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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Considerations for using Privacy Preserving Machine Learning Techniques for Safeguards

In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Immobilization of Uranium in a Wetland 8-km Downstream from an Industrial Source (Report)

A three-day walk-over survey with portable gamma detecting equipment was conducted over a uranium-contaminated wetland located 8-km downstream from a former fuel fabrication facility. 97,000 spectra were collected, individually processed to extract uranium activity data, and then integrated with topographical information (LIDAR) to create maps. These maps reveal that 50 years after being released into the environment, 11% of the uranium is retained in the 9-ha wetland area. Combined with measurement collected closer to the source, it was determined that 94% of the released uranium is presently being held in downstream wetlands. This demonstrates that wetlands can be very effective at immobilizing contaminants for long durations. The survey also showed that previous flyover maps overestimated the size of the contaminated areas by 140% but had the advantage of being completed in orders-of-magnitude fewer labor hours. Depending on project needs, the walk-over technique may be useful to include in tandem with flyover measurements to acquire maps with greater sensitivity and spatial resolution.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques

Opioids exert their analgesic effect by binding to the µ opioid receptor (MOR), which initiates a downstream signaling pathway, eventually inhibiting pain transmission in the spinal cord. However, current opioids are addictive, often leading to overdose contributing to the opioid crisis in the United States. Therefore, understanding the structure-activity relationship between MOR and its ligands is essential for predicting MOR binding of chemicals, which could assist in the development of non-addictive or less-addictive opioid analgesics. This study aimed to develop machine learning and deep learning models for predicting MOR binding activity of chemicals. Chemicals with MOR binding activity data were first curated from public databases and the literature. Molecular descriptors of the curated chemicals were calculated using software Mold2. The chemicals were then split into training and external validation datasets. Random forest, k-nearest neighbors, support vector machine, multi-layer perceptron, and long short-term memory models were developed and evaluated using 5-fold cross-validations and external validations, resulting in Matthews correlation coefficients of 0.528–0.654 and 0.408, respectively. Furthermore, prediction confidence and applicability domain analyses highlighted their importance to the models’ applicability. Our results suggest that the developed models could be useful for identifying MOR binders, potentially aiding in the development of non-addictive or less-addictive drugs targeting MOR.

Research & Experimental Medicine↗
DOE OSTI2018

Ultrafast table-top dynamic radiography of spontaneous or stimulated events

Disclosed herein are representative embodiments of methods, apparatus, and systems for performing radiography. For example, certain embodiments concern X-ray radiography of spontaneous events. Particular embodiments of the disclosed technology provide continuous high-speed x-ray imaging of spontaneous dynamic events, such as explosions, reaction-front propagation, and even material failure. Further, in certain embodiments, x-ray activation and data collection activation are triggered by the object itself that is under observation (e.g., triggered by a change of state detected by one or more sensors monitoring the object itself).

36 MATERIALS SCIENCE↗
DOE OSTI2018

Evolution, Current State of the Art, and Interpretation of Aircraft-Based Methane Emission Quantification at the Natural Gas Basin-Level

Accurately quantifying methane (CH4) emissions from the oil and gas (O&G) industry is challenging given the existence of many potential emission sources, differences in industry practices and regulations, and often incomplete or out-of-date activity data. It is thus important to regularly assess emission inventories and support improvements by deriving independent estimates using state-of-the-science approaches. Recent research has provided CH4 emission estimates from the O&G supply chains based on different measurement techniques at multiple spatio-temporal scales. Natural gas basin-scale aircraft-based atmospheric measurements indicate greater CH4 emissions than emission inventories based on up-scaling of component-/facility-level emission measurements from a relatively small sample size. Here we present results from a coordinated field study that compares concurrent CH4 emission measurements using different techniques and scales to better understand the strengths and weaknesses of each approach, with a focus on the aircraft-based estimation method. We describe the importance of (i) decades of measurement technology development, (ii) experience in designing measurement sampling strategies and aligning system boundaries, and (Hi) properly interpreting CH4 emission estimates for successfully reconciling aircraft-based measurements with emission inventories and for identifying CH4 emission mitigation potential. We illustrate that aircraft-based top-down estimates represent a brief snapshot of total basin-level CH4 emissions. Thus, a high spatio-temporally resolved bottom-up emission inventory is needed to identify key CH4 sources (including episodic sources) at the process level. While this paper provides a brief overview of the overall study design and findings, its focus is on the implementation of the aircraft measurements and its interpretation and results. A separate paper describes in more detail the overall organizational structure of the study as well as the study implications from an industry perspective.

