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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Expanded Accident Scenarios and Frequency Characteristics for a Fast Spectrum Molten Salt Reactor in an Integrated Energy System

Integration of advanced nuclear reactors to industrial processes in an integrated energy system (IES) has many potential advantages. The coupling of nuclear power to varied industrial processes introduces additional accident scenarios unique to the specific systems included in the IES. Similarly, noise in one system of the IES could potentially create negative effects in the other systems. Because of the limited amount of comparable nuclear IES operating experience, there is added value in scoping the inherent behavior of these systems in response to these transients. Here, this paper investigates the safety-related behavior of an open-source IES dynamic model of a fast spectrum molten salt reactor (MSR) powering a regenerative Rankine cycle for electricity production and a hybrid sulfur (HyS) cycle for hydrogen production. The simulations include a loss of cooling water to the Rankine cycle, a loss of sulfur in the HyS cycle, and the frequency characteristics of the entire IES across six orders of magnitude of frequency. The results show safe behavior in response to the two accident scenarios, wherein a disruption in heat transfer leads to increased salt temperatures and a decrease in reactor power. The frequency analysis shows that at higher frequencies, temperature changes propagating through an IES are increasingly damped to the point of negligibility.

Dunkle, Nicholas [Univ. of Tennessee, Knoxville, T↗
NASA NTRS1991

Hole-Impeded-Doping-Superlattice LWIR Detectors

Hole-Impeded-Doping-Superlattice (HIDS) InAs devices proposed for use as photoconductive or photovoltaic detectors of radiation in long-wavelength infrared (LWIR) range of 8 to 17 micrometers. Array of HIDS devices fabricated on substrates GaAs or Si. Radiation incident on black surface, metal contacts for picture elements serve as reactors, effectively doubling optical path and thereby increasing absorption of photons. Photoconductive detector offers advantages of high gain and high impedance; photovoltaic detector offers lower noise and better interface to multiplexer readouts.

Maserjian, Joseph↗
DOE OSTI2023

Machine Learning Classification of Molten Salt Heat Exchanger Channel Plugging using Synthetic Data

This report addresses the requirements of Milestone M3.4 AI capability to identify and predict maintenance events. Development of digital twins (DT) for molten salt reactor (MSR) components is crucial for reducing operating and maintenance costs (O&M) and ensuring commercial viability of these reactors. Our focus is on development of DT for MSR primary system heat exchanger (HX), a critical component, the fault in which can reduce operating efficiency and force reactor shutdown. We are investigating the feasibility of a conceptual DT of HX consisting of internal distributed temperature sensing with fiber optics and machine learning (ML) algorithms to detect and localize faults. To determine the optimal approach to detection and localization of channel plugging, we benchmark seven different ML models: Logistic Regression, K-Nearest Neighbors (KNN), Gaussian Naïve Bayes, Support Vector Machines (SVM), Decision Tree Classifier, Random Forest Tree Classifier, and Feed-Forward Neural Network. ML algorithms are benchmarked using synthetic HX plugging data generated with computational fluid dynamics COMSOL software, with added brown noise to represent experimental noise. We show that the best performance is obtained with the Decision Tree classifier.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2013

Johnson Noise Thermometry System Requirements

This document is intended to capture the requirements for the architecture of the developmental electronics for the ORNL-lead drift-free Johnson Noise Thermometry (JNT) project conducted under the Instrumentation, Controls, and Human-Machine Interface (ICHMI) research pathway of the U.S. Department of Energy (DOE) Advanced Small Modular Reactor (SMR) Research and Development (R&D) program. The requirements include not only the performance of the system but also the allowable measurement environment of the probe and the allowable physical environment of the associated electronics. A more extensive project background including the project rationale is available in the initial project report [1].

