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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Automated reactor physics analysis framework of High Flux Isotope Reactor low-enriched uranium silicide dispersion fuel designs

The High Flux Isotope Reactor (HFIR) is a versatile research reactor that provides one of the highest steady-state neutron fluxes of any reactor in the world. The HFIR reactor physics team investigated the conversion of the current 93 wt% highly enriched uranium U 3 O 8 -Al dispersion fuel to a 19.75% low-enriched uranium (LEU) U 3 Si 2 -Al dispersion fuel. The team continuously develops a Python module to streamline the analysis steps required for an LEU core design to ensure reproducible and agile design iteration. The Python module automates the data processing between analysis steps and automates the input perturbation for branch calculations and design changes. The automated framework has proven to significantly increase the efficiency and reproducibility of the reactor physics team to design High Flux Isotope Reactor (HFIR) LEU cores and thoroughly analyze performance metrics, safety metrics, and thermal safety margins. Consequently, the team can now respond rapidly to fuel fabrication engineer and thermal-hydraulic-structural analyst requests. Numerous combinations of LEU fuel designs are explored, of which two LEU fuel designs are presented here in this paper: a low density silicide design, and a high-density silicide design. Results show that both designs meet or exceed safety and performance metrics with exception for minor differences caused by the hardened spectrum from LEU.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Review of Current Software for Analyzing Total X-ray Scattering Data from Liquids

The popularity of the pair distribution function (PDF) analysis of X-ray total scattering data has steadily grown as access to ex situ synchrotron data has expanded. Due to the broadening of the PDF user community, there is a growing demand for software that can be used to extract PDFs and is accessible to non-expert users. While user-friendly options have been developed over the past decade for fast, streamlined data analysis, care must be taken in both processing the data and understanding any limitations, especially in the case of liquids. In this review, the same scattering data are analyzed using different total X-ray scattering software, in order to compare the accuracy of the extracted structure factors and associated pair distribution functions. The goal is to assess the best practices for extracting the most accurate liquid data for each software package. The importance of absolute normalization and the application of the most appropriate corrections are emphasized via quantitative comparisons between liquid sulfur and water. Additionally, an awareness of the competing conventions used to define the PDF in crystallography and liquids/glasses is crucial for both the downstream analyses of the data and a comparison with the previous results in the literature.

Gallington, Leighanne C. (ORCID:0000000203837522)↗
NASA NTRS2011

Toward Intelligent Software Defect Detection

Source code level software defect detection has gone from state of the art to a software engineering best practice. Automated code analysis tools streamline many of the aspects of formal code inspections but have the drawback of being difficult to construct and either prone to false positives or severely limited in the set of defects that can be detected. Machine learning technology provides the promise of learning software defects by example, easing construction of detectors and broadening the range of defects that can be found. Pinpointing software defects with the same level of granularity as prominent source code analysis tools distinguishes this research from past efforts, which focused on analyzing software engineering metrics data with granularity limited to that of a particular function rather than a line of code.

Benson, Markland J.↗
DOE OSTI2024

Tetrafluoroboric Acid Digestion for Accurate Determination of Rare Earth Elements in Coal and Fly Ash by ICP-MS Analysis

Coal and coal-related fly ash often contain rare earth elements (REEs) that have the potential to be utilized as valuable mineral resources. Accurately determining the REE content in coal and fly ash is crucial for resource evaluation. The conventional approach involves using hydrofluoric acid (HF) to dissolve silicates and release REEs, which, however, prolongs the digestion process due to the additional step of complexing fluoride ions (F−) with boric acid (H3BO3). Determining the correct amount of H3BO3 for neutralization can be challenging, and in some instances, the binding of fluoride ions with certain lanthanides (Lns) hampers the accurate determination of all 14 naturally occurring rare earth elements in a single digestion batch by inductively coupled plasma mass spectrometry (ICP-MS). In this study, we present an alternative method that achieves the accurate determination of all 14 naturally occurring REEs using tetrafluoroboric acid (HBF4) followed by ICP-MS analysis. This approach eliminates the need for an F− complexing step. We tested this method on certified REE reference materials, including NIST 1632e (coal) and NIST 1633c (fly ash), as well as the REE geological reference material USGS AGV-1 (andesite). Our results demonstrated excellent recovery rates (relative standard deviation, RSD < ±10%), with a correlation coefficient (r2) exceeding 0.99. Using this method, we investigated the concentrations of all 14 REEs in coal and fly ash samples collected from various locations in the southwestern USA. This improved digestion technique streamlines the analysis process and enhances the accuracy of REE determination, facilitating a more comprehensive evaluation of REE-rich coal and fly ash deposits for resource exploration.

