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Biofilm Management in a Microgravity Water Recovery System

Biofilm growth continues to be a significant concern for NASA’s current and future water systems. The International Space Station (ISS) Water Processor Assembly (WPA) produces potable water from a combination of humidity condensate and urine distillate. The WPA waste tank requires significant management to prevent biofilm growth from impacting downstream components. This issue is magnified for future NASA manned missions due to the need to place the vehicle’s life support system in a dormant state during uncrewed operations (e.g., when the vehicle is in Mars orbit during the surface mission). NASA personnel and the Center for Biofilm Engineering at Montana State University are evaluating various methods for mitigating biofilm growth, including reduced nutrient levels, thermal treatment, sonic waves treatment, and identifying effective biocides in this application. This paper provides an overview of the current status of this effort.

Yo-ann Velez Justiniano↗

Maven Orbital Trajectory Analysis: Design and Implementation of Lander Relay Support

NASA’s successful Mars Atmosphere and Volatile Evolution Mission (MAVEN), currently engaged in a survey of the Martian atmosphere and its evolution over time, has successfully completed a series of orbit changes, including a two-month aerobraking campaign, to satisfy additional mission roles. Launched on 18 November 2013, it entered Martian orbit on 22 September 2014. Having finished its primary one-year mission, the NASA Mars Program Office (MPO) started discussions with the MAVEN project on feasible options for extending the mission lifetime along with providing support for various Mars lander functions. MAVEN is the latest of the three active NASA Mars orbiters, with no others planned for the foreseeable future. Given that the landers need orbital relay support for years to come, and the MRO and Odyssey orbiters are aging, MAVEN’s role in providing critical orbital relay support has become increasingly important. After several years of studies, MPO directed the MAVEN project in 2018 to reduce its orbit size to provide better telecommunications relay support for Martian surface landers and to support Mars 2020 EDL under the constraint that MAVEN will have enough fuel to operate until 2030. As a part of this effort, the recent aerobraking campaign was one of several steps crucial to accomplishing this goal. This paper discusses the challenges of the entire mission design and its distillation into a concrete set of targeted objectives. With the successful orbit change, MAVEN will be able to provide support for the Mars 2020 relay activities from EDL through surface operations beyond 2030. This effort can serve as an excellent example for future missions having similar needs and requirements.

Jakosky, B. M.↗

Comparison of Two Community Noise Models Applied to a NASA Urban Air Mobility Concept Vehicle

Predictions of community noise exposure from a NASA urban air mobility (UAM) concept vehicle have been conducted for representative operations using the FAA Aviation Environmental Design Tool (AEDT) in order to demonstrate modeling tool interoperability and assess applicability, capabilities and limitations of integrated noise modeling tools. To both quantify limitations and highlight other capabilities, a comparative analysis is performed using a time-domain simulation method, in particular, using the Volpe Advanced Acoustic Model (AAM). Using the same source noise model, the 3D directivity of a UAM concept vehicle is predicted in terms of aeroacoustic pressure time histories at a sphere of observer positions near the vehicle. In addition to distilling those data to a set of noise-power-distance data for input to AEDT, the data are processed preserving spectral directivity, into one-third and one-twelfth octave bands for input to AAM. Results from AEDT and AAM modeling are provided for a variety of metrics to demonstrate the effect that source noise directivity and propagation modeling fidelity have on predicted results at receptors over a study area.

urban air mobility↗

Comparison of Two Community Noise Models Applied to a NASA Urban Air Mobility Concept Vehicle

Predictions of community noise exposure from a NASA urban air mobility (UAM) concept vehicle have been conducted for representative operations using the FAA Aviation Environmental Design Tool (AEDT) in order to demonstrate modeling tool interoperability and assess applicability, capabilities and limitations of integrated noise modeling tools. To both quantify limitations and highlight other capabilities, a comparative analysis is performed using a time-domain simulation method, in particular, using the Volpe Advanced Acoustic Model (AAM). Using the same source noise model, the 3D directivity of a UAM concept vehicle is predicted in terms of aeroacoustic pressure time histories at a sphere of observer positions near the vehicle. In addition to distilling those data to a set of noise-power-distance data for input to AEDT, the data are processed preserving spectral directivity, into one-third and one-twelfth octave bands for input to AAM. Results from AEDT and AAM modeling are provided for a variety of metrics to demonstrate the effect that source noise directivity and propagation modeling fidelity have on predicted results at receptors over a study area.

