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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Long-term Global Morphology of Gravity Wave Activity Using UARS Data

This quarter was largely devoted to a detailed study of temperature data acquired by the Cryogenic Limb Array Etalon Spectrometer (CLAES) on UARS. Our analysis used the same sequence of methods that have been developed, tested and refined on a more limited subset of temperature data acquired by the CRISTA instrument. We focused on a limited subset of our reasoning that geographical and vertical trends in the small-scale temperature variability could be compared with similar trends observed in November 1994 by the CRISTA-SPAS satellite. Results, backed up with hindcasts from the Mountain Wave Forecast Model (MWFM), reveal strong evidence of mountain waves, most persuasively in the Himalayas on 16-17 November, 1992. These CLAES results are coherent over the 30-50 km range and compare well with MWFM hindcasts for the same period. This constitutes, we believe, the first clear evidence that CLAES explicitly resolved long wavelength gravity waves in its CO2 temperature channel. A series of other tasks, related to mesoscale modeling of mountain waves in CRISTA data and fitting of ground-based and HRDI data on global scales, were seen through to publication stage in peer-reviewed journals.

Eckermann, Stephen D.↗
NASA NTRS2004

Study of Elongated Superbubble around CYG OB1

OB association superbubbles and supernova remnants are believed to be the primary sources of the hot component of the interstellar medium. Super- bubbles are also thought to "blow out" of the galactic plane and are thus the potential source of galactic coronal gas. Quantifying the energetics and accurately modeling the evolution of superbubbles is thus an important task in the understanding of the evolution of our Galaxy. The superbubble surrounding the Cyg OB1 association is an excellent example for study of the early stages of superbubble evolution. This superbubble, discovered in IRAS data, is an elongated structure 2 x 5 degrees (26 x 65 pc at distance=1.5 kpc), with at least 2 known supernova remnants believed to be associated with the superbubble. The elongated shape is more extended than can be explained by differential galactic rotation. A major problem in understanding the evolution of the Cyg OB1 superbubble is the discrepancy between the age of the superbubble and the derived age of the parent star association. The superbubble is believed to be no more than 1 Myr old, while the association is about 4-5 Myr old.

Sonneborn, George↗
NASA NTRS2005

Experimental Stage Separation Tool Development in NASA Langley's Aerothermodynamics Laboratory

As part of the research effort at NASA in support of the stage separation and ascent aerothermodynamics research program, proximity testing of a generic bimese wing-body configuration was conducted in NASA Langley's Aerothermodynamics Laboratory in the 20-Inch Mach 6 Air Tunnel. The objective of this work is the development of experimental tools and testing methodologies to apply to hypersonic stage separation problems for future multi-stage launch vehicle systems. Aerodynamic force and moment proximity data were generated at a nominal Mach number of 6 over a small range of angles of attack. The generic bimese configuration was tested in a belly-to-belly and back-to-belly orientation at 86 relative proximity locations. Over 800 aerodynamic proximity data points were taken to serve as a database for code validation. Longitudinal aerodynamic data generated in this test program show very good agreement with viscous computational predictions. Thus a framework has been established to study separation problems in the hypersonic regime using coordinated experimental and computational tools.

Murphy, Kelly J.↗
NASA NTRS2009

Ares I-X 30 Day Report

This slide presentation represents the 30 day report on the Ares I-X test flight. Included in the review is information on the following areas: (1) Ground Systems, (2) Guidance, Navigation and Control, (3) Roll Response, (4) Vehicle Response, (5) Control System Performance, (6) Structural Damping, (7) Thrust Oscillation, (8) Stage Separation, (9) Connector Assessment, (10) USS Splashdown, (11) Data Recorder and (12) FS Hardware Assessment.

