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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Multivariate Statistical Analysis of Orthogonal Mass Spectral Data for the Identification of Chemical Attribution Signatures of 3-Methylfentanyl

Critical to many modern forensic investigations is the chemical attribution of the origin of an illegal drug. This process greatly relies on identification of compounds indicative of its clandestine or commercial production. The results of these studies can yield detailed information on method of manufacture, sophistication of the synthesis operation, starting material source, and final product. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic 3- methylfentanyl, N-(3-methyl-1-phenethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods were studied in an effort to identify and classify route-specific signatures. These methods were chosen to minimize the use of scheduled precursors, complicated laboratory equipment, number of overall steps, and demanding reaction conditions. Using gas and liquid chromatographies combined with mass spectrometric methods (GC-QTOF and LC-QTOF) in conjunction with inductivelycoupled plasma mass spectrometry (ICP-MS), over 240 distinct compounds and elements were monitored. As seen in our previous work with CAS of fentanyl synthesis the complexity of the resultant data matrix necessitated the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 62 statistically significant, route-specific CAS were identified. Statistical classification models using a variety of machine learning techniques were then developed with the ability to predict the method of 3-methylfentanyl synthesis from three blind crude samples generated by synthetic chemists without prior experience with these methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Identification of Chemical Attribution Signatures of Fentanyl Syntheses Using Multivariate Statistical Analysis of Orthogonal Analytical Data

Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. The results of these studies can yield detailed information on method of manufacture, starting material source, and final product - all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. 160 distinct compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (GC-MS and LCMS/ MS-TOF) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least squares discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. This work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Isotope Identification

The objective of this training modules is to examine the process of using gamma spectroscopy for radionuclide identification; apply pattern recognition to gamma spectra; identify methods of verifying energy calibration; and discuss potential causes of isotope misidentification.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Identification and Characterization of Thermal Fluid Phenomena Associated with Selected Operating/Accident Scenarios in Modular High Temperature Gas-cooled Reactors

Dominant phenomena are identified and characterized for the loss-of-forced-convection (LOFC) scenario, the depressurized loss-of-forced-convection (DLOFC) scenario, and the moisture ingress scenario with relief valves available scenario. These scenarios are considered for the modular high temperature gas-cooled reactor (MHTGR) baseline design—with both a prismatic core and a pebble-bed core. The objectives of this effort, described in the above paragraph, were achieved by following a methodology identified by the U.S. Nuclear Regulatory Commission (NRC) and recommended in their Regulatory Guide 1.203. The methodology employed is described. For the above scenarios, baseline phenomena identification and ranking tables (PIRTs) generated by a NRC-sponsored effort in 2008 for the LOFC and DLOFC scenarios and a baseline PIRT generated by a Department of Energy-sponsored effort in 2011 by their Next Generation Nuclear Plant (NGNP) Moisture Ingress Assessment Committee were used to identify the dominant phenomena and existing levels-of-knowledge. Ten dominant phenomena (with only medium or low levels-of-knowledge) were identified for the LOFC and DLOFC scenarios including: (1) Inlet plenum stratification and plumes, (2) core coolant flow distribution, (3) radiant heat transfer from the top of the core to the upper vessel head, (4) reactor cavity air circulation and heat transfer, (5) reactor cavity cooling system behavior, (6) decay heat distributions, (7) core effective thermal conductivity, (8) molecular diffusion in air ingress DLOFC, (9) duct exchange flow, (10) phenomena that affect cavity gas composition for air-ingress. Five dominant phenomena, with high levels of knowledge, were identified for the moisture-ingress scenario with relief valves available including: (1) flow through break, (2) moisture level in the primary, (3) transport to moisture monitor, (4) moisture monitor instrument response, and (5) heat removal by shutdown cooling system. These phenomena are discussed and characterized. A listing of experiments—both completed and ongoing—is given with a view toward including data useful for further more detailed examination of the dominant phenomena listed in the previous paragraph. Note that only data for the LOFC and DLOFC scenarios are listed—since the moisture ingress scenario with relief valves available –have been identified as having adequate levels-of-knowledge by the PIRT committee. Finally, a preliminary computational fluid dynamics (CFD) calculation of inlet plenum stratification and plumes is described. Techniques employed for this calculation are typical for preliminary scoping calculations performed to validate numeric tools.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Optimal Floating Vertical-Axis Wind Turbine Platform Identification Design and Cost Estimation

