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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Statistical interpretation of sterile neutrino oscillation searches at reactors

A considerable experimental effort is currently under way to test the persistent hints for oscillations due to an eV-scale sterile neutrino in the data of various reactor neutrino experiments. The assessment of the statistical significance of these hints is usually based on Wilks’ theorem, whereby the assumption is made that the log-likelihood is χ 2 -distributed. However, it is well known that the preconditions for the validity of Wilks’ theorem are not fulfilled for neutrino oscillation experiments. In this work we derive a simple asymptotic form of the actual distribution of the log-likelihood based on reinterpreting the problem as fitting white Gaussian noise. From this formalism we show that, even in the absence of a sterile neutrino, the expectation value for the maximum likelihood estimate of the mixing angle remains non-zero with attendant large values of the log-likelihood. Our analytical results are then confirmed by numerical simulations of a toy reactor experiment. Finally, we apply this framework to the data of the Neutrino-4 experiment and show that the null hypothesis of no-oscillation is rejected at the 2.6 σ level, compared to 3.2 σ obtained under the assumption that Wilks’ theorem applies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Accuracy and convergence of coupled finite-volume/Monte Carlo codes for plasma edge simulations of nuclear fusion reactors

The plasma and neutral transport in the plasma edge of a nuclear fusion reactor is usually simulated using coupled finite volume (FV)/Monte Carlo (MC) codes. However, under conditions of future reactors like ITER and DEMO, convergence issues become apparent. This paper examines the convergence behaviour and the numerical error contributions with a simplified FV/MC model for three coupling techniques: Correlated Sampling, Random Noise and Robbins Monro. Also, practical procedures to estimate the errors in complex codes are proposed. Moreover, first results with more complex models show that an order of magnitude speedup can be achieved without any loss in accuracy by making use of averaging in the Random Noise coupling technique.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Development of a Convolutional Neural Network Classifier for Data Starved Spectra - 20199

The Institute for Clean Energy Technology (ICET) at Mississippi State University is exploring the utility of machine learning in augmenting its mobile radiation surveying platforms, which are currently being developed as means to survey depleted uranium contaminated areas in support of remediation and decommissioning efforts. Mobile survey platforms provide a means to efficiently scan large areas of interest while reducing human exposure to radiation and other hazards. The survey platforms can also be used for scanning for any gamma emitting isotope in addition to depleted uranium. The spectral data that the platforms collect may be data starved with relatively low counts and poorly defined spectral features depending on the speed of the platforms and scintillation detector selection. Such data-starved spectra are difficult to use for isotope identification, requiring advanced knowledge of the possible radionuclides that could be present and environmental factors that could attenuate signals or introduce background noise. These factors in combination with the volume of survey data increases the time it takes to perform analysis of survey data when the source type is unknown. There are a number of algorithms in the field of machine learning that can be used to classify data that would be challenging and time-consuming for a human to identify. Supervised machine learning algorithms train models based on extensive amounts of human-labeled training data. Once sufficiently trained, these models can be used to quickly make high-fidelity predictions on new data. Convolutional neural networks are machine learning algorithms that excel in learning representations of 'shapes'. They do this by taking numerical input data and convolving them with spatial feature detectors referred to as filters. These filters are incrementally adjusted to reduce the prediction error on the data during the backpropagation step of training. Discussed in this paper is the development of a convolutional neural network classifier (CNNC) that can utilize spectral survey data for source discrimination and isotope identification. Bench-top laboratory experiments data using LaBr{sub 3}(Ce) scintillation detectors were used to train and evaluate the performance of the developed CNNC. The CNNC is capable of discriminating a variety of gamma emitting source types, differentiating different forms of uranium (depleted vs. natural), and estimating the amount of uranium for a known geometry. The discussed CNNC may be useful in scenarios where survey systems are deployed in situations where hazardous radioactive material maybe present, but the type is unknown. When used in remediation applications the CNNC can be used to screen-out false positives, helping reduce remediation costs. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2019