58 GEOSCIENCES↗
DOE OSTI2016

Streamlining Building Efficiency Evaluation with DOE's Asset Score Preview

Building Energy Asset Score (Asset Score), developed by the Pacific Northwest National Laboratory (PNNL) for the U.S. Department of Energy (DOE), is a tool to help building owners and managers assess the efficiency of a building's energy-related systems and encourage investment in cost-effective improvements. The Asset Score uses an EnergyPlus model to provide a quick assessment of building energy performance with minimum user inputs of building characteristics and identifies upgrade opportunities. Even with a reduced set of user inputs, data collection remains a challenge for wide-spread adoption, especially when evaluating a large number of buildings. To address this, Asset Score Preview was developed to allow users to enter as few as seven building characteristics to quickly assess their buildings before a more in-depth analysis. A streamlined assessment from Preview to full Asset Score provides an easy entry point and also enables users who manage a large number of buildings to screen and prioritize buildings that can benefit most from a more detailed evaluation and possible energy efficiency upgrades without intensive data collection.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Generation of macro- and microplastic databases by high-throughput FTIR analysis with microplate readers

Abstract FTIR spectral identification is today’s gold standard analytical procedure for plastic pollution material characterization. High-throughput FTIR techniques have been advanced for small microplastics (10–500 µm) but less so for large microplastics (500–5 mm) and macroplastics (> 5 mm). These larger plastics are typically analyzed using ATR, which is highly manual and can sometimes destroy particles of interest. Furthermore, spectral libraries are often inadequate due to the limited variety of reference materials and spectral collection modes, resulting from expensive spectral data collection. We advance a new high-throughput technique to remedy these problems using FTIR microplate readers for measuring large particles (> 500 µm). We created a new reference database of over 6000 spectra for transmission, ATR, and reflection spectral collection modes with over 600 plastic, organic, and mineral reference materials relevant to plastic pollution research. We also streamline future analysis in microplate readers by creating a new particle holder for transmission measurements using off-the-shelf parts and fabricating a nonplastic 96-well microplate for storing particles. We determined that particles should be presented to microplate readers as thin as possible due to thick particles causing poor-quality spectra and identifications. We validated the new database using Open Specy and demonstrated that additional transmission and reflection spectra reference data were needed in spectral libraries. Graphical abstract

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

hpMCA: A Python-based Graphical Program for Energy Dispersive X-Ray Diffraction Data Collection and Analysis

Energy-dispersive X-ray diffraction (EDXD) at synchrotron beamlines is commonly used for the study of material properties under high pressure and/or high temperature. Experimenters typically rely on the availability of robust data collection and analysis at a beamline, but this has become increasingly difficult, especially with the introduction of multi-element detectors that generate complex, multi-dimensional data sets. These data sets have energy resolution, and they can also be resolved in relation to sample position, diffraction angle, or different external stimuli. We report a new Python-based graphical program, hpMCA, for EDXD data collection and analysis that streamlines the experimental process for the beamline users. The program features a user-friendly interface, capability for online viewing and analyzing data from multi-element energy-dispersive detectors, and includes features useful for working with samples under high pressure and/or high temperature, such as crystal phase identification, real-time unit cell lattice refinement, and pressure determination based on an equation of state.

data analysis software↗
NASA NTRS2012

SOFIA: Flying the Telescope

The primary focus of this paper is how the flight test team for the Stratospheric Observatory For Infrared Astronomy (SOFIA) re-cast an extensive developmental test program to meet key milestones while simultaneously ensuring safe certification of the airframe and delivery of an operationally relevant platform, ultimately saving the overall program from financial demise. Following a brief introduction to the observatory and what it is designed to do, SOFIAs planned developmental test program is summarized, including analysis and design philosophy, envelope expansion, model validation and airframe certification. How NASA used lessons learned from other aircraft that employed open cavities in flight is explained as well as how and why the chosen design was selected. The approach to aerodynamic analysis, including bare airframe testing, wind tunnel testing, computational fluid dynamics and finite element modeling proved absolutely critical. Despite a solid analytical foundation, many unknowns remained. History provides several examples of disastrous effects on both systems and flight safety if cavity design is not approached properly. For these reasons, an extensive test plan was developed to ensure a safe and thorough build-up for envelope expansion, airframe certification and early science missions. Unfortunately, as is often the case, because of chronic delays in overall program execution, severe schedule and funding pressures were present. If critical milestones were not met, domestic as well as international funding was in serious jeopardy, and the demise of the entire program loomed large. Concentrating on rigorous model validation, the test team challenged certification requirements, increased test efficiency and streamlined engineering analysis. This resulted in the safe reduction of test point count by 72%, meeting all program milestones and a platform that soundly satisfied all operational science requirements. Results from early science missions are shown and a proof of concept mission for which SOFIA was opportunely positioned is showcased. Success on this time-critical mission to observe a rare astronomical event proved the usefulness of an airborne observatory and the value in waiting for the capability provided by SOFIA. Finally, lessons learned in the test program are presented with emphasis on how lessons from previous aircraft and successful test programs were applied to SOFIA. Effective application of these lessons was crucial to the success of the SOFIA flight test program. SOFIA is an international cooperative program between NASA and the German Space Agency, DLR. It is a 2.5 meter (100-inch) telescope mounted in a Boeing 747SP aircraft used for astronomical observations at altitudes above 35,000 feet. SOFIA will accommodate a host of scientific instruments from the international science community and has a planned operational lifespan of more than 20 years.