This report houses the deliverables provided by Stress Engineering Services on the floating platform design identification studies and the detailed final design iterations. The results were obtained under contract to and in partnership with Sandia to iterate between the platform design and the aero-hydro-elastic load simulations of the coupled vertical-axis wind turbine system. Through the analysis summarized in this report, a tension-leg platform with multiple columns was identified as the optimal platform when considering cost and performance. The detailed design and cost estimate of this platform architecture was produced in the final phase of study which is also described within this report.

17 WIND ENERGY↗
DOE OSTI2017

Automated Work Packages: Radio Frequency Identification, Bluetooth Beacons, and Video Applications in the Nuclear Power Industry

The need to move conventional paper-based work packages into an electronic state motivated the development of a large span of electronic work package forms that ranged from simple Portable Document Format (PDF) files to adaptive and intelligent electronic forms. In parallel to this development, the Department of Energy Light Water Reactor Sustainability Program (LWRS) initiated the automated work package as a pilot project to evaluate implementing state of the art automation techniques into the work package process to improve the economic and safety of the work process. Through a multiple year’s effort, the automated work package project have developed a vision of the future work package that relies on automation in all critical and human factor challenging tasks. The vision identified gaps in the work process, the means to automate them, and evaluated the implementation, feasibility and benefits of these means. This was conducted through continues engagement with the nuclear power industry. The aim of this report is to study the potential of three automation technologies, which have been advancing in various industries, in the current work process of nuclear power plants. The study is to determine the feasibility and benefits of incorporating the technologies and determine potential gaps, if any, that need to be addressed to facilitate deployment of the studied technologies. The three automation technologies identified for this effort are ultra-high frequency (UHF) radio frequency identification (RFID), Bluetooth low energy (BLE) beacons, and video monitoring and recording. As part of this effort, the Idaho National Laboratory (INL) collaborated with Xcel Energy Inc. to evaluate these technologies. During the research and development scope of the effort, INL engaged Xcel Energy Inc. periodically and exchanged valuable insight on the means to implement these technologies in a nuclear power plant. Multiple pilot prototypes were developed and tested in a user study conducted at Xcel Energy Inc. in Minneapolis, Minnesota. The outcome of this effort resulted in significant findings that included the detailed means to custom fit the technologies to fit the nuclear power industry need, and identifying and evaluating several applications of high economic or safety potential. The limitations and obstacles that could impact deployment of the technologies were also identified, and the means of how to address these limitations and obstacles were studied.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Real-Time Automated Pathogen Identification by Enhanced Ribotyping (RAPIER) LDRD Final Report

Funded through the IHNS/E&HS investment area for FY16-18, the RAPIER LDRD sought to evaluate the potential benefits and applicability of the new Oxford MinION nanopore sequencer to pathogen diagnostic applications in biodefense, biosurveillance, and global/public health. The project had four primary objectives: 1) to investigate the performance of the MinION sequencer while building facility with its operation, 2) to develop microfluidic library prep automation facilitating the use of the MinION in field-forward or point-of-care applications, 3) to leverage CRISPR/Cas9 technology to enable targeted identification of bacterial pathogens, and 4) to capitalize on the real- time data output capabilities of the MinION to enable rapid sequence-based diagnostics. While the rapid evolution of the MinION sequencing technology during the course of the project posed a number of challenges and required a reassessment of initial project priorities, it also provided unique opportunities, notably culminating in our development of the RUBRIC real-time selective sequencing software.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Identification of Potential Locations for Placement of Strategic Transformers Reserve in Puerto Rico