Ess, Bob↗
NASA NTRS2003

Software for Allocating Resources in the Deep Space Network

TIGRAS 2.0 is a computer program designed to satisfy a need for improved means for analyzing the tracking demands of interplanetary space-flight missions upon the set of ground antenna resources of the Deep Space Network (DSN) and for allocating those resources. Written in Microsoft Visual C++, TIGRAS 2.0 provides a single rich graphical analysis environment for use by diverse DSN personnel, by connecting to various data sources (relational databases or files) based on the stages of the analyses being performed. Notable among the algorithms implemented by TIGRAS 2.0 are a DSN antenna-load-forecasting algorithm and a conflict-aware DSN schedule-generating algorithm. Computers running TIGRAS 2.0 can also be connected using SOAP/XML to a Web services server that provides analysis services via the World Wide Web. TIGRAS 2.0 supports multiple windows and multiple panes in each window for users to view and use information, all in the same environment, to eliminate repeated switching among various application programs and Web pages. TIGRAS 2.0 enables the use of multiple windows for various requirements, trajectory-based time intervals during which spacecraft are viewable, ground resources, forecasts, and schedules. Each window includes a time navigation pane, a selection pane, a graphical display pane, a list pane, and a statistics pane.

Wang, Yeou-Fang↗
NASA NTRS2010

Feature Extraction and Selection Strategies for Automated Target Recognition

Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.

computer vision↗
DOE OSTI2012

Offshore Wind Plant Balance-of-Station Cost Drivers and Sensitivities (Poster)

With Balance of System (BOS) costs contributing up to 70% of the installed capital cost, it is fundamental to understanding the BOS costs for offshore wind projects as well as potential cost trends for larger offshore turbines. NREL developed a BOS model using project cost estimates developed by GL Garrad Hassan. Aspects of BOS covered include engineering and permitting, ports and staging, transportation and installation, vessels, foundations, and electrical. The data introduce new scaling relationships for each BOS component to estimate cost as a function of turbine parameters and size, project parameters and size, and soil type. Based on the new BOS model, an analysis to understand the non-turbine costs associated with offshore turbine sizes ranging from 3 MW to 6 MW and offshore wind plant sizes ranging from 100 MW to 1000 MW has been conducted. This analysis establishes a more robust baseline cost estimate, identifies the largest cost components of offshore wind project BOS, and explores the sensitivity of the levelized cost of energy to permutations in each BOS cost element. This presentation shows results from the model that illustrates the potential impact of turbine size and project size on the cost of energy from US offshore wind plants.

17 WIND ENERGY↗
DOE OSTI2012

U.S. Balance-of-Station Cost Drivers and Sensitivities (Presentation)

With balance-of-system (BOS) costs contributing up to 70% of the installed capital cost, it is fundamental to understanding the BOS costs for offshore wind projects as well as potential cost trends for larger offshore turbines. NREL developed a BOS model using project cost estimates developed by GL Garrad Hassan. Aspects of BOS covered include engineering and permitting, ports and staging, transportation and installation, vessels, foundations, and electrical. The data introduce new scaling relationships for each BOS component to estimate cost as a function of turbine parameters and size, project parameters and size, and soil type. Based on the new BOS model, an analysis to understand the non‐turbine costs has been conducted. This analysis establishes a more robust baseline cost estimate, identifies the largest cost components of offshore wind project BOS, and explores the sensitivity of the levelized cost of energy to permutations in each BOS cost element. This presentation shows results from the model that illustrates the potential impact of turbine size and project size on the cost of energy from U.S. offshore wind plants.

17 WIND ENERGY↗
DOE OSTI2015

Improved Estimates of Air Pollutant Emissions from Biorefinery

We have attempted to use detailed kinetic modeling approach for improved estimation of combustion air pollutant emissions from biorefinery. We have developed a preliminary detailed reaction mechanism for biomass combustion. Lignin is the only biomass component included in the current mechanism and methane is used as the biogas surrogate. The model is capable of predicting the combustion emissions of greenhouse gases (CO2, N2O, CH4) and criteria air pollutants (NO, NO2, CO). The results are yet to be compared with the experimental data. The current model is still in its early stages of development. Given the acknowledged complexity of biomass oxidation, as well as the components in the feed to the combustor, obviously the modeling approach and the chemistry set discussed here may undergo revision, extension, and further validation in the future.

