Results for “industrial data” — Page 12

NASA NTRS2022

Quantifying urban, industrial, and background changes in NO2 during the COVID-19 lockdown period based on TROPOMI satellite observations

The COVID-19 lockdown had a large impact on anthropogenic emissions of air pollutants and particularly on nitrogen dioxide (NO 2 ). While the overall NO 2 decline over some large cities is well-established, understanding the details remains a challenge since multiple source categories contribute. In this study, a new method of isolation of three components (background NO 2 , NO 2 from urban sources, and NO 2 from industrial point sources) is applied to estimate the impact of the COVID-19 lockdown on each of them. The approach is based on fitting satellite data by a statistical model with empirical plume dispersion functions driven by a meteorological reanalysis. Population density and surface elevation data as well as coordinates of industrial sources were used in the analysis. The tropospheric NO 2 vertical column density (VCD) values measured by the Tropospheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor over 261 urban areas for the period from 16 March to 15 June 2020 were compared with the average VCD values for the same period in 2018 and 2019. While the background NO 2 component remained almost unchanged, the urban NO 2 component declined by −18 % to −28 % over most regions. India, South America, and a part of Europe (particularly, Italy, France, and Spain) demonstrated a −40 % to −50 % urban emission decline. In contrast, the decline over urban areas in China, where the lockdown was over during the analysed period, was, on average, only -4.4± 8 %. Emissions from large industrial sources in the analysed urban areas varied greatly from region to region from -4.8 ± 6 % for China to -40 ± 10 % for India. Estimated changes in urban emissions are correlated with changes in Google mobility data (the correlation coefficient is 0.62) confirming that changes in traffic were one of the key elements in the decline in urban NO2 emissions. No correlation was found between changes in background NO2 and Google mobility data. On the global scale, the background and urban components were remarkably stable in 2018, 2019, and 2021, with averages of all analysed areas all being within ±2.5 % and suggesting that there were no substantial drifts or shifts in TROPOMI data. The 2020 data are clearly an outlier: in 2020, the mean background component for all analysed areas (without China) was -6.0 % ± 1.2 % and the mean urban component was -26.7 ± 2.6 % or 20σ below the baseline level from the other years.

COVID-19↗

NASA NTRS1996

Development of a material property database on selected ceramic matrix composite materials

Ceramic Matrix Composites, with fiber/whisker/particulate reinforcement, possess the attractive properties of ceramics such as high melting temperature, high strength and stiffness at high temperature, low density, excellent environmental resistance, combined with improved toughness and mechanical reliability. These unique properties have made these composites an enabling technology for thermomechanically demanding applications in high temperature, high stress and aggressive environments. On a broader scale, CMC's are anticipated to be applicable in aircraft propulsion, space propulsion, power and structures, in addition to ground based applications. However, it is also true that for any serious commitment of the material toward any of the intended critical thermo-mechanical applications to materialize, vigorous research has to be conducted for a thorough understanding of the mechanical and thermal behavior of CMC's. The high technology of CMC'S is far from being mature. In view of this growing need for CMC data, researchers all over the world have found themselves drawn into the characterization of CMC's such as C/SiC, SiC/SiC, SiC/Al203, SiC/Glass, SiC/C, SiC/Blackglas. A significant amount of data has been generated by the industries, national laboratories and educational institutions in the United States of America. NASA/Marshall Space Flight Center intends to collect the 'pedigreed' CMC data and store those in a CMC database within MAPTIS (Materials and Processes Technical Information System). The task of compilation of the CMC database is a monumental one and requires efforts in various directions. The project started in the form of a summer faculty fellowship in 1994 and has spilled into the months that followed and into the summer faculty fellowship of 1995 and has the prospect of continuing into the future for a healthy growth, which of course depends to a large extent on how fast CMC data are generated. The 10-week long summer fellowship has concentrated, basically, on establishing the procedure for a smooth transfer of data into a CMC database on MAPTIS which is a vital part of the following broader picture of the project.

