Results for “LAB” — Page 35

NASA NTRSDate not supplied

The Effects to Exposure of Simulated Spaceflight Radiation on Behavioral Health of Male and Female Mice

Exposure to space radiation is a principal consideration of spaceflight missions as risk is leveraged as time and dose—both expected to increase with future missions to the Moon, Mars, and beyond. Previous mission exposure levels, galactic cosmic radiation (GCR) and solar particle events (SPE), have been characterized as increased compared to those natural to Earth and are predicted to cause robust deficits at higher doses and longer durations. The cognitive health implications of this critical difference are understood as risks to mission and crew operations. We examined potential radiation-induced disruptions on brain health through resting-state in-cage behavior. 23–24-week-old male and female mice were exposed to 0 cGy (Sham), 5 cGy, 15 cGy, and 50 cGy via Five-Ion GCR Simulation (H, Si, He, O, Fe) at the NASA Space Radiation Lab in Brookhaven National Labs. Behavioral and cognitive performance were evaluated via frequency/duration of digging, rearing, and grooming within the 72-hour period immediately following irradiation. Additionally, during this time we evaluated nestlet building using a 5-stage Deacon score, rating shredding and shelter assembly of cotton material from untouched (1) to shredded and formed into a crater shape (5). We have observed differences in Deacon score only among the 15 cGy subset. Further comparative performance analysis will be completed evaluating the differences in irradiation effects between male and female mice. These experimental design aspects that allot for gender-inclusivity is supportive of the diversification of future space travel mission plans. Investigating gender differences is an element under our main objective of determining radiation dose-response curves. In brief, these studies identified a space-relevant radiation dose of 15 cGy that that can be utilized for future standardized ground studies on the nervous system.

spaceflight cognition impairment↗

NASA NTRSDate not supplied

ACAS Xr Part Task Sim, Preliminary Experiment Design

In early 2022, the Human Autonomy Teaming Lab (NASA Ames Research Center) will conduct a manned, human-in-the-loop (HITL) simulation. This part task HITL will begin the lab’s Hazard Perception and Avoidance (HPA) technical work under NASA’s Advanced Air Mobility (AAM), Automated Flight and Contingency Management (AFCM) Sub-Project. The goals will be to assess levels of automation for manned, electric vertical takeoff and landing (eVTOL) aircraft. This simulation will test manual and automated Resolution Advisory (RA) responses and return-to-course (RTC) maneuvers for the first version of the Airborne Collision Avoidance System’s (ACAS) rotary-wing (Xr) variant. This will be conducted on a fixed-based simulator designed to fly eVTOL aircraft while maneuvering for intruding traffic. Variables for this study include levels of autonomy (i.e., manual and automated) as well as the types of alerts at the onset of conflicts (i.e., Corrective and RA). The data collected will include response times, losses of well clear, and maneuver sizes and durations as well as subjective ratings like acceptability, perceived workload, and meaningful human control. Additional details and future anticipations are also discussed.

air taxis↗

NASA NTRSDate not supplied

Accuracy of Center of Pressure Determination via Motion Capture

BACKGROUND: This study was conducted to support the stability assessment for tasks in lunar gravity and exercises on a Vibration Isolation and Stabilization (VIS) system in microgravity based on the dynamic feasibility criterion of whether the calculated position of the center of pressure (COP) falls within the base of support (BOS) which outlines the subject’s feet. Motion capture data combined with biomechanical modeling and simulation allows the forces and moments between the human and the VIS platform to be computed and the position of the COP as well as the location and shape of the BOS to be determined. The goal of this study was to assess the accuracy of the COP trajectory calculated using motion capture-based data. METHODS AND RESULTS: To obtain the dynamic quantities from which COP is calculated, motion capture data is first collected in the 1g lab environment by recording the trajectories of passive retroreflective markers placed on a subject during exercise or performance of a given task. The OpenSim [1] inverse kinematics (IK) tool is used to fit a scaled subject model to recorded marker trajectories while minimizing marker error to obtain joint angles. Then, a custom OpenSim plugin [2] is used to determine the subject’s time-varying moment of inertia and its time derivative, center of mass (CM) position, velocity, and acceleration, as well as the angular momentum and its time derivative relative to the subject’s CM. Some of these quantities are not needed for modeling tasks performed on a stationary lunar surface but, due to the moving exercise platform, are needed to model VIS response to the subject’s motion. Hand positions, used in calculating a cable force if present, are recorded as well. These quantities are used to calculate the total force (F ⃗^((plate) )) and moment (M ⃗^((plate) )) exerted by the lunar surface or the VIS plate on the subject’s shoe soles. COP is then calculated from the following equations: r_x^((cop) )= M_z^((plate) )/F_y^((plate) ) and r_z^((cop) )= 〖-M〗_x^((plate) )/F_y^((plate) ), where the y axis is normal to the surface. COP accuracy for feasibility assessments is then determined by whether it falls within the BOS, which is also computed by the plugin. To study the accuracy of COP calculated from motion capture, we first investigated whether COP remained within the BOS, as it must, for exercises performed in the 1g lab environment. Standard exercises such as back squat and deadlift were analyzed, as well as more explosive exercises including hang clean and press. Cases in which the COP exited the BOS indicated that COP accuracy required further investigation. In this study, an exercise device with cables was used, so cable force modeling accuracy should also be considered. In a separate study, we collected motion capture and force plate data for twenty-seven motions not involving an exercise device. About a third were genuine countermeasures exercises (e.g., hang clean and press), some were relevant for lunar tasks (e.g., object pick up), and the rest were of a “unit test” nature (e.g., swaying back and forth or side to side). Motion capture-based COP positions were compared with force plate measured COP. We found that while force plate measured COP remained within the BOS, motion capture-based COP was observed to briefly exit the BOS on occasion. Techniques to mitigate IK artifacts and filtering of calculated data could be used to improve the agreement of calculated and measured results, resolving excursions from the BOS within this dataset. The mean error between calculated and measured COP was found to be less than 6 mm. Additionally, we derived and investigated equations for the COP in terms of the cable force, cable location, as well as the human CM position, acceleration, and angular momentum with respect to the CM, and analyzed them for sensitivity to errors in individual quantities. Several were found, but the most significant one was that when the vertical force on the feet approaches zero, indicating a near-detachment or ‘jump off’ condition, errors are amplified. This is consistent with the observation that in the absence of pressure, the concept of the center of pressure would become meaningless.

