Results for “mixed conduction” — Page 71

NASA NTRS2019

Detailed Infrared Study of Amorphous to Crystalline Propionitrile Ices Relevant to Observed Spectra of Titan's Stratospheric Ice Clouds

We have conducted a comprehensive study of propionitrile (C2H5CN) ice from the amorphous to crystalline phase in order to provide detailed information on this specific cyanide, which may potentially contribute to the chemical composition of the Haystack ice cloud observed in Titan's stratosphere by the Cassini Composite InfraRed spectrometer (CIRS). Infrared transmission spectra of thin films of pure propionitrile ices deposited at low temperature (30–160 K) were collected from 50 cm(exp −1) to 11,700 cm(exp −1) (200–0.85 μm). The far-infrared spectral region was specifically targeted to compare with CIRS far-infrared limb spectra. The temperature and time evolution of C2H5CN ice was thoroughly investigated to better understand discrepancies reported in previously published laboratory studies on the crystalline phase of C2H5CN. Specifically, we observe peculiar temperature and time-driven ice phase transitions, revealed by significant spectral variations in the ice, which stabilizes once a complete crystalline phase is achieved. From these results, the crystalline phase of propionitrile ice was identified at deposition temperatures greater than or equal to 135 K and <140 K. Our findings corroborate previous studies that ruled out pure propionitrile ice as the sole chemical identity of Titan's observed Haystack emission feature. In order to understand and identify the Haystack cloud, we have initiated co-deposition experiments that incorporate mixtures of Titan-relevant organics, many of which have corresponding vapors that are abundantly present in Titan's stratosphere. In this paper, we present the result of one example of a co-deposited ternary ice mixture containing 16% hydrogen cyanide (HCN), 23% C2H5CN, and 61% benzene (C6H6). Although this co-condensed ice mixture is the best fit thus far obtained to match the broad width of the Haystack, it is still not the appropriate chemical candidate. However, it reveals an intriguing result: the strong lattice mode of pure C2H5CN ice is drastically altered by the surrounding molecules as a result of mixing in a co-condensed phase. The laboratory results reported here on propionitrile ice may help to further constrain the chemical identification of Titan's stratospheric Haystack ice cloud, as well as improve on the current state of knowledge of Titan's stratospheric ice cloud chemistry.

Delphine Nna-Mvondo↗

NASA NTRS1995

Mode Transitions in Glass Cockpit Aircraft: Results of a Field Study

One consequence of increased levels of automation in complex control systems is the presence of modes. A mode is a particular configuration of a control system that defines how human command inputs are interpreted. In complex systems, modes also often determine a specific allocation of control authority between the human and automated systems. Even in simple static devices (e.g., electronic watches, word processors), the presence of modes has been found to cause problems in either-the acquisition or production of skilled performance. Many of these problems arise due to the fact that the selection of a mode causes device behavior to be mediated by hidden internal state information. For these simple systems, many of these interaction problems can be solved by the design of appropriate feedback to communicate internal state information to the human operator. In complex dynamic systems, however, the design issues associated with modes seem to trancend the problem of merely communicating internal state information via displayed feedback. In complex supervisory control systems (e.g., aircraft, spacecraft, military command and control), a key function of modes is the selection of a particular configuration of control authority between the human operator and automated control systems. One mode may result in full manual control, another may result in a mix of manual and automatic control, while a third may result in full automatic control over the entire system. The human operator selects an appropriate mode as a function of current goals, operating conditions, and operating procedures. Thus, the operator is put in a position of essentially trying to control two coupled dynamic systems: the target system itself, and also a highly complex suite of automation controlling the target system. From a historical perspective, it should probably not come as a surprise that very little information is available to guide the design of mode-oriented control systems. The topic of function allocation (i.e., the proper division of control authority among human and computer) has a long history in human-machine systems research. Although this research has produced some relevant guidelines, a design approach capable of defining appropriate allocations of control function between the human and automation is not yet available. As a result, the function allocation decision itself has been allocated to the operator, to be performed in real-time, in the operation of mode-oriented control systems. A variety of documented aircraft accidents and incidents suggest that the real-time selection and monitoring of control modes is a weak link in the effective operation of complex supervisory control systems. Research in human-machine systems and human-computer interaction has barely scraped the surface of the problem of understanding how operators manage this task.The purpose of this paper is to present the results of a field study which examined how operators manage mode selection in a complex supervisory control system. Data on mode engagements using the Boeing B757/767 auto-flight system were collected during approach and descent into four major airports in the East Coast of the United States. Protocols documenting mode selection, automatic mode changes, pilot actions, quantitative records of flight-path variables, and verbal reports during and after mode engagements were collected by an observer from the jumpseat. Observations were conducted on two typical trips between three airports. Each trip was be replicated 11 times, which yielded a total of 22 trips and 66 legs on which data were collected. All data collected concerned the same flight numbers, and therefore, the same time of day, same type of aircraft, and identical operational environments (e.g., ATC facilities, weather patterns, traffic flow etc.)

