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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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116 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Investigation of Density Fluctuations in Supersonic Free Jets and Correlation with Generated Noise

The air density fluctuations in the plumes of fully-expanded, unheated free jets were investigated experimentally using a Rayleigh scattering based technique. The point measuring technique used a continuous wave laser, fiber-optic transmission and photon counting electronics. The radial and centerline profiles of time-averaged density and root-mean-square density fluctuation provided a comparative description of jet growth. To measure density fluctuation spectra a two-Photomultiplier tube technique was used. Crosscorrelation between the two PMT signals significantly reduced electronic shot noise contribution. Turbulent density fluctuations occurring up to a Strouhal number (Sr) of 2.5 were resolved. A remarkable feature of density spectra, obtained from the same locations of jets in 0.5< M<1.5 range, is a constant Strouhal frequency for peak fluctuations. A detailed survey at Mach numbers M = 0.95, 1.4 and 1.8 showed that, in general, distribution of various Strouhal frequency fluctuations remained similar for the three jets. In spite of the similarity in the flow fluctuation the noise characteristics were found to be significantly different. Spark schlieren photographs and near field microphone measurements confirmed that the eddy Mach wave radiation was present in Mach 1.8 jet, and was absent in Mach 0.95 jet. To measure correlation between the flow and the far field sound pressure fluctuations, a microphone was kept at a distance of 50 diameters, 30 deg. to the flow direction, and the laser probe volume was moved from point to point in the flow. The density fluctuations in the peripheral shear layer of Mach 1.8 jet showed significant correlation up to the measurement limit of Sr = 2.5, while for Mach 0.95 jet no correlation was measured. Along the centerline measurable correlation was found from the end of the potential core and at the low frequency range (Sr less than 0.5). Usually the normalized correlation values increased with an increase of the jet Mach number. The experimental data point out eddy Mach waves as a strong source of sound generation in supersonic jets and fail to locate the primary noise mechanism in subsonic jets.

Panda, J.↗
NASA NTRS2023

Fine particulate concentrations over East Asia derived from aerosols measured by the Advanced Himawari Imager using machine learning

Fine particulate matter with a diameter below 2.5 μm (PM 2.5 ) is deleterious to the cardiovascular and respiratory systems. It is often difficult to assess the effects of PM 2.5 on human health over regions with limited ground monitoring sites, especially in East Asia. As an alternative, we estimated near-surface PM 2.5 concentrations by analyzing Advanced Himawari Imager (AHI) Yonsei Aerosol Retrieval (YAER) products. This study incorporates daytime data for East Asia covering the Korean Peninsula, China, Japan, Southeast Asia, and southern Mongolia. We collocated AHI YAER product pixels with meteorological, land-cover, and other ancillary data for the period from March 2018 to February 2019. To estimate PM 2.5 concentrations over wide areas spanning many countries displaying various relationships between aerosol optical depth and PM 2.5 , monthly models were developed by considering both the spatial and temporal characteristics of ground-based PM 2.5 measurements. Random forest machine learning model estimated ground-level mass concentrations of PM 2.5 ; subsequent 10-fold cross validation (CV) yielded a CV R 2 value of 0.81 and a CV root mean squared error (RMSE) of 12.3 μg m -3 . We investigated the spatial pattern of PM 2.5 concentrations over multiple countries and seasonal variation in PM 2.5 concentrations. Diurnal variation of a severe PM 2.5 event in the Korean Peninsula was investigated as a case study. The model captured the extremely heterogeneous spatial distribution of PM 2.5 concentrations peaked around local noon. To measure the capability of the developed model to estimate PM 2.5 concentrations in areas with few in-situ data, its predictive performance was evaluated using a dataset independent of the training process with an R 2 of 0.60 and RMSE of 8.18 μg m −3 . This study demonstrates the potential for satellite-based PM 2.5 estimation for areas with insufficient measuring stations.

Pm2.5↗
NASA NTRS2015

Spatiotemporal Prediction of Fine Particulate Matter Using High-Resolution Satellite Images in the Southeastern US 2003-2011