urban air mobility↗

Flame Structure Comparison using Jet-A and an isoparaffinic fuel in a lean direct injection flame tube combustor

This paper offers a first look at some recently acquired optical data. The effect of reference velocity on flame thickness, fuel atomization, and relative speciation of OH, CH, C2, and soot for two National Jet Fuel Combustion Program (NJFCP) fuels are explored in this paper. The NJFCP fuels are an average Jet-A, designated A-2 with a derived cetane number (DCN) of 49, and an isoparaffinic kerosene, C-4. C-4 fuel is made up primarily of C9 - C13, and C16 isoparaffins and is a combination of 60% Sasol IPK and 40% GEVO alcohol-to-jet (ATJ). C-4 has a DCN of 28. Both fuels begin distillation at about the same temperature, but C-4 completes about 30 degrees Celsius lower than A-2. Chemiluminescence images were acquired in a 5-atm gas turbine flame tube combustor using the NASA 7-point lean direct injector (LDI). The flame characteristics were determined through imaging with three scientific cameras. OH*, CH*, and C2* chemiluminescence were collected through narrow bandpass filters centered at 313nm, 430nm and 515nm onto an intensified high-speed camera framed at 8-kHz. A second high-speed camera collected color flame images at rates between 40 and 50 kHz. An ICCD camera framed at 20 Hz was used to record planar laser scatter from liquid fuel. Differences in speciation for the chemiluminescent species was observed between the two fuels. Some reasons for the differences are explored in this paper.

gas turbine combustion↗

Method Development for In Situ Microbiome Profiling of the Water Recovery System’s Wastewater Tank Onboard the International Space Station

A distinctive microbial community has inhabited the wastewater tank within the International Space Station Water Recovery System (WRS) for over 14 years and experienced the stressors associated with the microgravity environment. The WRS generates potable water for the crew from urine distillate, humidity condensate, Sabatier product water, and the occasional off-loading of ground-supplied water. The reservoir for these products, the wastewater tank, does not have a means of microbial control. While samples are occasionally collected for analysis, the time between sample collection and the return to Earth, as well as the lack of preservation, results in a skewed depiction of the microbiome. Routinely observed from these returned samples are high counts (105 – 106 colony forming units per mL) and two prevailing genera, Burkholderia and Ralstonia. The wastewater tank likely contains a more diverse microbiome, as a higher diversity of bacteria and fungus has been noted upstream and downstream of the tank. To characterize the microbial profile of the tank, analysis needs to occur at the time of sample collection. Toward this goal, a method for in situ analysis based on nanopore sequencing was developed. The filter-to-sequencer method evolved from previous work that has been validated onboard the ISS (BEST payload and the BioMole Crew Health Care Systems Facility). The method, including filtration, DNA extraction, purification, amplification, library preparation, and nanopore sequencing will be described. Additionally, data collected with this method from both ISS and terrestrial samples will be detailed. The consumables needed to support in situ analysis of the tank are set to the launch to the ISS in the spring of 2023. This investigation will allow for the first accurate characterization of the microbiome of the tank providing insight for crew health, planetary protection, and has the potential to enable engineering controls for future space station water systems.

Sarah Stahl-Rommel↗

Exploration Helmet Permanent Anti-fog Study

For the current Extravehicular Mobility Unit (EMU) spacesuit, an astronaut applies an anti-fog solution to the interior of the helmet bubble before each EVA. However, the anti-fog solution has been reported to cause eye discomfort during at least seven EMU EVAs when the anti-fog solution contacted the crew member’s eyes. During STS-100, astronaut Chris Hadfield reported the eye irritation temporarily blinded him during his spacewalk. In addition, the wipe on anti-fog solution is a consumable that needs to be accounted for and a supply launched for missions. To solve this, the Exploration EMU (xEMU) pressure bubble investigated using a permanent anti-fog coating during Design Verification and Test (DVT) human in the loop (HITL) events. Chosen for previous use in Constellation Space Suit develop, HTAF-601, a water based permanent anti-fog solution, was tested. However, major issues have arisen with further HITL testing with the coating. Cleaning the helmet has been a challenge to avoid damaging the permanent anti-fog coating. NASA has completed a set of different methods for cleaning the anti-fog to try to document a preferred method. It was found isopropyl alcohol (IPA) cannot be used to clean the interior of the helmet because it will strip and delaminate the permanent anti-fog coating. Even with using a very gentle cleaning method of flushing with distilled or DI water and dabbing at facial oils, the permanent anti-fog starts to delaminate consistently after 50 hours manned pressurized time (MPT). Finally, the HTAF-601 coating is being discontinued by the vendor. Due to these issues, further investigation is being completed evaluating two solvent based anti-fog solutions: Exxene’s HCF-100 and FSI’s Visgard 106-94. Each coating will be evaluated on polycarbonate samples for application consistency, steam cycles (mimicking breath cycles), cleaning durability, haze, and light transmission. This study will determine if either coating is a viable option to pursue as a permanent anti-fog for spacesuit helmet applications.

