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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Application of research reactor environments for validation of the IRDFF-II dosimetry cross section library

Activation data from seven different reactor-based reference neutron fields are examined to provide enhanced validation evidence for the newly released IRDFF-II library. A least-squares based spectrum adjustment methodology is used and rigorous statistical metrics demonstrate the consistency of the set of IRDFF-II dosimetry cross sections. The use of these reactor-based neutron fields provides validation evidence for nine more reactions than have been addressed in the 252 Cf(sf) and 235 U(th) benchmark validation testing. The use of covers to shift their energy response increases the power of the response validation but makes it challenging to properly capture response correlations.

Griffin, Patrick↗
DOE OSTI2018

Temporal variability largely explains top-down/bottom-up difference in methane emission estimates from a natural gas production region

Here, this study spatially and temporally aligns top-down and bottom-up methane emission estimates for a natural gas production basin, using multiscale emission measurements and detailed activity data reporting. We show that episodic venting from manual liquid unloadings, which occur at a small fraction of natural gas well pads, drives a factor-of-two temporal variation in the basin-scale emission rate of a US dry shale gas play. The midafternoon peak emission rate aligns with the sampling time of all regional aircraft emission studies, which target well-mixed boundary layer conditions present in the afternoon. A mechanistic understanding of emission estimates derived from various methods is critical for unbiased emission verification and effective greenhouse gas emission mitigation. Our results demonstrate that direct comparison of emission estimates from methods covering widely different timescales can be misleading.

03 NATURAL GAS↗
DOE OSTI2023

Mitigation of ventilation air methane (VAM) using novel methanotrophic coating materials: a technical analysis

Abstract Ventilation air methane (VAM) is a potent greenhouse gas source originating from geological wells, current and extinct mineshafts and other terrestrial conduits venting methane to the atmosphere, contributing to global methane emissions and disproportionate warming potential. Herein, we introduce the concept of the methanotrophic material as an engineering solution. Such materials should be capable of converting methane at ambient temperatures and pressures to a binder product, capturing and permanently sequestering the methane while simultaneously restricting its further emission. While such materials are currently under research development, this goal is supported and facilities by the mathematical framework, introduced and used herein, to evaluate the ability to convert methane, using currently published activity data. We include a case study of the conversion of a characteristic stream of VAM (0.6% methane in air, 1.7 × 10 8 l hr −1 equivalent to 100 000 standard cubic feet per minute). We show that when appropriately designed, such systems require a surface coverage of less than 1000 m of mine tunnel length (equivalent to 20 000 m 2 areal coverage) in order to reduce the methane emission from this stream by over 99%. Finally, we highlight formaldehyde as a reactive intermediate of methane oxidation which may itself be incorporated into these coating materials. As a component of binders and polymers already used ubiquitously in commercial products, this intermediate ultimately allows these systems to sequester the carbon from methane in a stable and solid form. The results presented here are easily extended to the treatment of other methane streams—either more concentrated or dilute—and the results herein will guide the design and development of a new class of carbon-negative materials.

Environmental Sciences & Ecology↗
DOE OSTI2019

Evaluating the Environmental Impacts of Connected and Automated Vehicles: Potential Shortcomings of a Binned-Based Emissions Model

In addition to providing safety and mobility benefits, Connected and Automated Vehicles (CAVs) have the potential to reduce fuel consumption and emissions. As new CAV applications are developed, it is valuable to estimate these potential environmental benefits, typically using vehicle activity data and emissions models. To date, most researchers in the U.S. have used the MOVES vehicle emissions model, developed and maintained by the U.S. Environmental Protection Agency (EPA). However, because MOVES uses a binning approach, it is likely underestimating the true energy and emissions savings that occur when CAV applications smooth traffic flow. To illustrate this problem, we measure and model the fuel consumption and CO 2 emissions for a real-world CAV application: Eco-Approach and Departure (EAD) at signalized intersections. Real-world measurements are compared to a MOVES-based estimate, as well as to an estimate provided by the physical-based Comprehensive Modal Emissions Model (CMEM). Results show that MOVES consistently underestimates the energy and emissions benefits of the CAV application, primarily since the bin sizes in MOVES are too large to catch the nuances of traffic smoothing. On the other hand, CMEM provided a more accurate energy and emissions estimate, primarily since it uses analytical functions to model emissions and does not suffer from the same binning problem.

42 ENGINEERING↗
DOE OSTI2017

Estuary ecosystem restoration: implementing and institutionalizing adaptive management: Institutionalizing adaptive management

We successfully implemented and institutionalized an adaptive management (AM) process for the Columbia Estuary Ecosystem Restoration Program, which is a large-scale restoration program focused on improving ecosystem conditions in the 234-km lower Columbia River and estuary. For our purpose, “institutionalized” means the AM process and restoration program are embedded in the work flow of the implementing agencies and affected parties. While plans outlining frameworks, processes, or approaches to AM of ecosystem restoration programs are commonplace, establishment for the long term is not. This paper presents the basic AM framework and explains how AM was implemented and institutionalized. Starting with a common goal, we pursued included a well-understood governance and decision-making structure, routine coordination and communication activities, data and information sharing, commitment from partners and upper agency management to the AM process, and meaningful cooperation among program managers and partners. The overall approach and steps to implement and institutionalize AM for ecosystem restoration explained here are applicable to situations where it has been less than successful or, as in our case, the restoration program is just getting started.

adaptive management↗
DOE OSTI2017

Comparison of methane emission estimates from multiple measurement techniques at natural gas production pads