Britton, Jr., Charles L.↗
DOE OSTI2021

Performance of Compact Pulsed Thermal Imaging System for In-Service Applications. Pulsed thermal tomography nondestructive examination of additively manufactured reactor materials and components

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of complex topology nuclear reactor parts from high-strength corrosion resistance alloys, such as stainless steel and Inconel. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which has the capability of melting metallic powder and net shaping the structures with relatively high precision. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to creep in high temperature nuclear reactor environment. Currently, there exist limited capabilities to evaluate actual AM structures nondestructively. Pulsed Thermography (PT) imaging provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. The data cube of PT measurements consists of surface temperature taken at sequential time intervals T(x,y,t). Material defects can be detected either by analyzing the thermograms T(x,y,t) data cube, or by using thermal tomography (TT) algorithm to obtain 3D spatial reconstruction of thermal effusivity e(x,y,z). To reduce the cost and enable in-service NDE in spatially constrained environment, it is highly desirable to develop PT with compact and inexpensive IR camera. Following initial qualification of an AM component for deployment in a nuclear reactor, a compact PT system can also be used for in-service nondestructive evaluation (NDE) applications. However, data cube obtained with PT based on compact IR camera suffers from strong thermal noises and loss of features due to relatively low sampling rate. In this report we describe two unsupervised machine learning (ML) algorithms for enhancement of PT images obtained with compact IR camera. In one approach, we introduce Sparse Coding Discrete Cosine Transform (SC/DCT) algorithm to remove additive white Gaussian noise (AWGN) from spatial thermal effusivity reconstructions. In another approach we introduce a Spatial Temporal Denoised Thermal Source Separation (STDTSS) ML algorithm to process thermograms. The STDTSS algorithm consists of spatial and temporal denoising using Gaussian and Savitzky–Golay filtering, followed by the matrix decomposition using Principal Component Analysis (PCA), and Independent Component Analysis (ICA) to automatically detect flaws. In the work described in this report, we constructed a compact PT system using a relatively small and low-cost FLIR A65 camera, consisting on uncooled microbolometer detector. Performance of SC/DCT algorithm was demonstrated on enhancing TT images of Inconel 718 AM plate. Performance of the STDTSS methods was investigated using thermography data obtained from imaging stainless steel 316L specimens produced with LPBF method with imprinted calibrated porosity defects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

FHR Process Instruments

Fluoride salt-cooled High temperature Reactors (FHRs) are entering into early phase engineering development. Initial candidate technologies have been identified to measure all of the required process variables. The purpose of this paper is to describe the proposed measurement techniques in sufficient detail to enable assessment of the proposed instrumentation suite and to support development of the component technologies. This paper builds upon the instrumentation chapter of the recently published FHR technology development roadmap. Locating instruments outside of the intense core radiation and high-temperature fluoride salt environment significantly decreases their environmental tolerance requirements. Under operating conditions, FHR primary coolant salt is a transparent, low-vapor-pressure liquid. Consequently, FHRs can employ standoff optical measurements from above the salt pool to assess in-vessel conditions. For example, the core outlet temperature can be measured by observing the fuel s blackbody emission. Similarly, the intensity of the core s Cerenkov glow indicates the fission power level. Short-lived activation of the primary coolant provides another means for standoff measurements of process variables. The primary coolant flow and neutron flux can be measured using gamma spectroscopy along the primary coolant piping. FHR operation entails a number of process measurements. Reactor thermal power and core reactivity are the most significant variables for process control. Thermal power can be determined by measuring the primary coolant mass flow rate and temperature rise across the core. The leading candidate technologies for primary coolant temperature measurement are Au-Pt thermocouples and Johnson noise thermometry. Clamp-on ultrasonic flow measurement, that includes high-temperature tolerant standoffs, is a potential coolant flow measurement technique. Also, the salt redox condition will be monitored as an indicator of its corrosiveness. Both electrochemical techniques and optical spectroscopy are candidate fluoride salt redox measurement methods. Coolant level measurement can be performed using radar-level gauges located in standpipes above the reactor vessel. While substantial technical development remains for most of the instruments, industrially compatible instruments based upon proven technology can be reasonably extrapolated from the current state of the art.