Degani, Asaf↗

NASA NTRS1997

The Complex Stratigraphy of the Highland Crust in the Serenitatis Region of the Moon Inferred from Mineral Fragment Chemistry

Large impact basins are natural drill holes into the Moon, and their ejecta carries unique information about the rock types and stratigraphy of the lunar crust. We have conducted an electron microprobe study of mineral fragments in the poikilitic melt breccias collected from the Taurus Mountains at the Apollo 17 landing site. These breccias are virtually unanimously agreed to be impact melt produced in the Serenitatis impact event. They contain lithic fragments and much more abundant mineral fragments of crustal origin. We have made precise microprobe analyses of minor element abundances in fragments of olivine, pyroxene, and plagioclase to provide new information on the possible source rocks and the crustal stratigraphy in the Serenitatis region. These data were also intended to elucidate the nature of the cryptic geochemical component in breccias such as these with low-K Fra Mauro basalt compositions. We chose the finest-grained (i.e., most rapidly quenched) breccias for study, to avoid reacted and partly assimilated fragments as much as possible. Most of the mineral fragments appear to have been derived from rocks that would fall into the pristine igneous Mg-suite as represented by lithic fragments in the Apollo collection, or reasonable extensions of it. Gabbroic rocks were more abundant in the target stratigraphy than is apparent from the Apollo sample collection. Some pyroxene and plagiociase, but probably not much olivine, could be derived from feldspathic granulites, which are metamorphosed polymict breccias. Some mineral fragments are from previously unknown rocks. These include highly magnesian olivines (up to Fo(sub 94)), possibly volcanic in origin, that exacerbate the difficulty in explaining highly magnesian rocks in the lunar crust. It appears that some part of the lunar interior has an mg*(= 100 x Mg/(Mg/Fe) atomic) greater than the conventional bulk Moon value of 80-84. Other volcanic rocks, including mare basalts, and rapidly- cooled impact melt rocks do not contribute significantly to the fragment population. Nor do ferroan anorthosites contribute more than a tiny part of even the plagiociase fragment population. A few mineral fragments that are consistent with the cryptic low-K Fra Mauro chemical component were found, and these appear to be from gabbroic sources. The mineral fragment populations cannot be mixed in their observed proportions to produce the whole rock composition, because the fragments are more refractory and deficient in Ti, P, and alkalis. A preferential contribution to the melt from a rock similar to sodic ferrogabbro can partly resolve the discrepancy. The population of mineral fragments requires a very diverse population of igenous rocks that are not all related to each other, demonstrating the existence of a complex crust built of numerous separate igneous plutons. Many of these plutons may have crystallized at shallow depths. The chemical composition of the melt breccias, in combination with the mineral fragment data and an understanding of the cratering process, suggests that the deepest crust sampled by the Serenitatis impace (not necessarily the deepest crust) was basaltic in composition, including KREEP and gabbroic rocks like sodic ferrogabbro, and lacking abundant olivine-rich material. These were overlain by Mg-suite rocks of varied types, including norites and troctolites that supplied most of the olivine mineral fragments. Granulities, which are metamorphosed and more feldspathic breccias, were abundant near the surface. Remote sensing indicates that the entire Serenitatis region lacks ferroan anorthosite, consistent with the results of our study.