Asher, Troy A.↗
DOE OSTI2024

Accurate and Data‐Efficient Micro X‐ray Diffraction Phase Identification Using Multitask Learning: Application to Hydrothermal Fluids

Traditional analysis of highly distorted micro X‐ray diffraction (μ‐XRD) patterns from hydrothermal fluid environments is a time‐consuming process, often requiring substantial data preprocessing and labeled experimental data. Herein, the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations is demonstrated. MTL models are trained to identify phase information in μ‐XRD patterns, minimizing the need for labeled experimental data and masking preprocessing steps. Notably, MTL models show superior accuracy compared to binary classification convolutional neural networks. Additionally, introducing a tailored cross‐entropy loss function improves MTL model performance. Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieve close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor‐intensive experimental datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Practical considerations for amino acid isotope analysis

Over the last few decades, isotopic analysis of amino acids at the compound- and position-specific levels has been rapidly advancing across diverse fields. As these techniques progress, evaluation of isotopic fractionation associated with sample workup is essential. This critical review of analytical methods through the lens of isotope geochemistry provides a benchmark for researchers across disciplines seeking to make compound- and position-specific amino acid isotope measurements. We focus on preparation, acid hydrolysis, clean-up, derivatization, separation, and C, H, N, and S isotope measurement. Despite substantial customizability across these steps, the following general recommendations should maximize recovery while minimizing isotopic fractionation. Samples should be freeze-dried and stored anoxically at ≤ –20 °C prior to conventional acid hydrolysis (6N HCl, 110 °C, 20–24 h, anoxic), which suffices for many residues. Both gas and liquid chromatographic (GC and LC, respectively) techniques are well-established and separate about 15 amino acids; LC bypasses the need for derivatization, while GC provides higher sensitivity. Furthermore, when derivatization is needed, n-acetyl and alkoxycarbonyl esters provide the most reproducible C isotope ratios. For compound-specific analyses, online GC–IRMS and LC–IRMS systems offer the easiest workflow, but EA–IRMS enables potential multi-element isotope analysis. Emerging techniques like high-resolution mass spectrometry are also promising for multi-element analysis and recover position-specific isotopic information. Looking forward to the next decade of innovation, isotope geochemists and ecologists can improve amino acid isotope analysis by focusing on streamlining multi-element analysis and standardizing calibration practices across laboratories.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Automated electrosynthesis reaction mining with multimodal large language models (MLLMs)

Leveraging the chemical data available in legacy formats such as publications and patents is a significant challenge for the community. Automated reaction mining offers a promising solution to unleash this knowledge into a learnable digital form and therefore help expedite materials and reaction discovery. However, existing reaction mining toolkits are limited to single input modalities (text or images) and cannot effectively integrate heterogeneous data that is scattered across text, tables, and figures. In this work, we go beyond single input modalities and explore multimodal large language models (MLLMs) for the analysis of diverse data inputs for automated electrosynthesis reaction mining. We compiled a test dataset of 65 articles (MERMES-T24 set) and employed it to benchmark five prominent MLLMs against two critical tasks: (i) reaction diagram parsing and (ii) resolving cross-modality data interdependencies. The frontrunner MLLM achieved ≥96% accuracy in both tasks, with the strategic integration of single-shot visual prompts and image pre-processing techniques. We integrate this capability into a toolkit named MERMES (multimodal reaction mining pipeline for electrosynthesis). Our toolkit functions as an end-to-end MLLM-powered pipeline that integrates article retrieval, information extraction and multimodal analysis for streamlining and automating knowledge extraction. This work lays the groundwork for the increased utilization of MLLMs to accelerate the digitization of chemistry knowledge for data-driven research.

