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Magnus A Haw

Publications and source records attributed to Magnus A Haw.

Expanding the Measurement Capabilities of the mARC II Arc-Jet to Map the Operating Envelope for High-Enthalpy Air Flows

The mARC II is a 30 kW arc-jet facility at NASA Ames Research Center developed to produce high-enthalpy flows for low-cost technology development purposes. In this work, we introduce new measurement capabilities following the latest facility upgrades and begin characterizing the operating envelope for the two-disk arc-heater configuration with air as the working gas. The lower bound of the envelope corresponds to the lowest set current and the farthest sensor distance from the nozzle (𝐼set = 40 A, 𝑧 = 69 mm), while the upper bound corresponds to the highest current and closest sensor distance (𝐼set = 200 A, 𝑧 = 2 mm). Stagnation point heat flux was measured using a water-cooled Gardon gauge (⌀4.76 mm, 3/16" hemispherical), with values ranging from 15 ≤ q̇₀ ≤ 900 W/cm 2 . Stagnation pressure was measured using a water-cooled Pitot probe (⌀4.76 mm, 3/16" hemispherical), with values ranging from 18 ≤ p₀ ≤ 1100 Pa. The upgraded vacuum system is demonstrated to significantly extend the lower end of the operating envelope of the mARC II. Additionally, measurements of bulk enthalpy (3 ≤ h ≤ 13 MJ/kg) and stagnation enthalpy (4 ≤ h₀ ≤ 34 MJ/kg) demonstrate the mARC II facility's high-enthalpy capabilities. Completion of the operating envelope characterization is ongoing in tandem with the development of complementary diagnostic capabilities and numerical simulations.

instrumentation↗

Defining the Operational Envelope for Air Flows in the Miniature Arc-Jet Research Chamber (mARC II)

The second-generation 30 kW miniature Arc-jet Research Chamber (mARC II) at NASA Ames Research Center produces high enthalpy flows relevant for entry systems ground testing. The mARC II facility has recently undergone upgrades, including the installation of a new vacuum system to address the issues preventing it from maintaining underexpanded flow under test conditions. In this work, we present data obtained from an Integrated Systems Testing campaign and provide an initial assessment of arc-jet performance following the upgrades. Air is used as the working gas for the standard mARC II arc-heater configuration with two constrictor disks. Seven runs were investigated for five test conditions to assess the lowest achievable stagnation point heat fluxes for air flow rates of 0.15 or 0.25 g s −1 . The heat flux was measured using a water-cooled Gardon gauge (Ø4.76 mm, 3/16" hemispherical) at 70 mm from the nozzle exit plane. The new vacuum system produced test box pressures in the medium (fine) vacuum range (~0.03 torr, 4 Pa) prior to gas addition and successfully maintained underexpanded flow after gas addition. The upgrade yielded a ~4X reduction in heat flux relative to the previous system for the same set test conditions. We report the lowest heat fluxes measured in mARC II to date, ranging from 26 to 81 W cm −2 , for sonic flow enthalpies of 4–14 MJ kg −1 . Bulk enthalpies estimated using an energy balance method (EB 2 ) are reported for the first time using mARC II.Initial data suggests EB 2 generally estimates lower enthalpies than sonic flow methods for mARC II. Lastly, laminar axisymmetric Navier–Stokes simulations were performed using the NASA DPLR code. Numerical heat flux results show good agreement with experiments at low arc powers (3–15% difference at the minimum set arc current), but discrepancy increases with arc power (49% difference at the maximum set arc current).

arc-jet↗

Defining the Operational Envelope for Air Flows in the Miniature Arc-Jet Research Chamber (mARC II)

The second-generation 30 kW miniature Arc-jet Research Chamber (mARC II) at NASA Ames Research Center produces high enthalpy flows relevant for entry systems ground testing. The mARC II facility has recently undergone upgrades, including the installation of a new vacuum system to address the issues preventing it from maintaining underexpanded flow under test conditions. In this work, we present data obtained from an Integrated Systems Testing campaign and provide an initial assessment of arc-jet performance following the upgrades. Air is used as the working gas for the standard mARC II arc-heater configuration with two constrictor disks. Seven runs were investigated for five test conditions to assess the lowest achievable stagnation point heat fluxes for air flow rates of 0.15 or 0.25 g s −1 . The heat flux was measured using a water-cooled Gardon gauge (Ø4.76 mm, 3/16" hemispherical) at 70 mm from the nozzle exit plane. The new vacuum system produced test box pressures in the medium (fine) vacuum range (~0.03 torr, 4 Pa) prior to gas addition and successfully maintained underexpanded flow after gas addition. The upgrade yielded a ~4X reduction in heat flux relative to the previous system for the same set test conditions. We report the lowest heat fluxes measured in mARC II to date, ranging from 26 to 81 W cm −2 , for sonic flow enthalpies of 4–14 MJ kg −1 . Bulk enthalpies estimated using an energy balance method (EB 2 ) are reported for the first time using mARC II.Initial data suggests EB 2 generally estimates lower enthalpies than sonic flow methods for mARC II. Lastly, laminar axisymmetric Navier–Stokes simulations were performed using the NASA DPLR code. Numerical heat flux results show good agreement with experiments at low arc powers (3–15% difference at the minimum set arc current), but discrepancy increases with arc power (49% difference at the maximum set arc current).