03 NATURAL GAS↗
DOE OSTI2020

Adaptive sensing for a gas leak detection

A method, a system and an apparatus for adaptively sensing a gas leak. The method includes: obtaining wind data from a wind sensor, identifying at least one wireless mote in a plurality of wireless motes that is most likely to detect a gas plume from the wind data, activating the identified at least one wireless mote to trigger measuring gas concentration of the gas plume, and identifying the location of the gas leak if the gas concentration is above a predetermined threshold. Also included is a system for adaptively sensing a gas leak having a wind sensor, a plurality of wireless motes and a server in communication therewith. An apparatus for adaptively sensing a gas leak in an industrial site is also provided.

03 NATURAL GAS↗
DOE OSTI2014

Identification of Leakage Spectra from Neutron Activation in Glass/Cherenkov Detectors - Paper 96

Oak Ridge National Laboratory has developed a new detector system concept that can be deployed in major US cities to identify the leakage spectrum of a nuclear weapon detonation. The detector is based on a small piece of glass that has been doped with an element that has a high activation cross section and creates a radioactive activation product with a reasonable length half-life. The decay of the activation product(s) after exposure creates Cherenkov light which can propagate to a photomultiplier and can be turned into a count-rate signal. The signal from each glass in each detector in a city can be individually analyzed to determine the amount of activation products produced in the initial exposure. Several activation products can be determined in a single glass if the half-lives are not the same. With a known source location and a detailed 3D model of a city, neutron transport simulations can be made by using a library of known sources to predict the activation amounts each source would have made in the glass detectors. A simple comparison of the measured activation amounts and the predicted activation amounts can identify the source spectrum contained in the library that is the best match. Choosing dopants with a large range in the threshold energy for the activation reactions can help to differentiate the spectra in the library. The method of matching the measured activation amounts to the predicted activation amounts to identify the source spectrum is similar to the analysis used for the passive Activation Foil Integrated Detector System (AFIDS), also developed at Oak Ridge National Laboratory. In AFIDS, wires or foils of known mass and isotopic composition are deployed before and then collected after exposure. Counting gammas in a nearby laboratory determines the activation amounts. The glass/Cherenkov system is active - it uses a battery-powered detector to count flashes of Cherenkov light and to communicate its count rates to a remote location for further analysis. Work has been performed in several areas, including glass making, automated detection and communication systems and software tools for analysis of the signal and source spectrum identification. Bell and Boatner have developed phosphate glasses that incorporate elements with high capture cross sections, and Hayward has simulated the response with Geant4 and measured the response with isotopic gamma sources. This paper will focus on the software tools for determining the activation amounts from the Cherenkov count-rate signal and finding the best matching source spectrum. A test problem in an urban environment is used to demonstrate the potential of a spectrum identification system using neutron activation. The analysis methods developed to identify the neutron leakage spectra from Cherenkov light given off by neutron activation in doped glasses work well. The calculation used to unfold the count-rate information into the number of actual activation products uses robust algorithms that can also compute associated uncertainties. These uncertainties and the known uncertainties of the glass tube material compositions are incorporated into the matching analysis to determine which of the source spectra in the library best matches the measured activation data. Any measurement method used to identify a neutron source spectrum relies at some point on a comparison against known sources; no identification method is definitive. Detection systems measure some physical aspect of the source, not its serial number. Many systems measure the physical aspect to determine that the source has some property, and then leave it to experts to determine which potential sources share that property. The analysis system presented here incorporates the comparison into the analysis itself. The statistical tests show which of the known library sources match the measured data the best and whether that match is a good match. If the best match still does not fit the measured data well (a very high χ{sup 2} value), that indicates the true source is not one of the sources in the library. This is still valuable information for decision makers. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Precision measurement of positron decay modes of Xe-125 in the LUX-ZEPLIN experiment