Results from 2015 and the 2016 upgrade of the CONNIE experiment for detecting coherent neutrino nucleus scattering

The CONNIE Experiment (Coherent Neutrino Nucleus Interaction Experiment) is currently collecting reactor neutrino data to search for the undiscovered standard model process of coherent neutrino-nucleus scattering (CNNS). The detector is composed of a silicon target of thick, fully-depleted, low-noise CCD detectors. Results from data collected in 2015 indicate backgrounds are controlled, and allow an estimate of sensitivity to be presented for a larger scale detector. A 2016 upgrade, adding additional target mass, and reducing readout noise, has been performed, increasing the total yield of signal events by a factor of 30, and already yielding science-quality data. Low-energy nuclear calibrations have been performed, enabling calibration down to the device energy threshold. An estimate of the sensitivity expected for measuring the coherent neutrino process is presented. Future prospects with improved detector energy thresholds are estimated.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1989

Conceptual arcjet system design considerations for the SP-100 mission

Conceptual spacecraft design considerations for using 30-kW class arcjet engines as an active load for a 100-kWe SP-100 space nuclear reactor power system flight experiment are discussed. These design considerations involve configuration trades; interfaces with power, attitude and velocity control, thermal management, orbit insertion and reaction control propulsion systems; imposed environments concerned with electromagnetic noise, survivability, and ammonia exhaust plumes; and associated diagnostic instrumentation.

Zafran, Sidney↗
DOE OSTI2012

Simultaneous {sup 233}U and{sup 235}U characterization through the assay of delayed neutron temporal behavior

Aqueous solutions containing dissolved uranium-233 and uranium-235 were irradiated for 60's in the SLOWPOKE-2 reactor at the Royal Military College of Canada. The temporal behavior of the delayed neutrons produced was recorded by the Facility's Delayed Neutron Counting (DNC) system. The percentage of uranium-233 as a function of total fissile mass present in each sample ranged from 0 to 100% and was predicted by the DNC system with average absolute errors of {+-} 4%. Future work will upgrade the system electronics and software to reduce both uncertainties in timings and electrical noise. Mixture analysis will also be expanded to include plutonium-239 and fissile materials contained in non-aqueous matrices. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

Investigation of Magnetic Signatures and Microstructures for Heat-Treated Ferritic/Martensitic HT-9 Alloy

There is increased interest in improved methods for in-situ nondestructive interrogation of materials for nuclear reactors in order to ensure reactor safety and quantify material degradation (particularly embrittlement) prior to failure. Therefore, a prototypical ferritic/martensitic alloy, HT-9, of interest to the nuclear materials community was investigated to assess microstructure effects on micromagnetics measurements – Barkhausen noise emission, magnetic hysteresis measurements, and first-order reversal curve analysis – for samples with three different heat-treatments. Microstructural and physical measurements consisted of high-precision density, resonant ultrasound elastic constant determination, Vickers microhardness, grain size, and texture. These were varied in the HT-9 alloy samples and related to various magnetic signatures. In parallel, a meso-scale microstructure model was created for alpha iron and effects of polycrystallinity and demagnetization factor were explored. It was observed that Barkhausen noise emission decreased with increasing hardness and decreasing grain size (lath spacing) while coercivity increased. The results are discussed in terms of the use of magnetic signatures for nondestructive interrogation of radiation damage and other microstructural changes in ferritic/martensitic alloys.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2012

Exploration of High-dimensional Scalar Function for Nuclear Reactor Safety Analysis and Visualization: A User's Guide to TopoXG*

Large-scale simulation datasets can be modeled as high-dimensional scalar functions defined over a discrete sample of the domain. The goals of our proposed research are two-fold. First, we would like to provide structural analysis of a function at multiple scales and provide insight into the relationship between the input parameters and the output. Second, we enable exploratory analysis for users, where we help the users to differentiate features from noise through multi-scale analysis on an interactive platform, based on domain knowledge and data characterization. TopoXG is a software package that is designed to address these goals. The unique contribution of TopoXG lies in exploiting the topological and geometric properties of the domain, building statistical models based on its topological segmentations and providing interactive visual interfaces to facilitate such explorations. We provide a user’s guide to TopoXG, by highlighting its analysis and visualization capabilities, and giving several use cases involving datasets from nuclear reactor safety simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Progress Towards Prognostic Health Management of Passive Components in Advanced Reactors – Model Selection and Evaluation