Ryder, Graham↗

NASA NTRS2018

Design and Development of a Laboratory-Scale Ice Adhesion Testing Device

When an aircraft traverses through clouds containing supercooled water droplets, in-flight icing can occur that negatively affects vehicle performance by increasing weight and drag leading to loss of lift. Super-cooled water droplets present in clouds that impact vehicle surfaces can lead to inflight icing any time during the year.1 Most events occur at temperatures ranging from 0 to -20degC. Ice generated on the aircraft can vary between clear/glaze, rime, and mixed (Fig. 1) depending on air temperature (-5 to -20degC), liquid water content (0.3-0.6 g/m3), and droplet size (median volumetric diameter of 15-40 μm). Current strategies to remove ice are based on active technologies such as pneumatic boots, heated surfaces, and deicing agents (i.e., ethylene- and propylene-based glycols). The latter have potential environmental concerns. A passive approach to mitigate accreting ice that is actively being investigated are protective coatings. An ice mitigating coating could potentially be used as a stand-alone material, but more likely in combination with an active approach. In the latter scenario, potential reduction in power consumption by the active approach may be realized. To determine the ice adhesion strength of impact ice that is representative of the aircraft environment is not a trivial matter. Test methods utilizing slowly formed ice (i.e., freezer ice) do not accurately simulate this environment. Likewise, some testing methodologies involve sample relocation from the icing environment to the test chamber that can result in thermal shock to the sample, thus affecting the results. The Adverse Environment Rotor Test Stand (AERTS) located at Pennsylvania State University (PSU) has been demonstrated to simulate impact icing conditions within the icing envelope for the determination of ice adhesion shear strength (IASS) without removal/relocation of the sample.2 Due to the confidence in results obtained from AERTS, this instrument is in high demand and requires a significant amount of lead time and capital investment to obtain IASS results. As a solution for quickly and economically screening coatings in a controlled manner under impact icing conditions, a laboratory-scale ice adhesion test and dead blades were then removed from the rotor/blade assembly to obtain the final mass. The IASS of the live blade was determined from the difference in mass (before and after testing) of the live and dead blades, the ice shed area, and the rpm of the shed event. The same live blade sample was tested in triplicate at all three test temperatures. Surface roughness was determined using a Bruker Dektak XT Stylus Profilometer. Measurements were conducted using a 12.5 μm tip at a vertical range of 65.5 μm with an applied force of 3 mg. Data were collected over a 1.0 mm length at a resolution of 0.056 μm/point. Five single line scans at different locations were collected and processed using a two-point leveling subtraction. The resultant Ra (arithmetic roughness) and Rq (root mean square roughness) average values were calculated.

Smith, Joseph G., Jr.↗

NASA NTRS1998

Experiment 13: The Study of Dopant Segregation Behavior During the Growth of GaAs in Microgravity on USML-2

An investigation into the segregation behavior of selenium doped gallium arsenide (Se/GaAs) during directional solidification in the microgravity environment was conducted using the Crystal Growth Furnace (CGF) aboard the second United States Microgravity Laboratory (USML-2). Two crystals were successfully processed on USML-2, which lasted from October 20 to November 7, 1995. The first sample was processed for 67 hours, 45 minutes (MET 5/04:53:45-8/00:23:50) and included 19 hours of growth at 0.5 microns/sec which yielded 3.42 cm of sample length, and 5 hours of growth at 1.5 microns/sec which yielded 2.7 cm of sample. During the second experiment, the furnace temperature was adjusted to move the melt-solid interface position towards the hot end of the furnace. The second sample was processed for 50 hours, 10 minutes (MET 8/18:48:49-10/21:58:54) and included 11 hours of growth at 0.5 microns/sec which yielded 1.98 cm of sample, and 1 hour, 25 minutes of growth at 5.0 microns/sec which yielded 2.6 cm of sample. This sample provides an order of magnitude change in growth rate and reproduces one of the growth rates used during USML-1. In contrast to the results from USML-1, no voids were present in either crystal grown on USML-2. The absence of voids in either sample indicates that growth rate changes alone were not responsible for the formation of voids found in the crystals grown on USML-1. Sections of the ground-based and flight crystals grown on USML-2 were cut and polished. All of the interface demarcation lines expected from the current pulse interface demarcation (CPID) system have been identified. These measurements have been analyzed for interface positions, interface shapes, and growth rates. Using a newly developed technique, based on experimental and numerical results, the seeding interface reproducibility from run to run was <= 2.5 mm. The seeding interface position could be controllably moved, with respect to the furnace zones, by adjusting the control set points of the heating zones. The interface shapes flattened slightly as the interface position moved closer to the hot zone but was always an unfavorable concave into the solid shape. The growth rate was found to equal the furnace translation rate, after a 2 -hour transient, for growth rates <= 1.0 microns/sec. Segregation measurements for the ground-based crystals are indicative of complete mixing behavior, as expected. Segregation measurements of the flight crystals are still in progress.