09 BIOMASS FUELS↗
NASA NTRS2020

Characterizing spatiotemporal patterns of crop phenology across North America during 2000–2016 using satellite imagery and agricultural survey data

Crop phenology represents an integrative indicator of climate change and plays a vital role in terrestrial carbon dynamics and sustainable agricultural development. However, spatiotemporal variations of crop phenology remain unclear at large scales. This knowledge gap has hindered our ability to realistically quantify the biogeochemical dynamics in agroecosystems, predict future climate, and make informed decisions for climate change mitigation and adaptation. In this study, we improved an EVI-curve-based approach and used it to detect spatiotemporal patterns in cropping intensity and five major phenological stages over North America during 2000–2016 using vegetation index in combination with agricultural survey data and other ancillary maps. Our predicted crop phenological stages showed strong linear relationships with the survey-based datasets, with R(exp 2), RMSEs, and MAEs in the ranges of 0.35 –0.99, three to ten days, and two to eight days, respectively. During the study period, the planting dates were advanced by 0.60 days/year (p < 0.01), and harvesting dates were delayed by 0.78 days/year (p < 0.01) over North America. A minimum temperature increase by 1 °C caused a 4.26-day planting advance (r = −0.50, p < 0. 01) or a 0.66-day harvest delay (r = 0.10, p < 0.01). While, a higher maximum temperature resulted in a planting advance by 4.48 days/°C (r = −0.62, p < 0.01) or a harvest advance by 2.22 days/°C (r = −0.40, p < 0.01). Our analysis illustrated evident spatiotemporal variations in crop phenology in response to climate change and management practices. The derived crop phenological datasets and cropping intensity maps can be used in regional climate assessments and in developing adaptation strategies.

North America↗
NASA NTRS1957

Investigation of two-stage air-cooled turbine suitable for flight at Mach number of 2.5 II : blade design

A blade design study is presented for a two-stage air-cooled turbine suitable for flight at a Mach number of 2.5 for which velocity diagrams have been previously obtained. The detailed procedure used in the design of the blades is given. In addition, the design blade shapes, surface velocity distributions, inner and outer wall contours, and other design data are presented. Of all the blade rows, the first-stage rotor has the highest solidity, with a value of 2.289 at the mean section. The second-stage stator also had a high mean-section solidity of 1.927, mainly because of its high inlet whirl. The second-stage rotor has the highest value of the suction-surface diffusion parameter, with a value of 0.151. All other blade rows have values for this parameter under 0.100.

Miser, James W↗
NASA NTRSDate not supplied

Aerodynamic loss generation in a low-pressure turbine stage with high lift blading

The present work aims to investigate the loss generation mechanisms in a highly loaded low-pressure turbine (LPT) stage. The turbine employs high-lift airfoils to reduce the number of blades and therefore the overall engine weight. Large Eddy Simulation (LES) is applied to study the detailed flow fields at two representative Reynolds numbers (150,000 and 40,000). The calculated flow fields are compared with available measured data. LES calculates the measured aerodynamic performance of the current turbine stage well. Both the measurements and the CFD simulations indicate that most of the aerodynamic loss is generated by the development of secondary flows near the rotor hub. An area of low momentum and high aerodynamic loss forms at roughly 25 % span above the hub downstream of the rotor. Formation and migration of a vortex system near the hub is the primary source of the loss generation. The hub wall vortex system in this highly loaded turbine stage is quite different from the horse-shoe vortex system in a typical stationary turbine cascade. Calculated instantaneous and time-averaged flow fields at two Reynolds numbers are examined in detail to study Reynolds number effects on the turbine stage performance. Steady and unsteady flow separation on the rotor suction side is calculated at both high and low Reynolds numbers. At the lower Reynolds number of 40,000, thicker blade boundary layers increase the area of flow separation on the rotor suction side, resulting in a 1.9 % increase in aerodynamic loss compared to the higher Reynolds number case.

