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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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108 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Persistent Acoustic Sensing for Monitoring A Reactor Facility

Measurements over the past few years, taking place within the Multi-Informatics for Nuclear Operations Scenarios project, show that infrasound and low-frequency acoustic monitoring can detect, and often quantify, activities that occur on-site at the High Flux Isotope Reactor. Observable activities include: crane translation, lifting, and lowering; differentiation between loaded and unloaded crane operations; access door opening and closing; vehicle operations; and cooling tower fan operations. Advanced data analytic and spectral feature extraction methods can be used to interpret selected signatures to reach a deeper understanding of different reactor activities. These measurements are being conducted using a network of smartphones that continuously operate in conjunction with cloud-based architectures. A recent addition to the system is the development and deployment of a real-time, cloud-based analytic framework that supports near-real-time alarming which can facilitate tip-and-cue protocols. This paper presents recent research in this area including the use of low-frequency acoustics to tip-off transfer operations and cue a more comprehensive analysis by other sensor networks.

42 ENGINEERING↗
DOE OSTI2022

An Overview in the Development of a Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

To enhance nuclear nonproliferation stewardship, Idaho National Laboratory is building the Beartooth nuclear fuel cycle processing test bed. The Beartooth test bed will allow researchers the opportunity to study nuclear fuel processing operations including the use of centrifugal contactors in solvent extraction processes. The test bed is designed to support data collection and machine learning to monitor process operations. As part of this project, researchers will study data collected from a host of sensors that have not been typically used to monitor solvent extraction processes such as vibration, acoustic, colormetric, and thermal measurement data. The goal of this research is to employ machine learning and data analytics to study the confluence of signals collected from both traditionally and non-traditionally used sensors to provide operator process awareness and discover process anomalies. An overview of planned sensors and experiments that will focus on signal discovery will be presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

An Overview in the Development of a Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

To enhance nuclear nonproliferation stewardship, Idaho National Laboratory is building the Beartooth nuclear fuel cycle processing test bed. The Beartooth test bed will allow researchers the opportunity to study nuclear fuel processing operations including the use of centrifugal contactors in solvent extraction processes. The test bed is designed to support data collection and machine learning to monitor process operations. As part of this project, researchers will study data collected from a host of sensors that have not been typically used to monitor solvent extraction processes such as vibration, acoustic, colormetric, and thermal measurement data. The goal of this research is to employ machine learning and data analytics to study the confluence of signals collected from both traditionally and non-traditionally used sensors to provide operator process awareness and discover process anomalies. An overview of planned sensors and experiments that will focus on signal discovery will be presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Proliferation Detection via Acoustic Monitoring of Pyroprocessing Equipment and Related Systems

In support of nuclear safeguards and nonproliferation, this effort is leveraging acoustic and seismic sensors to advance detection of pyroprocessing activities. Pyroprocessing, or electrochemical processing, is a method of separating irradiated nuclear fuel into its actinide and fission product components, enabling the reuse of fuel materials and reducing the radiotoxicity of the remaining waste. Verification and accountability in the process poses several challenges because of the high radiation environment and the presence of material holdup through the facility. Irregular or unaccounted-for equipment operation can signify diversion or misuse of critical materials. Acoustic and seismic sensors offer the capability to identify periods of equipment operation and help verify adherence to accountancy records.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Use of Sensors and Machine Learning for Signal Discovery in a Solvent Extraction Process

Reprocessing is an important step in the nuclear fuel cycle where usable nuclear materials are extracted from used fuel for recycling. The separation of materials for reuse simultaneously reduces not only the volume of nuclear waste, but its decay time to radioactivity levels similar to that of the originating uranium ore. As part of an initiative to steward research, development, and innovation into the nuclear fuel cycle, Idaho National Laboratory is designing and constructing a solvent extraction testbed named Beartooth. This testbed will allow researchers to refine separation processes, test innovative extraction processes, and give early career scientists opportunities to gain skills in performing separations chemistry utilizing centrifugal contactors. In addition, the Beartooth testbed is being uniquely designed to enable novel technologies including machine learning capabilities for the characterization of chemical process operations in near real-time. To aid in the design of Beartooth, a team of researchers are installing a variety of atypical sensors into a system of contactors for signal discovery. The team will implement machine learning methods on acquired sensor data to extract signal features. The goal is to provide a process operator with a deeper understanding of the chemical process and equipment usage. This work will summarize sensors utilized and preliminary results from an infrared camera.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Development of a Multi Sensor Data Science System for Monitoring a Solvent Extraction Process

