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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O (sup 1) D (Deuterium) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2019

Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS1987

Space vehicle approach velocity judgments under simulated visual space conditions

Thirty-five volunteers responded when they first perceived an increase in apparent size of a collimated, 2-D image of an Orbiter vehicle. The test variables of interest included the presence of a fixed angular reticle within the field of view (FOV); three initial Orbiter distances; three constant Orbiter approach velocities corresponding to 1.6, 0.8, and 0.4 percent of the initial distance per second; and two background starfield velocities. It was found that: (1) at each initial range, increasing approach velocity led to a larger distance between the eye and Orbiter image at threshold; (2) including the fixed reticle in the FOV produced a smaller distance between the eye and Orbiter image at threshold; and (3) increasing background star velocity during this judgment led to a smaller distance between the eye and Orbiter image at threshold. The last two findings suggest that other detail within the FOV may compete for available attention which otherwise would be available for judging image expansion; thus, the target has to approach the observer nearer than otherwise if these details were present. These findings are discussed in relation to previous research and possible underlying mechanisms.

Haines, Richard F.↗
NASA NTRS1987

Space vehicle approach velocity judgments under simulated visual space conditions

Thirty-five volunteers responded when they first perceived an increase in apparent size of a collimated, 2-D image of an Orbiter vehicle. The test variables of interest included the presence of a fixed angular reticle within the field of view (FOV); three initial Orbiter distances; three constant Orbiter approach velocities corresponding to 1.6, 0.8, and 0.4 percent of the initial distance per second; and two background starfield velocities. It was found that: (1) at each initial range, increasing approach velocity led to a larger distance between the eye and Orbiter image at threshold; (2) including the fixed reticle in the FOV produced a smaller distance between the eye and Orbiter image at threshold; and (3) increasing background star velocity during this judgment led to a smaller distance between the eye and Orbiter image at threshold. The last two findings suggest that other detail within the FOV may compete for available attention which otherwise would be available for judging image expansion; thus, the target has to approach the observer nearer than otherwise if these details were present. These findings are discussed in relation to previous research and possible underlying mechanisms.

Haines, Richard F.↗
NASA NTRS1991

Control concept for maneuvering in hypersonic flight

This research investigates an approach to provide precise, coordinated maneuver control during excursions from a hypersonic cruise flight path while observing the necessary flight condition constraints. The approach achieves specified guidance commands by resolving altitude and cross-range errors into a load factor and bank angle command through a coordinate transformation which acts as an interface between outer loop guidance controls and inner loop flight controls. This interface, referred to as a 'resolver', applies constraints on angle-of-attack and dynamic pressure perturbations while prioritizing altitude regulation over crossrange. An unpiloted test simulation, in which the resolver was used to drive inner-loop flight controls, produced time histories of responses to guidance commands at Mach numbers of 6, 10, 15, and 20. It is shown that angle-of-attack and throttle perturbation constraints, combined with high-speed flight effects and the desire to maintain constant dynamic pressure, significantly impact the maneuver envelope for a hypersonic vehicle. Turn rate, climb rate, and descent rate limits are expressed in terms of these constraints.

Raney, David L.↗
DOE OSTI2021

Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks : *Full/Regular Research Paper submission for the symposium CSCI-ISAI: Artificial Intelligence

We use a stable parallel approach to train Wasserstein Conditional Generative Adversarial Neural Networks (W-CGANs). The parallel training reduces the risk of mode collapse and enhances scalability by using multiple generators that are concurrently trained, each one of them focusing on a single data label. The use of the Wasserstein metric reduces the risk of cycling by stabilizing the training of each generator. We apply the approach on the CIFAR10 and the CIFAR100 datasets, two standard benchmark datasets with images of the same resolution, but different number of classes. Performance is assessed using the inception score, the Fréchet inception distance, and image quality. An improvement in inception score and Fréchet inception distance is shown in comparison to previous results obtained by performing the parallel approach on deep convolutional conditional generative adversarial neural networks (DC-CGANs). Weak scaling is attained on both datasets using up to 100 NVIDIA V100 GPUs on the OLCF supercomputer Summit.

Lupo Pasini, Massimiliano↗
NASA NTRS1992

Control integration concept for hypersonic cruise-turn maneuvers

Piloting difficulties associated with conducting aircraft maneuvers in hypersonic flight are caused in part by the nonintuitive nature of the aircraft response and the stringent constraints anticipated on allowable angle of attack and dynamic pressure variations. An approach is documented that provides precise, coordinated maneuver control during excursions from a hypersonic cruise flight path and the necessary flight condition constraints. The approach is to achieve specified guidance commands by resolving altitude and cross range errors into a load factor and bank angle command by using a coordinate transformation that acts as an interface between outer and inner loop flight controls. This interface, referred to as a 'resolver', applies constraints on angle of attack and dynamic pressure perturbations while prioritizing altitude regulation over cross range. An unpiloted test simulation, in which the resolver was used to drive inner loop flight controls, produced time histories of responses to guidance commands and atmospheric disturbances at Mach numbers of 6, 10, 15, and 20. Angle of attack and throttle perturbation constraints, combined with high speed flight effects and the desire to maintain constant dynamic pressure, significantly impact the maneuver envelope for a hypersonic vehicle.