Mahanta, Kamala↗

NASA NTRS2020

Johns Hopkins University Stereoscopic Particle Image Velocimetry (SPIV) Test Results

Professor J. Katz at the Johns Hopkins University conducted an experimental investigation of the effect of varying the clearance over the tip of a compressor rotor in an embedded stage through a NASA funded award. The activity provided very detailed stereo Particle Image Velocimetry (PIV) measurements of the tip flow field in their optically index-matched facility. The researchers were able to measure and visualize the unsteady velocity and pressure field in the tip gap area for the first time from which they have published more than 10 Journal papers. The dataset has been used widely within NASA for internal program development. The compressor geometry is part of the NASA Low-Speed Axial Compressor (LSAC) compressor stage geometry which is publicly available. All the casing treatment geometries were published by the researchers at JHU. The PIV data has also been published in several ASME papers and presented at recent TurboExpo Conferences. Many research groups at universities and US industry would like access to this data; therefore, we would like to prepare the data package and release it to public. The data package will be announced on data.nasa.gov.

Compressor↗

NASA NTRS2004

The Airline Quality Rating 2004

The Airline Quality Rating (AQR) was developed and first announced in early 1991 as an objective method of comparing airline quality on combined multiple performance criteria. This current report, the Airline Quality Rating 2004, reflects monthly Airline Quality Rating scores for 2003. AQR scores for the calendar year 2003 are based on 15 elements in four major areas that focus on airline performance aspects important to air travel consumers. The Airline Quality Rating 2004 is a summary of month-by-month quality ratings for U.S. airlines that have at least 1 % of domestic passenger volume during 2003. Using the Airline Quality Rating system of weighted averages and monthly performance data in the areas of on-time arrivals, involuntary denied boardings, mishandled baggage, and a combination of 12 customer complaint categories, airlines comparative performance for the calendar year of 2003 is reported. This research monograph contains a brief summary of the AQR methodology, detailed data and charts that track comparative quality for domestic airline operations for the 12-month period of 2003, and industry results. Also, comparative Airline Quality Rating data for 2002 are included, where available, to provide historical perspective regarding performance quality in the industry.

Fink, Mary M.↗

NASA NTRS2002

An Analysis of U.S. Civil Rotorcraft Accidents by Cost and Injury (1990-1996)

A study of rotorcraft accidents was conducted to identify safety issues and research areas that might lead to a reduction in rotorcraft accidents and fatalities. The primary source of data was summaries of National Transportation Safety Board (NTSB) accident reports. From 1990 to 1996, the NTSB documented 1396 civil rotorcraft accidents in the United States in which 491 people were killed. The rotorcraft data were compared to airline and general aviation data to determine the relative safety of rotorcraft compared to other segments of the aviation industry. In depth analysis of the rotorcraft data addressed demographics, mission, and operational factors. Rotorcraft were found to have an accident rate about ten times that of commercial airliners and about the same as that of general aviation. The likelihood that an accident would be fatal was about equal for all three classes of operation. The most dramatic division in rotorcraft accidents is between flights flown by private pilots versus professional pilots. Private pilots, flying low cost aircraft in benign environments, have accidents that are due, in large part, to their own errors. Professional pilots, in contrast, are more likely to have accidents that are a result of exacting missions or use of specialized equipment. For both groups judgement error is more likely to lead to a fatal accident than are other types of causes. Several approaches to improving the rotorcraft accident rate are recommended. These mostly address improvement in the training of new pilots and improving the safety awareness of private pilots.