low pressure turbine↗
DOE OSTI2017

Peer Review Lessons Learned - DOE and Regulator Perspectives - 17140

The DOE NNSA Nevada Field Office (NFO) Underground Test Area (UGTA) Activity requires an external peer review to complete an extensive stage of site characterization and groundwater flow and contaminant transport model(s) development. The external peer review evaluates whether assumptions, methods, and conclusions derived from model(s) outputs are based on sound scientific principles and examines the scientific appropriateness of the model(s) outputs for informing the regulatory decision. The regulatory decision which is made by the Nevada Division of Environmental Protection (NDEP) is whether the model is acceptable to advance to the next UGTA strategy stage. Depending on the specific corrective action unit, the next stage is a Corrective Action Decision Document/Corrective Action Plan or Closure Stage. Three external peer reviews have taken place to date for the UGTA Activity. The first one resulted in a second phase of characterization and model development. The second external peer review resulted in advancement to the next UGTA strategy stage. While advancement to the next strategy stage was recommended by the third external peer review, a series of supplemental data collection and analyses and model simulation activities to address external peer review comments, including drilling three new wells and sampling multiple wells, occurred before moving into the next UGTA strategy stage. Twelve participants representing different aspects of the peer reviews (NDEP, NNSA/NFO, science advisors, presenters, and external peer reviewers) were interviewed to generate a combined lessons learned. Questions were asked regarding their experience with the process including panel selection, focused questions, presentations, interactions between UGTA Activity participants and reviewers, and results. The interview results were combined with the outcome of a published lessons learned report developed following the first external peer review. In general, those interviewed believed that all three external peer reviews were satisfactory and met the goals of the FFACO process. The following seven lessons learned were identified: (1) Ensure the panel is not biased and has a balance of technical and regulatory background; (2) Clearly state the review objective(s) at the start of the review; (3) Ask focused questions that support the peer review objective(s) but that do not ask the specific regulatory question; (4) Establish a peer review panel earlier in the strategy process; (5) Establish one point of contact for the entire review and conduct periodic meetings with the peer review panel to ensure focus remains on the review objective(s) and to ensure that pertinent information is provided; (6) Ensure that presentations are well coordinated, anomalous data or analysis results are addressed, and are focused on supporting the peer review objective(s); and (7) Carefully consider the panel's recommendations prior to embarking on a particular path forward. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Visualizing group II intron dynamics between the first and second steps of splicing

Group II introns are ubiquitous self-splicing ribozymes and retrotransposable elements evolutionarily and chemically related to the eukaryotic spliceosome, with potential applications as gene-editing tools. Recent biochemical and structural data have captured the intron in multiple conformations at different stages of catalysis. Here, we employ enzymatic assays, X-ray crystallography, and molecular simulations to resolve the spatiotemporal location and function of conformational changes occurring between the first and the second step of splicing. We show that the first residue of the highly-conserved catalytic triad is protonated upon 5’-splice-site scission, promoting a reversible structural rearrangement of the active site (toggling). Protonation and active site dynamics induced by the first step of splicing facilitate the progression to the second step. Our insights into the mechanism of group II intron splicing parallels functional data on the spliceosome, thus reinforcing the notion that these evolutionarily-related molecular machines share the same enzymatic strategy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Deep-learning methods for contrast enhancement and artifact reduction in cryo-electron tomography: a systematic analysis of the state of the art and proposed improvements

Cryo-electron tomography (cryo-ET) has emerged as the preferred technique for visualizing the organization of macromolecular complexes in situ and resolving their structures at subnanometre resolution [Tegunov et al. (2021)View full citation, Nat. Methods, 18, 186–193]. Despite improvements in data quality as a result of advances in detector technology, microscope stability and stage precision, the analysis and interpretation of tomograms remains challenging due to a low signal-to-noise ratio and reconstruction artifacts stemming from experimental constraints in specimen tilt during data collection resulting in a missing wedge in the Fourier space. Recently, self-supervised deep-learning methods have been proposed for contrast enhancement and reduction of resolution anisotropy in reconstructed tomograms. Here, we evaluate several state-of-the-art deep-learning methods which aim to improve the interpretability of cryo-ET reconstructions, with a focus on their performance on downstream tasks of template matching, sub­tomogram averaging and segmentation. We propose new training architectures and a loss function based on Fourier shell correlation that show improved performance over the standard U-Net with L1/L2 losses. We demonstrate our analysis on four diverse experimental datasets: purified 80S ribosomes, in situ Chlamydomonas reinhardtii, immature HIV-1 virus-like particles and INS-1E cells.