Pitot tube↗

Characterizing Heat Flux in the Miniature Arc Jet Research Chamber (mARC II)

The second-generation miniature Arc jet Research Chamber (mARC II) at NASA Ames Research Center produces small-scale high enthalpy flows relevant for entry systems ground testing. This facility is aimed at applications such as new arc jet flow diagnostics and new arc column diagnostics. The mARC system has previously been shown capable of delivering heat fluxes between 800 and 1900 W/cm2. New work shows that the facility is also capable of delivering heat fluxes as low as 200 W/cm2.

arc jet↗

Initial Characterization of the 30 kW Miniature Arc Jet (mARC II) at NASA Ames Research Center

The second-generation Miniature Arc jet Research Chamber (mARC II) at NASA Ames is undergoing integrated systems testing. Once fully operational, this facility will be added to the range of available test facilities operated by the NASA Ames Thermophysics Facilities Branch, with primary focus on applications such as new arc jet flow diagnostics and new arc column diagnostics. Initial characterization of the mARC II is reported here, specifically, measured arc current, arc voltage, column and chamber pressures, mass flow rates, and initial emission measurements carried out at a range of conditions.

arc jet↗

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↗

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↗

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗

Preliminary 3D Simulations of Anomalous Thermocouple Coupling to Surface Electric Fields

Anomalous measurements from subsurface thermocouples have been observed during re-entry of the Orion capsule (EFT-1 plugs). These measurements are considered anomalous because the amplitude and frequency of the inferred temperature modulations are too large to be due to plausible heating processes. Consequently, it is believed the anomalies are caused by electrical interference generated by the re-entry plasma. Quantifying these effects requires several steps: modeling the external plasma flows, modeling the plasma-surface interaction, and finally modeling the surface-thermocouple coupling. This paper addresses this last step using 3D finite element simulations of a wire embedded in a weakly conductive medium.

thermocouple anomaly↗

1d-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D CNN architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.

Machine learning↗

1D-Convolutional Neural Network Architecture for Generalized Time-series Segmentation

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization will be described as well as the results of application to three separate data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation. In all three test cases the 1D-CNN performs better than tailored integral/derivative/thresholding algorithms across a range of signal-to-noise levels.

CNN↗

Wireless Sensor Instrumentation for Distributed Sensing in Ground-Test Facilities

Wireless sensors have a number of advantages over wired sensors for flexible or temporary diagnostic deployment in a field or lab setting. Relative to wireless alternatives, traditional wired systems encounter limitations including spatial and weight constraints, maintenance costs, and signal integrity in high-noise environments. Ground-test facilities often face these challenges and cause increase down-time costs and failure of data integrity. This paper describes the development and implementation of flexible wireless sensor boards for applications within ground-test facilities using different network architectures. These boards implement state-of-the- art STM32 microprocessor chips for high-speed data acquisition and computational power. Additionally, the design integrates long-range radio transceivers with power output of +20dBm, easily reaching 2 km range, while maintaining high interference immunity and minimizing current consumption. The wireless sensor boards were implemented to demonstrate performance through benchmark testing and by taking distributed electrically-isolated measurements on a high-voltage lab apparatus while using different network architectures. Although this test case is in a laboratory setting, this network architecture could be easily repurposed for various other forms of monitoring.

Wireless Sensor Network↗

STARscan: Spatial Targeting and Alignment Rig for Scanning

The Spatial Targeting and Alignment Rig for Scanning (STARScan) is a 3D photogrammetry system developed at NASA Ames Research Center to address bottlenecks in pre/post-test scanning of arcjet test articles. It reduces scan time from 15 minutes with handheld laser scanners to under 2 minutes, while maintaining high accuracy (±0.2-0.5 mm). STARScan integrates an array of cameras, a 3D-printed rack, turntable, and LED light panels, all controlled via a user-friendly graphical user interface (GUI). The system offers tools for scan visualization, mesh analysis, and data export, automating tasks such as alignment of pre/post-test scans, material recession measurements, surface roughness assessment, and curvature analysis. By integrating scanning, imaging, and post-processing into one application, STARScan significantly improves efficiency in scanning and analyzing arcjet test samples.

