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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Development of the NASA/FLAGRO computer program

A preliminary version of a new computer program has been developed for calculating crack propagation in cyclically loaded structures. The new program, titled NASA/FLAGRO, has numerous enhanced features over previous programs used for safe life analysis on space systems. The essential features of the new program consist of modular design, promptive input using a cathode ray tube terminal, improved crack growth rate analytical methods, many new crack case solutions, and incorporation of user-defined files for material properties and fatigue spectrums. Also, five options made available in the program are to compute: (1) safe life of a part with a preexisting crack; (2) critical crack size for a specified stress level; (3) stress-intensity factor numerical values for making comparisons or checks of solutions; (4) least-squares fit of growth rate equations to crack growth rate data; and (5) the conversion of a versus N data to Delta K versus da/dN.

Forman, Royce G.↗
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 OSTI2022

Thermal modeling of directed energy deposition additive manufacturing using graph theory

Purpose: The purpose of this paper is to develop, apply and validate a mesh-free graph theory–based approach for rapid thermal modeling of the directed energy deposition (DED) additive manufacturing (AM) process. Design/methodology/approach: Here, the authors develop a novel mesh-free graph theory–based approach to predict the thermal history of the DED process. Subsequently, the authors validated the graph theory predicted temperature trends using experimental temperature data for DED of titanium alloy parts (Ti-6Al-4V). Temperature trends were tracked by embedding thermocouples in the substrate. The DED process was simulated using the graph theory approach, and the thermal history predictions were validated based on the data from the thermocouples. Findings: The temperature trends predicted by the graph theory approach have mean absolute percentage error of approximately 11% and root mean square error of 23°C when compared to the experimental data. Moreover, the graph theory simulation was obtained within 4 min using desktop computing resources, which is less than the build time of 25 min. By comparison, a finite element–based model required 136 min to converge to similar level of error. Research limitations/implications: This study uses data from fixed thermocouples when printing thin-wall DED parts. In the future, the authors will incorporate infrared thermal camera data from large parts. Practical implications: The DED process is particularly valuable for near-net shape manufacturing, repair and remanufacturing applications. However, DED parts are often afflicted with flaws, such as cracking and distortion. In DED, flaw formation is largely governed by the intensity and spatial distribution of heat in the part during the process, often referred to as the thermal history. Accordingly, fast and accurate thermal models to predict the thermal history are necessary to understand and preclude flaw formation. Originality/value: This paper presents a new mesh-free computational thermal modeling approach based on graph theory (network science) and applies it to DED. The approach eschews the tedious and computationally demanding meshing aspect of finite element modeling and allows rapid simulation of the thermal history in additive manufacturing. Although the graph theory has been applied to thermal modeling of laser powder bed fusion (LPBF), there are distinct phenomenological differences between DED and LPBF that necessitate substantial modifications to the graph theory approach.

42 ENGINEERING↗
NASA NTRS2005

Saturn radiation (SATRAD) model

The Saturnian radiation belts have not received as much attention as the Jovian radiation belts because they are not nearly as intense—the famous Saturnian particle rings tend to deplete he belts near where their peak would occur. As a result, there has not been a systematic development of engineering models of the Saturnian radiation environment for mission design. A primary exception is that of Divine (1990). That study used published data from several charged particle experiments aboard the Pioneer 11, Voyager 1, and Voyager 2 spacecraft during their flybys at Saturn to generate numerical models for the electron and proton radiation belts between 2.3 and 13 Saturn radii. The Divine Saturn radiation model described the electron distributions at energies between 0.04 and 10 MeV and the proton distributions at energies between 0.14 and 80 MeV. The model was intended to predict particle intensity, flux, and fluence for the Cassini orbiter. Divine carried out hand calculations using the model but never formally developed a computer program that could be used for general mission analyses. This report seeks to fill that void by formally developing a FORTRAN version of the model that can be used as a computer design tool for missions to Saturn that require estimates of the radiation environment around the planet. The results of that effort and the program listings are presented here along with comparisons with the original estimates carried out by Divine. In addition, Pioneer and Voyager data were scanned in from the original references and compared with the FORTRAN model’s predictions. The results were statistically analyzed in a manner consistent with Divine’s approach to provide estimates of the ability of the model to reproduce the original data. Results of a formal review of the model by a panel of experts are also presented. Their recommendations for further tests, analyses, and extensions to the model are discussed.