spacesuit↗

Method Development for In Situ Microbiome Profiling of the Water Recovery System’s Wastewater Tank Onboard the International Space Station

A distinctive microbial community has inhabited the International Space Station (ISS) Water Recovery System (WRS) for over 14 years and has experienced the stressors associated with the microgravity environment. The WRS generates potable water for the crew from urine distillate, humidity condensate, Sabatier product water, and the occasional off-loading of ground-supplied water (1). The reservoir for these products, the wastewater tank, does not have a means of microbial control. Current in situ microbial monitoring of the WRS is limited to quarterly culture-based assessments of the potable water product using a microbial capture device and coliform detection bag. Additional analysis of the wastewater and condensate sources are collected into Teflon bags for analysis following return to the ground. The time between sample collection and the return to Earth, as well as the lack of preservation, results in a skewed depiction of the microbiome. Routinely observed from these returned wastewater samples are high counts (105 – 106 colony forming units per mL) and two prevailing genera, Ralstonia and Cupriavidus, as well as a high abundance of unidentified organisms (Table 1). The wastewater tank likely contains a more diverse microbiome, as a higher diversity of bacteria and fungi has been noted upstream and downstream of the tank.

Sarah Stahl-Rommel↗

State-of-the-Art: Small Spacecraft Technology

When the first edition of NASA’s Small Spacecraft Technology State-of-the-art report was published in 2013, 247 CubeSats and 105 other non-CubeSat small spacecraft under 50 kilograms (kg) had been launched worldwide, representing less than 2% of launched mass into orbit over multiple years. In 2013 alone, around 60% of the total spacecraft launched had a mass under 600 kg, and of those under 600 kg, 83% were under 200 kg and 37% were nanosatellites (1). Of the total 1,849 spacecraft launched in 2021, 94% were small spacecraft with an overall mass under 600 kg, and of those under 600 kg, 40% were under 200 kg, and 11% were nanosatellites (1). Since 2013, the fight heritage for small spacecraft has increased by over 30% and has become the primary source to space access for commercial, government, private, and academic institutions. The total number of spacecraft launched in the past 10 years is 5,681 and 45% of those had a mass. As with all previous editions of this report, the 2022 edition captures and distills a wealth of new information available on small spacecraft systems from NASA and other publicly available sources. This report is limited to publicly available information and cannot reflect major advances in development that are not publicly disclosed. We encourage any opportunity to publish mission outcomes and technology development milestones (e.g., via conference papers, press releases, company website) so they can be reflected in this report. Overall, this report is a survey of small spacecraft technologies sourced from open literature; it does not endeavor to be an original source, and only considers literature in the public domain to identify and classify devices. Commonly used sources for data include manufacturer datasheets, press releases, conference papers, journal papers, public filings with government agencies, news articles, presentations, the compendium of databases accessed via NASA’s Small Spacecraft Systems Virtual Institute (S3VI) Information Search, and engagement with companies. Data not appropriate for public dissemination, such as proprietary, export controlled, or otherwise restricted data, are not considered. As a result, this report includes many dedicated hours of desk research performed by subject matter experts reviewing resources noted above. Content in this 2022 edition is based on data available by October 2022. This report should not be considered as a comprehensive overview of all the technologies but a great reference for the current state-of-the-art SmallSat technologies. The organizational approach for each chapter is relatively consistent with previous editions and includes an introduction of the technology, current development status of the technology’s procurable systems, and summary tables of technologies surveyed. The content in each chapter is uniquely organized to present a mini-stand-alone report on spacecraft subsystems. As in previous years, chapters include information from previous editions but are updated with new and maturating technologies and reference missions. Tables in each section provide a convenient summary of the technologies