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NASA NTRS2013

Progress in Operational Analysis of Launch Vehicles in Nonstationary Flight

This paper presents recent results in an ongoing effort to understand and develop techniques to process launch vehicle data, which is extremely challenging for modal parameter identification. The primary source of difficulty is due to the nonstationary nature of the situation. The system is changing, the environment is not steady, and there is an active control system operating. Hence, the primary tool for producing clean operational results (significant data lengths and data averaging) is not available to the user. This work reported herein uses a correlation-based two step operational modal analysis approach to process the relevant data sets for understanding and development of processes. A significant drawback for such processing of short time histories is a series of beating phenomena due to the inability to average out random modal excitations. A recursive correlation process coupled to a new convergence metric (designed to mitigate the beating phenomena) is the object of this study. It has been found in limited studies that this process creates clean modal frequency estimates but numerically alters the damping.

James, George↗

NASA NTRS1991

ESCA study of the effect of hydrocarbon contamination on poly(tetrafluoroethylene) exposed to atomic oxygen plasma

The ESCA spectra and data obtained by Morra et al. (1989) on poly(tetrafluoroethylene) (PTFE) exposed to atomic oxygen plasma are closely reexamined. It is shown that the spikes observed in Morra et al. plots of O/C or F/C versus time of the exposure of PTFE to atomic oxygen plasma were not characteristic of PTFE per se but were instead a result of a contamination by hydrocarbon present in their PTFE samples. This was demonstrated experimentally by comparing data derived for a very clean PTFE sample exposed for 10, 20, and 30 min to oxygen plasma with data obtained on PTFE samples with very small amounts of hydrocarbon contamination.

Golub, Morton A.↗

NASA NTRS2020

Systems Development, Datamining and Knowledge Discovery Abstract

This summer, 2020, during my NASA internship I worked with my mentor, Ali Shaykhian, as well as a group of four other interns: Javel Gramling, Janelisse Morales, Tristian Running Crane, and Zulmarie Jiménez. Our research and projects all differ but work together to solve datamining unstructured data into an easy to read format. Turning unstructured data into something easier to follow is important for helping quickly pull data out of larger documents, so that one doesn’t have to go through multiple pages to find certain data points. By being able to structure data pulled from a document, it can be usedto collect data from mass amounts of forms and arrange it in an easy to glance at table instead of multiple forms. I chose to focus mainly on creating form templates with both Microsoft Word and Excel and getting used to the types of data that can be collected; as well as learning where both programs differed. After I was familiar with what could be gathered, I worked towards taking data collected by a Word form and importing it into an Excel spreadsheet. By being able to transfer data from a Word document to an Excel document, there is an added layer of functionality to the datamining. Moving data around between Excel sheets isn’t that complex of a process, but when you try to import from a Word document a lot of formatting and readability can be lost. The purpose of my research is to reduce that loss by using Visual Basic scripts to clean and arrange imported data.

Makayla Amber Renfro↗

NASA NTRS2019

Success of Montreal Protocol Demonstrated by Comparing High-Quality UV Measurements with “World Avoided” Calculations from Two Chemistry-Climate Models

The Montreal protocol on Substances that Deplete the ozone Layer has been hailed as the most successful environmental treaty ever (https://www.unenvironment.org/news-and-stories/story/montreal-protocol-triumph-treaty). Yet, although our main concern about ozone depletion is the subsequent increase in harmful solar UV radiation at the earth’s surface, no studies to date have demonstrated its effectiveness in that regard. Here we use long-term UV Index (UVI) data derived from high-quality UV spectroradiometer measurements to demonstrate its success in curbing increases in UV radiation. Without this landmark agreement, UVi values would have increased at mid-latitude locations by approximately 20% between the early 1990s and today and would approximately quadruple at mid-latitudes by 2100. In contrast, an analysis of UVI data from multiple clean-air sites shows that maximum daily UVI values have remained essentially constant over the last ~20 years in all seasons, and may even have decreased slightly in the southern hemisphere, especially in Antarctica, where effects of ozone depletion were larger. Reconstructions of the UVi from total ozone data show evidence of increasing UVI levels in the 1980s, but unfortunately, there are no high-quality UV measurements available prior to the early 1990s to confirm these increases with direct observations.