The radioisotope $^{125}\text{Xe}$ is a short-lived ($T_{1/2}\sim16.9 h$) activation product of the neutron calibrations performed in the LUX-ZEPLIN experiment. Subsequently, $^{125}$Xe decays primarily ($>99\%$) via electron capture, but positron emission has been confirmed by direct measurement to at least the 243 keV level of $^{125}\text{I}$. An additional decay to the 188keV level is expected from triple-coincident measurements of the annihilation and relaxation $γ$ rays, but has not been directly confirmed. By utilizing multiple-scatter event analysis and the pre-activation data to constrain backgrounds, this work reports positron emission with a statistical significance of 5.5$σ$. This corresponds to a total branching ratio of $0.29\pm0.08_{\text{stat.}}\pm0.04_{\text{sys.}}$%, and is the first constraint to the individual branching levels of $^{125}\text{I}$.

Akerib, D. S. [SLAC; KIPAC, Menlo Park]↗
DOE OSTI2022

Applications of Autonomous Data Collection and Active Learning

Advances in sensors and robotics have dramatically improved the diversity of experimental approaches available to the materials community. Autonomous data collection platforms, either custom-made or commercially available, provide researchers with novel tools with which to probe materials behavior and perform advanced materials characterization. The application of novel control algorithms and active learning approaches can create much more robust experimental data, or can be used to improve the performance of existing characterization tools. Five papers within this special topic focus on experimental and computational methodologies for use in automatic data collection routines for materials characterization. From novel platforms for materials discovery to new statistical frameworks for assessing the autonomous experimentation process, these five papers highlight the diverse range of applications of automation for advancing materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Measuring and Extracting Activity from Time Series Data

This report summarizes the results of an LDRD focused on developing and demonstrating statistically rigorous methods for analyzing and comparing complex activities from remote sensing data. Identifying activity from remote sensing data, particularly those that play out over time and span multiple locations, often requires extensive manual effort because of the variety of features that describe the activity and the required domain expertise. Our results suggest that there are some hidden challenges in extracting and representing activities in sensor data. In particular, we found that the variability in the underlying behaviors can be difficult to overcome statistically, and the report identifies several examples of the issue. We discuss key lessons learned in the context of the project, and finally conclude with recommendations on next steps and future work.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Deep Learning for Full Waveform Inversion of Elastic Active-Source Seismic Data to Estimate P-Wave Velocity Models

Seismic imaging methods are critical for Global Security and Energy & Homeland Security missions and activities that rely on subsurface characterization, but traditional methods remain computationally expensive and require significant labor hours and expertise to execute. Within the past few years, machine learning (ML), namely deep learning (DL), has been used to develop data-driven end-to-end full waveform inversion (FWI) methods to estimate 2D P-wave velocity (Vp) models in a fraction of the time as conventional FWI. These methods, however, are trained on simplistic acoustic wave seismic data and Vp models that are not realistic nor representative of real-world observations, leaving a large gap between the state-of-the-art and deployable, feasible, and practical DL FWI methods. Here, we generate a synthetic active-source, 3D, elastic wave seismic data set and a variety of Vp models with realistic geologic structure for training DL FWI methods. We evaluate six different methods that have performed well for acoustic DL FWI or medical imaging tasks using our more realistic dataset. We find that these six trained models do not match the performance of published acoustic end-to-end DL FWI methods, indicating more training data may be needed, physics may need to be incorporated to achieve good accuracy at the sacrifice of the end-to-end advantage, and/or novel methods need to be developed to enable end-to-end DL FWI methods to perform well for real-world seismic data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2020

