Results for “data cleaning” — Page 7

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

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

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↗

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↗

NASA NTRSDate not supplied

arcjetCV: automating recession extraction from video

Arc jet Computer Vision (arcjetCV)[1][2] is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV 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.

Recession tracking↗

NASA NTRSDate not supplied

ArcjetCV: Automating Recession Tracking

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV 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.

Recession tracking↗

NASA NTRSDate not supplied

ArcjetCV: Automating Arc Jet Analysis

Arc jet Computer Vision (arcjetCV) is a software application built to automate time-resolved recession tracking of edges in test videos, specifically for tracking material recession and the shock-material standoff. This provides a new capability to resolve and validate new physics associated with non-linear processes and an essential step to reduce testing, modeling, and validation uncertainties for heatshield material performance. ArcticCV 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.

Recession tracking↗

DOE OSTI2025

Development and Preliminary Analysis of a U.S. Geothermal Heat Pump Installation Database

This paper seeks to addresses the significant gap in the literature regarding the installation and adoption of geothermal heat pump (GHP) systems in the United States. While the "2021 U.S. Geothermal Power Production and District Heating Market Report" published by the National Renewable Energy Laboratory (NREL) focused on direct-use geothermal district heating systems, it did not include an analysis of GHP installations (Robins et al. 2021). To bridge this gap, NREL has compiled a novel database currently containing 70,470 records of GHP installations, primarily sourced from state well permits and small-scale studies. Our methodology emphasizes the collection, cleaning, and standardization of data, addressing challenges such as inconsistent reporting formats and privacy concerns. Despite limitations in data on capacity, costs, and performance, our preliminary geospatial analysis reveals insights into the distribution of GHP systems across urban and rural areas and climate zones. The paper highlights the importance of publicly accessible data for advancing GHP technology adoption with a discussion of existing data sources and their limitations, advocating for improved collaboration between NREL and industry stakeholders.

data collection↗

DOE OSTI2025

Dwarf galaxy halo masses from spectroscopic and photometric lensing in DESI and DES

We present the most precise and lowest-mass weak lensing measurements of dwarf galaxies to date, enabled by spectroscopic lenses from the Dark Energy Spectroscopic Instrument (DESI) and photometric lenses from the Dark Energy Survey (DES) calibrated with DESI redshifts. Using DESI spectroscopy from the first data release, we construct clean samples of galaxies with median stellar masses $\log_{10}(M_*/M_{\odot})=8.3-10.1$ and measure their weak lensing signals with sources from DES, KiDS, and SDSS, achieving detections with $S/N$ up to 14 for dwarf galaxies ($\log_{10}(M_*/M_{\odot})<$9.25) -- opening up a new regime for lensing measurements of low-mass systems. Leveraging DES photometry calibrated with DESI, we extend to a photometric dwarf sample of over 700,000 galaxies, enabling robust lensing detections of dwarf galaxies with combined $S/N=38$ and a significant measurement down to $\log_{10}(M_*/M_{\odot})=8.0$. We show that the one-halo regime (scales $\lesssim 0.15h^{-1}\rm Mpc$) is insensitive to various systematic and sample selection effects, providing robust halo mass estimates, while the signal in the two-halo regime depends on galaxy color and environment. These results demonstrate that DESI already enables precise dwarf lensing measurements, and that calibrated photometric samples extend this capability. Together, they pave the way for novel constraints on dwarf galaxy formation and dark matter physics with upcoming surveys like the Vera C. Rubin Observatory's LSST.

Treiber, Helena [Princeton U., Astrophys. Sci. Dep↗

NASA NTRS1995

Prepsolv (TM): The optimum alternative to 1,1,1-trichloroethane and methyl ethyl ketone for hand-wipe cleaning of aerospace materials

Engineers at Hercules Aerospace, a rocket motor manufacturer in Utah, have worked closely with chemists at Glidco Organics to study the feasibility of using terpenes for zero-residue wipe cleaning. The result of this work is a technological breakthrough, in which the barrier to ultra-low non-volatile residue formation has been broken. After 2 years of development and testing, SCM Glidco Organics has announced the availability of Glidsafe(registered trademark) Prepsolv(TM): a state-of-the-art ultra-low residue terpene wipe cleaning agent that does not require rinsing. Prepsolv(TM) can successfully be used in simple hand-wipe cleaning processes without fear of leaving surface residues. Industry testing has confirmed that Prepsolv(TM) is not only highly effective, but can even be less expensive to use than traditional cleaning solvents like methyl chloroform. This paper addresses the features and benefits of Prepsolv(TM), and presents performance and material compatibility data that characterizes this unique cleaning agent. Since its commercialization, Hercules Aerospace has chosen Prepsolv(TM) as the optimum cleaning agent to replace ozone-depleting solvents in their weapons factory in Magna, UT. Likewise, Boeing has approved Prepsolv(TM) for cleaning components in the manufacture of commercial aircraft at their facilities in Seattle, WA and Wichita, KS. Additional approvals are forthcoming for this uniquely safe and effective solvent.

Gallagher, R. Scott↗

DOE OSTI2025

BLDAP Intro to Python/Data Science Curriculum v1

The Github repository contains the Jupyter notebooks for the intro to Python / Data Science course for Berkeley Lab Director's Apprenticeship Program (BLDAP). This course is designed for students with little to no experience in coding to learn skills in Python necessary for data science. Students utilize Jupyter notebooks throughout the course. The overall goal is for students to learn how to use Python to clean, analyze, and visualize large data sets in order to communicate effectively their conclusions about the data set. Students apply the skills they learned on actual data sets provided by researchers in Berkeley Lab.

Hales, Laurel [Lawrence Berkeley National Laborato↗

NASA NTRS2019

Adaptive Control Law for PID

This paper is the final report for my Spring 2019 internship at the Kennedy Space Center in Cape Canaveral, Florida. The official title of my internship is 'Launch Vehicle Control Study internship,' and I spent the spring working in GMRO- Granular Mechanics and Regolith Operations. There are two components to my project involving developing an adaptive control law for a PID controller. The first component involves performing a deep study and analysis of adaptive control laws, with the goal of developing stability proofs about the control law and the gain and phase margins. The second component involves analyzing the data received after implementing the adaptive control law. Unfortunately, as of the time of writing this paper, the data is not considered â€˜clean' enough to analyze. Therefore, in this paper I will give an overview of adaptive control law stability proofs and will write about the data analysis separately. Section II includes several general definitions, and the later sections have additional definitions at the end of each section.A PID (proportional integral derivative) controller is a common control algorithm which isimplemented in NASA’s rocket launch system. The idea behind the PID controller is to calculatethe error (current position – desired position) and drive it to zero by using proportional, integral,and derivative influences on the controller. For example, when a sailor steers a ship which isheading towards location 𝑥 but would like to turn the ship to reach location 𝑥∗, he/she wouldinitially turn the wheel significantly, and as the ship proceeds towards 𝑥∗ the sailor would slowlyshift the wheel back to its original position, and thereby drive the error, 𝑥∗ − 𝑥, to zero.

Eisenberg, Yael↗