Leong, Shi Xuan↗
DOE OSTI2023

Performance of the reconstruction of large impact parameter tracks in the inner detector of ATLAS

Searches for long-lived particles (LLPs) are among the most promising avenues for discovering physics beyond the Standard Model at the Large Hadron Collider (LHC). However, displaced signatures are notoriously difficult to identify due to their ability to evade standard object reconstruction strategies. In particular, the ATLAS track reconstruction applies strict pointing requirements which limit sensitivity to charged particles originating far from the primary interaction point. To recover efficiency for LLPs decaying within the tracking detector volume, the ATLAS Collaboration employs a dedicated large-radius tracking (LRT) pass with loosened pointing requirements. During Run 2 of the LHC, the LRT implementation produced many incorrectly reconstructed tracks and was therefore only deployed in small subsets of events. In preparation for LHC Run 3, ATLAS has significantly improved both standard and large-radius track reconstruction performance, allowing for LRT to run in all events. This development greatly expands the potential phase-space of LLP searches and streamlines LLP analysis workflows. This paper will highlight the above achievement and report on the readiness of the ATLAS detector for track-based LLP searches in Run 3.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

BiRD (BioReactorDesign) [SWR-24-35]

Numerical simulations can accelerate scale up and optimization of bioreactors. This project provides a toolbox to facilitate such numerical simulations in OpenFOAM. The tool box contains 1) mesh generations tools for several typical bio reactor types in OpenFOAM 2) Inverse modeling tools to calibrate models using available experimental data 3) post processing tools that accelerate and streamline the analysis of the numerical simulations.

Hassanaly, Malik↗
DOE OSTI2016

Long-Term Monitoring of Desert Land and Natural Resources and Application of Remote Sensing Technologies

Monitoring environmental impacts over large, remote desert regions for long periods of time can be very costly. Remote sensing technologies present a promising monitoring tool because they entail the collection of spatially contiguous data, automated processing, and streamlined data analysis. This report provides a summary of remote sensing products and refinement of remote sensing data interpretation methodologies that were generated as part of the U.S. Department of the Interior Bureau of Land Management Solar Energy Program. In March 2015, a team of researchers from Argonne National Laboratory (Argonne) collected field data of vegetation and surface types from more than 5,000 survey points within the eastern part of the Riverside East Solar Energy Zone (SEZ). Using the field data, remote sensing products that were generated in 2014 using very high spatial resolution (VHSR; 15 cm) multispectral aerial images were validated in order to evaluate potential refinements to the previous methodologies to improve the information extraction accuracy.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

COTS Data Analytics Software User Manual: Version 1.0

Large volumes of data are being collected by Sandia National Laboratories as part of an active commercial-off-the-shelf (COTS) part testing and surveillance program. This user manual documents Python-based COTS Data Analytics software that has been developed for standardizing, displaying, visualizing, and analyzing the resulting COTS part testing and surveillance data. It is the objective of these software tools to streamline the analysis of COTS testing and surveillance data and improve the efficiency with which test engineers and data analytics experts can pinpoint possible performance and reliability problems in COTS parts.

42 ENGINEERING↗
NASA NTRS1998

A modernized approach to meet diversified earth observing system (EOS) AM-1 mission requirements

From a flight dynamics perspective, the EOS AM-1 mission design and maneuver operations present a number of interesting challenges. The mission design itself is relatively complex for a low Earth mission, requiring a frozen, Sun-synchronous, polar orbit with a repeating ground track. Beyond the need to design an orbit that meets these requirements, the recent focus on low-cost, 'lights out' operations has encouraged a shift to more automated ground support. Flight dynamics activities previously performed in special facilities created solely for that purpose and staffed by personnel with years of design experience are now being shifted to the mission operations centers (MOCs) staffed by flight operations team (FOT) operators. These operators' responsibilities include flight dynamics as a small subset of their work; therefore, FOT personnel often do not have the experience to make critical maneuver design decisions. Thus, streamlining the analysis and planning work required for such a complicated orbit design and preparing FOT personnel to take on the routine operation of such a spacecraft both necessitated increasing the automation level of the flight dynamics functionality. The FreeFlyer(TM) software developed by AI Solutions provides a means to achieve both of these goals. The graphic interface enables users to interactively perform analyses that previously required many parametric studies and much data reduction to achieve the same result In addition, the fuzzy logic engine enables the simultaneous evaluation of multiple conflicting constraints, removing the analyst from the loop and allowing the FOT to perform more of the operations without much background in orbit design. Modernized techniques were implemented for EOS AM-1 flight dynamics support in several areas, including launch window determination, orbit maintenance maneuver control strategies, and maneuver design and calibration automation. The benefits of implementing these techniques include increased fuel available for on-orbit maneuvering, a simplified orbit maintenance process to minimize science data downtime, and an automated routine maneuver planning process. This paper provides an examination of the modernized techniques implemented for EOS AM-1 to achieve these benefits.