C A Bell↗

NASA NTRSDate not supplied

Development of a Comprehensive Physics-Based Model for Study of NASA Gateway Lunar Dust Contamination

NASA is committed to landing the first woman and first person of color on the Moon by 2025 to begin a sustaining presence. A major component of this mission is NASA’s orbiting lunar outpost, Gateway, that will be subjected to the harsh environment of space during its 7-day orbit around the Moon. One aspect of this environment that is not well quantified is microscopic lunar regolith particles, or simply, lunar dust. As Apollo 17 Mission Commander Eugene Cernan said, “I think dust is probably one of our greatest inhibitors to a nominal operation on the moon. I think we can overcome other physiological or physical or mechanical problems except dust” [1]. These dust particles, most of which are smaller than the width of two human hairs and of highly irregular shape, are composed mainly of dielectric materials. Such characteristics can allow the dust particles to collect electric charge from the surrounding environment, resulting in electrostatic interactions between the dust, electromagnetic fields, and electrically charged surfaces [2-3]. Due to the nature of these fields, dust can collect on and contaminate charged surfaces. Lunar dust introduced into the Gateway environment by a lunar ascent vehicle element returning from the surface of the Moon presents risks to Gateway hardware such as radiators, solar arrays, antennae, and docking mechanisms. To quantify this risk and inform NASA, International Partner, and Commercial Provider stakeholders, Booz Allen, in partnership with NASA, is developing a toolset to model the interaction of charged lunar dust particles with the cis-lunar and deep-space environments. This includes a comprehensive physics model of the space plasma and solar radiation environment and electromagnetic interaction of spacecraft and lunar dust. Spacecraft charging and plasma environment are solved using the open-source code from The French Aerospace Lab (ONERA) and the European Space Agency (ESA), known as Spacecraft Plasma Interaction Software (SPIS) [4]. The physics of particle charging and motion is handled by a time-dependent implementation of Orbital-Motion-Limited (OML) theory which accounts for plasma flows and positive potential grains [5-7], using Siemens STAR-CCM+ as the framework. Model validation includes lab experiments by NASA experts and academia, as well as future on-orbit dust detecting payloads on the exterior of Gateway and Commercial Lunar Payload Services (CLPS) missions to the lunar surface [7-9]. The results and analyses will inform Gateway Program system owners at risk for lunar dust contamination.

Complex Plasma↗

NASA NTRSDate not supplied

Meteorite Sample Section Repair at NASA Johnson Space Center

Introduction: Meteorite thin and thick sections are routinely shipped from NASA Johnson Space Center to fulfill sample allocation requests from principle investigators around the world. Sections are also re-turned to JSC when researchers are finished studying them since, in most cases, they can be reused for other studies. The sections are very fragile unfortunately, and sometimes return to us needing repairs. The fol-lowing should give you an idea of how we repair sections here in the very lab where they were created. NOTE: Please do not attempt to repair ANSMET meteorite sections that are in your possession. We will perform the repairs for you at NASA JSC if you send the section back to us. Section Delamination: The majority of the meteorite sections that we produce here are secured to the glass slide using a high quality, two-part epoxy. Occasionally, we are asked to use superglue if the researcher wishes to dismount the section from the slide. Both epoxy and superglue are excellent adhesives, but they both tend to embrittle with time which results in delamination from the slide. Exposure to vacuum can also degrade the adhesion between the sample section and the glass slide. Repeated handling of the slide edges can accelerate delamination and, as a preventive measure, the outer 1-2 mm of epoxy is trimmed from newly created sections at JSC. If conductive tapes (copper, carbon, etc.) are used on the section during analysis, great care must be taken in removing the tape so that the epoxy is not pulled up with it. If in doubt, the tape can be left on the section when it is returned to JSC. Before we perform any repairs to sample sections, carbon, gold, or other coatings are removed. We accomplish this using a slurry of 0.05 micron alumina and 190 proof ethyl alcohol applied to a felt polishing pad fitted to a rotating lap wheel. Coatings are re-moved in this manner from all sections that are re-turned to JSC. The extent of the delamination determines how we proceed with the section repair. If the meteorite sample area of the section is not disturbed, then we carefully remove the delaminated epoxy. This is done using a binocular microscope with a 6X zoom, cut-proof gloves, and a very sharp, single edged razor blade. We cut the delaminated epoxy with the blade angled away from the sample area and using very light pressure. The trimmed section is then cleaned in an ultrasonic bath of 200 proof ethyl alcohol for no more than 10 seconds and carefully dried using a lint-free clean room wipe. The section is then placed into a lab oven at 110o F in preparation for epoxy. We mix the resin and hardener components of the low viscosity epoxy and very small amounts are applied to the cut edges of the section using a needle probe and the binocular microscope. Warming the epoxy helps secure the existing section by filling any voids between the glass slide and the section. After the new epoxy cures, we give the section a light polish on a lap wheel fitted with cotton polishing paper that is charged with 1 micron diamond paste. If the section has delaminated to the point of sample area being lifted from the glass, then it may be irreparable. We employ the above technique along with clamping the section in a Teflon pad arrangement in order to flatten the sample while the epoxy cures. Otherwise, the sample will tend to curl. This works to some degree, but once the sample area curls, it seldom re-turns to the original flatness without cracking or bending. Canada Balsam and Crystalbond: We repair damaged sections that had originally been prepared using Canada Balsam or Crystalbond adhesives through the gradual application of heat. We take great care with these samples since these bonding materials tend to get brittle with age. The section is heated in gradual steps (40-50o F per hour) to the melting point of the adhesive. We repair the sample while the adhesive is fluid and then the section is cooled in the same gradual manner in which it was heated. Slide Cracks and Breaks: Accidents happen. Especially with something as small and fragile as a thin/thick section. We all know someone who has driven a microscope objective into a section. As bad as the damage may look, the section can be repaired in most instances. NOTE: Please do not try to tape or glue section pieces back together prior to returning the dam-aged section. This practice usually renders the section irreparable. If the glass slide is cracked but the section is still in one piece, we repair it by infilling the crack with the low viscosity epoxy mentioned earlier. If the slide is in pieces, we can reassemble it with epoxy on a new backer slide. This is a tricky task as the pieces need to be in the correct plane with respect to each other, especially if the sample area is split among several pieces.