Iseler, Laura↗

NASA NTRS2018

Mapping Pine Plantations in the Southeastern U.S. Using Structural, Spectral, and Temporal Remote Sensing Data

The southeastern U.S. produces the most industrial roundwood in the U.S. each year, largely from commercial pine plantations. The extent of plantation forests and management dynamics can be difficult to ascertain from periodic forest inventories, yet short-rotation tree plantations also present challenges for remote sensing. Here, we integrated spectral, temporal, and structural information from airborne and satellite platforms to distinguish pine plantations from natural forests and evaluate the contribution from planted forests to regional forest coverin the southeastern U.S. Within flight lines from NASA Goddard's Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, lidar metrics of forest structure had the highest overall accuracy for pine plantations among single-source classifications (90%), but the combination of spectral and temporal metrics from Landsat generated comparable accuracy (91%). Combined structural, temporal, and spectral information from G-LiHT and Landsat had the highest accuracy for plantations (92%) and natural forests (88%). At a regional scale, classifications using Landsat spectral and temporal metrics had between 74 and 82% mean class accuracy for plantations.Regionally, plantations accounted for 28% of forest cover in the southeastern U.S., a result similar to plot-based estimates, albeit with greater spatial detail. Regional maps of plantation forests differed from existing map products, including the National Land Cover Database. Combining plantation extent in 2011 with Landsat based forest change data identified strong regional gradients in plantation dynamics since 1985, with distinct spatial patterns of rotation age (east-west) and plantation expansion (interior). Our analysis demonstrates the potential to improve the characterization of dynamic land cover classes, including economically important timber plantations, by integrating diverse remote sensing datasets. Critically, multi-source remote sensing provides an approach to leverage periodic forest inventory data for annual monitoring of managed forest landscapes.

Fagan, M. E.↗

NASA NTRS1992

A sophisticated, multi-channel data acquisition and processing system for high frequency noise research

A sophisticated, multi-channel computerized data acquisition and processing system was developed at the NASA LeRC for use in noise experiments. This technology, which is available for transfer to industry, provides a convenient, cost-effective alternative to analog tape recording for high frequency acoustic measurements. This system provides 32-channel acquisition of microphone signals with an analysis bandwidth up to 100 kHz per channel. Cost was minimized through the use of off-the-shelf components. Requirements to allow for future expansion were met by choosing equipment which adheres to established industry standards for hardware and software. Data processing capabilities include narrow band and 1/3 octave spectral analysis, compensation for microphone frequency response/directivity, and correction of acoustic data to standard day conditions. The system was used successfully in a major wind tunnel test program at NASA LeRC to acquire and analyze jet noise data in support of the High Speed Civil Transport (HSCT) program.

Hall, David G.↗

NASA NTRSDate not supplied

GeneLab: The NASA Systems Biology Platform for Space Omics Repository, Analysis and Visualization

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics dataand collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 220 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetery data from experiments flown on the Space Shuttle. GeneLab has also recently implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretationof the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 120 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. Discoveries made using GeneLabhave begunand will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

Samrawit Getachew Gebre↗

NASA NTRSDate not supplied

GeneLab: The NASA Systems Biology Platform for Space Omics Repository, Analysis and Visualization

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics data, and collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 220 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetery data from experiments flown on the Space Shuttle. GeneLab has also recently implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretation of the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 120 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

Samrawit Gebre↗

NASA NTRSDate not supplied

WEBINAR, May 6: New Discoveries Using GeneLab

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics data and collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 220 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetry data from experiments flown on the Space Shuttle. GeneLab has also recently implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretation of the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 120 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