discussed, with explanations and references in the body text. The authors have attempted to isolate trends in the small spacecraft industry to point out which technologies have been adopted after successful demonstration missions. Lastly, the authors tried to use the terms “SmallSat,” “microsatellite,” “nanosatellite,” and “CubeSat” in a consistent manner, even as these terms are often used interchangeably in the space industry. Every subsystem chapter contains updated information to reflect the growth in the small spacecraft market. Significant changes are included in several chapters. The “Complete Spacecraft Platforms” chapter now includes information on the two main market options, hosted payload services and dedicated buses. The “Power” chapter provides information on the development of solid-state batteries with significantly higher energy than the current state-of-theart lithium-ion batteries. A large effort was made to update the “Communications” chapter to appropriately capture the recent technology maturation of optical communications for SmallSats. The “Ground Data Systems and Mission Operations” chapter was updated to reflect the recent establishment of the Near Space Network and influx of SmallSat Optical Ground Stations. The “Guidance, Navigation and Control” chapter was updated to include Lidar sensor technology. The “Deorbit Systems” chapter includes a discussion of recently proposed changes by the Federal Communications Commission (FCC) to limit a spacecraft’s lifetime to no longer than 5 years after end-of-mission. The “Identification and Tracking” Chapter includes updated information on the progress of SmallSat tracking. Finally, this report now encompasses technology funded by NASA’s Small Spacecraft Technology (SST) program’s SmallSat Technology Partnerships (STP) initiative which is described further in this Introduction. The reader can find the included SST technology in the “On the Horizon” section of the “Thermal Systems”, “Communications”, and “Guidance, Navigation, and Control” chapters. A central element of this report is to list state-of-the-art technologies by NASA standard Technology Readiness Level (TRL) as defined by the 2020 NASA Engineering Handbook, found in NASA NPR 7123.1C NASA Systems Engineering Processes and Requirements. The authors have endeavored to independently verify the TRL value of each technology by reviewing and citing published test results or publicly available data to the best of their ability. Where test results and data disagree with vendors’ own advertised TRL, the authors have attempted to engage the vendors to discuss the discrepancy. Readers are strongly encouraged to follow the references cited in the literature describing the full performance range and capabilities of each technology. Readers of this report should reach out to individual companies to further clarify information. It is important to note that this report takes a broad system-level view. To attain a high TRL, the subsystem must be in a flight-ready configuration with all supporting infrastructure—such as mounting points, power conversion, and control algorithms—in an integrated unit. An accurate TRL assessment requires a high degree of technical knowledge on a subject device, and an in-depth understanding of the mission (including interfaces and environment) on which the device was flown. There is variability in TRL values depending on design factors for a specific technology. For example, differences in TRL assessment based on the operating environment may result from the thermal environment, mechanical loads, mission duration, or radiation exposure. If a technology has flown on a mission without success, or without providing valid confirmation to the operator, such claimed “flight heritage” was discounted. The authors believe TRLs are most accurately determined when assessed within the context of a program’s unique requirements. While the overall capability of small spacecraft has matured since the 2021 edition of this report, technologies are still being developed to make deep space SmallSat missions more routine and more cost effective. Future editions of this report may include content dedicated to the rapidly growing fields of assembly, integration, and testing services, and mission modeling and simulation–all of which are now extensively represented at small spacecraft conferences. Many of these subsystems and services are still in their infancy, but as they evolve and reliable conventions and standards emerge, the next iteration of this report may also evolve to include additional chapters.

Bruce Yost↗

The Chemical and Isotopic Signatures of the Hibonite-rich FUN Inclusion "HIDALGO" in Dar al Gani 027 (CO3)