Meteorite↗

NASA NTRSDate not supplied

A Remote, Human-in-the-Loop Evaluation of a Multiple-Drone Delivery Operation

Over time, advances in unmanned aircraft systems (UAS) have enabled a shift in the operational paradigm from one operator managing one aircraft to that of multiple operators working together to manage multiple aircraft. This shift has highlighted the need for effective human-autonomy teaming methods to maintain manageable workload levels for operators as well as high standards of system performance and safety. This paper presents a study aimed at evaluating whether automation can help operators manage workload during small UAS (sUAS) package delivery scenarios featuring contingency situations. These contingency situations, resulting from unplanned UAS Volume Reservations (UVRs), required flight path reroutes for multiple aircraft simultaneously. The study manipulated the number of aircraft affected by the UVRs and the level of automation support. The presence of terrain conflicts was also controlled within each scenario. Due to the COVID-19 pandemic, subjects were not able to gain direct access to the Ground Control System (GCS). Therefore, the study was conducted using a subject-surrogate paradigm that required subjects to relay commands through a verbal protocol from remote locations outside of the lab to a researcher surrogate who had direct control of the GCS interfaces at the lab location. Results show that the automated support condition was associated with faster reroute response times, more efficient reroute maneuvers, and significantly lower levels of perceived workload than the manual reroute condition. However, the automation support level did not significantly impact pilots’ ability to avoid the UVR successfully; pilots were overwhelmingly capable of avoiding the UVR in all conditions. The presence of terrain conflicts primarily impacted pilot performance by leading to multiple uploads per vehicle, which was not typically required when pilots only needed to maneuver laterally. Although subjects did not have direct control over the GCS, subjective ratings indicate that the displays under test provided them with sufficient information to manage their aircraft and promptly respond to the unplanned UVRs. Overall, the objective and subjective data strongly suggest that the verbal protocol and subject-surrogate paradigm were effective methods for collecting data remotely amid the COVID-19 pandemic.

multi-UAS↗

NASA NTRS2022

Identifying Shocked Feldspar on Mars Using Perseverance Spectroscopic Instruments: Implications for Geochronology Studies on Returned Samples

The Perseverance rover (Mars 2020) mission, the first step in NASA’s Mars Sample Return (MSR) program, will select samples for caching based on their potential to improve understanding Mars’ astrobiological, geological, geochemical, and climatic evolution. Geochronologic analyses will be among the key measurements planned for returned samples. Assessing a sample’s shock history will be critical because shock metamorphism could influence apparent sample age. Shock effects in one Mars-relevant mineral class, plagioclase feldspar, have been well- documented using various spectroscopy techniques (thermal infrared reflectance, emission, and transmission spectroscopy, Raman, and luminescence). A subset of these data will be obtained with the SuperCam and SHERLOC (Scanning Habitable Environments with Raman & Luminescence for Organics & Chemicals) instruments onboard Perseverance to inform caching decisions for MSR. Here, we review shock indicators in plagioclase feldspar as revealed in Raman, luminescence, and IR spectroscopy lab data, with an emphasis on Raman spectroscopy. We consider how this information may inform caching decisions for selecting optimal samples for geochronology measurements. We then identify challenges and make recommendations for both in situ measurements performed with SuperCam and SHERLOC and for supporting lab studies to enhance the success of geochronologic analyses after return to Earth.

mars↗

NASA NTRSDate not supplied

Open-source Wireless Sensor Network (Wi-Se Net) for Flexible Deployment

Wireless sensors, especially if battery powered, have a number of advantages over wired sensors for flexible or temporary diagnostic deployment in a field or lab setting. Recent advances in wireless technology and microprocessor boards have produced a variety of inexpensive off-the shelf chips which can communicate wirelessly with simple protocols. This paper describes the design and implementation of a highly customizable wireless sensor network (called WiSe Net) using inexpensive open-source hardware components as wireless nodes. These wireless sensor nodes can transmit data at a rate <250 Hz, can be battery powered, and have a small footprint (2x5 cm). In addition, a preliminary over-the-air programming system was developed to allow for simple wireless configuration when active. The network performance was demonstrated by taking distributed and electrically isolated temperature measurements on a high-voltage lab apparatus. 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↗