Idaho National Laboratory is designing and constructing a solvent extraction test bed, Beartooth, as part of a nuclear fuel cycle stewardship initiative. The test bed will provide facilities and equipment for testing novel extraction processes and give early career scientists opportunities to gain skills in performing separations chemistry. The test bed is being uniquely designed to enable machine learning capabilities for the characterization of chemical process operations. One of the intentions of incorporating machine learning methods is to provide process operators with enhanced situational awareness for the optimization of separations techniques and possible detection of a diversion event. As a result, the objective of this Laboratory Directed Research and Development project is to develop a multi-sensor data collections system and implement machine learning techniques on signals from an existing centrifugal contactor cascade to identify equipment usage and process events. This presentation will provide an overview of sensors used and experiments conducted to date.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Mapping Rare Earths and Toxics in E-Waste via Hyperspectral Imaging and Machine Learning

Electronic waste (e-waste) presents a mounting challenge to environmental sustainability due to its complex composition, which includes high-value rare earth elements, hazardous organic compounds, and non-recyclable plastics. Accurate and scalable material classification is essential for enabling efficient resource recovery and safe recycling practices. This study introduces a confidence-aware classification pipeline that combines mid-infrared hyperspectral imaging (HSI), spectral angle mapping (SAM), and iterative machine learning to perform pixel-level material identification across e-waste devices. A curated spectral library encompassing artificial materials (e.g., plastic iron oxide, galvanized metals), minerals (e.g., allanite, hematite), and organic compounds (e.g., benzanthracene, toluene) was used to generate pseudo-labels, each assigned a confidence score based on SAM-derived spectral similarity. High-confidence samples from seven consumer electronics—digital cameras, keyboards, laptop fans, modems, motherboards, TV remotes, and speakers—were iteratively expanded and classified using models such as Support Vector Machine (SVM), Random Forest, Gradient Boosting Classifier, Partial Least Squares Discriminant Analysis (PLSDA) and Logistic Regression. The best-performing classifiers achieved macro F1 scores approaching 1.0. Results revealed widespread plastic content (dominated by plastic iron oxide), the presence of rare earth-bearing minerals like cerium-containing allanite, and pervasive detection of hazardous organics such as benzanthracene. Principal Component Analysis (PCA) visualizations and confusion matrices confirmed high separability and robust classification performance. This methodology enables precise, non-destructive, and scalable classification of heterogeneous e-waste streams. It supports automated, hazard-aware sorting in recycling workflows, facilitating selective recovery of critical materials and compliance with circular economy goals. The confidence-aware framework provides a foundation for real-time deployment in industrial settings, offering significant implications for smart e-recycling infrastructure and policy-driven material stewardship.

Circular economy↗
DOE OSTI2024

Nontraditional Sensors for Aqueous Separation Research & Workforce Development

Idaho National Laboratory’s (INL) nuclear fuel cycle capabilities enable the deployment of technologies that sustain the current reactor fleet, support demonstration and deployment of new advanced reactors, and facilitate management and disposition of existing and future radiological waste materials. INL focuses on deploying nuclear energy systems with confidence by decreasing proliferation risk through research that demonstrates process transparency and supports safeguards and security by design. An example of these capabilities is the Beartooth test bed, set to begin operations toward the end of fiscal year 2026. Beartooth will include a cascade of centrifugal contactors, glove box lines, and solidification and dissolution equipment to aid in the progression of novel separation techniques and to provide hands-on experience to cultivate and maintain a robust workforce of experts. To support Beartooth’s enhanced instrumentation and monitoring equipment needs, several nontraditional sensors are being considered for future deployment. The non-traditional sensors include accelerometers, acoustic microphones, and infrared cameras. These nontraditional sensors have the potential to not only help monitor the process but also enhance nuclear safeguards.

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

Acoustic Measurements of Solvent Extraction Processes in Support of Safeguards

The proposed poster focuses on using acoustic monitoring to advance detection techniques for reprocessing equipment in support of nuclear safeguards. The usage of free air acoustic monitoring has been previously demonstrated at Idaho National Laboratory (INL) at facilities such as the Advanced Test Reactor and the National Security Test Range. However, this work focuses on a deployment environment dedicated to monitoring separation processes, using solvent extraction equipment such as centrifugal contactors. This environment offers an opportunity for signal discovery and in characterizing acoustic signatures of the equipment in operation. This data can support the development of safeguards by design and security by design measures for aqueous reprocessing facilities. Furthermore, this type of monitoring can aid in early detection and identification of removed materials indicating diversion, which is essential for initiating material recovery and actor identification. The results of this work include data from nine low-frequency acoustic sensors used to investigate potential acoustic characteristics of centrifugal contactors used in multi-stage processes. The results also discuss the correlation between the signatures and the operation of the contactor banks.