Matthiesen, David H.↗

NASA NTRSDate not supplied

rHealth One Demonstration Aboard ISS: A Microfluidic Bioanalyzer Based on Sheath/Hydrodynamic Focusing Flow Cytometry

The Exploration Medical Capability (ExMC) element aims to provide astronauts with the means for their own health monitoring, diagnosis, and treatment during exploration missions. As space flight ventures further from earth, the need for autonomous medical care increases under greater constraints on size, mass, and resources. One pillar for diagnosis that crew would be separated from is laboratory analysis. Even now on ISS, blood samples must be collected and returned to earth for testing. In response, ExMC is assessing how assays for hematology, bone health, radiation exposure etc. could be addressed through commercial-off-the-shelf (COTS) and Small Business Innovation Research (SBIR) funded bioanalyzers that are miniaturizing lab technology. Since missions will reach distances where there are no timely replacements, validation on the International Space Station (ISS) is a necessary part of that assessment. In partnership with NASA Johnson Space Center (JSC) Immunology Lab and the Research Operations and Integration (ROI) element, ExMC conducted a technology demonstration on ISS of the rHEALTH ONE, a flow cytometry based bioanalyzer, to assess future devices based on this design. In flow cytometry, there are predominantly three ways to focus the cells (or particles) into a single file stream: hydrodynamic focusing, microcapillary, and acoustic focusing. The rHEALTH ONE utilizes the commercial standard, sheath-based hydrodynamic focusing. rHEALTH itself represents both a company and a suite of medical tools NASA has funded through SBIR grants towards the development of a promising diagnosis instrument for exploration missions. rHEALTH ONE is the interim version of the technology, functional as a benchtop analyzer and test bed for the next generation of rHEALTH in development. Several modifications were made to the rHEALTH ONE analyzer for operation in microgravity. For function, fluid management was key. A sheath-based analyzer uses sheath fluid to flow the sample, cleaning fluid to prevent biological contamination, and a reservoir to collect the liquid waste. The rHEALTH ONE analyzer uses bottles dependent on gravity to separate the air and liquid pathways and keep the liquids contained. It uses only 1 psig of air pressure to directly push the liquid through the device, requiring little-to-no resistance at inlet and outlet. A microtubing assembly with self-sealing luer connectors was designed – featuring 0.014 mm thick durable medical balloons to hold water inside the supply bottles – to create safe containment and easy access for the crew while maintaining the analyzer’s function. For safety, copper tape was added to the interior of the plastic housing to reduce electromagnetic interference, fluid and electrical connections were secured against vibration and leaks, and gaps were further sealed to ensure containment of the optical block and lasers. Water as the sheath and cleaning fluid and TOX 0 samples were used to reduce the biohazard risk to the crew. In May 2022, European Space Agency astronaut Samantha Cristoforetti demonstrated the rHEALTH ONE aboard ISS for its sample loading, flow cytometry, and data collection capability in microgravity. The JSC Immunology Lab provided flow cytometry expertise, designed the sample test protocol, and manufactured and benchmarked the flight samples on a ‘gold-standard’ flow cytometer. The analyzer was primed with water, purged of air, and four calibration solutions of polystyrene microparticles were tested to characterize its performance. Tightly controlled procedures and excellent execution prevented air bubble interference during air/water separation, fluid transfer, sample mixing and loading. Data showed a slight increase in signal noise on 3 of 5 channels and an anomaly of fluorophores migrating in one sample (confirmed by JSC post-flight). Flight results correctly detected the change in sample, matched the analyzer’s ground performance, and were consistent with the gold-standard’s ground results. Although areas were noted for improvement, these outcomes signified complete mission success.