NASA NTRSDate not supplied

Modeling and Simulation of Tank Pressure Control using Zero-Boil Off Active Thermal Control for LOXSAT Technology Demonstration Mission

To-date, research and modeling of cryogenic fluid management technologies (CFM) for spaceflight has been limited to ground tests, short-duration zero-g simulations (e.g. drop towers), and small-scale experiments on-orbit. There has not been a large-scale flight demonstration of a flight-like system. As future NASA missions to take humans further from Earth will require large, in-space cryogenic propulsion vehicles, it is imperative to begin collecting flight data for these systems to accurately model and design future vehicles. To meet this goal, NASA awarded tipping point technology demonstration awards to Eta Space, Lockheed Martin, Space Exploration Technologies (SpaceX), and United Launch Alliance (ULA) to demonstrate on-orbit storage and transfer of cryogenic propellant. For its award, Eta Space is developing LOXSAT-1. It is a small satellite that will be launched on a Rocket Lab Electron rocket. The spacecraft consists of a Rocket Lab Photon spacecraft bus with a primary payload of a spherical liquid oxygen (LOX) storage tank with thermodynamic control systems. The primary objective of the mission is to demonstrate zero-boil-off storage of liquid oxygen. To accomplish this objective, the payload is equipped with an active thermal control system fluid loop that consists of propellant management device (PMD), positive-displacement pump, heat exchanger connected to a cryocooler, and a spray bar mixing injector. When the fluid loop is operating, fluid is drawn from the tank by the PMD and pumped through the heat exchanger, lowering the fluid temperature below the fluid temperature in the tank. This subcooled liquid is then injected back into the tank through the spray bar. The subcooled injected liquid has two effects. If it is sprayed into the ullage space, the injected liquid will form into jets or droplets and exchange heat with the ullage gas. This will cool and condense the gas, reducing the pressure in the tank. Additionally, the liquid that is not sprayed through the ullage, as well as any remaining liquid spray from the ullage, will rejoin the liquid mass of the tank, lowering the bulk temperature of the liquid. These combines effects provide for zero-boil off pressure control by lowering the tank pressure and the liquid saturation pressure simultaneously, ensuring the liquid stays subcooled. To model these complex mechanics and predict the performance of the active thermal control system, NASA is providing Eta Space with 3 parallel models of the tank. The approach of providing 3 different models allows for cross-checking and comparisons between the three to better understand how different modeling assumptions and selection semi-empirical factors affects the modeling result. Additionally, developing 3 models provides three different schemes for numerical simulation, providing confidence that results depict real physical phenomenon and not numerical quirks of the program. Within the tank thermodynamics, there are two primary areas of heat transfer we concern ourselves with: the heat transfer between the ullage space and the droplet spray, and between the ullage space and bulk liquid. For the droplet heat transfer, there are multiple sets of assumptions that can be made and correlations that can be used. Currently, two working models - the TankSIM model and Easy5 model – provide for an overview of the different approaches available. The TankSIM model and Easy5 model use two different models for droplet heating and evaporation that illustrate how the models use different types of mechanisms to arrive at the same answer. For the TankSIM model, droplets are treated as spheres of constant radius. Heat is transferred from the ullage to the droplet and warms the droplet until it reaches saturation, then the droplet begins evaporating and reducing its radius and mass. To calculate the heat transfer coefficient between the droplet and ullage, the Ranz-Marshall correlation is used. To determine the number of droplets in the ullage, a resident mass approach is used. This approach averages the number of droplets such that residuals at the start-up and shut-off of the spray bar cancel out. This same approach is used in the Easy5 model. The GFSSP model implements a linked list to track individual droplet “nodes” within the model. For the Easy5 model, the droplet is assumed to have an interface at a temperature equal to the saturation temperature corresponding to the pressure of the gas phase. The heat transfer from the gas to the interface and the interface to the droplet bulk is then calculated, and the net mass transfer between the droplet and interface is determined by performing an energy balance across the interface. For the gas side of the interface, the Ranz-Marshall correlation is used. For the liquid side of the interface, a variety of correlations were tried, including Kronig and Brink (1950) and effective conductivity models. As a result of these assumptions, the Easy5 model currently predicts faster depressurization, as at saturated vapor conditions, the heat transfer coeffect on the liquid side for the Easy5 model is greater than the heat transfer coefficient predicted by Ranz-Marshall used in the TankSIM code. This greater heat flux translates into faster condensation of the saturated ullage gas. At the bulk liquid to ullage interface, the models are in much closer agreement. Both models model the ullage as a sphere centered within the bulk liquid in the tank, and both use the energy-jumping boundary condition to model heat and mass transfer across the interface. There are slight differences in how the interface temperature is calculated, however. The Easy5 model assumes the temperature of the interface is equal to the saturation temperature associated with the pressure of the gas phase. The TankSIM model calculates this temperature with Alabovskii’s equation, which provides a correction factor for interface temperatures. Analysis tasks are focused on determining rates of depressurization within the tank during active cooling operation. To maintain net positive suction head at the pump inlet, the tank pressure cannot fall faster than the saturation pressure associated with the temperature of the bulk liquid. Additionally, there is interest in analyzing the performance of the loop at different pump speeds and cryocooler input powers. Adjusting the flowrate affects both the performance of the heat exchanger between the cryocooler and pumped liquid, and the heat transfer between the droplet spray and the ullage. Ideally, a pump speed and cryocooler power can be selected that will allow the tank to operate in zero-boil-off mode with a very narrow range of storage pressure.

