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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Influence of Catalysis and Oxidation on Slug Calorimeter Measurements in Arc Jets

Arc jet tests play a critical role in the characterization and certification of thermal protection materials and systems (TPS). The results from these arc jet tests feed directly into computational models of material response and aerothermodynamics to predict the performance of the TPS in flight. Thus the precise knowledge of the plasma environment to which the test material is subjected, is invaluable. As one of the environmental parameters, the heat flux is commonly measured. The measured heat flux is used to determine the plasma enthalpy through analytical or computational models. At NASA Ames Research Center (ARC), slug calorimeters of a geometrically similar body to the test article are routinely used to determine the heat flux. A slug calorimeter is a thermal capacitance-type calorimeter that uses the temperature rise in a thermally insulated slug to determine the heat transfer rate, see Figure 1(left). Current best practices for measuring the heat flux with a slug calorimeter are described in ASTM E457 - 96. Both the calorimeter body and slug are made of Oxygen Free High Conductivity Copper, and are cleaned before each run.

Nawaz, Anuscheh↗
NASA NTRS2013

Buckling Design and Analysis of a Payload Fairing One-Sixth Cylindrical Arc-Segment Panel

Design and analysis results are reported for a panel that is a 16th arc-segment of a full 33-ft diameter cylindrical barrel section of a payload fairing structure. Six such panels could be used to construct the fairing barrel, and, as such, compression buckling testing of a 16th arc-segment panel would serve as a validation test of the buckling analyses used to design the fairing panels. In this report, linear and nonlinear buckling analyses have been performed using finite element software for 16th arc-segment panels composed of aluminum honeycomb core with graphiteepoxy composite facesheets and an alternative fiber reinforced foam (FRF) composite sandwich design. The cross sections of both concepts were sized to represent realistic Space Launch Systems (SLS) Payload Fairing panels. Based on shell-based linear buckling analyses, smaller, more manageable buckling test panel dimensions were determined such that the panel would still be expected to buckle with a circumferential (as opposed to column-like) mode with significant separation between the first and second buckling modes. More detailed nonlinear buckling analyses were then conducted for honeycomb panels of various sizes using both Abaqus and ANSYS finite element codes, and for the smaller size panel, a solid-based finite element analysis was conducted. Finally, for the smaller size FRF panel, nonlinear buckling analysis was performed wherein geometric imperfections measured from an actual manufactured FRF were included. It was found that the measured imperfection did not significantly affect the panel's predicted buckling response

composite structures↗
NASA NTRS2016

Consolidated Laser-Induced Fluorescence Diagnostic Systems for the NASA Ames Arc Jet Facilities

The spectroscopic diagnostic technique of two photon absorption laser-induced fluorescence (LIF) of atomic species for non-intrusive arc jet flow property measurement was first implemented at NASA Ames in the mid-1990s. In 2013-2014, NASA combined the agency's large-scale arc jet test capabilities at NASA Ames. Concurrent with that effort, the agency also sponsored a project to establish two comprehensive LIF diagnostic systems for the Aerodynamic Heating Facility (AHF) and Interaction Heating Facility (IHF) arc jets. The scope of the project enabled further engineering development of the existing IHF LIF system as well as the complete reconstruction of the AHF LIF system. The updated LIF systems are identical in design and capability. They represent the culmination of over 20 years of development experience in transitioning a specialized laboratory research tool into a measurement system for large-scale, high-demand test facilities. This paper will document the latest improvements of the LIF system design and demonstrations of the redeveloped AHF and IHF LIF systems.

wind tunnels↗
NASA NTRS2016

FT4 Data Analysis Summary (SSI-ARC)

Standards for Unmanned Aircraft System (UAS) Detect-and-Avoid (DAA) systems are currently being developed under the auspices of the RTCA Special Committee 228 (SC-228). To support the development of these standards, a series of flight tests has been conducted at NASAs Armstrong Flight Research Center (NASA-AFRC). The fourth in this series of flight test activities (Flight Test 4, or simply FT4) was conducted during the Spring and Summer of 2016. FT4 supported the objectives of numerous organizations working toward UAS DAA Minimum Operational Performance Standards (MOPS) and UAS DAA Radar MOPS. The summary provided herein is limited to the objectives, analysis and conclusions of the NASA Ames Research Center (NASA-ARC) SSI team toward the refinement of UAS DAA MOPS. This document provides a high-level overview of FT4 and the SSI-ARC objectives, a summary of the data analysis methodology and recommendations for UAS DAA MOPS refinements based on the data analysis results. A total of 72 encounters were flown to support SSI-ARC objectives. Test results were generally consistent with acceptable UAS DAA system performance and will be considered in broader SC-228 requirements validation efforts. Observed alert lead times indicated acceptable UAS DAA alerting performance. Effective interoperability between the UAS DAA system and the Traffic Alert and Collision Avoidance System (TCAS) was observed with one notable exception: TCAS Resolutions Advisories (RA) were observed in the absence of any DAA alert on two occasions, indicating the need for alert parameter refinement. Findings further indicated the need for continued work in the areas of DAA Well Clear Recovery logic and alert stability for Mode-C-only intruders. Finally, results demonstrated a high level of compliance with a set of evaluation criteria designed to provide anecdotal evidence of acceptable UAS DAA system performance.

