit-cell model to perform transient simulations, while verifying accuracy of the multiphysics solution obtained with the NEAMS codes. Results on the full core focused on showcasing the multiphysics workflow rather than detailed verification of the results, at least for this report.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Optimization Modeling for Advanced Syngas to Olefin Reactive Systems

Reactor designs with mixed catalysts play an important role in transforming a multiple reactor system to single-shot reactors. In addition to savings in capital and ease of implementation, single-shot reactors are useful to break equilibrium limitations, thereby increasing the yield and selectivity of desired product as shown in previous studies. However, the nonlinear and highly exothermic nature of mixed-catalyst systems makes it difficult for commercial process simulation and optimization tools to optimize these systems. This study describes the development and application of optimization strategies for mixed-catalyst, single-shot reactors for syngas to olefin (STO) processes. Finding the optimal catalyst distribution is challenging and requires advanced solution strategies for singular optimal control problems, which are poorly conditioned and often lead to flat response surfaces. The graded bed and partial-moving finite-element approaches are used to find the optimal catalyst distribution that maximizes the olefins yield. A 1.3% increase in the yield is observed from one zone to three zones. The yield further improves from three zones to the exact infinite dimensional solution by 0.2%. This improvement can be realized in practice by changing only the catalyst distribution, without any extra investment. Lastly, the results suggest that a mixed-catalyst single shot reactor bed can be applied to other reaction mechanisms to increase reactor performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A comparative study on deep learning models for condition monitoring of advanced reactor piping systems

Advanced nuclear reactors offer innovative applications due to their portability, reliability, resiliency, and high capacity factors. To operate them on a wider scale, reducing maintenance life-cycle costs while ensuring their integrity is essential. Autonomous operations in advanced nuclear reactors using augmented Digital Twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. A key component of nuclear DT frameworks is the condition monitoring of safety systems, such as piping-equipment systems, which involves acquiring and monitoring the plant’s sensor data. Here, this research proposes a condition monitoring methodology utilizing deep learning algorithms, such as multilayer perceptions (MLP) and convolutional neural networks (CNNs), to detect degradation and its severity in nuclear piping-equipment systems. Sensor signals are processed to obtain the power spectral density and the Short-Time Fourier transform, and feature extraction methodologies are proposed to develop degradation-sensitive data repositories. The performance of MLP, one-dimensional (1D) CNN, and 2D CNN within the proposed condition monitoring framework is compared using a finite element model of a 3D piping system subjected to seismic loads as the application case study. Various approaches, such as dropout, k-Fold validation, regularization, and early stopping of training the network, are investigated to avoid overfitting the models to the input sensor data. The predictive capability and computational capacity of the deep learning algorithms are also compared to detect degradation in the Z-pipe system of the Experimental Breeder Reactor II (EBRII). The Z-pipe system is subjected to harmonic excitations that represent normal operating loads, such as pump-induced vibrations. The findings of the study indicate that the proposed artificial intelligence (AI)-driven condition monitoring framework demonstrates superior prediction accuracies with a 2D CNN, whereas the MLP exhibits higher computational efficiency.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Enhancements to the New TREAT Automatic Reactor Control System (ARCS)

The Transient Reactor Test (TREAT) facility recently replaced the automatic reactor control system (ARCS). Here, this paper discusses some of the enhancements that were made during the ARCS replacement such as calculations to take into account the nonadiabatic effects and increasing the dynamic range for power and period indication. This paper also discusses upgrades that had been planned but were unable to implemented and potential upgrades for the future.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Investigation of Frozen Chemistry for Molten Salt Reactors (MSRs)