FHR↗
DOE OSTI2022

Quantifying the impact of temporal analysis of products reactor initial state uncertainties on kinetic parameters

The temporal analysis of products (TAP) reactor, a transient kinetic tool, provides users with information as the catalyst state evolves. However, the state of the art for TAP uncertainty quantification only considers the experimental noise present in the outlet flow signal. Additional sources of uncertainty, including, but not limited to, initial surface coverages, the catalyst zone location, the inert void fraction, and the gas pulse intensity and pulse delay, are not included. For this reason, a framework for quantifying all uncertainty sources present in TAP experiments is presented and applied to a carbon monoxide oxidation case study. Herein, two methods for quantifying these sources of uncertainty are introduced. The first utilizes initial state sensitivities to approximate the parameter variances, as well as to provide insights into the structural certainty of the model. The second generates parameter confidence distributions through an ensemble-based sampling algorithm. This initial state covariance matrix can ultimately be merged with the experimental noise covariance matrix, providing a unified description of the parameter uncertainties for a single TAP experiment.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Exploring low-energy neutrino physics with the Coherent Neutrino Nucleus Interaction Experiment

The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) uses low-noise fully depleted charge-coupled devices (CCDs) with the goal of measuring low-energy recoils from coherent elastic scattering ($\mathrm{CE}\mathcal{ν}\mathrm{NS}$) of reactor antineutrinos with silicon nuclei and testing nonstandard neutrino interactions (NSI). We report here the first results of the detector array deployed in 2016, considering an active mass 47.6 g (eight CCDs), which is operating at a distance of 30 m from the core of the Angra 2 nuclear reactor, with a thermal power of 3.8 GW. A search for neutrino events is performed by comparing data collected with the reactor on (2.1 kg-day) and reactor off (1.6 kg-day). The results show no excess in the reactor-on data, reaching the world record sensitivity down to recoil energies of about 1 keV (0.1 keV electron equivalent). A 95% confidence level limit for new physics is established at an event rate of 40 times the one expected from the standard model at this energy scale. The results presented here provide a new window to low-energy neutrino physics, allowing one to explore for the first time the energies accessible through the low threshold of CCDs. Finally, they will lead to new constraints on NSI from the $\mathrm{CE}\mathcal{ν}\mathrm{NS}$ of antineutrinos from nuclear reactors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2012

Analysis of BWR OPRM plant data and detection algorithms with DSSPP

All U.S. BWRs are required to have licensed stability solutions that satisfy General Design Criteria (GDC) 10 and 12 of 10 CFR 50 Appendix A. Implemented solutions are either detect and suppress or preventive in nature. Detection and suppression of power oscillations is accomplished by specialized hardware and software such as the Oscillation Power Range Monitor (OPRM) utilized in Option III and Detect and Suppress Solution - Confirmation Density (DSS-CD) stability Long-Term Solutions (LTSs). The detection algorithms are designed to recognize a Thermal-Hydraulic Instability (THI) event and initiate control rod insertion before the power oscillations increase much higher above the noise level that may threaten the fuel integrity. Option III is the most widely used long-term stability solution in the US and has more than 200 reactor years of operational history. DSS-CD represents an evolutionary step from the stability LTS Option III and its licensed domain envelopes the Maximum Extended Load Line Limit Analysis Plus (MELLLA +) domain. In order to enhance the capability to investigate the sensitivity of key parameters of stability detection algorithms, GEH has developed a new engineering analysis code, namely DSSPP (Detect and Suppress Solution Post Processor), which is introduced in this paper. The DSSPP analysis tool represents a major advancement in the method for diagnosing the design of stability detection algorithms that enables designers to perform parametric studies of the key parameters relevant for THI events and to fine tune these system parameters such that a potential spurious scram might be avoided. Demonstrations of DSSPPs application are also presented in this paper utilizing actual plant THI data. A BWR/6 plant had a plant transient that included unplanned recirculation pump transfer from fast to slow speed resulting in about 100% to {approx}40% rated power decrease and about 99% to {approx}30% rated core flow decrease. As the feedwater temperature is reduced to equilibrium conditions, the power increased from about {approx}40% to about {approx}60% with little change inflow. A THI event developed and subsequently, an OPRM initiated scram occurred with Option III. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Sensor Anomaly Detection for Nuclear Reactor Systems Utilizing Linear Regression and K-Means Unsupervised Machine Learning

