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MEDLI2: MISP Inferred Aerothermal Environment and Flow Transition Assessment

The Mars Entry, Descent, and Landing Instrumentation 2 (MEDLI2) sensor suite on the Mars2020 mission contained multiple sensors on the aeroshell to measure the aerothermal environment during entry into the Martian atmosphere. These sensors performed superbly and successfully returned forebody and aftbody heating measurements. Analysis of MEDLI2 data indicated flow transitioning from a laminar to turbulent state on the heatshield. No evidence of flow transition was observed on the backshell. Two methods were used to estimate flow transition times on the heatshield: (1) temperature gradient of near-surface thermocouple data and (2) heat flux gradient from an inverse reconstruction approach using thermocouple data and material response modeling. Both methods produced similar transition times with an estimated accuracy of ±1 s. To assess various transition criteria, transition parameters were evaluated at each sensor location using flow field solutions from computational fluid dynamics (CFD) simulations. The idea was to use conservative values inferred from MEDLI2 data as transition criteria for other Mars missions. To test this hypothesis, MEDLI data from the Mars Science Laboratory (MSL) mission was used to compare predicted vs. actual flow transition times. The comparisons suggest smooth wall transition criteria are not well-suited in modeling the rapid progression of a turbulent transition front. Transition criteria containing a roughness element parameter agreed better with the flight data. In summary, critical transition values derived from MEDLI2 data may be used as a starting point in constructing a flow transition model for future Mars missions.

Chun Y Tang↗

Updates on the Predictive Materials Modeling Software Tools

Updates on NASA‘s efforts to build a Predictive Material Modeling (PMM) framework from the micro-scale to the macro-scale are presented in this abstract. The PMM effort is part of the Entry Systems Modeling (ESM) project under NASA’s Game Changing Development (GCD) program. To reduce the need for extensive testing and accelerate the design cycle process, ESM is developing simulation and modeling tools that enable the characterization of the properties of thermal protection materials and their response to extremely hot plasma. The Porous Microstructure Analysis (PuMA) software has been developed to compute effective material properties and perform material response simulations on digitized microstructures of porous media. PuMA is able to import three-dimensional digital images obtained from X-ray microtomography or to generate artificial microstructures that mimic real materials. PuMA also provides a module for interactive 3D visualizations. Version 3, which was recently released as open-source, includes modules to compute simple morphological properties such as porosity, volume fractions, pore diameter, and specific surface area. Additional capabilities include the determination of effective thermal and electrical conductivity (both radiative and solid conduction - including the ability to simulate local anisotropy for the latter); effective diffusivity and tortuosity from the continuum to the rarefied regime; techniques to determine the local material orientation, as well as mechanical properties (elasticity coefficients), and permeability. Computed properties are then used to inform a macro-scale material response model, such as those implemented in the Porous material Analysis Toolbox based on OpenFOAM (PATO) software developed within ESM. The computational model in PATO is a generic heat and mass transfer model for porous reactive materials containing several solid phases and a single gas phase. The detailed chemical interactions occurring between the solid phases and the gas phase are modeled at the pore scale, assuming Local Thermal Equilibrium. Recent efforts include the development of a mechanical erosion model as well as a unified model allowing an intrinsic coupling between fluid and material. Comparison to flight data (Mars Science Laboratory [MSL] Entry Descent and Landing Instrument [MEDLI] and Mars 2020 MEDLI2) is critical in order to validate these computational tools. Examples of ablative material response using the code will be presented, including 3D simulations of the full-scale heatshield of the MSL capsule. The simulations demonstrated the ability of the modern material response code, PATO, to handle the material response of geometrically complex and large domains through the use of massively parallel computations.

material modeling↗

Evaluating The Relationships Between Supine Propriorception Assessments with Upright Functional Mobility and Balance Tests