UAS Test Flight↗
NASA NTRS2017

Computational Simulations of the NASA Langley HyMETS Arc-Jet Facility

The Hypersonic Materials Environmental Test System (HyMETS) arc-jet facility located at the NASA Langley Research Center in Hampton, Virginia, is primarily used for the research, development, and evaluation of high-temperature thermal protection systems for hypersonic vehicles and reentry systems. In order to improve testing capabilities and knowledge of the test article environment, an effort is underway to computationally simulate the flow-field using computational fluid dynamics (CFD). A detailed three-dimensional model of the arc-jet nozzle and free-jet portion of the flow-field has been developed and compared to calibration probe Pitot pressure and stagnation-point heat flux for three test conditions at low, medium, and high enthalpy. The CFD model takes into account uniform pressure and non-uniform enthalpy profiles at the nozzle inlet as well as catalytic recombination efficiency effects at the probe surface. Comparing the CFD results and test data indicates an effectively fully-catalytic copper surface on the heat flux probe of about 10% efficiency and a 2-3 kpa pressure drop from the arc heater bore, where the pressure is measured, to the plenum section, prior to the nozzle. With these assumptions, the CFD results are well within the uncertainty of the stagnation pressure and heat flux measurements. The conditions at the nozzle exit were also compared with radial and axial velocimetry. This simulation capability will be used to evaluate various three-dimensional models that are tested in the HyMETS facility. An end-to-end aerothermal and thermal simulation of HyMETS test articles will follow this work to provide a better understanding of the test environment, test results, and to aid in test planning. Additional flow-field diagnostic measurements will also be considered to improve the modeling capability.

Brune, A. J.↗
NASA NTRS2018

Testing with the Laser-Enhanced Arc Jet Facility (LEAF) at NASA Ames Research Center

NASA's new Laser-Enhanced Arcjet Facility (LEAF) employs 200 kilowatts of continuous-wave fiber lasers, combined with a 60-megawatt arc plasma jet, to simulate the combined radiative and convective heating is now operational and Orion heat-shield material is undergoing testing. This upgrade to the existing Interaction Heating Facility (IHF) involves the addition of high-power fiber lasers that project 1070nm radiative energy into the existing vacuum chamber. The optically expanded laser beam enters a side window of the test chamber to form either a uniform 150 x 150 mm or a 425 x 425 mm square spot on a wedge-shaped model holder. Combined heating levels of up to 180 W/cm2 convective with either 300 W/cm2 or 100 W/cm2 are achievable. This allows the arc jet and lasers to simulate dynamic entry-heating profiles encountered during entry by the Orion crew capsule for the lunar return conditions. New computational investigations are underway to explore testing with the objective of defining ablative material failure limit. Typical arc-jet testing is performed with test article that are stagnation or wedge geometries. The existing convective heating test facility combined with test article design limits how high a combined shear, heat-flux and pressure conditions can be achieved. With the addition of laser power of 200 kW, much higher combined conditions of high heat-flux, pressure and shear is achievable on a wedge or a swept-cylinder article. Such testing possibilities will allow us to progressively increase the combined conditions and explore when an ablative TPS material may fail. The presentation provides an overview of the LEAF and present results from the on-going Orion Testing.

Venkatapathy, Ethiraj↗
NASA NTRS2018

Time-Varying Entry Heating Profile Replication with a Rotating Arc Jet Test Article

A new approach to arc jet testing that replicates the time-varying conditions of atmospheric entry heating was demonstrated. The concept relies on the spatial variation of heat flux and pressure around a cylindrical test article. The spatial variation is transformed to a temporal variation at one location by rotating the test article during exposure to the arc jet stream. The test article rotation mechanism is based on a programmable stepper motor. An entry heat flux profile was mapped to the cylinders heat flux distribution to determine the rotation angle vs. time executed by the mechanism. A series of arc jet tests with instrumented cylindrical TPS test articles was conducted to prove the concept.