Numerous studies have demonstrated that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with adverse health outcomes. The use of ground monitoring stations of PM(sub 2.5) to assess personal exposure, however, induces measurement error. Land-use regression provides spatially resolved predictions but land-use terms do not vary temporally. Meanwhile, the advent of satellite-retrieved aerosol optical depth (AOD) products have made possible to predict the spatial and temporal patterns of PM(sub 2.5) exposures. In this paper, we used AOD data with other PM(sub 2.5) variables, such as meteorological variables, land-use regression, and spatial smoothing to predict daily concentrations of PM(sub 2.5) at a 1 sq km resolution of the Southeastern United States including the seven states of Georgia, North Carolina, South Carolina, Alabama, Tennessee, Mississippi, and Florida for the years from 2003 to 2011. We divided the study area into three regions and applied separate mixed-effect models to calibrate AOD using ground PM(sub 2.5) measurements and other spatiotemporal predictors. Using 10-fold cross-validation, we obtained out of sample R2 values of 0.77, 0.81, and 0.70 with the square root of the mean squared prediction errors of 2.89, 2.51, and 2.82 cu micrograms for regions 1, 2, and 3, respectively. The slopes of the relationships between predicted PM2.5 and held out measurements were approximately 1 indicating no bias between the observed and modeled PM(sub 2.5) concentrations. Predictions can be used in epidemiological studies investigating the effects of both acute and chronic exposures to PM(sub 2.5). Our model results will also extend the existing studies on PM(sub 2.5) which have mostly focused on urban areas because of the paucity of monitors in rural areas.

PM2.5↗
NASA NTRS2009

Aerojet - Attitude Control Engines

All the engines were both qualification and acceptance tested at Marquardt s facilities. After we won the Apollo Program contract, we went off and built two vacuum test facilities, which simulated altitude continuous firing for as long as we wanted to run an engine. They would run days and days with the same capability we had on steam ejection. We did all of the testing in both for the qualification and the acceptance test. One of them was a large ball, which was an eighteen-foot diameter sphere, evacuated again with a big steam ejector system that could be used for system testing; that s where we did the Lunar Excursion Module testing. We put the whole cluster in there and tested the entire cluster at the simulated altitude conditions. The lowest altitude we tested at - typically an acceptance test - was 105,000 feet simulated altitude. The big ball - because people were interested in what they called goop formation, which is an unburned hydrazine product migrating to cold surfaces on different parts of spacecraft - was built to address those kinds of issues. We ran long-life tests in a simulated space environment with the entire inside of the test cell around the test article, liquid nitrogen cooled, so it could act as getter for any of the exhaust products. That particular facility could pull down to about 350,000 feet (atmosphere) equivalent altitude, which was pushing pretty close to the thermodynamic triple point of the MMH. It was a good test facility. Those facilities are no longer there. When the guys at Marquardt sold the company to what eventually became part of Aerojet, all those test facilities were cut off at the roots. I think they have a movie studio there at this point. That part of it is truly not recoverable, but it did some excellent high-altitude, space-equivalent testing at the time. Surprisingly, we had very few problems while testing in the San Fernando Valley. In the early 1960s, nobody had ever seen dinitrogen tetroxide (N2O4), so that wasn't too big a deal. We really did only make small, red clouds. In all the hundreds of thousands of tests and probably well over one million firings that I was around that place for, in all that thirty-something years, we had a total of one serious injury associated with rocket engine testing and propellants. Because we were trying to figure out what propellants would really be good, we tried all of the fun stuff like the carbon tetrafluoride, chlorine pentafluoride, and pure fluorine. The materials knowledge wasn't all that great at the time. On one test, the fluorine we had didn't react well with the copper they were using for tubing, and it managed to cause another unscheduled disassembly of the facility. It was very serious. It's like one of those Korean War stories. The technician happened to be walking past the test facility when it decided to blow itself up. A piece of copper tubing pierced one cheek and came out the other. That was the only serious accident in all of the engines handled in all those years. Now, we did have a problem with the EPA later because they figured out what the brown clouds were about. We built a whole bunch of exhaust mitigation scrubbers to take care of engine testing in the daytime. In general, we operated the big shuttle (RCS) engine, the 870- pounder at nominal conditions; they scrubbed the effluents pretty well. If you operated that same 870-pound force engine at a level where you get a lot of excess oxidizer, yeah, there s a brown cloud. But, you know, it doesn't show up well in the dark. They did do some of that. But, that s gone; it was addressed one way or another. RELEASED -

Pfeifer, Gerald R.↗
NASA NTRS2010

Discontinuous Inter-Granular Separations (DIGS) in the Gas Nitride Layer of ISS Race Rings

The starboard solar alpha rotary joint (SARJ) race ring on the International space station (ISS) failed due to severe spalling of the outer diameter, 45 degree (outer canted) nitrided surface. Subsequent analysis at NASA-KSC revealed that almost all of the debris generated due to the failure was nitrided 15-5 stainless steel. Subsequent analysis of the nitride control coupons (NCC) at NASA-JSC revealed the presence of discontinuous inter-granular separations (DIGS) in the gas nitride layer. These DIGS were present in the inter-granular networking located in the top 2 mils of the nitride layer. The manufacturer's specification requires the maximum white structure to be 0.0003 inches and intergranular networking below the allowable white structure depth to be cause for rejection; a requirement that the NCCs did not meet. Subsequent testing and analysis revealed that lower DIGS content significantly lowered the probability of nitride spalling in simulated, dry condition runs. One batch of nitride samples with DIGS content similar to the port SARJ (did not fail on orbit) which exhibited almost no nitride spalling after being run on one test rig. Another batch of nitride samples with DIGS content levels similar to the starboard SARJ exhibited significant nitride spalling on the same test rig with the same load under dry conditions. Although DIGS were not the root cause of starboard race ring failure, testing indicates that increased DIGS reduced the robustness of the gas nitride layer under dry operating conditions.