INTRODUCTION Upon return to Earth, spaceflight sensorimotor adaptations can result in impaired posture and locomotion [1], [2], and current exercise countermeasures in the International Space Station are not sufficient to maintain sensorimotor function. In-flight countermeasures and assessment tools for sensorimotor function are needed to mitigate risks associated with mission-critical task performance upon return to Earth or arrival to 0.38 G on Mars. One proposed countermeasure for proprioceptive deconditioning includes training on a tilt board device [3], which will be tested in an upcoming bed rest study. In preparation for the said study, the objective of this pilot study is to compare performance in supine proprioceptive assessments to performance in functional upright activities. The results are intended to guide the selection of supine assessment tools for the upcoming bedrest study and the development of future inflight proprioceptive training and assessment tools for exploration missions. METHODS Seventeen healthy participants (8 males and 9 females, 27.9 ± 8.5 years) provided informed consent as approved by the Institutional Review Board (IRB) at NASA. A horizontal air-bearing sled was used to provide a proprioceptive challenge in a supine body orientation, allowing for mediolateral motion with minimal friction during supine stance [4]. Participants were loaded axially (30 to 60% body weight) with their feet on a vertically oriented tilt board and instructed to perform supine assessment activities using custom software that displays a cursor controlled by tilting the tilt board. In a single-leg static activity on the tilt board (TB-St), performance was measured as the percent time spent on a center target during a 30-second trial. During a two-feet dynamic activity on the tilt board (TB-Dy), performance was measured as the number of targets captured in 30 seconds. These supine tasks were compared to performance during two upright activities used to represent upright performance: completion time for a functional mobility task (FMT) [2],[5] and a performance score from force plate sway data during 30-second single-leg upright standing (USL). RESULTS AND DISCUSSION A Spearman's rank-order correlation was run to assess the relationship between upright and supine assessments. There were statistically significant, strong correlations between scores for the FMT and each TB activity, including TB-St, rs(17) = 0.50, p<0.05, TB-Dy3, rs(17) = 0.73, p<0.001, and TB-Dy9, rs(15) = 0.53, p<0.05. There were no statistically significant correlations between USL and each TB activity. Despite a lack of correlation with upright single-leg balance, the ability of the TB to indicate upright performance in the FMT is promising, as this task is a standard post-flight measure designed to characterize locomotor dysfunction [2], [5]. CONCLUSION These efforts will inform the selection of an appropriate sensorimotor assessment method for the upcoming bed rest study and similar future studies. While current work uses a bed rest analog to develop these technologies, future work aims to prepare capabilities for future in-flight sensorimotor training and assessment to mitigate the risks of proprioception impairment after spaceflight. ACKNOWLEDGEMENTS This work was supported by the University Space Research Association Internship Program. REFERENCES [1] Mulavara A.P. et al (2018) Med Sci Sports Exerc, 50(9), 1961-1980. [2] Mulavara A.P. et al (2010) Exp Brain Res, 202(3), 649-659. [3] Macaulay T.R. et al (2021) Front. Syst. Neurosci., 15, 658-985. [4] Goel R. et al (2017) Front. Syst. Neurosci, 11. [5] Koppelmans V. et al (2013) BMC Neurology, 12.

R Bellisle↗

Material Response Analysis of PICA-NuSil at the Hypersonic Materials Environmental Test System