Composite Analysis Solid Waste Release Data Reduction of Activity Flux from Waste Sites to the Vadose Zone for Baseline Assessment

This environmental calculation file (ECF) describes the data reduction methodology for the solid waste release datasets generated by the Composite Analysis Solid Waste Release (CASWR) model (CP-62766, Model Package Report: Composite Analysis Solid Waste Release Model [CASWR Model]) and documents the resulting reduced datasets for the updated Composite Analysis (CA) baseline assessment. This ECF is in support of the updated Hanford Site CA. The purpose of the updated Hanford Site CA is to provide an estimate of the cumulative radiological impacts from active and planned low-level radioactive waste disposals and other potentially interacting radioactive waste disposal sources that will remain following Hanford Site closure. The vadose zone modeling activities associated with the CA includes solid waste forms as contaminant sources. To support the modeling of solid waste transport from the vadose zone to the groundwater at the Hanford Site, the CASWR model was designed to generate deterministic radionuclide release rates for the Hanford Site Central Plateau solid waste disposal sites. The complete CASWR model-generated dataset for the CA Baseline Assessment consists of 2,378,831 sets of time and release rates (1,073,869 sets for the 200 East Area and 1,304,962 sets for the 200 West Area), representing a course of the simulation from year 1953 to year 12,069, 16 radiological contaminants of potential concern (COPCs), and 237 solid waste sources as documented in ECFHANFORD- 19-0112, Solid Waste Release Calculations for the Composite Analysis Baseline Assessment. For several of the STOMP vadose zone models, the number of release rate data pairs required for release at all waste sites would exceed the limitations imposed by the STOMP modelling software package used to simulate contaminant transport through the vadose zone. As a result, a data reduction of the CASWR dataset is required prior to its use as a source input for the CA vadose zone modeling activities.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Validation Study of the Spacecraft Background Activation using RHESSI Data

This document serves as a brief writeup of the work done to validate CINDER2008 and the Activation Analysis Tools in the Activation in Accelerator Radiation Environments (AARE) distribution that are used to calculate the activation of spacecraft material while in orbit [1{3]. The validation study involves replicating the decay gamma spectrum as observed onboard the RHESSI spacecraft in the high-purity germanium (HPGe) detectors after crossing the South Atlantic Anomaly (SAA) [4]. The tools involved are a suite of Python scripts, the transmutation code CINDER2008 accompanied with a suite of scripts distributed with AARE,MCNPX, and MCNP6.2 [2,5{7]. Figure 1 shows the gamma ray spectrum this study replicates as the spectrum designated \Difference".

42 ENGINEERING↗
DOE OSTI2023

Systems and methods for active learning from sparse training data

A method for active learning using sparse training data can include training a machine learning model using less than ten first training data points to generate a candidate machine learning model. The method can include performing a Monte Carlo process to sample one or more first outputs of the candidate machine learning model. The method can include testing the one or more first outputs to determine if each of the one or more first outputs satisfy a respective convergence condition. The method can include, responsive to at least one first output not satisfying the respective convergence condition, training the candidate machine learning model using at least one second training data point corresponding to the at least one first output. The method can include, responsive to the one or more first outputs each satisfying the respective convergence condition, outputting the candidate machine learning model.