This study presents the results of a campaign that estimated methane emissions at 268 gas production facilities in the Fayetteville shale gas play using onsite measurements (261 facilities) and two downwind methods - the dual tracer flux ratio method (Tracer Facility Estimate - TFE, 17 facilities) and the EPA Other Test Method 33a (OTM33A Facility Estimate - OFE, 50 facilities). A study onsite estimate (SOE) for each facility was developed by combining direct measurements and simulation of unmeasured emission sources, using operator activity data and emission data from literature. The SOE spans 0-403 kg/h and simulated methane emissions from liquid unloadings account for 88% of total emissions estimated by the SOE, with 76% (95% CI [51%-92%]) contributed by liquid unloading at two facilities. TFE and SOE show overlapping 95% CI between individual estimates at 15 of 16 (94%) facilities where the measurements were paired, while OFE and SOE show overlapping 95% CI between individual estimates at 28 of 43 (65%) facilities. However, variance-weighted least-squares (VWLS) regressions performed on sets of paired estimates indicate statistically significant differences between methods. The SOE represents a lower bound of emissions at facilities where onsite direct measurements of continuously emitting sources are the primary contributor to the SOE, a sub-selection of facilities which minimizes expected inter-method differences for intermittent pneumatic controllers and the impact of episodically-emitting unloadings. At 9 such facilities, VWLS indicates that TFE estimates systematically higher emissions than SOE (TFE-to-SOE ratio = 1.6, 95% CI [1.2 to 2.1]). At 20 such facilities, VWLS indicates that OFE estimates systematically lower emissions than SOE (OFE-to-SOE ratio of 0.41 [0.26 to 0.90]). Given that SOE at these facilities is a lower limit on emissions, these results indicate that OFE is likely a less accurate method than SOE or TFE for this type of facility.

03 NATURAL GAS↗
DOE OSTI2015

Neutron Reference Benchmark Field Specifications: ACRR Polyethylene-Lead-Graphite (PLG) Bucket Environment (ACRR-PLG-CC-32-CL)

This report was put together to support the International Atomic Energy Agency (IAEA) REAL- 2016 activity to validate the dosimetry community’s ability to use a consistent set of activation data and to derive consistent spectral characterizations. The report captures details of integral measurements taken in the Annular Core Research Reactor (ACRR) central cavity with the Polyethylene-Lead-Graphite (PLG) bucket, reference neutron benchmark field. The field is described and an “a priori” calculated neutron spectrum is reported, based on MCNP6 calculations, and a subject matter expert (SME) based covariance matrix is given for this “a priori” spectrum. The results of 37 integral dosimetry measurements in the neutron field are reported.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Neutron Reference Benchmark Field Specification: ACRR 44 Inch Lead-Boron (LB44) Bucket Environment (ACRR-LB44-CC-32-CL)

This report was put together to support the International Atomic Energy Agency (IAEA) REAL- 2016 activity to validate the dosimetry community’s ability to use a consistent set of activation data and to derive consistent spectral characterizations. The report captures details of integral measurements taken in the Annular Core Research Reactor (ACRR) central cavity with the 44 inch Lead-Boron (LB44) bucket, reference neutron benchmark field. The field is described and an “a priori” calculated neutron spectrum is reported, based on MCNP6 calculations, and a subject matter expert (SME) based covariance matrix is given for this “a priori” spectrum. The results of 31 integral dosimetry measurements in the neutron field are reported.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

Neutron Reference Benchmark Field Specification: ACRR Free-Field Environment (ACRR-FF-CC-32-CL)

This report was put together to support the International Atomic Energy Agency (IAEA) REAL- 2016 activity to validate the dosimetry community’s ability to use a consistent set of activation data and to derive consistent spectral characterizations. The report captures details of integral measurements taken in the Annular Core Research Reactor (ACRR) central cavity free-field reference neutron benchmark field. The field is described and an “a priori” calculated neutron spectrum is reported, based on MCNP6 calculations, and a subject matter expert (SME) based covariance matrix is given for this “a priori” spectrum. The results of 31 integral dosimetry measurements in the neutron field are reported.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2015

New Routes for the Production of Fission Products and Their Separation from the Source Material for the Development of Realistic Nuclear Forensic Debris

In order to fully characterize the NIF neutron spectrum, SAND-II-SNL software was requested/received from the Radiation Safety Information Computational Center. The software is designed to determine the neutron energy spectrum through analysis of experimental activation data. However, given that the source code was developed in Sparcstation 10, it is not compatible with current version of FORTRAN. Accounts have been established through the Lawrence Livermore National Laboratory’s High Performance Computing in order to access different compiles for FORTRAN (e.g. pgf77, pgf90). Additionally, several of the subroutines included in the SAND-II-SNL package have required debugging efforts to allow for proper compiling of the code.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Black carbon emissions from diesel sources in Russia. Final report

This report presents a detailed inventory of Russian BC emissions from diesel sources. Drawing on a complete Russian vehicle registry with detailed information about vehicle types and emission standards, this report analyzes BC emissions from diesel on-road vehicles. On-road diesel vehicles emitted 21 Gg of BC in 2014: heavy-duty trucks account for 60% of the on-road BC emissions, while cars represent only 5% (light commercial vehicles and buses account for the remainder). Using Russian activity data and fuel-based emission factors, the report also presents BC emissions from diesel locomotives and ships, off-road engines in industry, construction and agriculture, and generators. The total emissions from diesel sources in Russia are estimated to be 49 Gg of BC in 2014.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

WANDA: AI/ML for Nuclear Data

A draft summary of the Workshop on Applied Nuclear Data Activities 2020 is presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
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↗