Phenolic impregnated carbon ablator (PICA) is a thermal protection system (TPS) that gained heritage during the Stardust mission and is the baseline TPS material for expeditions to Mars (e.g., Mars Science Laboratory and Mars 2020).1 PICA is friable; therefore, a polysiloxane resin (NuSil CV-1144-0) is deposited onto the surface of flight hardware before assembly, test, and launch operations (ATLO) to mitigate particle shedding. Furthermore, the Mars Science Laboratory (MSL) and Mars 2020 heatshields were instrumented with an array of thermocouple plugs (MEDLI Integrated Sensor Plugs or MISP) designed to measure the in-depth thermal response of PICA during flight. Post-flight analysis of MISP data suggests that recession was lower than predicted, and NuSil is suspected as a reasonable cause. NuSil primarily consists of a copolymer blend of dimethyl and diphenyl-substituted polysiloxane resin. Four decades of research in the field of polymer-derived ceramics demonstrate that polysiloxane resins form oxidation-resistant silicon oxycarbide materials at pyrolysis temperatures as low as 800 °C. -2 Furthermore, silicon oxycarbide materials exhibit excellent thermal stability at relatively high-temperatures (< 1200 °C) but eventually decompose via carbothermal reduction. -3 Therefore, it is reasonable to suggest that NuSil forms an oxidation-resistant thermal barrier coating on the TPS surface and influences the material response during atmospheric entry. Evidence for this notion was observed during prior arc-jet testing at both the Aerodynamic Heating Facility (AHF) and the Panel Test Facility (PTF) at the NASA Ames Research Center. Articles of PICA were subjected to testing in airflow, and a temperature discontinuity was recorded at the surface of coupons coated with NuSil. A glassy coating was observed to form at the surface in shear flow configurations (wedge and panel) under moderate heat fluxes. Furthermore, state-of-the-art material response models have yet to account for the underlying physics and chemistry of the temperature discontinuity. To this end, a pathfinder test campaign was executed at the Hypersonic Materials Environmental Test System (HyMETS) to enhance the fundamental understanding of PICA-NuSil ablation phenomena and provide a preliminary data set to aid in the development of material response models.

PICA↗

Overview of NASA's Detailed Investigation into the MEDLI2 Flight Data

- MEDLI2 “Deep Dive” - Why a focused investigation? - Deeper understanding of the MEDLI2 dataset, including TPS in-depth temperatures, backshell pressure, and radiative heat flux - Validate new tools for future Mars missions including Mars Sample Return and Humans-to-Mars - MEDLI2 is considerably more complex than MEDLI - Fully leverage the significant investment in the MEDLI2 instrumentation suite - Core research areas: - PICA-NuSil (PICA-N) testing, model development, and validation - Detailed aeroheating investigations - Sensor fusion of collocated measurements - High-fidelity analyses and uncertainty quantification - Trajectory and aerodynamics investigations

Tom West↗

Aerothermal Analysis and Thermal Protection System Design of the Mars Sample Retrieval Lander [SRL].

Mars Sample Retrieval Lander, part of the Mars Sample Return (MSR) mission, is being designed to land the heaviest payload yet, to the surface of Mars. SRL is being designed to carry the Lander, Sample Transfer System, Mars Acent Vehicle, and two Sample Recovery Helicopters. Compared to MSL and Mars 2020, SRL has a significantly higher ballistic coefficient, and flies at a higher lift/drag configuration. While the SRL heatshield is very similar to that of MSL and M2020, the backshell is very different, so as to accommode the payload. SRL is shielded by the same TPS materials as MSL and Mars 2020, with changes to design reflecting the SRL configuration and ConOPS. The aerothermal analysis and TPS design methodology of SRL relies on the successes of MSL and Mars 2020, and the lessons learned from MEDLI and MEDLI2. However, the constraints on mass require us to revisit all of our prediction models and analysis assumptions, in an attempt to reduce conservatism and TPS mass. MSL and Mars 2020 reconstruction, and detailed comparisons against MEDLI/MEDLI2 data are being used to justify our analysis approach and refine uncertainties and margins.