Refractory Ca-Al-rich Inclusions (CAIs) with FUN (Fractionation with Unidentified Nuclear effects) characteristics are peculiar samples among all high-temperature components in chondritic meteorites. They are generally characterized by strong mass-dependent isotopic fractionations in several elements (e.g., O, Mg, Ca, Ti), large (~5-20‰) enrichments or depletions in neutron-rich isotopes (e.g., 48 Ca, 50 Ti, 54 Cr), and low inferred abundances of 26 Al ( 26 Al/ 27 Al < 1×10 −5 ). Understanding the origins of these features in FUN CAIs can shed light on the astrophysical environment and chemical processes that took place in the early Solar System. A large fraction (slightly less than 50%) of the ~20 FUN CAIs discovered so far are hibonite-rich (such as HAL, SHAL and DH-H1, collectively called HAL-type inclusions hereafter). According to their elemental and isotopic signatures and the results of evaporation experiments, HAL-type inclusions are thought to have formed as a distillation residue. However, questions regarding the timing of the formation of HAL-type inclusions (or FUN inclusions in general) relative to those of regular CAIs and the decoupling between the 26 Al abundances and nucleosynthetic anomalies remain poorly understood. In 2021, we reported the discovery of a new HAL-type inclusion, HIDALGO (Hibonite in Dar al Gani CO3) in the CO3 chondrite Dar al Gani 027 (DaG027), based on its (fractionated) oxygen isotopic compositions and low inferred 26 Al/ 27 Al ratio of (1.50±0.02)×10 −5 . Since then, more work on other short-lived and stable isotope systems and trace element abundances has been conducted. Here we report these new results and discuss the implications for the possible formation history of HIDALGO and origins of shortlived radionuclides (SLRs).

Meteorite↗

An Overview of NASA’s Newest Engineering Model, ORDEM 4.0

Since the mid-1990s, one of the most important products produced by the NASA Orbital Debris Program Office (ODPO) has been the Orbital Debris Engineering Model (ORDEM). This series of models distills down our knowledge of the orbital debris environment to compute debris fluxes on satellites in a given orbit. This information can be used by spacecraft and upper stage designers and operators to design missions for better protection against the debris environment. The current version of the model is ORDEM 3.2, but the ODPO is working on the next generation of ORDEM, to be designated ORDEM 4.0. ORDEM 4.0 will include many known features from previous models, such as the ability to input a spacecraft orbit and time and to compute the flux as a function of debris size, impact speed, impact direction, and debris material densities, as well as uncertainty information on the flux. ORDEM 4.0 will update debris populations using the most recent measurements, including radar observations by the Haystack Ultrawideband Satellite Imaging Radar (HUSIR), NASA’s Goldstone radar, data from the new Space Surveillance Network Space Fence, and observations of Geosynchronous Earth Orbits (GEO) using the Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT). The latest in situ impact data from returned hardware surfaces will be used. In addition, ORDEM 4.0 will introduce a parameterized debris shape model based on laboratory hypervelocity impact tests, such as DebriSat. This will allow analysts to implement shape characteristics in their damage equations and more accurately predict impact damage risk by debris of different shapes and orientations. This paper provides an overview of some of the new features forthcoming in ORDEM 4.0 and a status report on its development.

Mark Matney↗

Combined Experimental and Modeling Study of the Interactions of Acid Gas with Common Spacecraft Surfaces for Fire Safety Applications