Evans, R. W.↗
DOE OSTI2018

Excited-State Electronic Properties in Zr-Based Metal–Organic Frameworks as a Function of a Topological Network

Molecular assemblies in metal-organic frameworks (MOFs) are reminiscent of natural light-harvesting (LH) systems and considered as emerging materials for energy conversion. Such applications require understanding the correlation between their excited-state properties and underlying topological net. Two chemically identical but topologically different tetraphenylpyrene (1,3,6,8-tetrakis(p-benzoicacid)pyrene; H4TBAPy)-based ZrIV MOFs, NU-901 (scu) and NU-1000 (csq), are chosen to computationally and spectroscopically interrogate the impact of topological difference on their excited-state electronic structures. Time-dependent density functional theory-computed transition density matrices for selected model compounds reveal that the optically relevant S1, S2, and Sn states are delocalized over more than four TBAPy linkers with a maximum exciton size of ~1.7 nm (i.e., two neighboring TBAPy linkers). Computational data further suggests the evolution of polar excitons (hole and electron residing in two different linkers); their oscillator strengths vary with the extent of interchromophoric interaction depending on their topological network. Femtosecond transient absorption (fs-TA) spectroscopic data of NU-901 highlight instantaneous spectral evolution of an intense S1 -> Sn transition at 750 nm, which diminishes with the emergence of a broad (580-1100 nm) induced absorption originating from a fast excimer formation. Although these ultrafast spectroscopic data reveal the first direct spectral observation of fast excimer formation (t = 2 ps) in MOFs, the fs-TA features seen in NU-901 are clearly absent in NU-1000 and the free H4TBAPy linker. Furthermore, transient and steady-state fluorescence data collected as a function of solvent dielectrics reveal that the emissive states in both MOF samples are electronically nonpolar; however, low-lying polar excited states may get involved in the excited-state decay processes in polar solvents. The present work shows that the topological arrangement of the linkers critically controls the excited-state electronic structures.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Precise determination of water exchanges on a mineral surface

Solvent exchanges on solid surfaces and dissolved ions are a fundamental property important for understanding chemical reactions, but the rates of fast exchanges are poorly constrained. In this paper, we probed the diffusional motions of water adsorbed onto nanoparticles of the mineral barite (BaSO 4 ) using quasi-elastic neutron scattering (QENS) and classical molecular dynamics (MD) to reveal the complex dynamics of water exchange along mineral surfaces. QENS data as a function of temperature and momentum transfer (Q) were fit using scattering functions derived from MD trajectories. The simulations reproduce the dynamics measured in the experiments at ambient temperatures, but as temperature is lowered the simulations overestimate slower motions. Decomposition of the MD-computed QENS intensity into contributions from adsorbed and unbound water shows that the majority of the signal arises from adsorbed species, although the dynamics of unbound water cannot be dismissed. The mean residence times of water on each of the four surface sites present on the barite {001} were calculated using MD: at room temperature the low barium site is 194 ps, whereas the high barium site contains two distributions of motions at 84 and 2.5 ps. These contrast to 13 ps residence time on both sulfate sites, with an additional surface diffusion exchange of 66 ps. Surface exchanges are similar to those of the aqueous ions calculated using the same force field: Ba aq 2+ is 208 ps and SO 4aq 2- is 5.8 ps. Finally, this work demonstrates how MD can be a reliable method to deconvolute solvent exchange reactions when quantitatively validated by QENS measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Spatially Explicit Modeling of Criteria Air Pollutants from Agricultural and Forestry Feedstock Production

The Feedstock Production Emissions to Air Model (FPEAM) is a spatially explicit supply chain model that quantifies criteria air pollutants and precursors generated by biomass production, harvest and transportation to biorefineries. FPEAM was most recently used for the 2016 Billion Ton Study (BTS) published by the U.S. Department of Energy Bioenergy Technologies Office. This presentation describes several use cases for the model, and provides an overview of recent restructuring that increases FPEAM's usability and functionality. FPEAM currently estimates emissions of volatile organic compounds, particulate matter, nitrogen oxides, sulfur oxides, and carbon monoxide from on-farm equipment operation, on-farm fugitive dust, on-farm chemical (fertilizer, herbicide and insecticide) application, and biomass transportation. These calculations are implemented within FPEAM as modules, which independently perform calculations for each emissions category. This structure allows users to include or exclude emissions categories according to the goal and scope of their analysis. Each module's input data defines the activities taking place with information including fuel combusted, chemicals applied, and equipment used. Users can replace or augment the default input data to quantify additional pollutant types, emissions categories, farming practices and biomass types. Emissions from biomass transportation and on-farm equipment operation are by default calculated by the U.S. Environmental Protection Agency's Motor Vehicle Emission Simulator (MOVES) and NONROAD model, respectively. These models - which are computationally intensive but provide detailed results - can optionally be replaced with user-provided emissions factors for fuel combustion and vehicle operation. This approach reduces FPEAM's runtime and allows users to run sensitivity analyses around biomass production and transportation scenarios. Other enhancements currently under development are the use of farm-to-biorefinery route information to increase the spatial resolution of transportation emissions, and a module to connect FPEAM and the Intervention Model for Air Pollution (InMAP), which will enable the quantification of human health impacts.