Raney, David L.↗
DOE OSTI2020

TomoGAN: low-dose synchrotron x-ray tomography with generative adversarial networks: discussion

Synchrotron-based x-ray tomography is a noninvasive imaging technique that allows for reconstructing the internal structure of materials at high spatial resolutions from tens of micrometers to a few nanometers. In order to resolve sample features at smaller length scales, however, a higher radiation dose is required. Therefore, the limitation on the achievable resolution is set primarily by noise at these length scales. We present TomoGAN, a denoising technique based on generative adversarial networks, for improving the quality of reconstructed images for low-dose imaging conditions. We evaluate our approach in two photon-budget-limited experimental conditions: (1) sufficient number of low-dose projections (based on Nyquist sampling), and (2) insufficient or limited number of high-dose projections. In both cases, the angular sampling is assumed to be isotropic, and the photon budget throughout the experiment is fixed based on the maximum allowable radiation dose on the sample. Evaluation with both simulated and experimental datasets shows that our approach can significantly reduce noise in reconstructed images, improving the structural similarity score of simulation and experimental data from 0.18 to 0.9 and from 0.18 to 0.41, respectively. Furthermore, the quality of the reconstructed images with filtered back projection followed by our denoising approach exceeds that of reconstructions with the simultaneous iterative reconstruction technique, showing the computational superiority of our approach.

42 ENGINEERING↗
NASA NTRS1989

Space vehicle approach velocity judgments under simulated visual space conditions

There were 35 volunteers who responded when they first perceived an increase in apparent size of a collimated, two-dimensional perspective image of an Orbiter vehicle. The variables of interest included the presence (or absence) of a fixed reticle within the field of view (FOV), background starfield velocity, initial range to the vehicle and vehicle closure velocity. It was found that: 1) increasing vehicle approach velocity yielded a very small (but significant) effect of faster detection of vehicle movement, nevertheless, response variability was relatively large; 2) including the fixed reticle in the FOV produced significantly slower detection of vehicle radial movement, however this occurred only at the largest range and the magnitude of the effect was only about 15% of the one sigma value; and 3) increasing background star velocity during this judgment led to slower detection of vehicle movement. While statistically significant, this effect was small and occurred primarily at the largest range. A possible explanation for the last two findings is that other static and dynamic objects within the visual field may compete for available attention which otherwise would be available for judging image expansion; thus, the target's retinal image has to expand more than otherwise for its movement to be detected. This study also showed that the Proximity Operations Research Mockup at NASA/Ames can be used effectively to investigate a variety of visual judgment questions related to future space operations. These findings are discussed in relation to previous research and possible underlying mechanisms.

Space Flight↗
DOE OSTI2021

Feedstock and Catalyst Impact on Bio-Oil Production and FCC Co-Processing to Fuels

NREL’s thermochemical biomass conversion research is focused on ex-situ upgrading of biomass fast-pyrolysis (FP) vapors as an efficient and economical route to completely biogenic pyrolysis-based fuel precursors, potential fuels, and value-added chemicals depending on catalyst choice and process conditions. In this approach, biomass vapors are generated via fast pyrolysis with destabilizing vapor components (char, inorganics, tar aerosols) removed by hot gas filtration to produce conditioned vapors more responsive to catalytic upgrading. A Davison Circulating Riser (DCR), a petroleum industry standard for fluidized catalytic cracking (FCC) catalyst evaluation, was coupled with a custom pyrolyzer system designed to produce consistent composition pyrolysis vapors as feed to the DCR. The coupled system is shown below (Figure 1). Pyrolysis vapors, derived from pure hardwood and softwood, were upgraded using commercially available and laboratory modified zeolite-based catalysts. Upgraded oils were analyzed via 31P and 13C NMR spectroscopy, GCxGC-TOF/MS, carbonyl and ultimate analysis (CHNO), and micro distillation to assess both oil chemistry and distillation behavior as they relate to catalyst and feedstock type for producing fungible hydrocarbon product liquids. Measured process parameters identified from technoeconomic process analysis included achieving oil oxygenate contents of 20% or less, carbon-conversion efficiencies of 30% or more, and catalyst coking rates of 10% or less. A proprietary modified zeolite catalyst from Johnson Matthey produced oils that met these requirements. Using the DCR equipped with a proprietary nozzle system, a second approach focused on reducing the carbon intensity of petroleum-derived hydrocarbon fuels by FCC co-processing these CFP oils with petroleum vacuum gas oil (VGO) to produce biogenic carbon containing fuels. The vapor-phase upgrading results demonstrated for the first time the feasibility of producing refinery-compatible hydrocarbon fuel intermediates entirely from biomass-derived fast-pyrolysis vapors using an industry-accepted DCR system for catalytic upgrading. The co-processing results demonstrated the feasibility of using CFP oils with VGO feeds in FCC refinery operations to produce biogenic carbon containing fuels.

