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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

TPSAS-NF1676L-32869-DND

Three unseeded molecular tagging velocimetry (MTV) techniques have been demonstrated in NASA Langley’s 0.3-meter Transonic Cryogenic Tunnel (TCT). High pressure, cryogenic gaseous N2 is used to produce high Reynolds number transonic flow in TCTs, allowing small aircraft and space vehicles to be tested at flight Reynolds numbers. These facilities generally cannot be seeded with particles or toxic gases so unseeded velocimetry methods are needed. Three Laser Electronic Excitation and Tagging (LEET) techniques were tested: Femtosecond LEET (FLEET), Picosecond LEET (PLEET) and Selective Two-photon Absorptive Resonance Femtosecond LEET (STARFLEET). All three techniques were used over a range of pressures (124 to 517 kPa), temperatures (100-300 K) and Mach numbers (0.2-0.8) to measure freestream conditions and were then compared to conditions determined by the facility data system using conventional wall pressures and probes. Measurement precision and accuracy are presented, as are signal intensity and lifetimes which impact measurement uncertainties and are useful for understanding and modelling the various LEET processes.

P Danehy↗
NASA NTRS2020

Change of Exposure Time Mid-test in High Temperature DIC Measurement

Performing digital image correlation (DIC) at extreme temperatures has been greatly challenging due to the radiation which saturates the camera sensor. At such high temperatures, the light intensity emitted from an object is occasionally so powerful that the acquired images are overwhelmingly saturated. This induces data loss, potentially ruining the test, thus requiring the user to restart the test. For this reason, selection of an appropriate camera sensitivity plays a crucial role prior to beginning the test. Exposure time is a factor contributing to camera sensitivity and it is the easiest setting to manipulate during the test since it introduces minimal errors when comparing to other factors, especially in quasi-static tests. This paper examines the influence of changing exposure time mid-test on DIC measurement uncertainty. The investigation was conducted by rigid body motion experiments at room temperature and 1600 ◦C, respectively. Thereby, some recommendations are given to help DIC users assess their images at room temperature to extrapolate the exposure at extreme temperatures along with accompanying solutions to salvage data at high temperature.

Digital Image Correlation↗
NASA NTRS2018

Far-Field Pattern Measurement and Simulation of VHF Antenna at 60 MHz for Europa Clipper Mission

This paper presents measurements and simulations of a linearly polarized VHF folded dipole operating at 60 MHz for NASA’s upcoming Europa Clipper mission. The spacecraft contains a ground penetrating radar, consisting of an array of four VHF antennas, that will characterize the surface of Jupiter’s icy moon Europa. Measured far-field radiation patterns and reflection coefficients above a 50×80m perfect ground plane are compared to predicted results using commercial software. Normalized Far-Field radiation pattern cuts are measured utilizing a biconical antenna and receiver mounted on a flying drone. Simulations of the radiation pattern show that the number of lobes increases with the distance of the antenna from the ground due to the ground-bounce phenomenon. In addition, the measured and simulated reflection coefficients stay unchanged for height above 2 meters, which matches the predicted calculation of the reactive-near field region of this antenna. However, the antenna electrical parameters are optimized for use on the spacecraft and become detuned in the presence of a ground plane. Simulated and measured return loss discrepancy less than 0.5 dB is presented. Radiation pattern comparisons show similar agreement when taking into account the effect of far-field measurement uncertainties.

Decrossas, Emmanuel↗
NASA NTRS2019

Field Calibration Techniques used to Characterize the Radiometric Stability of the GEO-CAPE Airborne Simulator (GCAS)

The GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) was developed at NASA’s Goddard Space Flight Center (GSFC) and has flown in multiple field campaigns to perform mapping of the regional-scale EPA criteria pollutants nitrogen dioxide, ozone, and formaldehyde. GCAS will also participate invalidation campaigns for NASA’s Tropospheric Emissions: Monitoring POllution (TEMPO) mission and the Korean Geostationary Environment Monitoring Spectrometer (GEMS) mission, both scheduled to launch in the early 2020s. GCAS houses two commercial Offner-type grating spectrometers that measure backscattered solar spectral radiance from the near-ultraviolet to near-infrared at high spatial resolution (typically 250 meters at 8.5 kilometer altitude). These radiances are used to retrieve spatial and temporal distributions of trace gases relevant to the boundary layer and free tropospheric atmospheric chemistry cycles. In this paper, we describe the field calibration techniques employed to characterize the spectral and temporal radiometric stability of the system during its most recent deployment in the 2018 Long Island Sound Trace Ozone Study (LISTOS) field campaign. Overall measurement uncertainty, retrieval impacts, and lessons learned for future deployments will also be described.

NASA’s Tropospheric Emissions: Monitoring POllutio↗
NASA NTRS2021

Spatial Variation of Fine Particulate Matter Levels in Nairobi Before and During the COVID-19 Curfew: Implications for Environmental Justice

The temporary decrease of fine particulate matter (PM(sub 2.5)) concentrations in many parts of the world due to the COVID-19 lockdown spurred discussions on urban air pollution and health. However there has been little focus on sub-Saharan Africa, as few African cities have air quality monitors and if they do, these data are often not publicly available. Spatial differentials of changes in PM(sub 2.5) concentrations as a result of COVID also remain largely unstudied. To address this gap, we use a serendipitous mobile air quality monitoring deployment of eight Sensirion SPS 30 sensors on motorbikes in the city of Nairobi starting on 16 March 2020, before a COVID-19 curfew was imposed on 25 March and continuing until 5 May 2020. We developed a random-forest model to estimate PM(sub 2.5) surfaces for the entire city of Nairobi before and during the COVID-19 curfew. The highest PM(sub 2.5) concentrations during both periods were observed in the poor neighborhoods of Kariobangi, Mathare, Umoja, and Dandora, located to the east of the city center. Changes in PM(sub 2.5) were heterogeneous over space. PM(sub 2.5) concentrations increased during the curfew in rapidly urbanizing, the lower-middle-class neighborhoods of Kahawa, Kasarani, and Ruaraka, likely because residents switched from LPG to biomass fuels due to loss of income. Our results indicate that COVID-19 and policies to address it may have exacerbated existing air pollution inequalities in the city of Nairobi. The quantitative results are preliminary, due to sampling limitations and measurement uncertainties, as the available data came exclusively from low-cost sensors. This research serves to highlight that spatial data that is essential for understanding structural inequalities reflected in uneven air pollution burdens and differential impacts of events like the COVID pandemic. With the help of carefully deployed low cost sensors with improved spatial sampling and at least one reference-quality monitor for calibration, we can collect data that is critical for developing targeted interventions that address environmental injustice in the African context.

Environmental justice↗
NASA NTRSDate not supplied

Towards a program of record of inland water quality: Exploiting present and heritage multispectral sensors for maximum information extraction

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This research exploits recent advancements in bio-optical modeling, cloud computing, and machine learning to enhance our capacity to leverage present and heritage satellite data. Recent research suggests that sensors with low spectral resolution, such as Sentinel 2 and Landsat missions, contain enough hidden spectral variation which can be exploited using data-driven approaches. The availability of three decades of archival imagery will open doors to discover global trends of eutrophication and increased cyanobacteria dominance and provide valuable insight to the development of predictive methodologies. Preliminary efforts in synthetic emulation of global natural inland waters will be discussed and contextualized against satellite radiometric measurement uncertainty, satellite data product uncertainties and causal signal ambiguity over the visible wavelength range, supported by high quality field and image data for selected inland aquatic sites. Insights on water quality estimation via data-driven machine learning models versus matrix inversions will be discussed, and how we can exploit spectral-spatial relationships in high spatial resolution data. A cross-sensor synergistic approach with detailed uncertainty analysis based on optical water types, will allow for unprecedented global snapshots of fine scale ecological dynamics of inland waters.