09 BIOMASS FUELS↗
DOE OSTI2025

The Profiled Feldman-Cousins Method for Confidence Interval Construction for the Nova 3-Flavor Oscillation Analysis

The small interaction cross-section of neutrinos makes experimental neutrino physics particularly responsive to technological advancements. A significant development leveraged by the NOvA experiment is large-scale parallel processing, enabling novel computational approaches to longstanding experimental challenges. Central to managing the resulting high-throughput data is NOvA’s implementation of the Freight Train model, designed for efficient data production and handling.This dissertation details the methodology and execution of the NOvA 2024 3-Flavor Oscillation Analysis, supported by a comprehensive dataset spanning ten years. It emphasizes frequentist results refined through the Feldman-Cousins (FC) technique, specifically addressing confidence interval corrections in parameter estimation. The computational intensity associated with Feldman-Cousins arises from extensive Monte Carlo simulations, which were substantially mitigated through parallel computing on the Perlmutter supercomputer at the National Energy Research Scientific Computing Center (NERSC), employing the MPI framework.To further enhance computational efficiency, an Importance Sampling method is introduced and evaluated, demonstrating significant potential to reduce complexity, particularly in exploring extreme parameter space regions. This thesis presents both the successful application of advanced computational resources and the development of sophisticated statistical techniques, aiming to enhance the precision and scope of neutrino oscillation analyses.

Dye ajdye11190@gmail.com, Andrew Joseph [Mississip↗
NASA NTRS2005

Saturn Radiation (SATRAD) Model

The Saturnian radiation belts have not received as much attention as the Jovian radiation belts because they are not nearly as intense-the famous Saturnian particle rings tend to deplete the belts near where their peak would occur. As a result, there has not been a systematic development of engineering models of the Saturnian radiation environment for mission design. A primary exception is that of Divine (1990). That study used published data from several charged particle experiments aboard the Pioneer 1 1, Voyager 1, and Voyager 2 spacecraft during their flybys at Saturn to generate numerical models for the electron and proton radiation belts between 2.3 and 13 Saturn radii. The Divine Saturn radiation model described the electron distributions at energies between 0.04 and 10 MeV and the proton distributions at energies between 0.14 and 80 MeV. The model was intended to predict particle intensity, flux, and fluence for the Cassini orbiter. Divine carried out hand calculations using the model but never formally developed a computer program that could be used for general mission analyses. This report seeks to fill that void by formally developing a FORTRAN version of the model that can be used as a computer design tool for missions to Saturn that require estimates of the radiation environment around the planet. The results of that effort and the program listings are presented here along with comparisons with the original estimates carried out by Divine. In addition, Pioneer and Voyager data were scanned in from the original references and compared with the FORTRAN model s predictions. The results were statistically analyzed in a manner consistent with Divine s approach to provide estimates of the ability of the model to reproduce the original data. Results of a formal review of the model by a panel of experts are also presented. Their recommendations for further tests, analyses, and extensions to the model are discussed.

Garrett, H. B.↗
DOE OSTI2021

The CanBikeCO Mini Pilot: Procedure and Preliminary Results

In fall 2020, the Colorado Energy Office, as part of the State of Colorado's "Can Do Colorado" initiative, initiated a project aimed at encouraging energy-efficient transportation during the COVID-19 pandemic. The initial mini-pilot provided e-bikes to 13 low-income households under an individual ownership model. This report assesses the impact of providing this additional mobility option on the travel behavior of participants. It also outlines the lessons learned from deploying a continuous monitoring platform to track the travel behavior. These lessons will influence the evaluation component for the full pilot, which will cover multiple geographic regions, start in summer 2021, and run for 2 years. The continuous data collection was enabled by a customized version of the open-source e-mission platform, called CanBikeCO, configured with a behavioral gamification feature. The Colorado Energy Office used this system to collect a unique data set consisting of 3 months of partially automated travel diaries, combining sensed and surveyed data and linked with demographic information, from 12 participants. The data collection process worked well overall: users generally liked the app, appreciated the game, and did not complain about battery life. The long tracking period introduced behavioral challenges in user engagement, which we plan to address using repeated patterns and automated status checks for the full pilot. The analysis results, based on the subset of trips with user-reported labels (68%), indicate that the e-bike was the dominant commute mode share (31%), in sharp contrast to the census bicycle commute mode share (<1%). E-bike trips primarily replaced single-occupancy vehicle (SOV) trips (28%), followed closely by walking (24%) and regular bike (20%). The non-motorized mode replacement corresponds to lower travel time and increased productivity enabled by the program. The emissions impact analysis of the program, computed using trip-level energy intensity factors, indicates savings of 1,367 lbs. of CO2. Although the results are strongly positive, the narrow demographic profile of study participants, their limited mobility alternatives, and nonuniform labeling indicate caution in broader interpretation. These preliminary results do suggest that such programs, supported by real-time education and support from program managers, can simultaneously meet equity and sustainability goals. The planned full pilot, addressing the data collection challenges and broadening the geographic scope, will provide additional insights into the generality of this approach.

ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1986

The effects of inlet turbulence and rotor/stator interactions on the aerodynamics and heat transfer of a large-scale rotating turbine model. Volume 2: Heat transfer data tabulation. 15 percent axial spacing

A combined experimental and analytical program was conducted to examine the effects of inlet turbulence on airfoil heat transfer. The experimental portion of the study was conducted in a large-scale (approx 5X engine), ambient temperature, rotating turbine model configured in both single stage and stage-and-a-half arrangements. Heat transfer measurements were obtained using low-conductivity airfoils with miniature thermcouples welded to a thin, electrically heated surface skin. Heat transfer data were acquired for various combinations of low or high inlet turbulence intensity, flow coefficient, first-stator/rotor axial spacing, Reynolds number and relative circumferential position of the first and second stators. Aerodynamic measurements obtained as part of the program include distributions of the mean and fluctuating velocities at the turbine inlet and, for each airfoil row, midspan airfoil surface pressures and circumferential distributions of the downstream steady state pressures and fluctuating velocities. Analytical results include airfoil heat transfer predictions produced using existing 2-D boundary layer computation schemes and an examination of solutions of the unsteady boundary layer equations. The results are reported in four separate volumes, of which this is Volume 2: Heat Transfer Data Tabulation; 15 Percent Axial Spacing.

Dring, R. P.↗
DOE OSTI2015

Advanced Performance Modeling with Combined Passive and Active Monitoring

To improve the efficiency of resource utilization and scheduling of scientific data transfers on high-speed networks, the "Advanced Performance Modeling with combined passive and active monitoring" (APM) project investigates and models a general-purpose, reusable and expandable network performance estimation framework. The predictive estimation model and the framework will be helpful in optimizing the performance and utilization of networks as well as sharing resources with predictable performance for scientific collaborations, especially in data intensive applications. Our prediction model utilizes historical network performance information from various network activity logs as well as live streaming measurements from network peering devices. Historical network performance information is used without putting extra load on the resources by active measurement collection. Performance measurements collected by active probing is used judiciously for improving the accuracy of predictions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The CanBikeCO Mini Pilot: Preliminary Results and Lessons Learned

In fall 2020, the Colorado Energy Office, as part of the State of Colorado’s “Can Do Colorado” initiative, initiated a project aimed at encouraging energy-efficient transportation during the COVID-19 pandemic. The initial mini-pilot provided e-bikes to 13 low-income households under an individual ownership model. This report assesses the impact of providing this additional mobility option on the travel behavior of participants. It also outlines the lessons learned from deploying a continuous monitoring platform to track the travel behavior. These lessons will influence the evaluation component for the full pilot, which will cover multiple geographic regions, starting in summer 2021, and run for 2 years. The continuous data collection was enabled by a customized version of the open-source e-mission platform, called CanBikeCO, configured with a behavioral gamification feature. The Colorado Energy Office used this system to collect a unique data set consisting of 3 months of partially automated travel diaries, combining sensed and surveyed data and linked with demographic information, from 12 participants. The data collection process worked well overall: users generally liked the app, appreciated the game, and did not complain about battery life. The long tracking period introduced behavioral challenges in user engagement, which we plan to address using repeated patterns and automated status checks for the full pilot. The analysis results, based on the subset of trips with user-reported labels (68%), indicate that the e-bike was the dominant commute mode share (31%), in sharp contrast to the census bicycle commute mode share (<1%). E-bike trips primarily replaced single-occupancy vehicle (SOV) trips (28%), followed closely by walking (24%) and regular bike (20%). The nonmotorized mode replacement corresponds to lower travel time and increased productivity enabled by the program. The emissions impact analysis of the program, computed using trip-level energy intensity factors, indicates savings of 1,367 lbs. of CO 2 . Although the results are strongly positive, the narrow demographic profile of study participants, their limited mobility alternatives, and nonuniform labeling indicate caution in broader interpretation. These preliminary results do suggest that such programs, supported by real-time education and support from program managers, can simultaneously meet equity and sustainability goals. The planned full pilot, addressing the data collection challenges and broadening the geographic scope, will provide additional insights into the generality of this approach.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Polycyclic Aromatic Hydrocarbons with Aliphatic Sidegroups: Intensity Scaling for the C–H Stretching Modes and Astrophysical Implications