Nuclear reactors and related systems are becoming increasingly complex due to advancing technologies in next-generation power reactors. This increased complexity necessitates enhanced automation and data management capabilities. To successfully realize autonomous systems, methods must be developed to handle vast volumes of data and effectively distinguish anomalous data from noise and expected data. While impressive models utilizing digital twins and similar approaches are under development, here we propose a simplified model for analyzing fundamental methods and techniques. Initially, we created a general dataset by using initial data from PCTRAN in order to represent ideal steady-state conditions. We then inserted anomalies based on prevalent sensor anomaly types (e.g., point anomalies, linear drift, and downward deviations), along with unusual anomalies such as exponential drift and upward deviations. To detect anomalies, we developed a program that employs data partitioning and linear regression to preprocess and filter the anomalous data. A K-Means machine learning (ML) method was then applied to separate and count the data within the anomalous partition. The results from all datasets—apart from exponential growth—demonstrated positive outcomes, with each returning multiple instances of greaterthan-95% accuracy. We conducted further investigations using Idaho National Laboratory’s RAVEN software to perform a sensitivity analysis on the input variables (R 2 Tolerance, Slope Tolerance, and Window Size) and found that the output variables (Accuracy and Time) were most sensitive to the Window Size. Despite the promising results published, further development is required to effectively apply these methods to nuclear systems. Nevertheless, the strengths of this approach are evident and hold promise for future applications in the field.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Data recovery via covert cognizance for unattended operational resilience

One of the important premises of unattended operation, a highly promoted characteristic of fission batteries and advanced microreactors, is the ability to automate the analysis of sensors data used in support of operational monitoring and control. Here, to meet this vision, this work proposes a new monitoring and data recovery paradigm to ensure resilience against data corruption which may be the result of malicious intrusion into the reactor operational network. This is paramount to ensure 100% availability under contingency scenarios such as cyberattacks. In support of this vision, earlier work has presented the concept of covert cognizance and demonstrated its mathematical ability to identify and embed cognizance parameters under the noise-dominated null space of the sensors data. This work extends this concept and applies it in real-time to demonstrate three key characteristics: zero-impact, zero-observability, and data recovery, where the first characteristic is to ensure no impact on operation, the second is immunity to discovery by pattern recognition techniques, and the third is to allow recovery of corrupt or falsified data. Recognizing that fission batteries are designed to operate under steady state most of the time, we elect to employ a small modular reactor model under transient operational conditions to demonstrate the operational resilience enabled by the covert cognizance paradigm. Specifically, the PI controller is augmented with the covert cognizance modules to develop self-awareness and enable automatic data recovery. The developed modules are expected to be equally applicable to a wide range of advanced reactor technologies relying on full or partial unattended control.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Applicability of alkali beam emission spectroscopy on NSTX-U

Understanding fast pedestal dynamics and turbulent transport in the edge and scrape-off layer (SOL) plasma of spherical tokamaks is crucial for the design and operation of future fusion reactors. The alkali beam emission spectroscopy diagnostic technique offers a means to measure the absolute electron density radial profile and fluctuation amplitude in these regions. Here, in this study, we demonstrate that injecting a sodium neutral beam radially into the plasma and analyzing the light emission from its 3p–3s atomic transition using near-orthogonal viewing angles allows for accurate measurement of the electron density profile and fluctuations in the National Spherical Torus Experiment (NSTX) Upgrade spherical tokamak. Our findings indicate a peak signal-to-noise ratio of 118 in the pedestal and 12 in the SOL under typical NSTX plasma conditions. The spatial resolution for the electron density profile is estimated to be between 2 and 8 mm, while for fluctuation measurements, it ranges from 12 to 15 mm.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Use of First Order Reversal Curve Measurements to Understand Barkhausen Noise Emission in Nuclear Steel

A prototypical ferritic/martensitic alloy, HT-9, of interest to the nuclear materials community was investigated for microstructure effects on Barkhausen noise emission and first-order reversal curve (FORC) analysis for three different heat-treated samples. It was observed that Barkhausen noise emission and reversible component of magnetization, computed from the FORC data, decreased with increasing measured mechanical hardness. The results are discussed in terms of the use of magnetic signatures for use in nondestructive interrogation of radiation damage and other microstructural changes in ferritic/martensitic alloys. FORC analysis is shown to be particularly useful for detailed characterization of defect density and pinning, which can be correlated to bulk non-destructive evaluation field measurements such as Barkhausen noise emission.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Detection of Defects in Additively Manufactured Metallic Materials with Machine Learning of Pulsed Thermography Images