Cameron J. Hines↗

NASA NTRSDate not supplied

Hazard Perception & Avoidance (HPA): Part Task 1 - Results Outbrief

n April 2022, the Human Autonomy Teaming Lab (NASA Ames Research Center) conducted a manned, human-in-the-loop (HITL) simulation. This part task HITL began the lab’s Hazard Perception and Avoidance (HPA) technical work under NASA’s Advanced Air Mobility (AAM), Automated Flight and Contingency Management (AFCM) Sub-Project. The goals were to assess levels of automation for manned, electric vertical takeoff and landing (eVTOL) aircraft. This simulation tested manual and automated Resolution Advisory (RA) responses and return-to-course (RTC) maneuvers for the first version of the Airborne Collision Avoidance System’s (ACAS) rotary-wing (Xr) variant. This was conducted on a fixed-based simulator designed to fly eVTOL aircraft while maneuvering for intruding traffic. Variables for this study included levels of autonomy (i.e., manual and automated) as well as the types of alerts at the onset of conflicts (i.e., Corrective and RA). The data collected included response times, losses of well clear, and maneuver sizes and durations as well as subjective ratings like acceptability, and perceived workload. Additional details and future anticipations are also discussed.

air taxis↗

NASA NTRSDate not supplied

A Rapid Assessment of a Lunar Surface Laboratory Module

Johnson Space Center has piloted an innovation team known as the Forge to perform rapid turnaround, concurrent engineering studies. The Forge is intended to be able to pull together a team of strategically selected domain experts to innovatively solve a specified problem within a short period of time. The Forge’s inaugural project was the development of a Lunar Surface Laboratory Module concept. The lab module is intended as an option for the NASA Lunar Architecture Team and may allow for increased science productivity and return on investment than with only the Surface Habitat. The Forge team developed two concepts, a stationary laboratory and a mobile laboratory. Human factors evaluations have been conducted to determine the acceptability of the resulting design concepts. The human factors evaluations conducted include a card sort evaluation of the laboratories by science discipline and the System Usability Scale (SUS). The card sort focused subject matter expert expectations for the relative positioning of the laboratory functional areas, determining whether a given lab area should be adjacent to, or separated from, another. The SUS focused on layout efficiency. Both used human factors personnel and both life and physical scientists as subject matter experts. Each of these evaluations will focus primarily on objective data with limited subjective comments, reducing the time required for processing responses. The results of the card sort and SUS evaluations and their implications will be discussed. Based on the evaluation data, an overall acceptability of the Lunar Surface Laboratory Module produced by the Forge study can be determined. Lessons learned and recommendations for future use of human factors rapid assessment processes in early design trades will also be provided.

Forge↗

NASA NTRSDate not supplied

Changes in Cognitive Performance and Behavior Induced by Space-like Environment

Exposure to space radiation is a principal consideration of spaceflight missions as risk is leveraged as time and dose—both expected to increase with future missions to the Moon, Mars, and beyond. Previous mission exposure levels, galactic cosmic radiation (GCR) and solar particle events (SPE), have been characterized as increased compared to those natural to Earth and are predicted to cause robust deficits at higher doses and longer durations. The cognitive health implications of this critical difference are understood as risks to mission and crew operations. We examined potential radiation-induced disruptions on brain health through resting-state in-cage behavior. 23–24-week-old male and female mice were exposed to 0 cGy (Sham), 5 cGy, 15 cGy, and 50 cGy via Five-Ion GCR Simulation (H, Si, He, O, Fe) at the NASA Space Radiation Lab in Brookhaven National Labs. Behavioral and cognitive performance were evaluated via frequency/duration of digging, rearing, and grooming within the 72-hour (acute) and 91-day (delayed) period following irradiation. Additionally, during this time we evaluated nestlet building using a 5-stage Deacon score, rating shredding and shelter assembly of cotton material from untouched (1) to shredded and formed into a crater shape (5). We have observed differences in behavior frequency and duration differences among the 15 cGy subset within the acute observation window. There were no significant differences in behavior frequencies nor duration during the delayed observation period. These experimental design aspects that allot for gender-inclusivity is supportive of the diversification of future space travel mission plans. Investigating gender differences is an element under our main objective of determining radiation dose-response curves. In brief, these studies identified a space-relevant radiation dose of 15 cGy that that can be utilized for future standardized ground studies on the nervous system.

O. Siu↗

NASA NTRSDate not supplied

Models for Facilitating Government-Funded Activities in the Post-ISS LEO Ecosystem

