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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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NASA NTRSDate not supplied

Measurement of material recession and shock standoff in plasma windtunnel using neural nets

Arcjets are plasma wind tunnels used to test the performance of heatshield materials for spacecraft atmospheric entry. These facilities present an extremely harsh flow environment with heat fluxes up to 10^9 W/m^2 for up to 30 minutes. The plasma is low-temperature (~1 eV) but high pressure (> 10 kPa) creating high-enthalpy supersonic flows similar to atmospheric entry conditions. Typically, material samples are measured before and after a test to characterize the total recession. However, this does not capture time-dependent effects such as material expansion and non-linear recession. This work will present new analysis of arcjet test videos which measure both the time-dependent 2D recession of the material samples and the shock standoff distance. New results showing non-linear material erosion rates will be highlighted. The material and shock edges are extracted from the videos by training and applying a convolutional neural network. Due to the consistent camera settings, the machine learning model achieves high accuracy (~99%) on new data with only a small number of training frames (~80). The new results will be discussed in the context of temperature dependent plasma-surface interaction.

Magnus A Haw↗
NASA NTRSDate not supplied

Measurement of Material Recession and Shock Standoff in Plasma Windtunnel using Neural Nets

Arcjets are plasma wind tunnels used to test the performance of heatshield materials for spacecraft atmospheric entry. These facilities present an extremely harsh flow environment with heat fluxes up to 109 W/m2 for up to 30 minutes. The plasma is low-temperature (∼1 eV) but high pressure (> 10 kPa) creating high-enthalpy supersonic flows similar to atmospheric entry conditions. Typically, material samples are measured before and after a test to characterize the total recession. However, this does not capture time-dependent effects such as material expansion and non-linear recession. This work will present new analysis of arcjet test videos which measure both the time-dependent 2D recession of the material samples and the shock standoff distance. The results show non-linear time-dependent effects are present for some conditions. The material and shock edges are extracted from the videos by training and applying a convolutional neural network. Due to the consistent camera settings, the machine learning model achieves high accuracy (± 2 px) relative to manually segmented images with only a small number of training frames (80).

Neural network↗
NASA NTRS2001

Integrated Thermal Response Tool for Earth Entry Vehicles

A system is presented for multi-dimensional, fully-coupled thermal response modeling of hypersonic entry vehicles. The system consists of a two-dimensional implicit thermal response, pyrolysis and ablation program (TITAN), a commercial finite-element thermal and mechanical analysis code (MARC), and a high fidelity Navier-Stokes equation solver (GIANTS). The simulations performed by this integrated system include hypersonic flow-field, fluid and solid interaction, ablation, shape change, pyrolysis gas generation and flow, and thermal response of heatshield and structure. The thermal response of the ablating and charring heatshield material is simulated using TITAN, and that of the underlying structural is simulated using MARC. The ablating heatshield is treated as an outer boundary condition of the structure, and continuity conditions of temperature and heat flux are imposed at the interface between TITAN and MARC. Aerothermal environments with fluid and solid interaction are predicted by coupling TITAN and GIANTS through surface energy balance equations. With this integrated system, the aerothermal environments for an entry vehicle and the thermal response of both the heatshield and the structure can be obtained simultaneously. Representative computations for a proposed blunt body earth entry vehicle are presented and discussed in detail.

Chen, Y.-K.↗
NASA NTRS2009

Validation of PICA Ablation and Thermal-Response Model at Low Heat Flux

Phenolic Impregnated Carbon Ablator (PICA) was the forebody heatshield material on the Stardust sample-return capsule and is also a primary candidate material for the Mars Science Lander (MSL), the Orion Crew Module, and the SpaceX Dragon vehicle. As part of the heatshield qualification for Orion, physical and thermal properties of virgin and charred PICA were measured, and an ablation and thermal response model was developed. We validated the model by comparing it with recession and temperature data from stagnation arcjet tests conducted over a wide range of stagnation heat flux of 107 to 1102 W/sq cm. The effect of orthotropic thermal conductivity was evident in the thermal response of the arcjet models. In general, model predictions compared well with the data; however, the uncertainty of the recession prediction was greatest for heat fluxes below 200 W/sq cm. More recent MSL testing focused on the low heat flux regime of 45 to 250 W/sq cm. The new results confirm the recession uncertainty, especially for pressures below 6 kPa. In this work we focus on improving the model predictions for MSL and Orion tests below 250 W/sq cm.