Understanding accident progression and the potential/conditions for fission product release from fuel is necessary to evaluate safety for any nuclear reactor system. Molten Salt Reactors (MSRs) under development need such analysis to support safety evaluations. Fission product chemistry specific to MSR concepts is a critical area that introduces distinct considerations relative to the current state-of-knowledge in reactor safety, primarily developed for water-moderated nuclear reactor systems. In Light Water Reactor (LWR) systems, it is necessary to capture the chemical interaction of fission products with the reactor evironment, containment and confinement systems. The overall effects at this point are relatively well understood for the purposes of performing safety evaluations. A key insight from LWR studies is that fission product chemical behavior can be reasonably captured by modeling approaches where the chemistry is "frozen". These modeling approaches assume that radionuclide reaction and speciation can be represented by chemical classes, each with characteristic transport behavior that is invariant under a broad range of thermochemical conditions. However, radionuclides can exhibit a range of behavior in the liquid salt-melt phase of the coolant used in MSRs. Radionuclides, salt, and the metal containment surfaces (i.e. pipes) can co-exist in dynamic equilibrium that could evolve with small system mass changes. A detailed investigation to the degree the equilibrium state can dynamically evolve with changes in the conditions of the molten salt mixture has not been previously conducted. It is currently not well understood where frozen chemistry assumptions are valid. Expanding the state-of-knowledge in this regard is relevant to better assessing the range of chemical effects that should be incorporated as part of MSR safety assessments. This investigation used the Oak Ridge Isotope GENeration (ORIGEN) module of the Standardized Computer-Analysis for Licensing Evaluation (SCALE) code to generate simulated radionuclide inventories for the MSR Experiment (MSRE) and then modeled reactor chemical speciation using the Molten Salt Thermodynamic Database – Thermochemical (MSTDB-TC) coupled with Thermochimica. The effect of composition variation during decay of fission product inventory in a molten salt over a period of 500 days prolonged post- at multiple temperatures was studied. Mass fractions for fluorine and berilium were varied in order to probe the effects of free fluorine control. Finally, speciation of fluoride reactors were showed by comparing MSRE readionuclide inventories with a FLiBe based molten salt breeder reactor (MSBR). The results showed that fission product mass change has little effect on phase mass changes and vapor pressures for fluoride species, but differ with varying carrier and fuel salt compositions. However, iodine species were found to have a vapor pressure not only dependent on temperature, but also the free fluorine potential, releasing iodine when the free fluorine potential is equal to the iodine inventory. This observation, however, arose under free fluorine potentials that are very unlikely to be realized in typical molten salt mixtures. Despite this observation, temperature was found to be the dominant parameter that drove phase change and fission product species vapor pressure. The results indicate that the current frozen chemistry approach is adequate for MSR analysis.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

A System-Level Model for Estimating Residual Strain and Life of Nuclear Reactor Coolant System Components Under Connected-System-Thermal–Mechanical Boundary Conditions

Background: Environmental-assisted fatigue (EAF) is a major issue for the long-term survival of nuclear power plant fleets in the U.S. and worldwide. Multi-material welded regions (e.g., nozzles) and other high-stress regions of reactor coolant system (RCS) components are prone to EAF-related damage. Objective: The discussed work describes a system-level finite element (FE) model of RCS components of a pressurized water reactor (PWR). This is with the goal of predicting the stress hotspots, strain residuals, strain amplitudes and the resulting fatigue lives. Methods: Herein, the FE model was developed considering system-level loading conditions (under connected system thermal-mechanical boundary conditions). Thermal-mechanical stress analysis was performed considering thermal stratification and a design-basis reactor loading cycle. Based on the FE model results, the strain residuals, strain amplitudes and resulting fatigue lives of RCS components were predicted. Results: The results show that some of the RCS components can have significantly different strain amplitudes, residual strain, and fatigue lives, despite having similar geometry and material. Higher residual strain can lead to accelerated cyclic hardening of material and the associated effect of EAF. The simulated component-level strain profile (under realistic multi-axial-multi-physics loading cycle) can guide the selection of appropriate test inputs for conducting laboratory-scale EAF tests, which is a focus of future works. Conclusions: Despite similar geometry and material the RCS component can have significantly different strain profiles and resulting fatigue lives. The discussed approach can help to identify and prioritize the RCS components for conducting expensive nondestructive evaluation (NDE) inspections.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