UV Index (UVI) data derived from high-quality UV s↗

NASA NTRS2022

A New Machine Learning Based Analysis for Improving Satellite Retrieved Atmospheric Composition Data: OMI SO2 as an Example

Despite recent progress, satellite retrievals of anthropogenic SO2 still suffer from relatively low signal-tonoise ratios. In this study, we demonstrate a new machine learning data analysis method to improve the quality of satellite SO2 products. In the absence of large ground-truth datasets for SO2, we start from SO2 slant column densities (SCDs) retrieved from the Ozone Monitoring Instrument (OMI) using a data-driven, physically based algorithm and calculate the ratio between the SCD and the root mean square (rms) of the fitting residuals for each pixel. To build the training data, we select presumably clean pixels with small SCD / rms ratios (SRRs) and set their target SCDs to zero. For polluted pixels with relatively large SRRs, we set the target to the original retrieved SCDs. We then train neural networks (NNs) to reproduce the target SCDs using predictors including SRRs for individual pixels, solar zenith, viewing zenith and phase angles, scene reflectivity, and O3 column amounts, as well as the monthly mean SRRs. For data analysis, we employ two NNs: (1) one trained daily to produce analyzed SO2 SCDs for polluted pixels each day and (2) the other trained once every month to produce analyzed SCDs for less polluted pixels for the entire month. Test results for 2005 show that our method can significantly reduce noise and artifacts over background regions. Over polluted areas, the monthly mean NN-analyzed and original SCDs generally agree to within ±15 %, indicating that our method can retain SO2 signals in the original retrievals except for large volcanic eruptions. This is further confirmed by running both the NN-analyzed and original SCDs through a topdown emission algorithm to estimate the annual SO2 emissions for ∼ 500 anthropogenic sources, with the two datasets yielding similar results. We also explore two alternative approaches to the NN-based analysis method. In one, we employ a simple linear interpolation model to analyze the original SCD retrievals. In the other, we develop a PCA–NN algorithm that uses OMI measured radiances, transformed and dimension-reduced with a principal component analysis (PCA) technique, as inputs to NNs for SO2 SCD retrievals. While the linear model and the PCA–NN algorithm can reduce retrieval noise, they both underestimate SO2 over polluted areas. Overall, the results presented here demonstrate that our new data analysis method can significantly improve the quality of existing OMI SO2 retrievals. The method can potentially be adapted for other sensors and/or species and enhance the value of satellite data in air quality research and applications.

Can Li↗

NASA NTRS1995

Ultrasonic frequency selection for aqueous fine cleaning

A study was conducted to evaluate ultrasonic cleaning systems for precision cleaning effectiveness for oxygen service hardware. This evaluation was specific for Rocketdyne Division of Rockwell Aerospace alloys and machining soils. Machining lubricants and hydraulic fluid were applied as soils to standardized complex test specimens designed to simulate typical hardware. The study consisted of tests which included 20, 25, 30, 40, 50, and 65 kHz ultrasonic cleaning systems. Two size categories of cleaning systems were evaluated, 3- to 10-gal laboratory size tanks and 35- to 320-gal industrial size tanks. The system properties of cavitation, frequency vs. cleaning effectiveness, the two types of transducers, and the power level of the system vs. size of the cleaning tank were investigated. The data obtained from this study was used to select the ultrasonic tanks for the aqueous fine clean facility installed at Rocketdyne.

Becker, Joann F.↗

NASA NTRS1994

Ultrasonic frequency selection for aqueous fine cleaning

A study was conducted to evaluate ultrasonic cleaning systems for precision cleaning effectiveness for oxygen service hardware. This evaluation was specific for Rocketdyne Div. of Rockwell Aerospace alloys and machining soils. Machining lubricants and hydraulic fluid were applied as soils to standardized complex test specimens designed to simulate typical hardware. The study consisted of tests which included 20, 25, 30, 40, 50, and 65 kHz ultrasonic cleaning systems. Two size categories of cleaning systems were evaluated, 3- to 10-gal laboratory size tanks and 35- to 320-gal industrial size tanks. The system properties of cavitation; frequency vs. cleaning effectiveness; the two types of transducers; and the power level of the system vs. size of the cleaning tank were investigated. The data obtained from this study was used to select the ultrasonic tanks for the aqueous fine clean facility installed at Rocketdyne.