bio-oil↗
NASA NTRS1996

Ozone Change from 1992 to 1993 as Observed from SSBUV on the ATLAS-1 and ATLAS-2 Missions

The Shuttle SBUV (SSBUV) conducted its fourth and fifth flights in late March 1992 and early April 1993 along with the ATLAS-1 and ATLAS-2 Shuttle missions, respectively. The two successive SSBUV flights yielded ozone data nearly one year apart. An analysis of the meteorological conditions, namely temperature and winds, during the two flight periods indicate that the conditions in the stratosphere were very similar. The temperatures had significantly warmed from winter throughout most of the stratosphere and the circulation was approaching normal summertime conditions for both periods. SSBUV-4, flown in 1992, measured ozone from approximately 30S to 60N while SSBUV-5, flown in 1993, measured ozone from approximately 55S to 60N. Zonal average column ozone amounts were derived from the two flights and compared to determine if a systematic change in ozone could be detected despite the fact that only a few days from each year were sampled. The comparison indicates that in the latitude range 30N to 60N total ozone was lower in 1993 than in 1992 by about 12%. This change is larger than the observational errors and the expected interannual variations. This result verifies similar data taken from ground and satellites.

Hilsenrath, E.↗
DOE OSTI2021

A consistent and conservative model and its scheme for N -phase- M -component incompressible flows

Here, we propose a consistent and conservative model for multiphase and multicomponent incompressible flows, where there can be arbitrary numbers of phases and components. Each phase has a background fluid called the pure phase, each pair of phases is immiscible, and components are dissolvable in some specific phases. The model is developed based on the multiphase Phase-Field model including the contact angle boundary condition, the diffuse domain approach, and the analyses on the proposed consistency conditions for multiphase and multicomponent flows. The model conserves the mass of individual pure phases, the amount of each component in its dissolvable region, and thus the mass of the fluid mixture, and the momentum of the flow. It ensures that no fictitious phases or components can be generated and that the summation of the volume fractions from the Phase-Field model is unity everywhere so that there is no local void or overfilling. It satisfies a physical energy law and it is Galilean invariant. A corresponding numerical scheme is developed for the proposed model, whose formal accuracy is 2nd-order in both time and space. It is shown to be consistent and conservative and its solution is demonstrated to preserve the Galilean invariance and energy law. Numerical tests indicate that the proposed model and scheme are effective and robust to study various challenging multiphase and multicomponent flows.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRSDate not supplied

Generating Global CH4 NASA GEOS Product by Assimilating TROPOMI

Radiative properties of methane (CH4) significantly contribute to climate change and the recent acceleration of global CH4 growth requires thorough investigation into its causes. Examination of temporal and spatial CH4 variability is crucial for better understanding the shifts that are currently taking place. Integrating different CH4 observations to create a reliable, easily to use global atmospheric CH4 product could contribute to assessment of emissions processes, but remains challenging due to the lack of long satellite data records. Here we present the NASA Goddard Earth Observing System (GEOS) based global CH4 product constrained by atmospheric transport from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and assimilated CH4 from the TROPOspheric Monitoring Instrument (TROPOMI). This product uses the Constituent Data Assimilation System (CoDAS) component of the GEOS capability, which has been expanded and generalized to assimilate any satellite, ground-based, or in situ observation of atmospheric composition that can be expressed as a vertical sounding or point sample. The product is similar to the one generated by assimilating carbon dioxide (CO2) data into GEOS from Orbiting Carbon Observatory 2 (OCO-2): OCO-2 GEOS Level 3 daily and monthly, 0.5x0.625 assimilated CO2 V10r. The preliminary product is investigated using a variety of quality check approaches with the help of in situ observations to examine bias correction, error inflation, and performance of the assimilation system. Potential applications of this approach include support for interpretation of high-resolution point source detection approaches, climate and greenhouse gas reanalyses, and boundary conditions for regional modeling approaches.