Milos, Frank S.↗
NASA NTRS2018

A Common Probe Design for Multiple Planetary Destinations

Atmospheric probes have been successfully flown to planets and moons in the solar system to conduct in-situ measurements. They include the Pioneer Venus multi-probes, the Galileo Jupiter probe, and Huygens probe. Probe mission concepts to five destinations, including Venus, Jupiter, Saturn, Uranus, and Neptune, have all utilized similar-shaped aeroshells and concept of operations, namely a 45 deg sphere cone shape with high density heatshield material and parachute system for extracting the descent vehicle from the aeroshell. Each concept designed its probe to meet specific mission requirements and to optimize mass, volume, and cost. At the 2017 IPPW, NASA Headquarters postulated that a common aero-shell design could be used successfully for multiple destinations and missions. This "common probe" design could even be assembled with multiple copies, properly stored, and made available for future NASA missions, potentially realizing savings in cost and schedule and reducing the risk of losing technologies and skills difficult to sustain over decades. Thus the NASA Planetary Science Division funded a study to investigate whether a common probe design could meet most, if not all, mission needs to the five planetary destinations with extreme entry environments. The Common Probe study involved four NASA Centers and addressed these issues, including constraints and inefficiencies that occur in specifying a common design.Study methodology: First, a notional payload of instruments for each destination was defined based on priority measurements from the Planetary Science Decadal Survey. Steep and shallow entry flight path angles (EFPA) were defined for each planet based on qualification and operational g-load limits for current, state-of-the-art instruments. Interplanetary trajectories were then identified for a bounding range of EFPA. Next, 3-DoF simulations for entry trajectories were run using the entry state vectors from the interplanetary trajectories. Aeroheating correlations were used to generate stagnation point convective and radiative heat flux profiles for several aeroshell shapes and entry masses. High fidelity thermal response models for various TPS materials were used to size stagnation point thicknesses, with margins based on previous studies. Backshell TPS masses were assumed based on scaled heat fluxes from the heatshield and also from previous mission concepts.

Hwang, Helen H.↗
NASA NTRS2018

A Common Probe Design for Multiple Planetary Destinations

Atmospheric probes have been successfully flown to planets and moons in the solar system to conduct in situ measurements. They include the Pioneer Venus multi-probes, the Galileo Jupiter probe, and Huygens probe. Probe mission concepts to five destinations, including Venus, Jupiter, Saturn, Uranus, and Neptune, have all utilized similar-shaped aeroshells and concept of operations, namely a 45-degree sphere cone shape with high density heatshield material and parachute system for extracting the descent vehicle from the aeroshell. Each concept designed its probe to meet specific mission requirements and to optimize mass, volume, and cost. At the 2017 International Planetary Probe Workshop (IPPW), NASA Headquarters postulated that a common aeroshell design could be used successfully for multiple destinations and missions. This "common probe" design could even be assembled with multiple copies, properly stored, and made available for future NASA missions, potentially realizing savings in cost and schedule and reducing the risk of losing technologies and skills difficult to sustain over decades. Thus the NASA Planetary Science Division funded a study to investigate whether a common probe design could meet most, if not all, mission needs to the five planetary destinations with extreme entry environments. The Common Probe study involved four NASA Centers and addressed these issues, including constraints and inefficiencies that occur in specifying a common design. Study methodology: First, a notional payload of instruments for each destination was defined based on priority measurements from the Planetary Science Decadal Survey. Steep and shallow entry flight path angles (EFPA) were defined for each planet based on qualification and operational g-load limits for current, state-of-the-art instruments. Interplanetary trajectories were then identified for a bounding range of EFPA. Next, 3-degrees-of-freedom simulations for entry trajectories were run using the entry state vectors from the interplanetary trajectories. Aeroheating correlations were used to generate stagnation point convective and radiative heat flux profiles for several aeroshell shapes and entry masses. High fidelity thermal response models for various Thermal Protection System (TPS) materials were used to size stagnation-point thicknesses, with margins based on previous studies. Backshell TPS masses were assumed based on scaled heat fluxes from the heatshield and also from previous mission concepts. Presentation: We will present an overview of the study scope, highlights of the trade studies and design driver analyses, and the final recommendations of a common probe design and assembly. We will also indicate limitations that the common probe design may have for the different destinations. Finally, recommended qualification approaches for missions will be presented.