A fire in a spacecraft poses detrimental consequences and risks mission success in addition to crew safety. This is compounded during long-duration missions when the crew has limited options to recover from a fire. A common spacecraft fire concern is the smoldering of wire insulation, typically made from Polyvinyl chloride (PVC) or Polytetrafluoroethylene (PTFE). This creates acid gases such as Hydrogen Chloride (HCl), Hydrogen Fluoride (HF) and Hydrogen Cyanide (HCN). These poisonous gases are hazardous to the crew. They also interact with common surfaces within the spacecraft more than dominant combustion products such as CO2 and H2O. This makes them more difficult to track for potential fire detection techniques, or for postfire clean-up. It is imperative to be able to understand and predict the fate of these poisonous species in a microgravity environment in order to design a safe vehicle. HCl interacts with a number of materials inside a spacecraft. Primary among these materials is aluminum, which is abundantly used due to its strong and light weight nature. Aluminum has a natural oxide layer that protects it from corrosion but is typically treated to enhance this oxide layer. Among these treatments is a chromate conversion coating (CCC), which provides a thin enough protective oxide layer to still conduct electricity, and a traditional anodized material that has a thicker oxide layer that does not conduct electricity. Nomex is another common material found inside a spacecraft. It is a flame-resistant woven polymer that is related to nylon. This commercially available material is used for cargo storage bags and as a fire barrier. Physics-based models were developed to predict the uptake of HCl by these materials. The ultimate objective of these models is to predict the fate of HCl within the spacecraft so that sensors can be placed in meaningful locations in future missions based on the model predictions. To support these modeling efforts, experiments were performed in a cast acrylic test cell that measured the difference between the inlet and outlet concentration of HCl after inserting a sample rod of the test material. Different uptake capacities were realized for each type of sample tested. A computational fluid dynamics model (CFD) model of the reactor was then constructed that used a one-step global reaction rate with calibratable reaction (or kinetic) constants. These constants were calibrated to match the HCl uptake on the CCC aluminum samples, and the same kinetic constants were then tested for the stock and anodized aluminum samples. Model predictions matched the experimental data for the stock aluminum, and to a much lesser extent, the anodized aluminum. The model was additionally validated at different flow rates, sample surface areas, and inlet concentrations, and showed good agreement for all stock and CCC samples. The model did not accurately predict the HCl uptake in the anodized samples compared to the other two types of aluminum. Adjusting the kinetic constants and transport properties did little to improve the prediction. X-Ray Photoelectron Spectroscopy (XPS) was used to determine that the oxide layer thickness of anodized aluminum is approximately 5,000 nm, compared to 250 nm for CCC and 50 nm for stock. XPS also revealed presence of chlorine further down in the aluminum oxide layer in anodized samples than CCC and stock samples after the samples were saturated with HCl, indicating that accounting for diffusion of HCl into the oxide layer is important for accurate prediction of HCl uptake onto anodized aluminum. Consequently, a multi-scale model was developed and tested. First, a single pore inside the anodized aluminum oxide layer was modeled and is referred to as the pore-scale model. In this model, HCl diffused through the pore and reacted with the aluminum oxide pore wall to create aluminum chloride. The sample was then saturated when the mass transfer resistance through the growing aluminum chloride layer became too large for the HCl to reach the aluminum oxide wall and continue the reaction. This pore-scale model was coupled to the reactor-scale model using a concentration-dependent diffusion coefficient, resulting in much more accurate predictions (approximately half the sum square error of the aforementioned reactor-scale model that produced good agreement for stock and CCC) for a variety of operating conditions. The amount of water vapor or relative humidity (RH) in the flow during a reactor experiment was determined to influence HCl uptake. Experiments were performed to understand the interaction of gaseous HCl with aluminum surfaces in the presence of water vapor. The results show that increasing levels of RH increased the capacity of aluminum to adsorb HCl but decreased the capacity of Nomex to uptake HCl. A series of tests were performed on individual aluminum samples after they had been saturated with a fixed concentration of HCl in dry air conditions with the goal of determining how their HCl uptake capacity changes after various treatments with water relative to the original saturation tests. HCl-saturated aluminum samples subjected to a second dry air flow at the same HCl concentration as the original test had an uptake of 23.5% of the original sample with no treatment in between. Saturated aluminum samples subjected to an air flow with a RH of 90% in between tests had an uptake of 35.6% of the original. Saturated aluminum samples submerged in distilled water for 12 hours in between tests had an uptake of 82.2% of the original sample. Previously saturated aluminum tested with HCl and a 50% RH air flow resulted in similar uptake characteristics in multiple repeated tests. The experiments show the profound effect water vapor has on HCl uptake onto aluminum surfaces. In the samples subjected to water vapor or liquid water, capillary condensation and capillary diffusion alters the transport of HCl significantly. A model was proposed that developed a relationship between RH and the coefficient of HCl diffusion in aluminum chloride. This produced an “S-shaped” curve with diffusion coefficient as a function of RH, with 45% RH represented as the point where the diffusion coefficient is halfway between no water saturation and 100% water saturation in the aluminum chloride product layer. No difference in uptake characteristics for the experiment or model were realized between 50% and 62% RH. The results from the large-scale microgravity experiment, Saffire, are discussed as they pertain to the fate of HCl throughout a spacecraft. HCl was released, both as a standalone event, and in concurrence with the burning of a structured cloth. These events only produced a small response in the far field HCl sensor, while a PMMA burn that did not produce HCl had a significantly greater response. A ground-based large-scale facility was constructed to flow acid gas at the scale and configuration realized in the Saffire experiments. A CFD model of this duct was constructed to test kinetic parameters developed in this work at a larger scale and different geometric configuration and to predict the results of the large-scale facility. The models developed in this work were used to interpret the results of the microgravity tests and lead the discussion on what further experiments and models are needed in order to predict the fate of acid gas in a spacecraft environment. To summarize, the major contributions of this work are as follows: the capacity to uptake HCl, with and without the presence of water vapor, was measured for a variety of real spacecraft surfaces. Several different models (single reactor-scale, multiscale, spacecraft-scale) were developed and with the aid of modeling, the rate of uptake for those surfaces was also predicted and validated. The kinetic parameters determined from the small-scale reactor experiments and models were used to predict large-scale and microgravity tests. Conclusions from this research will be used in the design of spacecraft vehicles and large-scale microgravity fire safety experiments. The models built by this work will aid designers in sensor placement and could be used to predict acid gas transport from fires in partial gravity, as would be seen in Lunar and Martian habitats.