Mars↗

Kinematic Sensors Evaluation for Spaceflight Exercise Data Collections

INTRODUCTION: On the International Space Station (ISS), exercise feedback from astronauts is very important to diagnose and mitigate any form-related injuries and ensure efficacious exercise prescriptions and systems. Going forward, exploration exercise efforts seek to gain further quantitative data of human and system performance. Currently, methods of collecting in-flight exercise data on the ISS are limited to marker-based motion capture (MoCap) where astronauts must wear reflective markers over their clothes and specialized cameras are used. The main objective of this work was to investigate the following alternative tracking options: markerless video-based MoCap and inertial measurement units (IMUs). These were compared against traditional marker-based MoCap to evaluate kinematic accuracy and inform feasible methods for future exercise data collections on the ISS, especially in support of future Vibration Isolation and Stabilization (VIS) system development. METHODS: Three test subjects performed a variety of flight-like resistance and aerobic exercises using the Miniature Exercise Device (MED-2), Concept-2 rowing ergometer, barbell mockup, bench (e.g., for bench press, hip thruster, and cycling), and a custom structure for dips. These were intended also to represent exercises which could be performed on the multi-modality European Enhanced Exploration Exercise Device (E4D) [1]. The marker-based MoCap data, collected through a 16-camera OptiTrack MoCap system, was regarded as the gold standard to compare the data against. Passive markers were affixed to each subject according to a modified full body Plug-in Gait marker set [2] with 46 total markers. The markerless MoCap data was collected using two GoPro Hero7 cameras and one GoPro Hero11 camera. For the IMU data, a full body set of 17 Xsens DOTs were placed on the subject: 10 upper body and 7 lower body IMUs. Biomechanical modeling and evaluation was conducted through OpenSim [3] (MoCap), OpenSense [4] (IMU), OpenCap [5] (markerless), ENABLE [6] (markerless), and other modeling software. Secondary objectives included comparing the volume of equipment, reducing mass and crew set-up time. RESULTS AND DISCUSSION: While there were issues with initial processing for the IMUs and markerless MoCap, the results aided in the understanding of each sensor, developing end-to-end processes, and identifying future needs. Some observed concerns with the markerless MoCap approaches included being cognizant of a cluttered background, number of people in field of view, camera number and placement. Some challenges with the IMUs included possible sliding, early deactivation possibly due to exercise pose, and large quantity sensor synchronization. Overall, the markerless MoCap option may be the preferred method of data collection and processing as it provides a solution for certain IMU shortcomings and may be least in equipment volume, upmass, and crew setup time. CONCLUSIONS: While this work was mainly focused on ISS data collection, these sensor data along with continued evaluation and development efforts will help to establish best methods for exercise data collection on Gateway, for other Artemis missions, and beyond. Details on the latest end-to-end processing of the data and results will be presented, along with lessons learned and recommended sensor selection and methods.

S. Faragalla↗

Artificial Intelligence Medical Support for Long-Duration Space Missions

We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth, minimizing the amount of Earth data that needs to be transferred into space. Then during deployment, the system would be constantly refined through active learning from diverse streams of data from sensors in the spacecraft, data collected daily from individual astronauts, and human-in-the-loop feedback from the crew. The model could be interrogated for predictions and recommendations on personalized crew health based on the overall status of the spacecraft, medicinal stores, and status of other crew members. Adaptation techniques would be used to incorporate spaceflight data that have very different distributions from the training data due to the extreme environment. Edge computing and the most advanced neuromorphic processing would enable computation in scenarios with low power and bandwidth, while dimensionality reduction would be employed to ensure that the input data streams from spaceflight are as small as possible. In order to realize this long-term vision, several hardware and software aspects need to be developed and assembled. First, models pretrained on Earth biomedical data would need to be evaluated for predictive accuracy, and the best one selected. That model would need to be adapted to learn from diverse, sparse, and inconsistently measured data streams, as well as human-in-the-loop feedback. A data integration, standardization, and dimensionality reduction methodology would need to be developed to handle all data types and feed them into the model. Once the software and data infrastructure is developed, it would need to be integrated with small footprint compute processors and tested in high-radiation, high-vibration, unregulated temperature situations. As a short-term goal, we recommend to focus on the development of the data and model software structure. Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios. Then, question and answer tasks should be designed to interrogate the model for predictions and recommendations, with acceptable answers already identified.