Hwang, H. H.↗
NASA NTRS2017

Mars2020 Entry, Descent, and Landing Instrumentation 2 (MEDLI2) Do No Harm Test Series

A total of seventeen instrumented thermal sensor plugs, eight pressure transducers, two heat flux sensors, and one radiometer are planned to be utilized on the Mars 2020 missions thermal protection system (TPS) as part of the Mars Entry, Descent, and Landing Instrumentation II (MEDLI2) project. Of the MEDLI2 instrumentation, eleven instrumented thermal plugs and seven pressure transducers will be installed on the heatshield of the Mars 2020 vehicle while the rest will be installed on the backshell. The goal of the MEDLI2 instrumentation is to directly inform the large performance uncertainties that contribute to the design and validation of a Mars entry system. A better understanding of the entry environment and TPS performance could lead to reduced design margins enabling a greater payload mass-fraction and smaller landing ellipses. To prove that the MEDLI2 system will not degrade the performance of the Mars 2020 TPS, an Aerothermal Do No Harm (DNH) test series was designed and conducted. Like Mars 2020s predecessor, Mars Science Laboratory (MSL), the heatshield material will be Phenolic Impregnated Carbon Ablator (PICA); the Mars 2020 entry conditions are enveloped by the MSL design environments, therefore the development and qualification testing performed during MEDLI is sufficient to show that the similar MEDLI2 heatshield instrumentation will not degrade PICA performance. However, given that MEDLI did not include any backshell instrumentation, the MEDLI2 team was required to design and execute a DNH test series utilizing the backshell TPS material (SLA-561V) with the intended flight sensor suite. To meet the requirements handed down from Mars 2020, the MEDLI2 DNH test series emphasized the interaction between the MEDLI2 sensors and sensing locations with the surrounding backshell TPS and substrucutre. These interactions were characterized by performing environmental testing of four 12 by 12 test panels, which mimicked the construction of the backshell TPS and the integration of the MEDLI2 sensors as seen in Figure 1. The testing included thermal vacuumcycling, random vibration, shock, and arc jet testing. The test panels were fabricated by Lockheed Martin, establishing techniques that will be utilized during the Mars 2020 vehicle installation. Each test panel included one thermal sensor plug (two embedded thermocouples), one heat flux sensor, and multiple pressure port holes for evaluation.This presentation will discuss the planning and execution of the MEDLI2 DNH test series. Selected highlights and results of each environmental test will be presented, and lessons learned will be addressed that will feed forward into the planning for the MEDLI2 flight system certification testing.

backshell↗
NASA NTRS2017

MELDI2 Do No Harm Test Series

Mars 2020 will fly the Mars Entry, Descent, and Landing Instrumentation II (MEDLI2) sensor suite consisting of a total of seventeen instrumented thermal sensor plugs, eight pressure transducers, two heat flux sensors, and one radiometer embedded in the thermal protection system (TPS). Of the MEDLI2 instrumentation, eleven instrumented thermal plugs and seven pressure transducers will be installed on the heatshield of the Mars 2020 vehicle while the rest will be installed on the backshell. The goal of the MEDLI2 instrumentation is to directly inform the large performance uncertainties that contribute to the design and validation of a Mars entry system. A better understanding of the entry environment and TPS performance could lead to reduced design margins enabling a greater payload mass-fraction and smaller landing ellipses. To prove that the MEDLI2 system will not degrade the performance of the Mars 2020 TPS, an Aerothermal Do No Harm (DNH) test series was designed and conducted. Like Mars 2020's predecessor, Mars Science Laboratory (MSL), the heatshield material will be Phenolic Impregnated Carbon Ablator (PICA); the Mars 2020 entry conditions are enveloped by the MSL design environments, therefore the development and qualification testing performed during MEDLI is sufficient to show that the similar MEDLI2 heatshield instrumentation will not degrade PICA performance. However, given that MEDLI did not include any backshell instrumentation, the MEDLI2 team was required to design and execute a DNH test series utilizing the backshell TPS material (SLA-561V) with the intended flight sensor suite. To meet the requirements handed down from Mars 2020, the MEDLI2 DNH test series emphasized the interaction between the MEDLI2 sensors and sensing locations with the surrounding backshell TPS and substrucutre. These interactions were characterized by performing environmental testing of four 12" by 12" test panels, which mimicked the construction of the backshell TPS and the integration of the MEDLI2 sensors as seen in Figure 1. The testing included thermal vacuum/ cycling, random vibration, shock, and arc jet testing. The test panels were fabricated by Lockheed Martin, establishing techniques that will be utilized during the Mars 2020 vehicle installation. Each test panel included one thermal sensor plug (two embedded thermocouples), one heat flux sensor, and multiple pressure port holes for evaluation. This presentation will discuss the planning and execution of the MEDLI2 DNH test series. Selected highlights and results of each environmental test will be presented, and lessons learned will be addressed that will feed forward into the planning for the MEDLI2 flight system certification testing.