The International Space Station National Lab (ISSNL) was established through Congressional authority in 2005 and has been managed by a nonprofit called the Center for the Advancement of Science in Space (CASIS) since 2011 to support research, development, education, and commercial activities in low-Earth orbit (LEO). NASA leadership is considering next steps for microgravity and orbital activities in LEO in preparation for the retirement of the International Space Station (ISS) and transition to Commercial LEO Destinations (CLDs) in the 2030 timeframe. Specifically, our study team within the NASA Office of Technology, Policy, and Strategy addressed the question: What are potential models for an ISS National Lab facilitating government-funded or subsidized activities on a commercial LEO platform after the transition of the ISS to one or more private platforms? To inform the trade space of potential models, we conducted over 40 discussions with stakeholders internal and external to NASA and reviewed over 35 documents related to LEO legislation, commercialization, models, and activities. Following the extensive literature review and discussions with diverse stakeholders, we defined and evaluated six models, in addition to the current ISSNL model. We assessed each model across three possible future scenarios (dynamic, steady, and limited growth of the commercial LEO market and activities) and across five stakeholder-driven model evaluation criteria (ability to meet NASA’s needs, adaptability, opportunity for collaboration, market sustainability, and equity and accessibility). The six models in the future trade space were: • Government Research Broker: Customizable research efforts on transport vehicles and CLDs • Innovation Campus: Modern terrestrial campus with workforce focus • Anchor Tenant: Long-term, substantial agreement for leasing space on a single CLD • Fee for Service: Free market approach with NASA-sponsored grants and service/data buys • Matchmaker: Neutral third-party connecting users to platforms • Institute Network: Network of separate but related efforts to enable commercial scaling The Government Research Broker model performed best across the scenarios, followed by Innovation Campus, Anchor Tenant, and Fee for Service. While Matchmaker and Institute Network exhibited positive aspects, the two models performed most favorably in future scenarios with well-established communities and markets. The six models presented in this study are representative of the potential trade space and are illustrative examples. NASA leadership can adjust models as desired to align to their priorities more closely by using combinations of the unique model features provided in our analysis. The optimal model is likely a combination of features from multiple models. Building upon this work to best position NASA and the diverse stakeholders of LEO activities for success post-ISS, further work will explore the near-term impact of preparing for the transition of the ISS to CLDs by identifying opportunities for modifying the current ISSNL-CASIS partnership. ACKNOWLEDGEMENTS We thank the many reviewers and discussants, internal and external to NASA, that contributed to the work.

Erica Rodgers↗

NASA NTRSDate not supplied

Human-Autonomy Teaming Research in Support of m:N Operations

Since 2019, the NASA and industry partners have been involved in research focused on a novel paradigm for operations of remotely piloted aircraft. This paradigm involves multiple people sharing a fleet of multiple vehicles between them. Referred to as m:N (pronounced “em-to-en”), this configuration describes a ratio where m is the number of operators and N is the number of vehicles. Through force and asset multiplication, the m:N concept seeks to enable a scalable and resilient operation of remotely crewed vehicles. The primary means of obtaining such a robust operation is through allowing a flexible crew of variable size to dynamically attend to the needs of assets in while performing real-time operator workload management. It is in that sense that assets are shared between operators: as needed (such as in events of elevated workload) an operator in an m:N context can “handoff” the responsibility for some amount of assets, nh < N, to be absorbed by the m – 1 crew members on staff. At some time later, these nh assets could be returned to their original owner or they may be further distributed to other crew if called for by the mission. During this panel, I will elaborate on the research activity undertaken by the Human-Autonomy Teaming (HAT) Laboratory at NASA Ames Research Center over the previous three years. The studies conducted by the HAT Lab range from interviews with subject matter experts, a cognitive walkthrough, a task analysis, and two simulation experiments to-date. During experimentation, pilots made use of an advanced Ground Control Station developed by the HAT Lab and industry partners to simulate m:N operations in two large, metropolitan areas of Southern California: Los Angeles and San Diego. Further experimentation planned over the next few years. Results from our research to-date indicates that pilots of a moderately sized fleet of about a dozen remotely crewed aircraft adequately maintained safety performance and situation awareness of their aircraft, even when presented with unexpected situations of heightened workload.

multi-vehicle control↗

NASA NTRSDate not supplied

Assured Vehicle Automation 1 Sim - Results Outbrief

In early 2022, the Human Autonomy Teaming Lab (NASA Ames Research Center) conducted a manned, human-in-the-loop (HITL) simulation. This part-task HITL began the lab’s Hazard Perception and Avoidance (HPA) technical work under NASA’s Advanced Air Mobility (AAM), Automated Flight and Contingency Management (AFCM) Sub-Project. The results of that sim guided the objectives of the current study, which were to examine pilots’ use of the Airborne Collision Avoidance System (ACAS) rotorcraft variant (Xr) v2 in multiple phases of flight with two separate Xr Modes, fully leverage Xr v2 features (e.g., use radar altimeter data to inform low altitude Resolution Advisory [RA] behavior, utilize the ability to designate “terminal-area intruders,” and display airspeed-based Detect and Avoid [DAA] guidance), emulate a “Traffic Advisory” (TA), and present Xr in a higher-fidelity environment. Therefore, this study was conducted in the Vertical Motion Simulator, and the variables included Phase of Flight (En-route, Hover, and Approach) as well as ACAS Xr Mode (TA/RA and DAA). The data collected included response times, losses of well clear, and maneuver sizes and durations as well as subjective ratings like acceptability and usability. Additional details and future anticipations are also discussed.

air taxis↗

NASA NTRSDate not supplied

Open-source Wireless Sensor Network (Wi-Se Net) for Flexible Deployment

Wireless sensors, especially if battery powered, have a number of advantages over wired sensors for flexible or temporary diagnostic deployment in a field or lab setting. Recent advances in wireless technology and microprocessor boards have produced a variety of inexpensive off-the shelf chips which can communicate wirelessly with simple protocols. This paper describes the design and implementation of a highly customizable wireless sensor network (called Wi-Se Net) using inexpensive open-source hardware components as wireless nodes. These wireless sensor nodes can transmit data at a rate <250 Hz, can be battery powered, and have a small footprint (2x5 cm). In addition, a preliminary over-the-air programming system was developed to allow for simple wireless configuration when active. The network performance was demonstrated by taking distributed and electrically isolated temperature measurements on a high-voltage lab apparatus. 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↗