Inland↗
NASA NTRS2023

Low-Drag Acoustic Liner Development

Interest in characterization of the aerodynamic drag of acoustic liners has increased in the past several years. This report details experiments in the NASA Langley Grazing Flow Impedance Tube to quantify the relative drag of several perforate-over-honeycomb liner configurations at flow speeds of M=0.3 and 0.5. Various perforate geometries and orientations are investigated to determine their resistance factors using a static pressure drop approach. Comparison of these resistance factors gives a relative measurement of liner drag. For these same flow conditions, acoustic measurements are performed simultaneously with the drag measurements for tonal excitation from 400 to 3000 Hz at source sound pressure levels of 140 and 150 dB. Educed impedance and attenuation spectra are used to determine the impact of variations in perforate geometry on acoustic performance. The goal is to identify a perforate that will reduce the drag penalty associated with conventional round-hole perforates by 60%. One perforate design, based on a slot geometry, is shown to reduce this penalty by 50%. Further reductions may be possible but require a reduction in measurement uncertainty to allow a statistically rigorous evaluation.

drag↗
NASA NTRSDate not supplied

Construction of a Fluid Flowfield from Discrete Point Data using Machine Learning

Many verification and validation procedures in aerospace engineering involve the comparison of computational fluid dynamics (CFD) data to experimental results from sources like wind tunnel tests. However, an incongruity exists between the data available from these sources: flow visualization is available by default in computational data, whereas in most experimental setups the available data is far more discrete and far more limited: integrated forces and moments, discrete pressure and temperature probes, etc. When differences exist between quantities of interest like lift and drag coefficients, the lack of full-field flow data from the experiments complicates most attempts to reconcile why the different data sources disagree. To this end, a shallow neural network, constrained by certain fluid flow properties, was trained to approximate flow field snapshots given only discrete data like that available in a wind tunnel test. The constructed snapshots, even for complex incompressible fluid flows, were found to agree at the large scales with the true flow fields. With this tool, researchers can more readily and easily understand why quantities of interest differ between their experimental and computational datasets. This in turn improves the resulting data's uncertainty measures.

Yury Lebedev↗
NASA NTRSDate not supplied

Development of Neural Network for X-59 Air Data Probe Calibration

Two wind tunnel tests were conducted in the NASA Glenn Research Center (GRC) 8- by 6-Foot Supersonic Wind Tunnel to calibrate the nose-mounted air data probe for the X-59. The probe will be the primary instrument for the flight test of the X-59 for determining angle of attack, angle of sideslip, airspeed, and pressure altitude of the aircraft in flight. This paper documents the development of several neural net algorithms that can be used to relate air data probe port pressures from those tests to relevant X-59 flight parameters, such as angle of attack, angle of sideslip, mach number, and static and dynamic pressure. These calibration algorithms address accuracy effects for significant real-world conditions, such as initial sensor measurement uncertainties, assumed parameter constraints on NN modelling errors, and malfunctioning or plugged ports. Resultant integrated uncertainties are provided for each algorithm; these uncertainties are then used to estimate the effects on relevant X-59 mission parameters.

Kurtis Roy Long↗
NASA NTRSDate not supplied

Construction of a Fluid Flowfield from Discrete Point Data using Machine Learning

Many verification and validation procedures in aerospace engineering involve the comparison of computational fluid dynamics (CFD) data to experimental results from sources like wind tunnel tests. However, an incongruity exists between the data available from these sources: flow visualization is available by default in computational data, whereas in most experimental setups the available data is far more discrete and far more limited: integrated forces and moments, discrete pressure and temperature probes, etc. When differences exist between quantities of interest like lift and drag coefficients, the lack of full-field flow data from the experiments complicates most attempts to reconcile why the different data sources disagree. To this end, a shallow neural network, constrained by certain fluid flow properties, was trained to approximate flow field snapshots given only discrete data like that available in a wind tunnel test. The constructed snapshots, even for complex incompressible fluid flows, were found to agree at the large scales with the true flow fields. With this tool, researchers can more readily and easily understand why quantities of interest differ between their experimental and computational datasets. This in turn improves the resulting data's uncertainty measures.