This paper presents recent progress towards developing a prognostic health management framework for passive components of advanced reactors (AR). The focus of this paper is on lifecycle prognostics for passive components using a Bayesian prognostic algorithm that provides a natural framework for incorporating different sources of variability, and uncertainties inherent in the operations of AR. High-temperature creep damage, a prototypic failure mechanism in AR materials, is used as the context for this research. A Bayesian model selection approach is implemented to select the appropriate creep degradation model at any given time, using relevant sensor measurements reflecting the material degradation state. The model selection approach, based on reversible jump Markov chain Monte Carlo methods, is integrated with Bayesian particle filter-based prognostic framework. The proposed approach is evaluated using strain measurements obtained from accelerated creep testing of stainless steel specimens. Results indicate feasibility of the proposed approach in accurately identifying the creep degradation stage from the available measurements at a given time. Effect of uncertainties in material degradation model and measurement noise on the performance of the prognostic algorithm is also investigated.

advanced reactor prognostics↗
DOE OSTI2018

DEMAND, a Dimensional Extreme Magnetic Neutron Diffractometer at the High Flux Isotope Reactor

A two-dimensional (2D) Anger camera detector has been used at the HB-3A four-circle single-crystal neutron diffractometer at the High Flux Isotope Reactor (HFIR) since 2013. The 2D detector has enabled the capabilities of measuring sub-mm crystals and spin density maps, enhanced the efficiency of data collection and phase transition detection, and improved the signal-to-noise ratio. Recently, the HB-3A four-circle diffractometer has been undergoing a detector upgrade towards a much larger area, magnetic-field-insensitive, Anger camera detector. The instrument will become capable of doing single-crystal neutron diffraction under ultra-low temperatures (50 mK), magnetic fields (up to 8 T), electric fields (up to 11 kV/mm), and hydrostatic high pressures (up to 45 GPa). Furthermore, half-polarized neutron diffraction is also available to measure weak ferromagnetism and local site magnetic susceptibilities. With the new high-resolution 2D detector, the four-circle diffractometer has become more powerful for studying magnetic materials under extreme sample environment conditions; hence, it has been given a new name: DEMAND.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Variance reduction techniques for Monte Carlo neutron noise simulations

The small fluctuations of the neutron flux caused by small perturbations of the macroscopic cross-sections take the name of neutron noise. Advanced Monte Carlo methods have been recently proposed in order to solve the neutron noise equations in the frequency domain, which allows establishing reference solutions to validate faster but approximate deterministic solvers. Due to the presence of particles carrying two statistical weights (for the real and imaginary components of the noise field), both of which may be positive or negative, Monte-Carlo simulations of neutron noise pose distinct challenges in terms of variance reduction. In this work we investigate two variance-reduction techniques, namely branchless collisions and weight cancellation, and probe their effectiveness for a benchmark con- figuration concerning the noise field induced by a pin with oscillating cross sections in a fuel assembly.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Examination of decay heat measurements and their relevance for understanding the origin of the reactor antineutrino anomaly