Extreme weather events such as tropical storms cause extensive damage to energy infrastructures including generator stations, substations, poles, transformers, power transmission lines, etc. due to their presence in hazardous locations. Often these damages lead to long term power outage as was seen in Puerto Rico (PR) following the landfall of Hurricane Maria (2017). A potential solution to long-term power outage is strategic placement of reserve generators and transformers that would ensure continued power supply to enable restoration and recovery activities. The strategic placement of energy storage devices requires identification of potential sites such that (i) the damage from extreme events to energy infrastructures is minimized, (ii) power supply is optimized, and (iii) installation of these infrastructures is cost effective. To meet this goal, in this research, a Multi-Criteria Evaluation (MCE) technique-based site suitability analysis was implemented to identify potential sites for reserve generators and transformers in PR. To determine suitable sites and existing government facilities, the following steps were implemented: (i) identify physical, environmental, technical and socio-economic factors contributing to site suitability; (ii) determine siting constraints based on guidelines and standards used for energy sector (i.e., siting of nuclear plants, solar or wind farms), (iii) implement a raster data model using 30m x 30m spatial resolution to create suitability layers for each factor, (iv) combine suitability and exclusionary layers to create the final suitability layer. Results show that six hospitals, four colleges and universities, and five correctional facilities are in very high suitability areas and could be used for to store reserve generators. These facilities are in the eastern, south-eastern, south-western, and southern part of the island except for one correctional facility that is in the northern part of PR. Not surprisingly, coastal areas were found to be unsuitable for siting any energy facility. More than half (55.9%) of the island was also found to be unsuitable while only 14% of the island was found to be very highly suitable. Further assessment of current power plants and sub-stations indicated that only 16% and 42% of existing power plants and sub-stations respectively are in suitable areas. Given that northern part of the island lacks any facility that could be used for placement of reserve generators, suitable sites identified in this region could be used for placement of storage devices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Identification of Hydrogen Material Risk Research Areas

This report presents a research framework for the application of quantitative risk assessment to hydrogen materials, based on the identification of potential areas of research, a literature review, and a plan for future work in the area of hydrogen materials risk. After outlining basics on hydrogen infrastructure, with a focus on pressure vessels, a number of materials risk topics are identified and discussed. Of these, four important areas of risk application to hydrogen materials are highlighted and discussed in further detail. The four topics are initial crack distribution in metals, damage and stress rupture in composites, and polymer behavior in high pressure hydrogen. These topics vary in scope, specificity, level of anticipated resources required, and potential impact to the field. Finally, recommendations are made for future research on the highest priority topics.

08 HYDROGEN↗
DOE OSTI2021

Aftershock Identification Using a Paired Neural Network Applied to Constructed Data

This report is intended to detail the findings of our investigation of the applicability of machine learning to the task of aftershock identification. The ability to automatically identify nuisance aftershock events to reduce analyst workload when searching for events of interest is an important step in improving nuclear monitoring capabilities and while waveform cross - correlation methods have proven successful, they have limitations (e.g., difficulties with spike artifacts, multiple aftershocks in the same window) that machine learning may be able to overcome. Here we apply a Paired Neural Network (PNN) to a dataset consisting of real, high quality signals added to real seismic noises in order to work with controlled, labeled data and establish a baseline of the PNN's capability to identify aftershocks. We compare to waveform cross - correlation and find that the PNN performs well, outperforming waveform cross - correlation when classifying similar waveform pairs, i.e., aftershocks.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Tensor Text-Mining Methods for Malware Identification and Detection, Malware Dynamics Characterization, and Hosts Ranking

Malware is one of the most persistent and costly cyber threats endangering reputation, confidentiality, integrity, and availability for organizations and national security. Consequently, many of the incident detection and prevention systems, and incident responders have begun to utilize machine learning as a helper in the fight against malware and other cyber threats. However, cyber defenders rely on interpretability and generalizability, yet the popular machine learning methods are black-box and often use traditional supervised solutions that do not generalize to novel malware. Therefore, there is a need to improve the existing solutions. At the same time, the majority of the prior research ignored essential evaluation criteria when reporting the results of their methods, which disables the safe reproducibility of the methods in a production environment. Tensor decomposition, on the other hand, enables interpretable unsupervised analysis of the large-scale data for the discovery of hidden patterns. Our findings, performed on real-world and large-scale experiments, show that tensor factorization-based methods yield performance results that surpasses or competes with existing supervised solutions with the added benefit of interpretability and generalizability. With the ability to analyze complex and large-scale data using tensors, we report results that reflect real-world production environments. We propose to develop new game- changing tools for malware identification and characterization that can trace malware evolution, rank the infected or malicious hosts, and streamline the work of incident response teams, malware analysts, and incident detection and prevention systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Li-ion Identification For the Next Generation of Cathode Recycling