Yury Lebedev↗
NASA NTRSDate not supplied

Particle Size Distribution Parameters from SAGE III/ISS Extinction Spectra

Stratospheric aerosols play key roles in the chemistry and radiation balance of the atmosphere and are a key input parameter for global chemistry and climate models. The degree to which aerosols impact chemistry and radiation balance depends primarily on the relative abundance of different sized particles within the sample volume, often referred to as the particle size distribution (PSD). If the PSD is accurately known then other key modeling parameters (e.g., surface area density and effective radius) can be derived. Historically, occultation observations from orbital instruments such as SAGE III/ISS have been used to infer these PSD parameters by inverting the extinction coefficient spectra. However, past efforts routinely failed to account for measurement uncertainty and lacked a rigorous estimate of the inferred PSD uncertainty. We carried out a series of simulations to evaluate the accuracy of these inferences and, for every valid SAGE III/ISS extinction spectrum, determined the range of PSD parameters that fell withing the bounds of the extinction error bars. Special application of this method was applied to estimate the impact of the 2022 Hunga Tonga eruption had on particle size distributions.

Travis N Knepp↗
NASA NTRSDate not supplied

Sage III/ISS Stratospheric Aerosols and Gas Experiment An Earth Science Mission on the International Space Station

Stratospheric aerosols play key roles in the chemistry and radiation balance of the atmosphere and are a key input parameter for global chemistry and climate models. The degree to which aerosols impact chemistry and radiation balance depends primarily on the relative abundance of different sized particles within the sample volume, often referred to as the particle size distribution (PSD). If the PSD is accurately known then other key modeling parameters (e.g., surface area density and effective radius) can be derived. Historically, occultation observations from orbital instruments such as SAGE III/ISS have been used to infer these PSD parameters by inverting the extinction coefficient spectra. However, past efforts routinely failed to account for measurement uncertainty and lacked a rigorous estimate of the inferred PSD uncertainty. We developed a PSD solution algorithm that infers single mode and bimodal distribution parameters and applied this algorithm to the SAGE II and SAGE III/ISS data record. Herein we describe the algorithm, evaluate its performance, and show results from the 2022 Hunga Tonga eruption.

Travis N. Knepp↗
NASA NTRSDate not supplied

A Facility Effect Characterization Test of the BHT-6000 Hall Thruster

During the development of the BHT-6000 Hall thruster for flight on NASA’s Power and Propulsion Element, a facility effect test campaign was performed on an engineering unit of the thruster. Testing occurred in Vacuum Facility 5 at NASA Glenn Research Center. The goal of the test campaign was to characterize the behavior of the BHT-6000 Hall thruster as functions of facility effects such that the thruster’s in-flight behavior can be reasonably bounded. In particular, the behavior of the thruster with regard to the background pressure and electrical environment was tested. The background pressure was controlled by injecting supplemental propellant flow into the environment, while the electrical environment was controlled by biasing the facility beam dump. Thrust was found to remain constant over the tested range of pressures while specific impulse was found to increase slightly (1 to 1.5% over a range of up to 20 μTorr, depending on operating point). Plasma plume properties, particularly of ions exiting the side of the thruster, were found to be highly sensitive to pressure and may require pressures of less than a few μTorr for accurate measurements. The thruster was found to operate identically with changing beam dump bias voltage to within measurement uncertainties.

Electric propulsion↗
NASA NTRSDate not supplied

A Facility Effect Characterization Test of the BHT6000 Hall Thruster

During the development of the BHT-6000 Hall thruster for flight on NASA’s Power and Propulsion Element, a facility effect test campaign was performed on an engineering unit of the thruster. Testing occurred in Vacuum Facility 5 at NASA Glenn Research Center. The goal of the test campaign was to characterize the behavior of the BHT-6000 Hall thruster as functions of facility effects such that the thruster’s in-flight behavior can be reasonably bounded. In particular, the behavior of the thruster with regard to the background pressure and electrical environment was tested. The background pressure was controlled by injecting supplemental propellant flow into the environment, while the electrical environment was controlled by biasing the facility beam dump. Thrust was found to remain constant over the tested range of pressures while specific impulse was found to increase slightly (1 to 1.5% over a range of up to 20 μTorr, depending on operating point). Plasma plume properties, particularly of ions exiting the side of the thruster, were found to be highly sensitive to pressure and may require pressures of less than a few μTorr for accurate measurements. The thruster was found to operate identically with changing beam dump bias voltage to within measurement uncertainties.