Artificial Intelligence↗

Kinematic Sensors Evaluation for Spaceflight Exercise Data Collections

INTRODUCTION: On the International Space Station (ISS), exercise feedback from astronauts is very important to diagnose and mitigate any form-related injuries and ensure efficacious exercise prescriptions and systems. Going forward, exploration exercise efforts seek to gain further quantitative data of human and system performance. Currently, methods of collecting in-flight exercise data on the ISS are limited to marker-based motion capture (MoCap) where astronauts must wear reflective markers over their clothes and specialized cameras are used. The main objective of this work was to investigate the following alternative tracking options: markerless video-based MoCap and inertial measurement units (IMUs). These were compared against traditional marker-based MoCap to evaluate kinematic accuracy and inform feasible methods for future exercise data collections on the ISS, especially in support of future Vibration Isolation and Stabilization (VIS) system development. METHODS: Three test subjects performed a variety of flight-like resistance and aerobic exercises using the Miniature Exercise Device (MED-2), Concept-2 rowing ergometer, barbell mockup, bench (e.g., for bench press, hip thruster, and cycling), and a custom structure for dips. These were intended also to represent exercises which could be performed on the multi-modality European Enhanced Exploration Exercise Device (E4D) [1]. The marker-based MoCap data, collected through a 16-camera OptiTrack MoCap system, was regarded as the gold standard to compare the data against. Passive markers were affixed to each subject according to a modified full body Plug-in Gait marker set [2] with 46 total markers. The markerless MoCap data was collected using two GoPro Hero7 cameras and one GoPro Hero11 camera. For the IMU data, a full body set of 17 Xsens DOTs were placed on the subject: 10 upper body and 7 lower body IMUs. Biomechanical modeling and evaluation was conducted through OpenSim [3] (MoCap), OpenSense [4] (IMU), OpenCap [5] (markerless), ENABLE [6] (markerless), and other modeling software. Secondary objectives included comparing the volume of equipment, reducing mass and crew set-up time. RESULTS AND DISCUSSION: While there were issues with initial processing for the IMUs and markerless MoCap, the results aided in the understanding of each sensor, developing end-to-end processes, and identifying future needs. Some observed concerns with the markerless MoCap approaches included being cognizant of a cluttered background, number of people in field of view, camera number and placement. Some challenges with the IMUs included possible sliding, early deactivation possibly due to exercise pose, and large quantity sensor synchronization. Overall, the markerless MoCap option may be the preferred method of data collection and processing as it provides a solution for certain IMU shortcomings and may be least in equipment volume, upmass, and crew setup time. CONCLUSIONS: While this work was mainly focused on ISS data collection, these sensor data along with continued evaluation and development efforts will help to establish best methods for exercise data collection on Gateway, for other Artemis missions, and beyond. Details on the latest end-to-end processing of the data and results will be presented, along with lessons learned and recommended sensor selection and methods.

S Faragalla↗

Investigation of Hardware and Instrumentation to Measure Hand Grasp Activity with the Spacesuit Gloves