OnTo’s end-to-end solution will demonstrate (in partnership/collaboration with relevant industries) collection of lithium-ion batteries through recycling of cathode material. The DISCC project will prototype 1) deactivation of batteries 2) identification 3) sorting using automated techniques, 4) cutting with waterjet technology and 5) Cathode-Healing™ to produce valuable cathodes from scrap batteries.

25 ENERGY STORAGE↗
DOE OSTI2022

Overview of the Strategic Goods Identification Instructor Training [Slides]

The slides provide an overview of the training course, including course objectives and agenda topics for the 5 days expected. The goal of this course is to establish a core team of Malaysian interagency experts to serve as: 1. Instructors for STS’s recurring STA2010 and Strategic Goods Identification courses; and 2. Technical Strategic Goods List experts when either licensing or enforcement actions require technical consultation or “reachback".

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Phenomenon Identification and Ranking Table Analysis for Thermal Energy Storage Technologies Integration with Advanced Nuclear Reactors

This report provides an overview of the Phenomena Identification and Ranking Table (PIRT) analysis of thermal energy storage (TES) systems for possible integration with various types of advanced nuclear power plants (NPPs). Advanced NPPs will potentially need to operate in environments where power generation flexibility is more highly valued than the stability or baseload generation capability for conventional demand curves. TES systems would enable NPPs to respond nimbly to market variability and could also position advanced NPPs to participate differently in restructured markets, thus further enhancing their economic competitiveness. TES systems could also benefit the electric grid by eliminating the need for peaking plants, as well as by improving the economic performance of baseload NPPs. While TES technologies afford a unique opportunity to address many of these challenges, the applicability of these systems is also complicated by the fact that various advanced NPPs are designed differently, each with its own temperature range, size, operating fluids, and operating conditions. Hence, TES systems face significant barriers to investment, as more information on their compatibility and performance metrics is needed to quantify the advantages provided by each, as well as the challenges these technologies might face if coupled with a particular type of advanced NPP. This report explores the possibility of integrating a wide variety of TES technologies with various categories of advanced NPPs, based on their operating characteristics. To help users and developers decide which TES technology is best suited to a particular category of advanced NPPs, this research developed a PIRT of 10 TES systems that could potentially be coupled with advanced NPPs, which themselves are divided into nine categories based on their operating conditions. Each advanced NPP category is evaluated for compatibility with the 10 TES systems by assembling and discussing a database of information concerning 10 engineering questions (defined herein in as figures of merit [FOMs]), such as: technology readiness level (TRL), temperature compatibility, energy density, size, cycle frequency, ramp time, realignment frequency, geographic needs, environmental impact, and interventions. By assembling a database of information concerning the TES technologies’ compatibility with various advanced NPP systems, this study can help developers acquaint themselves with a particular TES technology before choosing to build a new integrated installation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Machine Learning Application for Frac-Hit Identification: Field Data Use Case

This study focusses on the development of an ML-based workflow that can be used for frac-hit identification and monitoring based on the readily available field data, such as drilling surveys, pressure records, production history (oil and water production measurements), and fracking time and duration information, etc.

02 PETROLEUM↗
DOE OSTI2022

Identification and Quantification of Photosynthetic Pigments in Algae (Laboratory Analytical Procedure (LAP))

The Laboratory Analytical Procedure (LAP) outlined here describes a method to quantitatively extract phytopigments from microalgae biomass, as well as to identify and quantify individual pigments based on separation and detection with a High-Performance Liquid Chromatography (HPLC) system coupled to a Diode Array Detector (DAD). Pigments were extracted with greater than 95 % extraction efficiency. Chromatographic conditions allowed for isomeric resolution between pigments and identification based on UV/Vis spectra and comparison to analytical standards.

09 BIOMASS FUELS↗