Ablation↗

Expanding the Measurement Capabilities of the mARC II Arc-Jet to Map the Operating Envelope for High-Enthalpy Air Flows

The mARC II is a 30 kW arc-jet facility at NASA Ames Research Center developed to produce high-enthalpy flows for low-cost technology development purposes. In this work, we introduce new measurement capabilities following the latest facility upgrades and begin characterizing the operating envelope for the two-disk arc-heater configuration with air as the working gas. The lower bound of the envelope corresponds to the lowest set current and the farthest sensor distance from the nozzle (𝐼set = 40 A, 𝑧 = 69 mm), while the upper bound corresponds to the highest current and closest sensor distance (𝐼set = 200 A, 𝑧 = 2 mm). Stagnation point heat flux was measured using a water-cooled Gardon gauge (⌀4.76 mm, 3/16" hemispherical), with values ranging from 15 ≤ q̇₀ ≤ 900 W/cm2. Stagnation pressure was measured using a water-cooled Pitot probe (⌀4.76 mm, 3/16" hemispherical), with values ranging from 18 ≤ p₀ ≤ 1100 Pa. The upgraded vacuum system is demonstrated to significantly extend the lower end of the operating envelope of the mARC II. Additionally, measurements of bulk enthalpy (3 ≤ h ≤ 13 MJ/kg) and stagnation enthalpy (4 ≤ h₀ ≤ 34 MJ/kg) demonstrate the mARC II facility's high-enthalpy capabilities. Completion of the operating envelope characterization is ongoing in tandem with the development of complementary diagnostic capabilities and numerical simulations.

TPS↗

An Overview of Experiments and Modeling of Polysiloxane-Coated Thermal Protection Systems for Missions to Mars, Titan, and Beyond.

Phenolic Impregnated Carbon Ablator (PICA) gained heritage during the historic Stardust mission, where it successfully returned samples from a comet’s tail and has since been instrumental in delivering payloads to the surface of Mars [1-3]. Most recently, PICA enabled the safe return of samples collected from asteroid Bennu as part of the OSIRIS-REx mission. This rich legacy underscores PICA’s critical role in allowing NASA’s most ambitious exploration missions. However, the friable nature of its phenolic phase presents challenges during handling and pre-launch activities. To mitigate this issue, PICA is coated with a polysiloxane resin system, which serves to suppress particulate dispersion and thereby safeguard spacecraft components. A comprehensive understanding of the polysiloxane resin’s behavior is imperative, as it profoundly shapes the material response of PICA during atmospheric entry by influencing its thermal and oxidative stability. This influence extends to thermocouple plugs embedded within thermal protection systems. These plugs have demonstrated their significance in missions such as Mars Science Laboratory (MSL) and Mars 2020, where the MEDLI and MEDLI2 instrumentation suites delivered in-valuable insights into the performance of thermal protection systems during entry into the Martian atmosphere [4]. Looking ahead, missions such as Dragonfly, set to descend into Titan’s dense atmosphere, aim to leverage advanced sensor technologies to further refine our understanding of thermal protection response [5]. Moreover, thermocouple plugs play an essential role in validating cutting-edge material response models, such as those pioneered under NASA’s Entry Systems Modeling Project (ESM), designed, in-part, to predict the operational integrity of thermal protection systems under the extreme stresses of atmospheric entry. To achieve these modeling goals, ground-based experiments are crucial to provide the foundational data necessary for developing and refining these predictive tools. To this end, an extensive test campaign was conducted at the Hypersonic Materials Environmental Test System (HyMETS) to investigate the high-temperature behavior of the polysiloxane resin in an air environment [6]. These experiments revealed critical phenomena, including the formation of a silicon oxycarbide layer that enhances oxidation resistance, moderates surface temperatures, and alters in-depth thermal response. Building on these findings, subsequent tests were designed to simulate atmospheric entry conditions in reactive gases, such as CO2 and N2, to mimic the environments of Mars and Titan, respectively, as well as non-reactive gases representing the atmospheres of the Ice Giants (Neptune and Uranus). A heating rate dependent decomposition mechanism has been identified for the polysiloxane resin under oxidizing conditions (Fig. 1). In the initial stage, the resin and the underlying thermal protection system undergo pyrolysis, rapidly generating a thin amorphous silicon oxycarbide interwoven with carbonaceous char and residual fibers from PICA. During the second stage, the nascent oxide layer establishes a robust, oxidation-resistant thermal barrier coating, which significantly impedes heat transfer to the underlying carbonaceous char, resulting in a stagnation of the surface temperature. A key factor contributing to this thermal resistance is the low recombination efficiency of atomic oxygen (γ), which further diminishes the heat load on the material’s interior layers [7]. Moreover, as the surface temperature stagnates, the silicon oxycarbide phase separates into distinct regions of silica and free graphite. Ultimately, when the heat flux reaches a critical threshold, a third stage is triggered, leading to the breakdown of the coating through carbothermal reduction, exposing the underlying char layer. This exposure leads to a dramatic surface temperature spike, driven by highly exothermic reactions between atomic oxygen and the char layer, further accelerating material degradation. A detailed mass and heat transfer model of PICA coated with polysiloxane resin was implemented in the Porous material Analysis Toolbox based on OpenFOAM, PATO [8]. The initial stage was considered negligible in this model because the resin decomposition occurs rapidly within a thin surface layer. Instead, the coating was directly considered as an oxygen-resistant thermal barrier coating. For the second stage, the thin amorphous silicon oxycarbide was treated as a pure silica surface to simplify the thermochemical behavior. The model ac-counts for surface equilibrium processes using representative elements of the coating-environment system. For the third stage, specific boundary conditions were developed to estimate the onset and progression of the coating removal. Two-dimensional material response simulations were conducted to compare uncoated and coated PICA using boundary conditions calibrated with HyMETS data. Fig. 2 illustrates that the simulations closely align with experimental data, successfully reproducing measured temperature profiles. This work will include the latest advancements in the coating model, including the calibration of recombination of atomic oxygen at the surface during the second phase. These simulated results will be further validated against additional CO2 data points from HyMETS, reinforcing the models’ predictive capabilities. These mechanisms and their effects on thermal protection systems, including thermochemical behavior and thermocouple probe performance in extreme environments, provide crucial insights for optimizing spacecraft designs that safeguard scientific payload and ensure mission success in future planetary exploration endeavors.