fire safety↗

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

Principles for Architecting Autonomous Systems

This paper distills principles for developing autonomous systems based on experience and lessons learned from past efforts. The purpose of these principles is to establish a common understanding and knowledge of architectural elements to guide the development of next-generation multi-mission autonomous systems and ensure the safe and productive operation of space assets. An attempt has been made to ground these principles in fundamentals that should withstand the test of time while allowing for and enabling the advancement of technologies. They are not intended to prescribe a design nor a software representation. There may be multiple designs that can honor these principles. These principles are focused on autonomy for robotic assets. As such, they do not address autonomy for crewed assets nor autonomy that can collectively generate intelligent behavior without top-level system cognizance (e.g., intelligent swarm behavior). These areas would be a subject of future efforts.

Day, John↗

Identifying and Closing Medical Capability Gaps for Human Spaceflight Missions Beyond Low Earth Orbit

BACKGROUND: Features of human space missions beyond low Earth orbit such as increasing distance from Earth, lack of real-time communication, and limited or no evacuation or resupply capability are expected to drive an increase in medical risk and require crews to operate in an increasingly autonomous fashion. A diverse set of stakeholders at NASA are seeking to fund the development of concepts of operations, medical requirements, and medical capabilities for such missions. However, a systematic approach to identification of current medical capability gaps and a strategic framework to gap closure is needed. OVERVIEW: The Exploration Medical Integrated Product Team (XMIPT) has developed a list of nine high-level medical capability gaps and associated activities required for gap closure. The list was derived based on inputs from subject matter experts at NASA including flight surgeons, other clinical providers, as well as operational and research communities regarding medical capabilities required to support human missions to the Moon and the Mars surface. Responses were reviewed and distilled to identify common themes across capabilities. To ensure alignment with established human system risks, the gap list was further refined based on inputs from NASA’s Human System Risk Board. Relevant medical gap closure activities outside of those funded by the XMIPT were identified through solicitation of inputs from Elements of NASA’s Human Research Program (particularly Exploration Medical Capability), the broader medical operations community, and other stakeholders. This medical capability gap list is reviewed and updated regularly as new information becomes available or new stakeholders are identified. DISCUSSION: The medical capability gap list has matured to include a large group of NASA stakeholders and development activities. This has enabled articulation of priorities to funding entities and programmatic stakeholders, while serving as an accessible resource summarizing gap closure activities, relevant programmatic infusion points, and opportunities for collaboration between stakeholders. This presentation will provide an overview of the nine NASA medical capability gaps and their associated gap closure activities.

Moriah Thompson↗

Advanced Metal Separation (AMS) via Marangoni Effect-driven Separation

For practical, economical, and sustainable human missions to the Moon, processing of lunar regolith and metal oxides is necessary for localized production of oxygen (O2) and metals that can be repurposed as rocket propellant, life support O2, and in-space manufactured parts. The primary candidates for in-situ resource utilization (ISRU) regolith processing are carbothermic reductions and molten regolith electrolysis (MRE) which both produce oxygen and a reduced, deoxygenated metallic slag. A metal separation technique is still required to capture and purify metals from the deoxygenated slag for reuse in areas such as metallic part fabrication, structural or design frameworks, and metal catalyst production. The Marangoni Effect is best explained as mass transfer along an interface driven by surface tension gradients induced by temperature and concentration differences. Due to the temperature gradients, molten metal can be transported in this fashion to create thin films. This method can transport molten metal in the absence of gravity or mechanical motion and greatly increases the available surface area for a vacuum fractional separation process. In this study, a novel vacuum furnace induces the Marangoni Effect to fractionally decompose and separate valuable metals (e.g., aluminum) from thin films of molten metal via vacuum distillation. The proposed work will examine lunar regolith, metal-based recycling feedstocks, and metallic slags produced from the carbothermal reduction of regolith. Data and observations from this study could produce a simple yet effective method of refining lunar derived metals by taking advantage of the high vacuum environment present on the lunar surface.

ISRU↗

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