Figert, John↗
NASA NTRS2013

Estimating Ground-Level PM(sub 2.5) Concentrations in the Southeastern United States Using MAIAC AOD Retrievals and a Two-Stage Model

Previous studies showed that fine particulate matter (PM(sub 2.5), particles smaller than 2.5 micrometers in aerodynamic diameter) is associated with various health outcomes. Ground in situ measurements of PM(sub 2.5) concentrations are considered to be the gold standard, but are time-consuming and costly. Satellite-retrieved aerosol optical depth (AOD) products have the potential to supplement the ground monitoring networks to provide spatiotemporally-resolved PM(sub 2.5) exposure estimates. However, the coarse resolutions (e.g., 10 km) of the satellite AOD products used in previous studies make it very difficult to estimate urban-scale PM(sub 2.5) characteristics that are crucial to population-based PM(sub 2.5) health effects research. In this paper, a new aerosol product with 1 km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields (e.g., wind speed) and land use parameters (e.g., forest cover, road length, elevation, and point emissions) as ancillary variables to estimate daily mean PM(sub 2.5) concentrations. The study area is the southeastern U.S., and data for 2003 were collected from various sources. A cross validation approach was implemented for model validation. We obtained R(sup 2) of 0.83, mean prediction error (MPE) of 1.89 micrograms/cu m, and square root of the mean squared prediction errors (RMSPE) of 2.73 micrograms/cu m in model fitting, and R(sup 2) of 0.67, MPE of 2.54 micrograms/cu m, and RMSPE of 3.88 micrograms/cu m in cross validation. Both model fitting and cross validation indicate a good fit between the dependent variable and predictor variables. The results showed that 1 km spatial resolution MAIAC AOD can be used to estimate PM(sub 2.5) concentrations.

aerosol optical depth↗
NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗
NASA NTRS2018

Design and Development of a Laboratory-Scale Ice Adhesion Testing Device

When an aircraft traverses through clouds containing supercooled water droplets, in-flight icing can occur that negatively affects vehicle performance by increasing weight and drag leading to loss of lift. Super-cooled water droplets present in clouds that impact vehicle surfaces can lead to inflight icing any time during the year.1 Most events occur at temperatures ranging from 0 to -20degC. Ice generated on the aircraft can vary between clear/glaze, rime, and mixed (Fig. 1) depending on air temperature (-5 to -20degC), liquid water content (0.3-0.6 g/m3), and droplet size (median volumetric diameter of 15-40 μm). Current strategies to remove ice are based on active technologies such as pneumatic boots, heated surfaces, and deicing agents (i.e., ethylene- and propylene-based glycols). The latter have potential environmental concerns. A passive approach to mitigate accreting ice that is actively being investigated are protective coatings. An ice mitigating coating could potentially be used as a stand-alone material, but more likely in combination with an active approach. In the latter scenario, potential reduction in power consumption by the active approach may be realized. To determine the ice adhesion strength of impact ice that is representative of the aircraft environment is not a trivial matter. Test methods utilizing slowly formed ice (i.e., freezer ice) do not accurately simulate this environment. Likewise, some testing methodologies involve sample relocation from the icing environment to the test chamber that can result in thermal shock to the sample, thus affecting the results. The Adverse Environment Rotor Test Stand (AERTS) located at Pennsylvania State University (PSU) has been demonstrated to simulate impact icing conditions within the icing envelope for the determination of ice adhesion shear strength (IASS) without removal/relocation of the sample.2 Due to the confidence in results obtained from AERTS, this instrument is in high demand and requires a significant amount of lead time and capital investment to obtain IASS results. As a solution for quickly and economically screening coatings in a controlled manner under impact icing conditions, a laboratory-scale ice adhesion test and dead blades were then removed from the rotor/blade assembly to obtain the final mass. The IASS of the live blade was determined from the difference in mass (before and after testing) of the live and dead blades, the ice shed area, and the rpm of the shed event. The same live blade sample was tested in triplicate at all three test temperatures. Surface roughness was determined using a Bruker Dektak XT Stylus Profilometer. Measurements were conducted using a 12.5 μm tip at a vertical range of 65.5 μm with an applied force of 3 mg. Data were collected over a 1.0 mm length at a resolution of 0.056 μm/point. Five single line scans at different locations were collected and processed using a two-point leveling subtraction. The resultant Ra (arithmetic roughness) and Rq (root mean square roughness) average values were calculated.

Smith, Joseph G., Jr.↗