Measurements of the decay energy released as a function of time following the thermal neutron induced fission of 235 U and 239,241 Pu were performed in the 1970s at Oak Ridge National Laboratory with the purpose of quantifying possible loss of coolant accident scenarios. The derivative of this decay energy with respect to time, known in technical parlance as decay heat, is mainly composed of two terms, that of the electrons produced together with antineutrinos in the $β$-minus decay of the neutron-rich fission products, and that of the $γ$ rays produced in the subsequent decay of excited nuclear levels. In this work we study if this extensive set of decay energy measurements can be used to assess the reactor antineutrino anomaly, that is, the approximately 5% deficit of electron antineutrinos produced by nuclear reactors, first deduced by Mention and collaborators in 2011, and observed by the major reactor antineutrino experiments since. Furthermore, with the assistance of nuclear databases, we are able to obtain the ratio of electron spectra under equilibrium conditions for 235 U to 239 Pu, in better agreement with the lower trend recently reported by Kopeikin and collaborators, as well as those for 235 U to 241 Pu and 241 Pu to 239 Pu, which do not agree well with those measured at the Institut Laue-Langevin in the 1980s. We conclude that a new experimental campaign is needed to measure the electron spectra utilizing a high resolution and signal-to-noise-ratio electron spectrometer and a highly precise fission normalization procedure.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Characterization and Quantification of Radiation-Induced Clusters/Precipitates in RPV Steels Using STEM-EDS and Machine Learning

Over the operational lifespan of a nuclear reactor, reactor pressure vessel (RPV) steels are subjected to significant neutron irradiation, resulting in complex microstructural changes and the consequent degradation of mechanical properties. Various physically motivated correlation models have been developed to predict neutron irradiation-induced embrittlement of RPVs under different irradiation conditions. However, the efficient and accurate characterizations and quantification of radiation-induced clusters in RPVs are still challenging, which will affect the precision of the predictive models for embrittlement of RPV components. In the DOE Visiting Faculty Program (VFP) research work at Oak Ridge National Lab (ORNL), I integrate machine learning to aid Scanning Transmission Electron Microscopy – Energy Dispersive X-ray Spectroscopy (STEM-EDS) analyses, which improve the characterization and quantification of radiation-induced clusters in RPV steels, thereby enabling more accurate predictions of material behavior under irradiation. The surveillance base- and welded- RPV steels were annealed at various temperatures of 340 °C, 450 °C and 500 °C for up to 168 hours, respectively. Afterwards, I have characterized radiation-induced clusters using advanced STEM-EDS techniques and subsequently applying machine learning algorithms to analyze and refine STEM-EDS datasets, enhancing the quantification of clusters compositions and distributions. In the end, an efficient workflow for integrating STEM-EDS data analysis with machine learning to address challenges including noise reduction has been developed. The completion of this VFP work will support bridge critical gaps in the accurate quantification of radiation-induced clusters in RPV steels using STEM-EDS and support the development of more precise models for predicting RPV embrittlement in the Light Water Reactor Sustainability program supported by Department of Energy and enhancing the collaboration between ORNL and Alred University. The outcome of the VFP project will leverage a few research papers submission to peer-reviewed journals in the relevant scientific field and a few oral presentations at national and international conferences.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Anomaly Detection in Liquid Sodium Cold Trap Operation with Multisensory Data Fusion Using Long Short-Term Memory Autoencoder

Sodium-cooled fast reactors (SFR), which use high temperature fluid near ambient pressure as coolant, are one of the most promising types of GEN IV reactors. One of the unique challenges of SFR operation is purification of high temperature liquid sodium with a cold trap to prevent corrosion and obstructing small orifices. We have developed a deep learning long short-term memory (LSTM) autoencoder for continuous monitoring of a cold trap and detection of operational anomaly. Transient data were obtained from the Mechanisms Engineering Test Loop (METL) liquid sodium facility at Argonne National Laboratory. The cold trap purification at METL is monitored with 31 variables, which are sensors measuring fluid temperatures, pressures and flow rates, and controller signals. Loss-of-coolant type anomaly in the cold trap operation was generated by temporarily choking one of the blowers, which resulted in temperature and flow rate spikes. The input layer of the autoencoder consisted of all the variables involved in monitoring the cold trap. The LSTM autoencoder was trained on the data corresponding to cold trap startup and normal operation regime, with the loss function calculated as the mean absolute error (MAE). The loss during training was determined to follow log-normal density distribution. During monitoring, we investigated a performance of the LSTM autoencoder for different loss threshold values, set at a progressively increasing number of standard deviations from the mean. The anomaly signal in the data was gradually attenuated, while preserving the noise of the original time series, so that the signal-to-noise ratio (SNR) averaged across all sensors decreased below unity. Results demonstrate detection of anomalies with sensor-averaged SNR < 1.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