Becker, Joann F.↗

NASA NTRS2016

Compiling a Comprehensive EVA Training Dataset for NASA Astronauts

Training for a spacewalk or extravehicular activity (EVA) is considered a hazardous duty for NASA astronauts. This places astronauts at risk for decompression sickness as well as various musculoskeletal disorders from working in the spacesuit. As a result, the operational and research communities over the years have requested access to EVA training data to supplement their studies. The purpose of this paper is to document the comprehensive EVA training data set that was compiled from multiple sources by the Lifetime Surveillance of Astronaut Health (LSAH) epidemiologists to investigate musculoskeletal injuries. The EVA training dataset does not contain any medical data, rather it only documents when EVA training was performed, by whom and other details about the session. The first activities practicing EVA maneuvers in water were performed at the Neutral Buoyancy Simulator (NBS) at the Marshall Spaceflight Center in Huntsville, Alabama. This facility opened in 1967 and was used for EVA training until the early Space Shuttle program days. Although several photographs show astronauts performing EVA training in the NBS, records detailing who performed the training and the frequency of training are unavailable. Paper training records were stored within the NBS after it was designated as a National Historic Landmark in 1985 and closed in 1997, but significant resources would be needed to identify and secure these records, and at this time LSAH has not pursued acquisition of these early training records. Training in the NBS decreased when the Johnson Space Center in Houston, Texas, opened the Weightless Environment Training Facility (WETF) in 1980. Early training records from the WETF consist of 11 hand-written dive logbooks compiled by individual workers that were digitized at the request of LSAH. The WETF was integral in the training for Space Shuttle EVAs until its closure in 1998. The Neutral Buoyancy Laboratory (NBL) at the Sonny Carter Training Facility near JSC opened in March 1997 and is the current site for US EVA training. Other space agencies also have used water to simulate weightlessness and train for EVAs. Russia has a training facility similar to the NBL named the Hydro Lab. The Hydro Lab began operations at the Gagarin Cosmonaut Training Center (GCTC) in 1980 and has been used extensively to the present. Although a majority of training in the Hydro Lab uses the Russian Orlan suit, a small number of sessions have been conducted using a NASA suit. The Japanese Weightlessness Environment Test System (WETS) went into service at the Tsukuba Space Center in 1997 but was closed in 2011 due to extensive earthquake damage. Several sessions were performed using a NASA suit, but these sessions were short and considered "development" runs. LSAH has assembled records from the WETF, NBL and Hydro Lab. Recording of the EVA training data has changed considerably from 1967 to present. The goal of early record keeping was to track use of hardware components, and the person involved was treated as a suited operator, not as a focus of interest. Records from the past two decades are fairly precise with the person, date, suit type and size noted. On occasion the length of the session was listed, but this data is not included on all records. Records were merged from data sources and extensive cleaning of the records was required since the multiple sources frequently overlapped and duplicated records. To date the LSAH EVA training dataset includes over 12,500 EVA training sessions performed by NASA astronauts since 1981. The following variables are included for most records: Name, Sex, Event date, Event name, HUT type, HUT size, Facility, and Estimated run time. For a smaller subset of records, the following variables are available: Actual run time, Time inverted, and the suit components Waist bearing type, Shoulder harness, Shoulder pads, and Teflon inserts. The LSAH dataset is currently the most complete resource for data regarding EVA training sessions performed by NASA astronauts. However, it is not 100 percent complete since the WETS (Japan) and NBS (Marshall) training facility data were not included. This dataset has been compiled by LSAH to study the relationship of EVA training to musculoskeletal injuries but has many other non-medical applications. This dataset can be provided to other groups in order to respond to program and research questions with appropriate board approvals.

Laughlin, M. S.↗

NASA NTRS2009

Nitric Oxide and Oxygen Air-Contamination Effects on Extinction Limits of Non-Premixed Hydrocarbon-Air Flames for a HIFiRE Scramjet