Heatshield↗
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↗
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

New Tools for Automating Arcjet Sample Recession Tracking and Analysis

Arcjet Computer Vision (arcjetCV) has been significantly upgraded to enhance accuracy and performance in tracking material recession and shock-material standoff in test videos. These improvements include integrating new machine learning models, developing a specialized edge detection class, and incorporating a more comprehensive training dataset. These upgrades have refined the software’s ability to automate time-resolved recession tracking, making it more precise and reliable for analyzing complex physical processes. In parallel, a new tool called STARscan (Spatial Targeting and Alignment Rig for Scanning) is being developed to capture detailed 3D surface data before and after testing. By comparing these pre- and post-test scans with arcjetCV’s automated video analysis results, users can achieve a more comprehensive assessment of material recession. This method enables cross-validation of results, improving confidence in the analysis of tested materials. The expanded capabilities of arcjetCV have been successfully demonstrated on videos from various facilities, including the NASA Ames arcjets, UIUC’s PlasmatronX, and the VKI Plasmatron. It has been adopted as a new standard for in-situ recession tracking by the Mars Sample Return Project and Orion. ArcjetCV’s improved efficiency and accuracy are critical for reducing testing uncertainties and validating heatshield material performance under extreme conditions. The software’s user-friendly graphical interface ensures ease of use, enabling seamless processing and precise analysis of arcjet videos, providing deeper insights into material behavior in hypersonic environments. ArcjetCV is now available on both PyPI and Conda, allowing easy installation via "pip install arcjetCV" or through the Conda package manager, ensuring broad accessibility and streamlined deployment for users across various platforms.

Ablation↗
NASA NTRS2007

Overview of the MEDLI Project

The Mars Science Laboratory Entry, Descent, and Landing Instrumentation (MEDLI) Project's objectives are to measure aerothermal environments, sub-surface heatshield material response, vehicle orientation, and atmospheric density for the atmospheric entry and descent phases of the Mars Science Laboratory (MSL) entry vehicle. The flight science objectives of MEDLI directly address the largest uncertainties in the ability to design and validate a robust Mars entry system, including aerothermal, aerodynamic and atmosphere models, and thermal protection system (TPS) design. The instrumentation suite will be installed in the heatshield of the MSL entry vehicle. The acquired data will support future Mars entry and aerocapture missions by providing measured atmospheric data to validate Mars atmosphere models and clarify the design margins for future Mars missions. MEDLI thermocouple and recession sensor data will significantly improve the understanding of aeroheating and TPS performance uncertainties for future missions. MEDLI pressure data will permit more accurate trajectory reconstruction, as well as separation of aerodynamic and atmospheric uncertainties in the hypersonic and supersonic regimes. This paper provides an overview of the project including the instrumentation design, system architecture, and expected measurement response.

Gazarik, Michael J.↗
NASA NTRS2008

Overview of the MEDLI Project

The Mars Science Laboratory Entry, Descent, and Landing Instrumentation (MEDLI) Project s objectives are to measure aerothermal environments, sub-surface heatshield material response, vehicle orientation, and atmospheric density for the atmospheric entry and descent phases of the Mars Science Laboratory (MSL) entry vehicle. The flight science objectives of MEDLI directly address the largest uncertainties in the ability to design and validate a robust Mars entry system, including aerothermal, aerodynamic and atmosphere models, and thermal protection system (TPS) design. The instrumentation suite will be installed in the heatshield of the MSL entry vehicle. The acquired data will support future Mars entry and aerocapture missions by providing measured atmospheric data to validate Mars atmosphere models and clarify the design margins for future Mars missions. MEDLI thermocouple and recession sensor data will significantly improve the understanding of aeroheating and TPS performance uncertainties for future missions. MEDLI pressure data will permit more accurate trajectory reconstruction, as well as separation of aerodynamic and atmospheric uncertainties in the hypersonic and supersonic regimes. This paper provides an overview of the project including the instrumentation design, system architecture, and expected measurement response.

Gazarik, Michael J.↗