NASA NTRSDate not supplied

Modeling and Simulation of Tank Pressure Control using Zero-Boil Off Active Thermal Control for LOXSAT Technology Demonstration Mission

To-date, research and modeling of cryogenic fluid management technologies (CFM) for spaceflight has been limited to ground tests, short-duration zero-g simulations (e.g. drop towers), and small-scale experiments on-orbit. There has not been a large-scale flight demonstration of a flight-like system. As future NASA missions to take humans further from Earth will require large, in-space cryogenic propulsion vehicles, it is imperative to begin collecting flight data for these systems to accurately model and design future vehicles. To meet this goal, NASA awarded tipping point technology demonstration awards to Eta Space, Lockheed Martin, Space Exploration Technologies (SpaceX), and United Launch Alliance (ULA) to demonstrate on-orbit storage and transfer of cryogenic propellant. For its award, Eta Space is developing LOXSAT-1. It is a small satellite that will be launched on a Rocket Lab Electron rocket. The spacecraft consists of a Rocket Lab Photon spacecraft bus with a primary payload of a spherical liquid oxygen (LOX) storage tank with thermodynamic control systems. The primary objective of the mission is to demonstrate zero-boil-off storage of liquid oxygen. To accomplish this objective, the payload is equipped with an active thermal control system fluid loop that consists of propellant management device (PMD), positive-displacement pump, heat exchanger connected to a cryocooler, and a spray bar mixing injector. When the fluid loop is operating, fluid is drawn from the tank by the PMD and pumped through the heat exchanger, lowering the fluid temperature below the fluid temperature in the tank. This subcooled liquid is then injected back into the tank through the spray bar. The subcooled injected liquid has two effects. If it is sprayed into the ullage space, the injected liquid will form into jets or droplets and exchange heat with the ullage gas. This will cool and condense the gas, reducing the pressure in the tank. Additionally, the liquid that is not sprayed through the ullage, as well as any remaining liquid spray from the ullage, will rejoin the liquid mass of the tank, lowering the bulk temperature of the liquid. These combines effects provide for zero-boil off pressure control by lowering the tank pressure and the liquid saturation pressure simultaneously, ensuring the liquid stays subcooled. To model these complex mechanics and predict the performance of the active thermal control system, NASA is providing Eta Space with 3 parallel models of the tank. The approach of providing 3 different models allows for cross-checking and comparisons between the three to better understand how different modeling assumptions and selection semi-empirical factors affects the modeling result. Additionally, developing 3 models provides three different schemes for numerical simulation, providing confidence that results depict real physical phenomenon and not numerical quirks of the program. Within the tank thermodynamics, there are two primary areas of heat transfer we concern ourselves with: the heat transfer between the ullage space and the droplet spray, and between the ullage space and bulk liquid. For the droplet heat transfer, there are multiple sets of assumptions that can be made and correlations that can be used. Currently, two working models - the TankSIM model and Easy5 model – provide for an overview of the different approaches available. The TankSIM model and Easy5 model use two different models for droplet heating and evaporation that illustrate how the models use different types of mechanisms to arrive at the same answer. For the TankSIM model, droplets are treated as spheres of constant radius. Heat is transferred from the ullage to the droplet and warms the droplet until it reaches saturation, then the droplet begins evaporating and reducing its radius and mass. To calculate the heat transfer coefficient between the droplet and ullage, the Ranz-Marshall correlation is used. To determine the number of droplets in the ullage, a resident mass approach is used. This approach averages the number of droplets such that residuals at the start-up and shut-off of the spray bar cancel out. This same approach is used in the Easy5 model. The GFSSP model implements a linked list to track individual droplet “nodes” within the model. For the Easy5 model, the droplet is assumed to have an interface at a temperature equal to the saturation temperature corresponding to the pressure of the gas phase. The heat transfer from the gas to the interface and the interface to the droplet bulk is then calculated, and the net mass transfer between the droplet and interface is determined by performing an energy balance across the interface. For the gas side of the interface, the Ranz-Marshall correlation is used. For the liquid side of the interface, a variety of correlations were tried, including Kronig and Brink (1950) and effective conductivity models. As a result of these assumptions, the Easy5 model currently predicts faster depressurization, as at saturated vapor conditions, the heat transfer coeffect on the liquid side for the Easy5 model is greater than the heat transfer coefficient predicted by Ranz-Marshall used in the TankSIM code. This greater heat flux translates into faster condensation of the saturated ullage gas. At the bulk liquid to ullage interface, the models are in much closer agreement. Both models model the ullage as a sphere centered within the bulk liquid in the tank, and both use the energy-jumping boundary condition to model heat and mass transfer across the interface. There are slight differences in how the interface temperature is calculated, however. The Easy5 model assumes the temperature of the interface is equal to the saturation temperature associated with the pressure of the gas phase. The TankSIM model calculates this temperature with Alabovskii’s equation, which provides a correction factor for interface temperatures. Analysis tasks are focused on determining rates of depressurization within the tank during active cooling operation. To maintain net positive suction head at the pump inlet, the tank pressure cannot fall faster than the saturation pressure associated with the temperature of the bulk liquid. Additionally, there is interest in analyzing the performance of the loop at different pump speeds and cryocooler input powers. Adjusting the flowrate affects both the performance of the heat exchanger between the cryocooler and pumped liquid, and the heat transfer between the droplet spray and the ullage. Ideally, a pump speed and cryocooler power can be selected that will allow the tank to operate in zero-boil-off mode with a very narrow range of storage pressure.