Unique nitric oxide (NO) and oxygen air-contamination effects on the extinction Flame Strength (FS) of non-premixed hydrocarbon (HC) vs. air flames are characterized for 7 gaseous HCs, using a new idealized 9.3 mm straight-tube Opposed Jet Burner (OJB) at 1 atm. FS represents a laminar strain-induced extinction limit based on cross-section-average air jet velocity, Uair, that sustains combustion of a counter jet of gaseous fuel just before extinction. Besides ethane, propane, butane, and propylene, the HCs include ethylene, methane, and a 64 mole-% ethylene / 36 % methane mixture, the writer s previously recommended gaseous surrogate fuel for HIFiRE scramjet tests. The HC vs. clean air part of the work is an extension of a May 2008 JANNAF paper that characterized surrogates for the HIFiRE project that should mimic the flameholding of reformed (thermally- or catalytically-cracked) endothermic JP-like fuels. The new FS data for 7 HCs vs. clean air are thus consolidated with the previously validated data, normalized to absolute (local) axial-input strain rates, and co-plotted on a dual kinetically dominated reactivity scale. Excellent agreement with the prior data is obtained for all 7 fuels. Detailed comparisons are also made with recently published (Univ. Va) numerical results for ethylene extinction. A 2009-revised ethylene kinetic model (Univ. Southern Cal) led to predicted limits within approx. 5 % (compared to 45 %, earlier) of this writer s 2008 (and present) ethylene FSs, and also with recent independent data (Univ. Va) obtained on a new OJB system. These +/- 5 % agreements, and a hoped-for "near-identically-performing" reduced kinetics model, would greatly enhance the capability for accurate numerical simulations of surrogate HC flameholding in scramjets. The measured air-contamination effects on normalized FS extinction limits are projected to assess ongoing Arc-Heater-induced "facility test effects" of NO production (e.g., 3 mole-%) and resultant oxygen depletion (from 21 to 19.5 %), for testing the "64/36" surrogate fuel in Langley s Arc-Heated Scramjet Test Facility for HIFiRE engine designs. The FS results show a generally small (< 4 %) "nitric oxide enhancement" effect, relative to clean air, for up to 3 % NO (freestream Mach number up to 7 in Arc Jet testing). However, a progressively large "oxygendeficiency weakening" effect develops. For 3 % NO, a net weakening of 26 % in FS is derived for the "64/36" fuel vs. air. The corresponding net weakening for pure ethylene is 20 %. A number of practical recommendations regarding facility test effects are offered.

Pellett, Gerald L.↗

NASA NTRS1972

Project Fog Drops. Part 2: Laboratory investigations

Measurements of the total nucleus concentration and cloud condensation nuclei (CCN) were acquired for several conditions representing both high normal and severe pollution levels for the Los Angeles Basin as well as clean filtered air. The data show that in filtered air there is a large photochemically induced increase in the total particle content within a few minutes after starting the lamp. The concentration of CCN remains near zero, until sufficient coagulation and condensation occurs on the smaller Aitken particles. The addition of gaseous pollutants to filtered air results in large increases in the photochemical production of both the cloud and Aitken nucleus concentration. Fogs were also generated under controlled, reproducible conditions in the cloud chamber and seeded with aerosols of various compounds which form monomolecular surface films at air-water interfaces. Visibility characteristics and droplet data were obtained. The data suggest that droplet growth on treated nuclei can be retarded but fog formation was not significantly altered by the chemical seeding.

Kocmond, W. C.↗

NASA NTRS1973

In-flight and laboratory vacuum-friction test results

Coefficient of friction measurements were made for six unlubricated metal couples exposed to the space environment aboard the OV-1-13 spacecraft and exposed to laboratory vacuum. Materials studied included mutually soluble, partially soluble, and insoluble metal combinations. Two samples of each material couple were tested in space and in the laboratory using the disk and rider technique. Linear velocity was 0.10 cm/s (2.5 in/min) and rider normal load was 4.45 N (1 lb) for the gold versus silver couples and 8.90 N (2lb) for the other combinations. Results showed that friction data obtained in a clean ion-pumped laboratory vacuum of 10 to the minus 10 power materials with low mutual solubility can be correlated to operation in the vicinity of a typical scientific spacecraft that is exposed to an ambient pressure as low as 10 to the minus 12 power torr. The expected increase in coefficient of friction with solubility was shown. Material couples with high mutual solubility present the hazard of unpredictable drastic friction increase in orbit which may not be evident in laboratory testing at levels down to 10 to the minus 10 power torr. It was also shown that gross cold welding of unlubricated metals exposed to a satellite environment does not occur.