Introduction: During the 2022 suited injury summit, it was hypothesized that there will be concerns for hand and glove injuries for future exploration space missions, especially given the fact that the “total number of Extravehicular activity (EVA) hours and frequency” for lunar surface missions is expected to vastly increase [1]. It has been reported that the hands experienced the greatest “absolute numbers” of reported injuries and far exceeds other injuries during EVA [1, 2]. It was reported that the most fatiguing part of the surface EVA was the repetitive gripping tasks [3]. It was recommended that a “glove sub-team” be created to look at possible injury mechanism and mitigation strategies. Some of the recommendations that were suggested [1] are as follows: examine hand fatigue, utilize motion capture, examine the duration and frequency of hand movements, and identify frequent hand motions. We started assessing hardware and instrumentation to measure hand grasp activity in the pressurized glove environment. The purpose of this test was to perform a hardware evaluation for motion capture (MoCap) gloves obtained from StretchSense (Auckland, New Zealand). The specific gloves used were the Pro Fidelity and SuperSplay to determine the repeatability, reliability, feasibility, and useability inside of a pressurized gloved environment. Methods: The MoCap gloves were customized (e.g., battery/Bluetooth pack relocated to upper arm) to better suit the pressurized testing environment and protect the subject from unintentional injury (Fig. 1). Fourteen total subjects from different demographics (i.e., gender and pressurized glove experience level) participated in this test series. Testing included one session each of a baseline data collection (NASA Johnson Space Center (JSC) building 21) and a spacesuit glove box (Fig. 2 at JSC building 7 room 2027) data collection (under vacuum down to 4.3 psid), where each session lasted 3-5 hours. Controlled and reproducible tasks to systematically evaluate the repeatability and reliability of the hardware were performed during baseline data collection. Additionally, subjects performed simulated EVA-like tasks in a pressurized gloved environment. For all sessions, MoCap gloves were placed on each of the subjects’ hands and the signal from it, or the raw capacitance (Fig. 3), was analysed. The raw capacitance was used to estimate the open and closed hand states between the testing conditions and allow us to provide an offset caused by the pressurized environment. Results & Discussion: Initial observation with the bare hands (baseline) condition showed that the MoCap gloves appeared to track grasping and releasing of the fingers (opening and closing fist) with both high- and low-speed conditions, while adduction and abduction of the fingers were not relatively tracked. A hardware evaluation was done outside of the glove box to assess the reliability and repeatability of the MoCap glove. In one task, a point force was applied to various locations on the back of the hand. When the point force was applied to the space between the 1st digit and the pointer finger, there was a noticeable distortion to the MoCap data. Another task examining an increasing force from a 10 lb. sandbag applied to the back of the hand while lying flat on a table, showed a constant flat line with only a distortion when the weight was increased or added to the back of the hand. Fig. 3 shows an object relocation task where you can see when each individual finger “opened” and “closed” (changed position) when picking up and setting down the dumbbell. When the fingers were stationary, the signal remained relatively flat compared to the peaks and valleys that can be observed in Fig. 3. This study showed promising results and imperative input into an attempt to discriminate between hand states across various functional tasks and should be evaluated with context to the repeatability and reliability outcomes. Depending on the task done inside of the pressurized glove box environment and outside, the results appear to be affected by many different factors (i.e., drift, pressure, hand size, etc.). Significance: If this hardware proves to be reliable and repeatable in determining the open and closed hand states then this may provide critical insight into assisting in the characterization of the pressurized gloved environment and the effect on crew member exertion level. Ultimately, this tool will provide useful data for quantifying the repetitive nature of EVA training and tasks. Acknowledgments: The authors would like to acknowledge the NASA Mars Campaign Office for providing funding for this research. Lastly, thanks to all the engineers and technicians at NASA JSC who helped with this data collection. References: [1] Reiber, et al. (2022), NASA/TM-20220007605; [2] Scheuring, et al. (2009), Av., Sp., and Envir. Med. 80(2). [3] Scheuring, et al. (2007), NASA/TM–2007–214755.

Rachel L Thompson↗

Aerothermal Analysis and Environment Predictions for the Mars Sample Retrieval Lander (SRL)

Mars Sample Retrieval Lander (SRL) is the next mission to Mars, and an integral part of the proposed Mars Sample Return (MSR) Program. Aerothermal Analysis of the SRL capsule takes advantage of the design and analysis of the Mars Science Laboratory and the Mars 2020 missions, findings from the MEDLI and MEDLI2 heatshield instrumentation campaigns, and developments in predictive capabilities over the last 20 years. In particular, SRL is being designed to enter the Mars atmosphere at velocities as high as 8 km/s, which would be the highest for a Mars entry, and is expected to encounter additional shock layer radiation physics compared to previous missions to Mars. This paper presents the status of analysis including the overall methodology, models and assumptions of the aerothermal environment predictions, with a focus on differences from the approaches and modeling used for Mars 2020 and MSL.

