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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
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↗
NASA NTRS2017

Evaluation of Series T22 Wet Tantalum Capacitors

Several types of advanced wet tantalum capacitors, and series T22 in particular, are designed without internal Teflon sealing that is used for military grade, CLR style capacitors. This raises concerns regarding hermeticity of the single seal parts and their capability to withstand high internal gas pressures that might develop during operation in space. To address these issues, T22 series capacitors rated to 50 V and 125 V were subjected to highly accelerated life testing (HALT) at 125 C and rated voltage and step stress random vibration testing (RVT). To simulate conditions of storage or operation under increased internal gas pressure, the parts were stored at temperature of 150 C for 2500 hr (HTS150). Electrical characteristics of the parts were measured through the storage testing and the hermeticity leak rate was tested before and after HTS150. To assess thermo-mechanical robustness of the part, capacitors were manually soldered onto printed wired boards (PWB) and stressed by 1000 temperature cycles between -55 C and +125 C. The effect of temperature cycling was assessed by additional HALT at different temperatures. Results show that T22 series capacitors have robust design and can satisfy requirements for space applications.

Wet tantalum capacitors↗
NASA NTRS2015

Model Generation to Support Model-Based Testing Applied on NASA DAT - An Experience Report

Model-based Testing (MBT), where a model of the system under tests (SUT) behavior is used to automatically generate executable test cases, is a promising and versatile testing technology. Nevertheless, adoption of MBT technologies in industry is slow and many testing tasks are performed via manually created executable test cases (i.e. test programs such as JUnit). In order to adopt MBT, testers must learn how to construct models and use these models to generate test cases, which might be a hurdle. An interesting observation in our previous work is that the existing manually created test cases often provided invaluable insights for the manual creation of the testing models of the system. In this paper we present an approach that allows the tester to first create and debug a set of test cases. When the tester is happy with the test cases, the next step is to automatically generate a model from the test cases. The generated model is derived from the test cases, which are actions that the system can perform (e.g. a button clicks) and their expected outputs in form of assert statements (e.g. assert data entered). The model is a Finite State Machine (FSM) model that can be employed with little or no manual changes to generate additional test cases for the SUT. We successfully applied the approach in a feasibility study to the NASA Data Access Toolkit (DAT), which is a web-based GUI. One compelling finding is that the test cases that were generated from the automatically generated models were able to detect issues that were not detected by the original set of manually created test cases. We present the findings from the case study and discuss best practices for incorporating model generation techniques into an existing testing process.

State Machines↗
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↗
DOE OSTI2024

Contactless Position Measurement System for Remote Alignment of Highly Reflective Objects

The Contactless Position Measurement System (CPMS) permits automatic alignment of highly-reflecting components that cannot or should not be touched by human hands, such as those used in SRF cavities. CPMS also has application during maintenance of fusion reactors, when the components in the reactor are radioactive. The length of time for which humans can handle the components in order to carry out maintenance or repairs is limited. We would like to measure the relative positions of such objects so we can bring them into alignment with motorized stages, thus eliminating the need for human contact. The CPMS is a computer vision system that measures the location of highly reflective components to within a fraction of a millimeter by finding the edges of silhouette images. This is done by placing a uniform, infrared backlight behind the components, and viewing each component with two low-aberration, infrared cameras. Each of these stereoscopic cameras are located within a coordinate system we set up with reference platforms, light sources, and survey cameras distributed around the perimeter of the string assembly room, or fusion reactor. We obtain stereoscopic silhouette images of each component, and we use these to determine the position of the component within our string assembly coordinate system. The CPMS combines the silhouette images with knowledge of the dimensions of the objects to obtain the relative positions of components in its field of view. These position measurements then allow us to mechanically maneuver the components into contact. Once we know where they are, we can move the components to where they are supposed to be with motorized stages or other mechanical means, check they are in the right place, and bolt them together. In Phase I, we built a prototype infrared backlights and cameras, tested it, wrote an analysis program to fit the images. At a range of 50 cm, we are able to measure the relative positions of two stainless steel flanges with an accuracy of 150 μm rms. The next steps are to test the analysis program with motorized stages, build a full-size prototype, and implement it in an cleanroom with SRF cavity assembly. This can be done in a cost effective way given the materials with which the system is designed. As Open Source Instruments achieved more in Phase I than anticipated, despite not receiving a Phase II SBIR grant, we hope to move forward with product development and sales with a testing partner.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Excerpt from Oxygen Monitor Clean Testing Report Performed in 2010

The Rosemount X-STREAM Series X2GP is a paramagnetic oxygen analyzer with a 0-25% oxygen measurement range. It claims a drift less than 2% of span (0.10% volume oxygen) per week, and a response time of less than 5 seconds (for the analyzer to report 90% of a step change). Our test equipment was set-up such that the same gas was running through each oxygen analyzer at the same time. This allowed Calibration Error, Calibration Drift, and Loss of Power Testing to be run on all monitors at the same time, eliminating any variability between the analyzers from tolerances in the gas cylinder, ambient temperature, ambient pressure, test duration, etc. An APTech pressure regulator was placed on the gas cylinder manifold and a 23 psia Paroscientific pressure transducer measured the regulator’s outlet pressure. This did not give the inlet pressure on any one analyzer but outlet pressure from the manifold before the stream was split. The APTech pressure regulator was used to keep operating conditions consistent. A DWYER rotameter was installed on each oxygen monitor’s outlet piping. With the exception of Variation of Flow Rate Testing, the rotameter was left fully open for all testing. The flow rate through each oxygen monitor was controlled by a Swagelok needle valve that was installed on each analyzer’s inlet piping. Additionally, a filter was installed in the each monitor’s inlet piping to keep particles from the measurement cells and a Swagelok ball valve was installed on each monitor’s outlet piping to isolate each monitor between tests. No pumps were installed. Each of the cylinders used was at a pressure greater than ambient room barometric pressure. This pressure differential was used to move gas through the testing equipment. The flow rate through each analyzer was controlled by the Swagelok needle valve installed on each analyzer’s inlet piping. Finally, for all tests, a Paroscientific Digiquartz Barometer was used to measure ambient room barometric pressure and a Fluke Thermo-hygrometer was used to measure ambient room temperature.