The so-called unidentified infrared emission (UIE) features at 3.3, 6.2, 7.7, 8.6, and 11.3 μm ubiquitously seen in a wide variety of astrophysical regions are generally attributed to polycyclic aromatic hydrocarbon (PAH) molecules. Astronomical PAHs may have an aliphatic component, as revealed by the detection in many UIE sources of the aliphatic C–H stretching feature at 3.4 μm. The ratio of the observed intensity of the 3.4 μm feature to that of the 3.3 μm aromatic C–H feature allows one to estimate the aliphatic fraction of the UIE carriers. This requires knowledge of the intrinsic oscillator strengths of the 3.3 μm aromatic C–H stretch (A{sub 3.3}) and the 3.4 μm aliphatic C–H stretch (A{sub 3.4}). Lacking experimental data on A{sub 3.3} and A{sub 3.4} for the UIE candidate materials, one often has to rely on quantum-chemical computations. Although the second-order Møller–Plesset (MP2) perturbation theory with a large basis set is more accurate than the B3LYP density functional theory, MP2 is computationally very demanding and impractical for large molecules. Based on methylated PAHs, we show here that, by scaling the band strengths computed at an inexpensive level (e.g., B3LYP/6-31G*), we are able to obtain band strengths as accurate as those computed at far more expensive levels (e.g., MP2/6-311+G(3df,3pd)). We calculate the model spectra of methylated PAHs and their cations excited by starlight of different spectral shapes and intensities. We find that (I{sub 3.4}/I{sub 3.3}){sub mod}, the ratio of the model intensity of the 3.4 μm feature to that of the 3.3 μm feature, is insensitive to the spectral shape and intensity of the exciting starlight. We derive a straightforward relation for determining the aliphatic fraction of the UIE carriers (i.e., the ratio of the number of C atoms in aliphatic units N{sub C,ali} to that in aromatic rings N{sub C,aro}) from the observed band ratios (I{sub 3.4}/I{sub 3.3}){sub obs}: N{sub C,ali}/N{sub C,aro}≈0.57×(I{sub 3.4}/I{sub 3.3}){sub obs} for neutrals and N{sub C,ali}/N{sub C,aro}≈0.26×(I{sub 3.4}/I{sub 3.3}){sub obs} for cations.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1974

Investigation of Northrop F-5A wing buffet intensity in transonic flight

A flight test and data processing program utilizing a Northrop F-5A aircraft instrumented to acquire buffet pressures and response data during transonic maneuvers is discussed. The data are presented in real-time format followed by spectral and statistical analyses. Also covered is a comparison of the aircraft response data with computed responses based on the measured buffet pressures.

Chintsun, H.↗
DOE OSTI2024

AI-assisted detector design for the EIC (AID(2)E)

Artificial Intelligence is poised to transform the design of complex, large-scale detectors like ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far forward and far backward regions, the ePIC experiment incorporates numerous design parameters and objectives, including performance, physics reach, and cost, constrained by mechanical and geometric limits. This project aims to develop a scalable, distributed AI-assisted detector design for the EIC (AID(2)E), employing state-of-the-art multiobjective optimization to tackle complex designs. Supported by the ePIC software stack and using G EANT 4 simulations, our approach benefits from transparent parameterization and advanced AI features. The workflow leverages the PanDA and iDDS systems, used in major experiments such as ATLAS at CERN LHC, the Rubin Observatory, and sPHENIX at RHIC, to manage the compute intensive demands of ePIC detector simulations. Tailored enhancements to the PanDA system focus on usability, scalability, automation, and monitoring. Ultimately, this project aims to establish a robust design capability, apply a distributed AI-assisted workflow to the ePIC detector, and extend its applications to the design of the second detector (Detector-2) in the EIC, as well as to calibration and alignment tasks. Additionally, we are developing advanced data science tools to efficiently navigate the complex, multidimensional trade-offs identified through this optimization process.

97 MATHEMATICS AND COMPUTING↗