contrast enhancement↗
DOE OSTI2021

Methods for a blind analysis of isobar data collected by the STAR collaboration

In 2018, the STAR collaboration collected data from $_{44}^{96}{\mathrm{Ru}}+_{44}^{96}{\mathrm{Ru}}$ and $_{40}^{96}{\mathrm{Zr}}+_{40}^{96}{\mathrm{Zr}}$ at $\sqrt{s_\text {NN}}=200$ GeV to search for the presence of the chiral magnetic effect in collisions of nuclei. The isobar collision species alternated frequently between $_{44}^{96}{\mathrm{Ru}}+_{44}^{96}{\mathrm{Ru}}$ and $_{40}^{96}{\mathrm{Zr}}+_{40}^{96}{\mathrm{Zr}}$ . In order to conduct blind analyses of studies related to the chiral magnetic effect in these isobar data, STAR developed a three-step blind analysis procedure. Analysts are initially provided a “reference sample” of data, comprised of a mix of events from the two species, the order of which respects time-dependent changes in run conditions. After tuning analysis codes and performing time-dependent quality assurance on the reference sample, analysts are provided a species-blind sample suitable for calculating efficiencies and corrections for individual $\approx 30$-min data-taking runs. For this sample, species-specific information is disguised, but individual output files contain data from a single isobar species. Only run-by-run corrections and code alteration subsequent to these corrections are allowed at this stage. Following these modifications, the “frozen” code is passed over the fully un-blind data, completing the blind analysis. As a check of the feasibility of the blind analysis procedure, analysts completed a “mock data challenge,” analyzing data from Au + Au collisions at $\sqrt{s_\text {NN}}=27$ GeV, collected in 2018. The Au + Au data were prepared in the same manner intended for the isobar blind data. Finally, the details of the blind analysis procedure and results from the mock data challenge are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

Life Cycle Analysis Data and Results for Geothermal and Other Electricity Generation Technologies

Life cycle analysis (LCA) is an environmental assessment method that quantifies the environmental performance of a product system over its entire lifetime, from cradle to grave. Based on a set of relevant metrics, the method is aptly suited for comparing the environmental performance of competing products systems. This file contains LCA data and results for electric power production including geothermal power. The LCA for electric power has been broken down into two life cycle stages, namely plant and fuel cycles. Relevant metrics include the energy ratio and greenhouse gas (GHG) ratios, where the former is the ratio of system input energy to total lifetime electrical energy out and the latter is the ratio of the sum of all incurred greenhouse gases (in CO2 equivalents) divided by the same energy output. Specific information included herein are material to power (MPR) ratios for a range of power technologies for conventional thermoelectric, renewables (including three geothermal power technologies), and coproduced natural gas/geothermal power. For the geothermal power scenarios, the MPRs include the casing, cement, diesel, and water requirements for drilling wells and topside piping. Also included herein are energy and GHG ratios for plant and fuel cycle stages for the range of considered electricity generating technologies. Some of this information are MPR data extracted directly from the literature or from models (eg. ICARUS - a subset of ASPEN models) and others (energy and GHG ratios) are results calculated using GREET models and MPR data. MPR data for wells included herein were based on the Argonne well materials model and GETEM well count results.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Evaluating Image Classification Deep Convolutional Neural Network Architectures for Remaining Useful Life Estimation of Turbofan Engines

Accurate estimation of the remaining useful life (RUL) is a key component of condition-based maintenance (CBM) and prognosis and health management (PHM). Data-based models for the estimation of RUL are of particular interest because expert knowledge of systems is not always available, and physical modeling is often not feasible. Additionally, using data-based models, which make decisions based on raw sensor data, allow features to be learned instead of manually determined. In this work, deep convolutional neural network (CNN) architectures are investigated for their ability to estimate the RUL of turbofan engines. To improve the accuracy of the models, CNN architectures, which have proven successful in image classification, are implemented and tested. Specifically, the blocks used in the Visual Geometry Group (VGG) architecture, inception modules used in the GoogLeNet architecture, and residual blocks used in the ResNet architecture are incorporated. To account for varying flight lengths, the input to the models is a window of time series data collected from the engine under test. Window locations at the climb, cruise, and descent stages are considered. To further improve the RUL estimations, multiple overlapping windows at each location are used. This increases the amount of training data available and is found to increase the accuracy of the resulting RUL estimations by averaging the estimates from all overlapping segments. The model is trained and tested using the new Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) data set, and high prognosis accuracy was achieved. Furthermore, this work expands on the model developed and used in the 2021 PHM Society Data Challenge, which received second place.

convolutional neural networks↗