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on laser powder bed fusion (LPBF) process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environment in a nuclear reactor. Quality control (QC) requires nondestructive evaluation (NDE) of actual AM structures. Pulsed thermography is a potentially promising QC technique because it is scalable to arbitrary structure size. However, detection sensitivity of this method is limited by noises. We investigate separation of signal from noise in thermography images using several machine learning (ML) methods, including new spatio-temporal blind source separation (STBSS) and spatio-temporal sparse dictionary learning (STSDL) methods. Performance of the ML methods is benchmarked using thermography data obtained from imaging stainless steel 316L and Inconel 718 specimens produced LPBF method with imprinted calibrated porosity defects. The ML methods are ranked by F-score and execution runtime. The ML methods with higher accuracy require longer run time. However, this runtime is sufficiently short to perform QC within a realistic time frame.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

A high-efficiency cryogenic neutron spin flipper for IMAGINE-X at the high flux isotope reactor

The Laue neutron macromolecular crystallography beamline, IMAGINE, at the High Flux Isotope Reactor (HFIR) is undergoing a major upgrade to incorporate the dynamic nuclear polarization (DNP) technique. Upon completion, the new IMAGINE-X instrument is expected to enhance the signal-to-noise ratio of the diffraction data by an order of magnitude. To take full advantage of the benefits of DNP, both a highly polarized neutron beam and a high-efficiency neutron spin flipper are required. We have developed a compact cryogenic spin flipper utilizing the Meissner effect in high-Tc yttrium–barium–copper–oxide superconducting films. The flipper has been successfully tested at two HFIR neutron development beamlines, Larmor and Poplar, with neutron wavelengths of 5.3 and 2.4 Å respectively. At Larmor, a flipping efficiency of 0.995 ± 0.004 was achieved for 5.3 Å neutrons, and at Poplar, efficiency of up to 0.9987 ± 0.0004 was obtained for 2.4 Å neutrons. Both tests demonstrated the flipper’s excellent performance and suitability for integration into IMAGINE-X.

Fu, Sichao [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2023

Improving an Acoustic Vehicle Detector Using an Iterative Self-Supervision Procedure

In many non-canonical data science scenarios, obtaining, detecting, attributing, and annotating enough high-quality training data is the primary barrier to developing highly effective models. Moreover, in many problems that are not sufficiently defined or constrained, manually developing a training dataset can often overlook interesting phenomena that should be included. To this end, we have developed and demonstrated an iterative self-supervised learning procedure, whereby models are successfully trained and applied to new data to extract new training examples that are added to the corpus of training data. Successive generations of classifiers are then trained on this augmented corpus. Using low-frequency acoustic data collected by a network of infrasound sensors deployed around the High Flux Isotope Reactor and Radiochemical Engineering Development Center at Oak Ridge National Laboratory, we test the viability of our proposed approach to develop a powerful classifier with the goal of identifying vehicles from continuously streamed data and differentiating these from other sources of noise such as tools, people, airplanes, and wind. Using a small collection of exhaustively manually labeled data, we test several implementation details of the procedure and demonstrate its success regardless of the fidelity of the initial model used to seed the iterative procedure. Finally, we demonstrate the method’s ability to update a model to accommodate changes in the data-generating distribution encountered during long-term persistent data collection.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Data analysis for valve leak detection of nuclear power plant safety critical components - 336

A nuclear plant consists of several safety-related components. These safety-related components are tested periodically in order to verify their functionality when they are called upon to perform. The data acquired during periodic testing provides information about possible degradation of the equipment or device under test. This research is focused on leak monitoring techniques to provide diagnostic information on the determination of valve aging and degradation. Several commercially available valve diagnostic-monitoring methods are evaluated, especially for the techniques based on measurements of acoustic emission, and accelerometer. The check valve is one of typical components being extensively used in safety systems of nuclear power plants. The failures of check valves have resulted in significant maintenance efforts, on occasion, have resulted in water hammer, over-pressurization of low-pressure systems, and damage to flow system components. In this paper, acoustic emission signals due to leak from check valves with artificial leakage and worn parts are investigated both analytically and experimentally. The results of advanced signal processing and de-noising techniques also have been identified. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