Sylvain V. Costes↗

NASA NTRSDate not supplied

NASA GeneLab: Open Science for Life in Space

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics data and collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 350 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab Sequencing Lab. The GLDS contains rich metadata about each experiment and has integrated radiation dosimetry data from experiments flown on the Space Shuttle, International Space Station, and Free Flying spacecrafts. With the increasing amount and complexity of omics data being generated, GeneLab utilizes community-defined, common models for metadata and terminology so that omics data and results are discoverable and reliably reproducible. GeneLab uses the ISA-Tab specification and semantic model for organizing and representing omics metadata. In addition to metadata standards, data files must be open-source file or common exchange formats to ensure accessibility and usability by all users. To ease data ingestion and transfer, the web-based submission tool allows PIs a user-friendly user interface to curate, organize, and publish their space relevant omics data. In the more recent years, data curation and submission portal has incorporated the FAIR principles making data findable, accessible, interoperable, and reusable. To increase reusability of data, GeneLab has implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretation of the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 200 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. To train the next generation of scientists, NASA offers training programs such as GeneLab 4 High School (GL4HS) and GeneLab 4 Universities. NLM Curation at a Scale Workshop 2022 | NASA GeneLab (GL4U) to teach students bioinformatics and computational biology methods to analyze omics data. Discoveries made using GeneLab have begun and will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

GeneLab↗

NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗

NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗

NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗

NASA NTRS1995

Laser Communication Demonstration System (LCDS) and future mobile satellite services

The Laser Communications Demonstration System (LCDS) is a proposed in-orbit demonstration of high data rate laser communications technology conceived jointly by NASA and U.S. industry. The program objectives are to stimulate industry development and to demonstrate the readiness of high data rate optical communications in Earth orbit. For future global satellite communication systems using intersatellite links, laser communications technology can offer reduced mass and power requirements and higher channel bandwidths without regulatory constraints. As currently envisioned, LCDS will consist of one or two orbiting laser communications terminals capable of demonstrating high data rate (greater than 750Mbps) transmission in a dynamic space environment. Two study teams led by Motorola and Ball Aerospace are currently in the process of conducting a Phase A/B mission definition study of LCDS under contracts with JPL/NASA. The studies consist of future application survey, concept and requirements definition, and a point design of the laser communications flight demonstration. It is planned that a single demonstration system will be developed based on the study results. The Phase A/B study is expected to be completed by the coming June, and the current results of the study are presented in this paper.

Chien-Chung Chen↗

NASA NTRS1995

Laser Communication Demonstration System (LSCS) and Future Mobile Satellite Services

The Laser Communications Demonstration System (LCDS) is a proposed in-orbit demonstration of high data rate laser communications technology conceived jointly by NASA and U.S. industry. The program objectives are to stimulate industry development and to demonstrate the readiness of high data rate optical communications in Earth Orbit. For future global satellite communication systems using intersatellite links (ISLs), laser communications technology can offer reduced mass , reduced power requirements, and increased channel bandwidths without regulatory restraint. This paper provides comparisons with radio systems and status of the program.

laser communications intersatellite links↗

NASA NTRS1974

Earth resources technology satellite /ERTS/ data collection and transmission buoys for inland, neritic and oceanic waters

As a result of a consortium of several industries and organizations, an economical, versatile, and stable data collection and transmission buoy has been designed, developed, and deployed to gather and transmit water quality data to a ground receiving station at three-minute intervals and to the earth resources technology satellite (ERTS) as it passes over the deployed buoy every 12 hours. The buoy system, designed for both fresh and salt water application, gathers data inclusive of temperature measurement, conductivity, relative acidity, dissolved oxygen, current speed, and direction. The mechanical design philosophy used to determine and satisfy boundary conditions involving stability, ease of deployment, servicing and maintenance, minimal manufacturing costs, and fresh and salt water installation capability is discussed. The development of peripheral handling equipment and anchoring systems is described.

Chapman, W. S.↗

NASA NTRS1974

Graphite thermal expansion reference for high temperature

The design requirements of the aerospace and high-temperature nuclear reactor industries necessitate reliable thermal expansion data for graphite and other carbonaceous materials. The feasibility of an acceptable reference for calibration of expansion measuring systems that operate in carbon-rich atmospheres at temperatures ranging to 2500 C is the prime subject of this work. Present-day graphite technology provides acceptable materials for stable, reproducible references, as reflected by some of the candidate materials. The repeatability for a single specimen in a given expansion measuring system was found to be plus or minus 1%, while the combined results of several tests made on a number of samples fell within a plus or minus 2.5% band.

Gaal, P. S.↗