Newman, Lauri Kraft↗
NASA NTRS1998

A Modernized Approach to Meet Diversified Earth Observing System (EOS) AM-1 Mission Requirements

From a flight dynamics perspective, the EOS AM-1 mission design and maneuver operations present a number of interesting challenges. The mission design itself is relatively complex for a low Earth mission, requiring a frozen, Sun-synchronous, polar orbit with a repeating ground track. Beyond the need to design an orbit that meets these requirements, the recent focus on low-cost, "lights out" operations has encouraged a shift to more automated ground support. Flight dynamics activities previously performed in special facilities created solely for that purpose and staffed by personnel with years of design experience are now being shifted to the mission operations centers (MOCs) staffed by flight operations team (FOT) operators. These operators' responsibilities include flight dynamics as a small subset of their work; therefore, FOT personnel often do not have the experience to make critical maneuver design decisions. Thus, streamlining the analysis and planning work required for such a complicated orbit design and preparing FOT personnel to take on the routine operation of such a spacecraft both necessitated increasing the automation level of the flight dynamics functionality. The FreeFlyer(trademark) software developed by AI Solutions provides a means to achieve both of these goals. The graphic interface enables users to interactively perform analyses that previously required many parametric studies and much data reduction to achieve the same result. In addition, the fuzzy logic engine .enables the simultaneous evaluation of multiple conflicting constraints, removing the analyst from the loop and allowing the FOT to perform more of the operations without much background in orbit design. Modernized techniques were implemented for EOS AM-1 flight dynamics support in several areas, including launch window determination, orbit maintenance maneuver control strategies, and maneuver design and calibration automation. The benefits of implementing these techniques include increased fuel available for on-orbit maneuvering, a simplified orbit maintenance process to minimize science data downtime, and an automated routine maneuver planning process. This paper provides an examination of the modernized techniques implemented for EOS AM-1 to achieve these benefits.

Newman, Lauri Kraft↗
DOE OSTI2014

MO-F-16A-06: Implementation of a Radiation Exposure Monitoring System for Surveillance of Multi-Modality Radiation Dose Data

Purpose: We have implemented a commercially available Radiation Exposure Monitoring System (REMS) to enhance the processes of radiation dose data collection, analysis and alerting developed over the past decade at our sites of practice. REMS allows for consolidation of multiple radiation dose information sources and quicker alerting than previously developed processes. Methods: Thirty-nine x-ray producing imaging modalities were interfaced with the REMS: thirteen computed tomography scanners, sixteen angiography/interventional systems, nine digital radiography systems and one mammography system. A number of methodologies were used to provide dose data to the REMS: Modality Performed Procedure Step (MPPS) messages, DICOM Radiation Dose Structured Reports (RDSR), and DICOM header information. Once interfaced, the dosimetry information from each device underwent validation (first 15–20 exams) before release for viewing by end-users: physicians, medical physicists, technologists and administrators. Results: Before REMS, our diagnostic physics group pulled dosimetry data from seven disparate databases throughout the radiology, radiation oncology, cardiology, electrophysiology, anesthesiology/pain management and vascular surgery departments at two major medical centers and four associated outpatient clinics. With the REMS implementation, we now have one authoritative source of dose information for alerting, longitudinal analysis, dashboard/graphics generation and benchmarking. REMS provides immediate automatic dose alerts utilizing thresholds calculated through daily statistical analysis. This has streamlined our Closing the Loop process for estimated skin exposures in excess of our institutional specific substantial radiation dose level which relied on technologist notification of the diagnostic physics group and daily report from the radiology information system (RIS). REMS also automatically calculates the CT size-specific dose estimate (SSDE) as well as provides two-dimensional angulation dose maps for angiography/interventional procedures. Conclusion: REMS implementation has streamlined and consolidated the dosimetry data collection and analysis process at our institutions while eliminating manual entry error and providing immediate alerting and access to dosimetry data to both physicists and physicians. Brent Stewart has funded research through GE Healthcare.

60 APPLIED LIFE SCIENCES↗