Sankaranarayanan, Subramanian↗
DOE OSTI2024

233 U oxide Measurement Campaign Data: Passive and Active Neutron Multiplicity Measurements of Uranium Oxide Samples at Oak Ridge National Laboratory

During FY2023, three measurement campaigns were conducted at Oak Ridge National Laboratory. The goal was to quantify the neutron signatures of samples of uranium oxide containing uranium 233 and uranium-235. This report presents the neutron multiplicity data obtained using the large volume active well coincidence counter (LV-AWCC).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Assessment of Modeling and Nuclear Data Needs for Active Neutron Interrogation

This document is the primary deliverable for a scoping study proposed to DOE National Laboratory Announcement Number LAB 19-2114 in the NNSA research area. The study supports user applications employing active neutron interrogation by providing a science plan to improve the modeling capability and the nuclear data that radiation transport codes use. Users rely on the accuracy of the elastic scattering and non-elastic cross-sections spanning thermal energies to 14 MeV (and higher in some cases) for modeling the neutron transport through complex geometries of materials potentially composed of many elements. While the elastic scattering cross-section data are accepted for all commonly occurring elements, the non-elastic cross-section data and the associated emission data include reaction channels that require attention. The study focused on the non-elastic reactions that emit secondary, also known as prompt, gammas with the premise that many users would benefit from improved modeling of these reactions. Many users develop material assay technologies based upon gamma signatures from radiative capture, inelastic scattering, and reactions on low-Z isotopes emitting multiple particles, so the nuclear data gaps, modeling deficiencies, and recommendations for addressing the shortfalls were assessed for these reactions. Fission gammas were excluded from this study because there are other efforts underway to address known shortfalls. Follow-on efforts that successfully execute the recommendations will tangibly improve to the ability to model gamma signatures and backgrounds for user applications, such as controlled substance detection, oil-well logging, and space exploration.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Integration of seismic-pressure-petrophysics inversion of continuous active-seismic monitoring data for monitoring and quantifying CO 2 plume (Final Report)

The overall objective of this project is to develop and validate an integrated package of joint seismic-pressure-petrophysics inversion (jSPPI) of continuous active-source seismic monitoring dataset capable of providing real-time monitoring of CO 2 plume during geologic carbon sequestration (GCS). The three specific developments include: (a) the methodologies for fast seismic full waveform inversion of continuous active source seismic monitoring, (CASSM) datasets for simultaneously estimating velocity and attenuation, and with data assimilation; (b) joint Bayesian petrophysical inversion of seismic models and pressure data for providing and updating CO 2 saturation models; (c) the methods using multiple datasets including (Crainfield and Frio-II borehole) synthetic, laboratory, and field CASSM datasets. The outcomes of jSPPI include (a) a workflow for processing CASSM data, (b) Bayesian inversion algorithms using CASSM data and pressure response data, and (c) integration with data assimilation algorithms for continuously updating site-specific models used for prediction and reservoir management. The validation of joint FWI will be conducted using synthetic models based on the Cranfield and Frio experiments as well as field CASSM datasets collected as part of the Frio-II pilot injection. To quantify and map the mass and distribution of CO 2 (saturation), we will jointly invert velocity and attenuation measurements from the FWI with a Bayesian approach using a rock physics model for attenuation (e.g., White’s attenuation model with two selected patch sizes (White, 1976; Dutta and Seriff, 1979)). The Bayesian inversion will be applied to each time step in the CASSM survey in an updating scheme, which integrates with an ensemble of reservoir simulations at each step. A more complete experimental validation dataset will be collected as part of a mesoscale (2-3 m) gas-CO 2 injection experiment utilizing a higher frequency version of the CASSM system developed for laboratory studies; the integrated inversion will be demonstrated using this dataset which will provide both a dense geometry as well as more precise secondary confirmation measurements (e.g. saturation) typically not available in the field. The resulting real-time map of CO 2 saturation is able to provide a deeper scientific understanding of the complex, time-varying dynamics of subsurface fluid flow migration path as well as the rapid detection of CO 2 leakage hazards.

25 ENERGY STORAGE↗
DOE OSTI2021

Replication Data for: Oxophilicity Drives Oxygen Transfer at a Palladium−Silver Interface for Increased CO Oxidation Activity

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