Nikolay Balashov↗
NASA NTRS1992

Two approaches to the rapid screening of crystallization conditions

A screening procedure is described for estimating conditions under which crystallization will proceed, thus providing a starting point for more careful experiments. The initial procedure uses the experimental setup of McPherson (1982) which supports 24 individual hanging drop experiments for screening variables such as the precipitant type, the pH, the temperature, and the effects of certain additives and which uses about 1 mg of protein. A second approach is proposed (which is rather hypothetical at this stage and needs a larger sample), based on the isoelectric focusing of protein samples on concentration gradients of common precipitating agents. Using this approach, crystals of concanavalin B and canavalin were obtained.

Mcpherson, Alexander↗
NASA NTRS1974

Model wall and recovery temperature effects on experimental heat transfer data analysis

Basic analytical procedures are used to illustrate, both qualitatively and quantitatively, the relative impact upon heat transfer data analysis of certain factors which may affect the accuracy of experimental heat transfer data. Inaccurate knowledge of adiabatic wall conditions results in a corresponding inaccuracy in the measured heat transfer coefficient. The magnitude of the resulting error is extreme for data obtained at wall temperatures approaching the adiabatic condition. High model wall temperatures and wall temperature gradients affect the level and distribution of heat transfer to an experimental model. The significance of each of these factors is examined and its impact upon heat transfer data analysis is assessed.

Throckmorton, D. A.↗
NASA NTRS2011

Assessment of Radiated Fan Noise Prediction Capabilities Using Static Engine Test Data

This paper describes further assessment of the CDUCT-LaRC code via comparison with static engine test data. In an effort to improve confidence in the use of CDUCT-LaRC for liner optimization studies addressing realistic three-dimensional geometries, inlet radiated fan noise predictions were performed at 54% and 87% engine speed settings. Predictions were then compared with far-field measurements to assess the approach and implementation. The particular configurations were chosen to exercise the three-dimensional capability of CDUCT-LaRC and it s applicability to realistic configurations and conditions. At the 54% engine speed setting, the predictions capture the general directivity and acoustic treatment effects quite well. Comparisons of the predicted and measured directivity at the 87% power setting were more problematic. This was likely due in part to the difficulties in source specification and possibly the nonlinear nature of buzz-saw tones at this engine operating condition. Overall, the approach captured the basic trends and provided a conservative estimate of liner effects from which relative performance metrics could be inferred.

Nark, Douglas M.↗
DOE OSTI2023

Let’s Unleash the Network Judgment: A Self-Supervised Approach for Cloud Image Analysis

Accurate cloud type identification and coverage analysis are crucial in understanding the Earth’s radiative budget. Traditional computer vision methods rely on low-level visual features of clouds for estimating cloud coverage or sky conditions. Several handcrafted approaches have been proposed; however, scope for improvement still exists. Newer deep neural networks (DNNs) have demonstrated superior performance for cloud segmentation and categorization. These methods, however, need expert engineering intervention in the preprocessing steps—in the traditional methods—or human assistance in assigning cloud or clear sky labels to a pixel for training DNNs. Such human mediation imposes considerable time and labor costs. We present the application of a new self-supervised learning approach to autonomously extract relevant features from sky images captured by ground-based cameras, for the classification and segmentation of clouds. We evaluate a joint embedding architecture that uses self-knowledge distillation plus regularization. We use two datasets to demonstrate the network’s ability to classify and segment sky images—one with ~85,000 images collected from our ground-based camera and another with 400 labeled images from the WSISEG database. We find that this approach can discriminate full-sky images based on cloud coverage, diurnal variation, and cloud base height. Additionally, it semantically segments the cloud areas without labels. The approach shows competitive performance in all tested tasks, suggesting a new alternative for cloud characterization.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Off-Design Analysis of Axisymmetric External-Compression Supersonic Inlets for Mach 1.4 to 2.0

Supersonic axisymmetric, external-compression inlets designed for freestream Mach numbers of Mach 1.4, 1.7, and 2.0 were evaluated to characterize their off-design performance. The inlets were characterized as isolated from the airframe with freestream conditions, engine-face geometry, and flow rates established from a reference NASA commercial aircraft concept. The off-design performance was characterized using computational fluid dynamics (CFD) simulations. The study characterized the off-design performance at the cruise condition for variations in the inlet flow rate, angle-of-attack, and freestream Mach number. Also characterized was the inlet performance at take-off and approach-to-landing conditions for which the use of auxiliary intakes was included. The inlet performance was characterized by the flow rates, total pressure recovery and distortion, and drag. An understanding of the off-design performance of the axisymmetric inlets provides information for the selection of inlets for commercial supersonic aircraft.

Supersonic Inlets↗