47 OTHER INSTRUMENTATION↗
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 NTRS1936

Tank Tests of Models of Flying Boat Hulls Having Longitudinal Steps

Four models with longitudinal steps on the forebody were developed by modification of a model of a conventional hull and were tested in the National Advisory Committee for Aeronautics (NACA) tank. Models with longitudinal steps were found to have smaller resistance at high speed and greater resistance at low speed than the parent model that had the same afterbody but a conventional V-section forebody. The models with a single longitudinal step had better performance at hump speed and as low high-speed resistance except at very light loads. Spray strips at angles from 0 degrees to 45 degrees to the horizontal were fitted at the longitudinal steps and at the chine on one of the two step models having two longitudinal steps. The resistance and the height of the spray were less with each of the spray strips than without; the most favorable angle was found to lie between 15 degrees and 30 degrees.

Allison, John M↗
NASA NTRS1998

Dynamics of Liquids in Edges and Corners (DYLCO): IML-2 Experiment for the BDPU

Knowledge of the behavior of fluids possessing free surfaces is important to many fluid systems, particularly in space, where the normally subtle effects of surface wettability play a more dramatic and often surprising role. DYLCO for the IML-2 mission was proposed as a simple experiment to probe the particular behavior of capillary surfaces in containers of irregular cross section. Temperature control was utilized to vary the fluid-solid contact angle, a questionable thermodynamic parameter of the system, small changes in which can dramatically influence the configuration, stability, and flow of a capillary surface. Container shapes, test fluid, and temperature ranges were selected for observing both local changes in interface curvature as well as a global change in fluid orientation due to a critical wetting phenomenon. The experiment hardware performed beyond what was expected and fluid interfaces could be readily digitized post flight to show the dependence of the interface curvature on temperature. For each of the containers tested surfaces were observed which did not satisfy the classic equations for the prediction of interface shape with constant contact angle boundary condition. This is explained by the presence of contact angle hysteresis arising from expansion and contraction of the liquid during the heating and cooling steps of the test procedure. More importantly, surfaces exceeding the critical surface curvature required for critical wetting were measured, yet no wetting was observed. These findings are indeed curious and pose key questions concerning the role of hysteresis for this critical wetting phenomena. The stability of such surfaces was determined numerically and it is shown that stability is enhance (reduced) when a surface is in its 'advancing' ('receding') state, The analysis shows complete instability as the critical wetting condition is reached. The case of ideal dynamic wetting is addressed analytically in detail with results of significant flow characteristics presented in closed form. The solutions indicate a square root of T dependence of the capillary 'rise' rate which is corroborated by drop tower tests. The analysis clearly shows that infinite time is necessary for surfaces to reorient at the critical wetting transition.

Langbein, D.↗
NASA NTRS2010

Space Suit Environment Testing of the Orion Atmosphere Revitalization Technology

An amine-based carbon dioxide (CO2) and water vapor sorbent in pressure-swing regenerable beds has been developed by Hamilton Sundstrand and baselined for the Orion Atmosphere Revitalization System (ARS). In three previous years at this conference, reports were presented on extensive Johnson Space Center (JSC) testing of this technology. That testing was performed in a sea-level pressure environment with both simulated and real human metabolic loads, and in both open and closed-loop configurations. The Orion ARS is designed to also support space-suited operations in a depressurized cabin, so the next step in developmental testing at JSC was to test the ARS technology in a typical closed space suit-loop environment with low-pressure oxygen inside the process loop and vacuum outside the loop. This was the first instance of low-pressure, high-oxygen, closed-loop testing of the Orion ARS technology, and it was conducted with simulated human metabolic loads in March 2009. The test investigated pressure drops and flow balancing through two different styles of prototype suit umbilical connectors. General swing-bed performance was tested with both umbilical configurations, as well as with a short jumper line installed in place of the umbilicals. Other interesting results include observations on the thermal effects of swing-bed operation in a vacuum environment and a recommendation of cycle time to maintain acceptable suit atmospheric CO2 and moisture levels.

Button, Amy B.↗
NASA NTRS2003

The Multipole Structure of Earth's STEP Signal

If there is an interaction in physical law which differentially accelerates the test bodies in a STEP satellite, then the di.erent elements that compose the Earth will most likely have source strengths for this interaction which are not proportional to their mass densities. The rotational flattening of Earth and geographical irregularities of our planet's crust then produces a multipole structure for the Equivalence Principle violating force field which differs from the multipole structure of Earth's ordinary gravity field. Measuring these differences yields key information about the new interaction in physical law which is not attainable by solely measuring differences of test body accelerations.

Nordtvedt, Kenneth↗