Active Oxidation↗

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗

BEAST: Expanding Sustainable Data Infrastructure for High-Enthalpy Facilities

Reproducible, data-driven thermal protection system (TPS) research requires that experimental records from high-enthalpy testing be consistently structured, traceable, and accessible across campaigns and institutions. In practice, however, arcjet and plasma facilities data remain largely fragmented: raw diagnostics are stored in ad hoc formats, material sample histories are disconnected from test conditions, and metadata standards are absent, precluding systematic cross-campaign analysis and long-term reuse. BEAST (Backend for Experiment Analysis, Storage, and Traceability) is an open-source, web-based platform that addresses these limitations by providing a unified, queryable infrastructure for high-enthalpy ground-test data [1]. First presented at the 15th Ablation Workshop [2], BEAST has since undergone significant development. The platform ingests and structures multi-channel time-series diagnostics, facility configurations, and material property records within a common provenance model, ensuring end-to-end traceability from raw sensor acquisition to reduced experimental quantities. A versioned material library links specimen identity and processing history to the specific runs in which each sample was tested. An integrated modeling workbench enables training and evaluation of regression models directly on archived experimental data, supporting condition interpolation and the construction of empirical material response databases. Beyond its original deployment at NASA Ames Research Center, BEAST has been designed to be facility-agnostic, with ongoing efforts to extend its adoption to other facilities. Its modular architecture accommodates heterogeneous diagnostic setups and facility types, and its future open-source distribution allows institutions to build on a common data standard rather than maintaining isolated, bespoke solutions. BEAST is further integrated within a broader ecosystem of companion tools: arcjetCV [3] extracts recession rates and shock standoff distances from high-speed video using computer vision, and miniSTARscan [4] provides sub-minute, portable photogrammetric surface reconstruction of test articles before and after exposure. All tools share a common data schema, enabling seamless ingestion of surface geometry, imagery, and time-series data into a single, coherent experimental record.

Database↗

Kalman Filtering and RTS Smoothing for Arc-Jet Sample Edge Tracking

Accurate and temporally consistent measurements of test-article recession are required to characterize material response during arc-jet ablation experiments. However, image-based boundary measurements are often affected by segmentation noise, brightness variations, and frame-to-frame variability. This work extends arcjetCV [1] with filtering methods for tracking the evolving boundaries of hemispherical and ISO-Q test articles. Two boundary-tracking approaches based on Kalman filtering [2] were implemented. The first applies a point-wise Kalman filter followed by a Rauch–Tung–Striebel smoother [3] to individual boundary-point locations. The second applies the same filtering and smoothing framework to a reduced set of geometry-dependent shape parameters. The point-wise method reduces local frame-to-frame fluctuations while preserving spatial details along the detected boundary. The shape-parameter method provides a compact and geometrically constrained estimate of the sample contour. Figure 1 illustrates the two approaches for a hemispherical test article. Both methods improve temporal consistency and support more robust estimation of surface recession from arc-jet imagery. The two filtering approaches provide complementary representations of boundary evolution and improve the reliability of image-based recession measurements during arc-jet experiments.

recession measurement↗