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

Acoustic Monitoring of Pyroprocessing for Safeguards

As pyroprocessing continues to be an attractive option for the reprocessing of spent nuclear fuel worldwide, safeguards technologies are needed to address the monitoring capabilities that can help state level authorities, or the International Atomic Energy Agency (IAEA) maintain continuity of knowledge of the plant operations. Idaho National Laboratory (INL) is studying the possibility of using acoustic monitoring as a means to monitor a pyroprocessing facility for safeguards purposes. This paper discusses the experimental design and some preliminary results of tests conducted at the Fuel Conditioning Facility, a pyrochemical capable facility, at INL.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗
DOE OSTI2025

Seismoacoustic Monitoring of Pyroprocessing Equipment

With the projected increase in world nuclear capacity comes the hurdle of spent nuclear fuel. Pyroprocessing is one method to process irradiated fuel by using high temperatures and electrochemical steps to separate radioactive components. High temperatures, high radiation levels, and equipment confined to a heavily shielded hot cell are a few of the challenges introduced regarding safeguards for pyroprocessing. The first stage of the process utilizes an element chopper to cut irradiated fuel into smaller pieces, offering the potential to identify equipment operation through seismoacoustic monitoring. Eleven seismic and infrasound sensors were deployed at various locations and distances near a pyroprocessing hot cell to evaluate signals emitted from the chopper. Signals were processed using short-time Fourier transforms and manually scanned to locate chopping events. Operator logs were then collected for a ground truth comparison. Results indicate that both infrasound and seismic signals offer the ability to accurately measure this stage of pyroprocessing, though seismic signals are more prominent. Sensor locations are also evaluated to determine where the signal is detectable. This type of monitoring offers a method to aid in safeguards and proliferation detection by validating a pyroprocessing facility’s schedule and identifying activity that does not align with records.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Vibroacoustic Process Monitoring of 5-Stage Centrifugal Contactors

Vibroacoustic monitoring was investigated to aid in informing process operators with parameters regarding aqueous separation techniques. These techniques have a wide range of applications including nuclear fuel reprocessing. A small-scale solvent extraction system was set up with five magnetic drive contactors and two piston pumps to collect realistic operation. Vibration sensors were placed on three of the contactors and both pumps, and a microphone was placed near the system. Contactor rotational speed and pump flow rate were varied with known values to compare against measured vibroacoustic signatures. Spectral analysis allowed for the determination of contactor rotational speed and the ratio of organic to aqueous flow rate, an important parameter for ensuring optimal extraction. Vibration metrics such as kurtosis and crest factor provided additional details regarding individual rotational speeds and have potential in examination of faults for predictive maintenance. Overall, results indicate the capability of vibroacoustic monitoring to improve operator awareness and process outcomes.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Benefits and challenges of vibroacoustic process monitoring to support operators in nuclear research facilities

According to the International Atomic Energy Agency in 2024, global nuclear energy capacity is projected to increase by 2.5 times by 2050 in their high-case scenario. Research on the laboratory scale of innovative processes within nuclear facilities is ongoing to improve nuclear fuel cycle capabilities. Vibroacoustic process monitoring of these techniques has potential to inform operators regarding process specific metrics, predictive maintenance, and anomalous event detection. Additionally, this type of monitoring has the potential to aid in nuclear safeguards. Challenges regarding vibroacoustic monitoring in these environments include high temperature, high radiation, limited access, and shielded equipment limiting sensor type and placement. Microphones, accelerometers, and temperature sensors were deployed both inside and near these environments during operation to determine environment driven limitations, sound attenuation of containment enclosures, process specific metrics, and future potential of vibroacoustic monitoring in these environments. Overall, this work highlights the benefits and limitations of vibroacoustic process monitoring in nuclear research facilities.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Validation and Verification of Python based Neutron Spectrum Unfolding Software

To validate and verify the python-based code (PySL), designed to replicate the programs used by STAYSL for Beam Correction Factor (BCF) and Self-Shielding Factor (SHIELD), a series of tests were performed. To test BCF a python script was written to generate a random flux history file and both versions of the code processed the data. The test verified matching values up to at least one decimal place, approximately 10,000 tests where run and each one passed. Isotopes began to fail the tests once neutron saturation was reached. To verify this the total time of exposure was varied the isotopes that failed were compared to a list of their half-lives. The test process for SHIELD was very similar but, in this case, the code began by producing an input file with varying thickness and device type/environment for the SHIELD input. The failure condition for this test was if any of the data points for an isotope had a difference above 3%. Approximately 40 of these tests were run and there were only 3 isotopes that had reoccurring failures but only 2% of their points were above the 3% difference. A visual comparison was conducted by plotting the results from both programs. Although the test failed, the differences between their values were minuscule, and the self-shielding factor’s shape was preserved when plotted. Next steps for this project will be validating and verifying the python-based SigPhi code and then reproducing and testing the least squares unfolding performed by STAYSL.

73 - NUCLEAR PHYSICS AND RADIATION PHYSICS↗