Devine, E. J.↗

NASA NTRS1975

Effect of a 180 deg-extent inlet pressure distortion on the internal flow conditions of a TF30-P-3

The measured effects of inlet pressure distortion on the internal flow temperatures and pressures of a TF30-P-3 afterburning turbofan engine are reported. Extensive inner-stage instrumentation combined with stepwise rotation of pressure distortion provided a high degree of circumferential resolution in the data. The steady-state spatial variation in pressures, temperature, and calculated flow velocity and the amplitude and extent of the distorted sectors are given. Data are presented for runs of 77 and 90 percent of low-speed-rotor design speed at pressure distortion levels two-thirds of that required to stall the engine. These data are compared with data taken at clean-inlet conditions. Results indicate that the inlet pressure distortion was quickly attenuated within the compressor, except at the hub of the low-pressure compressor. The distorted sectors also swirled and varied in extent as they passed through the engine. Average velocities within the compressor were about equal to the clean-inlet values.

Debogdan, C. E.↗

NASA NTRS1984

Strain gage calibration

A temporary bonding system for accurately predetermining the individual apparent strain curve characteristics of the gages is used, and subsequently employs a computer to watch the apparent strain curves of the individual gages to determine which gages should be used together on transducers. The temporary bonding system requires a test block on which the gages are temporarily bonded, several thermocouples for monitoring temperature, and a data acquisition system for recording apparent strain data. Initially, a group of strain gages are attached to the test block using a bonding agent that disintegrates at high temperatures. The gages are then wired to an appropriate data acquisition and data, collected throughout a predetermined temperature excursion. Once the data is obtained, the test block is heated until the bonding agent disintegrates freeing the gages from the test block. The gages are then disconnected from the data acquisition system and cleaned, thereby ready for use on transducers.

Moore, T. C.↗

NASA NTRS1990

New Voyager radio spectrograms of Uranus

New, high-resolution spectrograms of the Voyager-2 radio observations at Uranus were produced from the original, six-second Planetary Radio Astronomy (PRA) data and these show a number of new features which were not obvious in previous versions. Among these new features are the detailed structure of the so-called broadband-bursty (b-bursty) emissions, unexpected sloping striations in the smooth high-frequency (SHF) component, and the overlap of these two components during the first rotation after closest approach. In addition, a slightly different planetary rotation rate from the b-bursty emissions, was found, and at the initial onset of the SHF component, what appears to be the shadow of a Uranian plasmasphere. These new spectrograms were prepared using a special dithering algorithm to show signal strengths as gray shadings, and the data were also manually cleaned to suppress noise and interference. This produced spectrograms of exceptional quality and certain details of their production on a stand-alone personal computer are also discussed.

Calvert, W.↗

NASA NTRS2012

Sapphire Viewports for a Venus Probe

A document discusses the creation of a viewport suitable for use on the surface of Venus. These viewports are rated for 500 C and 100 atm pressure with appropriate safety factors and reliability required for incorporation into a Venus Lander. Sapphire windows should easily withstand the chemical, pressure, and temperatures of the Venus surface. Novel fixture designs and seals appropriate to the environment are incorporated, as are materials compatible with exploration vessels. A test cell was fabricated, tested, and leak rate measured. The window features polish specification of the sides and corners, soft metal padding of the sapphire, and a metal C-ring seal. The system safety factor is greater than 2, and standard mechanical design theory was used to size the window, flange, and attachment bolts using available material property data. Maintenance involves simple cleaning of the window aperture surfaces. The only weakness of the system is its moderate rather than low leak rate for vacuum applications.

Bates, Stephen↗

NASA NTRSDate not supplied

ArcjetCV: a new machine learning application for extracting time-resolved recession measurements from arc jet test videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession, sting arm motion, and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials.

machine learning↗

NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

machine learning↗

NASA NTRSDate not supplied

ArcjetCV: A New Machine Learning Application for Extracting Time-Resolved Recession Measurements From Arc Jet Test Videos

Arc jet Computer Vision (ArcjetCV) is a software application built to automate analysis of arc jet ground test video footage. This includes tracking material recession and the shock-material standoff distance. This provides a new capability to resolve and validate new physics associated with non-linear processes. This is an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcjetCV uses several types of machine learning (convolutional neural net: CNN, decision tree: DT, k-means unsupervised clustering: KM) to automate the video processing pipeline. These include inferring the start/stop of time segments of interest (1D CNN), measuring the time-dependent 2D recession of the material samples (2D CNN, DT), measuring the time-dependent shock standoff distance (2D CNN, DT), and post-processing cleaning of the recession data (KM). The software also provides a graphical user interface for ease of use. The results of using this tool on arc jet videos show non-linear time-dependent effects can be important for certain materials and characterizing certain failure modes.

machine learning↗