Mars↗

Aerothermal Analysis and Environment Predictions for the Mars Sample Retrieval Lander (SRL)

Mars Sample Retrieval Lander (SRL) is the next mission to Mars, and an integral part of the proposed Mars Sample Return (MSR) Program. Aerothermal Analysis of the SRL capsule takes advantage of the design and analysis of the Mars Science Laboratory and the Mars 2020 missions, findings from the MEDLI and MEDLI2 heatshield instrumentation campaigns, and developments in predictive capabilities over the last 20 years. In particular, SRL is being designed to enter the Mars atmosphere at velocities as high as 8 km/s, which would be the highest for a Mars entry, and is expected to encounter additional shock layer radiation physics compared to previous missions to Mars. This paper presents the status of analysis including the overall methodology, models and assumptions of the aerothermal environment predictions, with a focus on differences from the approaches and modeling used for Mars 2020 and MSL.

Mars↗

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↗

Lessons Learned from Aerothermal Flight Data on NASA Mars Entry Vehicles

The NASA Mars Science Laboratory (MSL) and Mars 2020 entry vehicles included temperature measurements inside and direct heat flux on the surface of the thermal protection system (TPS) material. The instrumentation was included to begin addressing predictive capabilities for aerodynamic heating and ablative TPS material response. The MSL aeroshell included temperature measurements at seven heatshield locations, from which total aerodynamic heating was reconstructed. The Mars 2020 instrumentation suite included eleven heatshield measurement locations and new backshell measurements at nine locations: six with temperature sensors, two with sensors to directly measure total heat flux, and one to measure radiative heat flux. Both instrumentation suites returned full sets of data, with all temperature sensors nearest to the surface surviving entry, indicating minimal material recession. Heatshield boundary layer transition (BLT) was observed for both vehicles prior to the time of peak heating, with transition starting near the shoulder and progressing towards the stagnation area. MSL’s higher speed on approach to Mars resulted in higher total heating compared to Mars 2020, with the maximum occurring near the heatshield’s leeside shoulder. The Mars 2020 backshell heating data indicate significant contributions from shock layer radiation. Heating from Navier-Stokes flowfield calculations are in family with reconstructed heating. The paper covers the instrumentation, BLT and reconstructed heating on both heatshields, backshell heating for Mars 2020, TPS performance, and predicted versus reconstructed heat fluxes. Opportunities for improved design approaches for future Mars missions are discussed, especially for the upcoming Mars Sample Return mission.

Mars Science Laboratory↗

Lessons Learned from Aerothermal Flight Data on NASA Mars Entry Vehicles

The NASA Mars Science Laboratory (MSL) and Mars 2020 entry vehicles included temperature measurements inside and direct heat flux on the surface of the thermal protection system (TPS) material. The instrumentation was included to begin addressing predictive capabilities for aerodynamic heating and ablative TPS material response. The MSL aeroshell included temperature measurements at seven heatshield locations, from which total aerodynamic heating was reconstructed. The Mars 2020 instrumentation suite included eleven heatshield measurement locations and new backshell measurements at nine locations: six with temperature sensors, two with sensors to directly measure total heat flux, and one to measure radiative heat flux. Both instrumentation suites returned full sets of data, with all temperature sensors nearest to the surface surviving entry, indicating minimal material recession. Heatshield boundary layer transition (BLT) was observed for both vehicles prior to the time of peak heating, with transition starting near the shoulder and progressing towards the stagnation area. MSL’s higher speed on approach to Mars resulted in higher total heating compared to Mars 2020, with the maximum occurring near the heatshield’s leeside shoulder. The Mars 2020 backshell heating data indicate significant contributions from shock layer radiation. Heating from Navier-Stokes flowfield calculations are in family with reconstructed heating. The paper covers the instrumentation, BLT and reconstructed heating on both heatshields, backshell heating for Mars 2020, TPS performance, and predicted versus reconstructed heat fluxes. Opportunities for improved design approaches for future Mars missions are discussed, especially for the upcoming Mars Sample Return mission.

Mars Science Laboratory↗