SPARTA: A flux adjustment methodology to interpret complex experiments

For the accurate determination of reactivity from a detector count rate, correction of spatial effects is of prime importance. This spatial correction is often provided using simulation methodologies, but this may introduce a bias if the result of the experiment is also used as input data for the simulation. Here, this work presents a flux adjustment methodology able to infer experimental reactivity and correction of spatial effects without the need for a simulation. It can process the signal from a complex experiment such as a heat balance measurement in the TREAT reactor, where control rods are continuously adjusted to maintain a constant power. In the present work, this methodology successfully computed the reactivity and the local spatial variation of the flux of a generated signal. It also proved to be robust against noise and errors on kinetic parameters and provides a credible interpretation of a heat balance experiment in TREAT. Efficiency of flux adjustment methods for complex experiment enable a better experiment interpretation less reliant on nuclear data evaluation.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Gamma Neutron Correlated Noise of the EUCLID Critical Experiment

In this work, we present results from gamma noise measurements of a fast plutonium critical system for the first time. These results are compared to the more traditional method of neutron noise. Additionally, we investigate cross correlations of neutron and gamma signals and discuss the advantages of doing so.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Practical Insights on Applying Simulation-Based Control Methods in Experimental Studies

Advanced nuclear reactors are crucial to the future of energy both in the United States and around the globe. In contrast to the current operating fleet, they are characterized as being deployable in remote locations and able to operate in semi-autonomous or autonomous fashion. This leap forward necessitates a new reactor control paradigm. Because advanced nuclear reactors are still under development in the United States, the creation of new control methods to achieve autonomous operations has been based on systems modeling and simulation. However, an important factor in successfully deploying these new control methods is the ability to seamlessly transition from simulation environments to real-world settings. Control methods tested in both simulation and experimental settings need to be investigated in the context of advanced reactor applications. This work developed a series of simple controllers for Idaho National Laboratory (INL)’s Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor operating in load-following scenarios. These controllers were tested in both simulation and experimental settings, and a comparative performance analysis was performed. The simulation tests leveraged the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND) software developed in a previous stage of the current effort, along with the MARVEL Reactor Excursion and Leak Analysis Program (RELAP5-3D) and Monte Carlo N-Particle (MCNP) models. The experimental tests leveraged the COMMAND software, MARVEL models, and the U.S. Department of Energy Microreactor Program’s Microreactor Automated Control System (MACS). MACS was developed to serve as a control method testbed. It was customized to mirror the MARVEL microreactor, and COMMAND enabled MACS to emulate the physics of MARVEL. The load-following controller was developed using the simulation platform, with efforts to emulate real systems by introducing actuator saturation and noise. These factors were incrementally accounted for in the controller design. After finalizing the controller design, it was implemented with the experimental setup. The experimental conditions tested included an initial test under conditions similar to the final simulation test, and two additional scenarios. The first scenario introduced additional actuator saturation to account for equipment aging over time, which was unknown to the controller. The second scenario introduced sensor delay, a phenomenon anticipated with the use of remote operations or wireless communication in advanced reactors. These tests revealed several notable differences. While the controller performed well in simulation, it exhibited several limitations when transitioning to hardware. The main challenges involved maintaining the steady-state target power, as evidenced by larger error values between the true reactor power and setpoint power, as well as persistent oscillations in controlled reactor power. These issues could lead to unacceptable transient conditions in real reactor testing. Introducing actuator aging and stochastic delays in the experimental setup significantly impacted controller performance, resulting in increased overshoot and undershoot, and exacerbated error and oscillations previously mentioned. These findings underscore the importance of experimental testbeds for testing and validating control methods, as controllers developed using only theory and/or simulation may perform unexpectedly when applied to actual hardware. This research emphasizes the need for an experimental testbed for achieving such validation.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