Aerothermodynamics↗
NASA NTRS2020

Asymmetric Surface Brightness Structure of Caustic Crossing Arc in SDSS J1226+2152: A Case for Dark Matter Substructure

We study the highly magnified arc SGAS J122651.3+215220 caused by a star-forming galaxy at z s = 2.93 crossing the lensing caustic cast by the galaxy cluster SDSS J1226+2152 (z l = 0.43), using Hubble Space Telescope observations. We report in the arc several asymmetric surface brightness features whose angular separations are a fraction of an arcsecond from the lensing critical curve and appear to be highly but unequally magnified image pairs of underlying compact sources, with one brightest pair having clear asymmetry consistently across four filters. One explanation of unequal magnification is microlensing by intracluster stars, which induces independent flux variations in the images of individual or groups of source stars in the lensed galaxy. For a second possibility, intracluster dark matter subhaloes invisible to telescopes effectively perturb lensing magnifications near the critical curve and give rise to persistently unequal image pairs. Our modelling suggests, at least for the most prominent identified image pair, that the microlensing hypothesis is in tension with the absence of notable asymmetry variation over a six-year baseline, while subhaloes of ∼10 6 –10 8 \Mꙩ anticipated from structure formation with cold dark matter typically produce stationary and sizable asymmetries. We judge that observations at additional times and more precise lens models are necessary to stringently constrain temporal variability and robustly distinguish between the two explanations. The arc under this study is a scheduled target of a Director’s Discretionary Early Release Science program of the James Webb Space Telescope, which will provide deep images and a high-resolution view with integral field spectroscopy.

Liang Dai↗
NASA NTRS2021

NASA Ames Arc Jet Complex Measurements Handbook

The Arc Jet Measurement System Handbook will serve as a reference of the calculations and formulas used by the Arc Jet Complex Data Acquisition System. It is the objective that Code ASF personnel and experimenters will be able to manually reproduce data calculations performed by the Arc Jet Data Acquisition System using these formulas as a guide.

Jerry Cheng↗
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 NTRS2024

‘Trapped Wave’ Resonance Tones Radiated in the Forward Arc of High-Speed Jets

An experimental study is conducted investigating the characteristics of ‘trapped wave’ resonance tones in the forward arc of jet noise radiation fields. While these spectral peaks disappear in the far acoustic field in the aft and sideline directions, which is why they went unnoticed in decades of jet noise measurements, the present study clearly shows that they radiate in the forward arc (shallow upstream polar locations in the approximate range of θ < 45°). Far-field jet noise spectra in the forward arc, data on which had been lacking in the literature, are not smooth but are characterized by these peaks. This is found for high subsonic to supersonic jets up to the highest jet Mach number covered in the experiment (MJ ≈ 1.9), for round, rectangular as well as convergent-divergent nozzles. With heated jets these spectral peaks are weakened especially around transonic conditions, however, they persist in supersonic conditions. Note that digital files containing all spectral data shown in the following are in a supplemental file to this NASA Technical Memorandum (TM-20240009873-SUPPL). These files can be obtained from the NASA Technical Report Server (NTRS) as a separate download from this report.

jets↗
DOE OSTI2024

Titanium Wire Arc Additive Manufacturing Inert Enclosure and Material Handling Safety Considerations

Wire arc additive manufacturing (WAAM) via metal inert gas (MIG)/gas metal arc welding (GMAW) is a viable option for fabrication of large-scale titanium parts; however, it introduces new safety hazards associated with both the material and the additional system hardware required for the process. Localized gas shielding of the weld arc via standard GMAW torch is inadequate for titanium due to its affinity for oxygen; thereby requiring the use of an inert enclosure to protect the weld from entraining oxygen. The use of the inert enclosure presents potential safety hazards such as operator asphyxiation and brings up discussion of confined space considerations. In addition, the titanium welding process creates pyrophoric titanium soot residue around the deposit, which can undergo deflagration during part cleaning and part removal. This paper provides an overview of the titanium WAAM process along with safety considerations for the design and operation of the inert enclosure as well as functional solutions for the safe handling of the titanium soot by-product.

Walters, Alex↗
DOE OSTI2025

Characterization and prediction of the electromechanical wear of contact tips during wire arc additive manufacturing of 316L stainless steel

Here, this study seeks to better understand the degradation of the contact tip with respect to WAAM for a 316L wire electrode as well as explore methods of monitoring the contact tip state from process data. The contact tip, a consumable component, positions the wire and serves as the electrical contact surface between the wire electrode and the welding power supply. The wear of the contact tip was characterized in terms of material loss and material contamination for a set of tips worn to discrete levels as measured by the amount of wire fed or arc time. Geometrical characterization found a 49% increase in the bore exit area at 180 meters of wire fed. Machine learning models were developed to predict the relative bore exit area of the contact tip from arc-based process data and a random forest classifier exhibited favorable performance with a cross-validated f1-score of 0.84. The regression architecture implemented a multi-layer perceptron with the ability to predict the relative exit area with an $R^2$ score of 0.75. Key features used in the prediction include the standard deviation of the voltage and the time between shorts.

Contact tip wear↗