Electric propulsion↗
NASA NTRSDate not supplied

Automation of Geometric Accuracy Assessment Algorithm

When end-users are using multiple satellite images for a study the first step is often the assessment and rectification of geolocation offsets. We have developed an automated algorithm to calculate the geometric offsets between two satellite images. It splits both images into subset images (chips) and resamples them to a common resolution. Offsets are imposed on matching chips and the Pearson Cross Correlation (PCC) value calculated for each offset, where the best PCC gives the true offsets. With the true offsets corrected for, a quality of image registration is calculated, measurement uncertainty (MU). The MU threshold value is non-uniform across sensors because it depends on spectral characteristics of each sensor. Previously, the MU threshold was determined by manual inspection for each new sensor assessed. In this study, we automated MU threshold determination. 1/MU vs calculated offset approximates a Gaussian function in shape, see abstract image. A Gaussian function is fit to this dataset, and the root of the third derivative is calculated and applied as a quality threshold, removing the data of lower quality from the final assessment of image offsets. We apply this algorithm to commercial satellite data for proof of concept.

Alana Semple↗
NASA NTRS2024

Mock Truss-Braced Wing Loads Calibration Research Utilizing Fiber-Optic Sensors

In support of the National Aeronautics and Space Administration Advanced Air Transonic Transport Truss-Braced Wing structural research, the Armstrong Flight Research Center Flight Loads Laboratory is performing pathfinder tests on a small, scaled test article (dubbed the mock Truss-Braced Wing) to study the application of a loads calibration on a truss-braced wing structure. The addition of a truss to the wing structure, as opposed to a purely cantilever beam setup, changes the strain-gage-based loads calibration techniques. In a truss-braced configuration, the internal shear and moments, inboard of the truss-wing connection, are not directly representative of the external aerodynamic loading as is the case for a traditional cantilever configuration. A 3-axis load cell was used at the wing-to-strut interface to estimate the load sharing between the two structural members. Crosstalk within the 3-axis load cell is added to the measurement uncertainties of the calibration effort, and the results, including crosstalk uncertainties, are presented. The calibration tests presented in this report are used as a pathfinder for developing an understanding how to perform loads calibration on a truss-braced wing structural configuration. The use of distributed fiber-optic strain sensors is leveraged to provide a near-full-span strain profile along the leading and trailing edge of the wing structure for strain-based loads calibration.

Francisco Peña↗
NASA NTRS2025

NAIRAS Version 3 Atmospheric Ionizing Radiation Validation: Comparisons to RaD-X Measurements

The Nowcast of Aerospace Ionizing RAdiation System (NAIRAS) model predicts dosimetric and radiative flux quantities for assessing human radiation exposure levels and radiation effects on flight electronic systems from the surface of the Earth to deep space. NAIRAS version 3 includes many updates and improvements to the model. In this paper, NAIRAS version 3 dosimetric quantities are compared to observations of the atmospheric radiation environment taken during the Radiation Dosimetry Experiment (RaD-X) flight campaign. The RaD-X campaign consisted of a high-altitude balloon flight, with altitudes up to 40 km, and four coordinated aircraft flights covering an altitude range from 6-20 km. The RaD-X measurement data from the different flight platforms were acquired in September 2015 and at nearly the same vertical cutoff rigidity (3-4 GV). High quality, flight-averaged measurement data were provided at seven altitudes, which correspond to regions of fundamentally different radiation transport and collisional interaction physics. The NAIRAS model agrees with the RaD-X measurements to within 30% over the entire altitude domain of the flight campaign (0-40 km), which is within the measurement uncertainty.

Christopher J Mertens↗