zero boil-off↗

NASA NTRSDate not supplied

Modeling and Simulation of Tank Pressure Control using Zero-Boil Off Active Thermal Control for LOXSAT Technology Demonstration Mission

To-date, research and modeling of cryogenic fluid management technologies (CFM) for spaceflight has been limited to ground tests, short-duration zero-g simulations (e.g. drop towers), and small-scale experiments on-orbit. There has not been a large-scale flight demonstration of a flight-like system. As future NASA missions to take humans further from Earth will require large, in-space cryogenic propulsion vehicles, it is imperative to begin collecting flight data for these systems to accurately model and design future vehicles. To meet this goal, NASA awarded tipping point technology demonstration awards to Eta Space, Lockheed Martin, Space Exploration Technologies (SpaceX), and United Launch Alliance (ULA) to demonstrate on-orbit storage and transfer of cryogenic propellant. For its award, Eta Space is developing LOXSAT-1. It is a small satellite that will be launched on a Rocket Lab Electron rocket. The spacecraft consists of a Rocket Lab Photon spacecraft bus with a primary payload of a spherical liquid oxygen (LOX) storage tank with thermodynamic control systems. The primary objective of the mission is to demonstrate zero-boil-off storage of liquid oxygen. To accomplish this objective, the payload is equipped with an active thermal control system fluid loop that consists of propellant management device (PMD), positive-displacement pump, heat exchanger connected to a cryocooler, and a spray bar mixing injector. When the fluid loop is operating, fluid is drawn from the tank by the PMD and pumped through the heat exchanger, lowering the fluid temperature below the fluid temperature in the tank. This subcooled liquid is then injected back into the tank through the spray bar. The subcooled injected liquid has two effects. If it is sprayed into the ullage space, the injected liquid will form into jets or droplets and exchange heat with the ullage gas. This will cool and condense the gas, reducing the pressure in the tank. Additionally, the liquid that is not sprayed through the ullage, as well as any remaining liquid spray from the ullage, will rejoin the liquid mass of the tank, lowering the bulk temperature of the liquid. These combines effects provide for zero-boil off pressure control by lowering the tank pressure and the liquid saturation pressure simultaneously, ensuring the liquid stays subcooled. To model these complex mechanics and predict the performance of the active thermal control system, NASA is providing Eta Space with 3 parallel models of the tank. The approach of providing 3 different models allows for cross-checking and comparisons between the three to better understand how different modeling assumptions and selection semi-empirical factors affects the modeling result. Additionally, developing 3 models provides three different schemes for numerical simulation, providing confidence that results depict real physical phenomenon and not numerical quirks of the program. Within the tank thermodynamics, there are two primary areas of heat transfer we concern ourselves with: the heat transfer between the ullage space and the droplet spray, and between the ullage space and bulk liquid. For the droplet heat transfer, there are multiple sets of assumptions that can be made and correlations that can be used. Currently, two working models - the TankSIM model and Easy5 model – provide for an overview of the different approaches available. The TankSIM model and Easy5 model use two different models for droplet heating and evaporation that illustrate how the models use different types of mechanisms to arrive at the same answer. For the TankSIM model, droplets are treated as spheres of constant radius. Heat is transferred from the ullage to the droplet and warms the droplet until it reaches saturation, then the droplet begins evaporating and reducing its radius and mass. To calculate the heat transfer coefficient between the droplet and ullage, the Ranz-Marshall correlation is used. To determine the number of droplets in the ullage, a resident mass approach is used. This approach averages the number of droplets such that residuals at the start-up and shut-off of the spray bar cancel out. This same approach is used in the Easy5 model. The GFSSP model implements a linked list to track individual droplet “nodes” within the model. For the Easy5 model, the droplet is assumed to have an interface at a temperature equal to the saturation temperature corresponding to the pressure of the gas phase. The heat transfer from the gas to the interface and the interface to the droplet bulk is then calculated, and the net mass transfer between the droplet and interface is determined by performing an energy balance across the interface. For the gas side of the interface, the Ranz-Marshall correlation is used. For the liquid side of the interface, a variety of correlations were tried, including Kronig and Brink (1950) and effective conductivity models. As a result of these assumptions, the Easy5 model currently predicts faster depressurization, as at saturated vapor conditions, the heat transfer coeffect on the liquid side for the Easy5 model is greater than the heat transfer coefficient predicted by Ranz-Marshall used in the TankSIM code. This greater heat flux translates into faster condensation of the saturated ullage gas. At the bulk liquid to ullage interface, the models are in much closer agreement. Both models model the ullage as a sphere centered within the bulk liquid in the tank, and both use the energy-jumping boundary condition to model heat and mass transfer across the interface. There are slight differences in how the interface temperature is calculated, however. The Easy5 model assumes the temperature of the interface is equal to the saturation temperature associated with the pressure of the gas phase. The TankSIM model calculates this temperature with Alabovskii’s equation, which provides a correction factor for interface temperatures. Analysis tasks are focused on determining rates of depressurization within the tank during active cooling operation. To maintain net positive suction head at the pump inlet, the tank pressure cannot fall faster than the saturation pressure associated with the temperature of the bulk liquid. Additionally, there is interest in analyzing the performance of the loop at different pump speeds and cryocooler input powers. Adjusting the flowrate affects both the performance of the heat exchanger between the cryocooler and pumped liquid, and the heat transfer between the droplet spray and the ullage. Ideally, a pump speed and cryocooler power can be selected that will allow the tank to operate in zero-boil-off mode with a very narrow range of storage pressure.

zero boil-off↗