R. S. Miller↗

NASA NTRS1999

Microgravity Transport Phenomena Experiment (MTPE) Overview

The Microgravity Transport Phenomena Experiment (MTPE) is a fluids experiment supported by the Fundamentals in Biotechnology program in association with the Human Exploration and Development of Space (BEDS) initiative. The MTP Experiment will investigate fluid transport phenomena both in ground based experiments and in the microgravity environment. Many fluid transport processes are affected by gravity. Osmotic flux kinetics in planar membrane systems have been shown to be influenced by gravimetric orientation, either through convective mixing caused by unstably stratified fluid layers, or through a stable fluid boundary layer structure that forms in association with the membrane. Coupled transport phenomena also show gravity related effects. Coefficients associated with coupled transport processes are defined in terms of a steady state condition. Buoyancy (gravity) driven convection interferes with the attainment of steady state, and the measurement of coupled processes. The MTP Experiment measures the kinetics of molecular migration that occurs in fluids, in response to the application of various driving potentials. Three separate driving potentials may be applied to the MTP Experiment fluids, either singly or in combination. The driving potentials include chemical potential, thermal potential, and electrical potential. Two separate fluid arrangements are used to study membrane mediated and bulk fluid transport phenomena. Transport processes of interest in membrane mediated systems include diffusion, osmosis, and streaming potential. Bulk fluid processes of interest include coupled phenomena such as the Soret Effect, Dufour Effect, Donnan Effect, and thermal diffusion potential. MTP Experiments are performed in the Microgravity Transport Apparatus (MTA), an instrument that has been developed specifically for precision measurement of transport processes. Experiment fluids are contained within the MTA fluid cells, designed to create a one dimensional flow geometry of constant cross sectional area, and to facilitate fluid filling and draining operations in microgravity. The fluid cells may be used singly for bulk solutions, or in a Stokes diaphragm configuration to investigate membrane mediated phenomena. Thermal and electrical driving potentials are applied to the experiment fluids through boundary plates located at the ends of the fluid cells. In the ground based instrument, two constant temperature baths circulate through reservoirs adjacent to the boundary plates, and establish the thermal environment within the fluid cells. The boundary plates also serve as electrodes for measurement and application of electrical potentials. The Fluid Manipulation System associated with the MTA is a computer controlled system that enables storage and transfer of experiment fluids during on orbit operations. The system is used to automatically initiate experiments and manipulate fluids by orchestrating pump and valve operations through scripted sequences. Unique technologies are incorporated in the MTA for measurement of fluid properties. Volumetric Flow Sensors have been developed for precision measurement of total fluid volume contained within the fluid cells over time. This data is most useful for measuring the kinetics of osmosis, where fluid is transported from one fluid cell to another through a semipermeable membrane. The MicroSensor Array has been designed to perform in situ measurement of several important fluid parameters, providing simultaneous measurement of solution composition at multiple locations within the experiment fluids. Micromachined sensors and interface electronics have been developed to measure temperature, electrical conductivity, pH, cation activity, and anion activity. The Profile Refractometer uses a laser optical system to directly image the fluid Index of Refraction profile that exists along the MTA fluid cell axis. A video system acquires images of the RI profile over time, and records the transport kinetics that occur upon application of chemical, thermal, or electrical driving potentials. Image processing algorithms have been developed to analyze the refractometer images on a pixel by pixel basis, calibrating and scaling the measured Index of Refraction profile to correlated solution properties of interest such as density, concentration, and temperature. Additional software has been developed to compile the processed images into a three dimensional matrix that contains fluid composition data as a function of experiment time and position in the fluid cell. These data are combined with data from the other sensor systems, and analyzed in the context of transport coefficients associated with the various transport phenomena. Analysis protocols have been developed to measure the transient kinetics, and steady state distribution of fluid components that occur in response to the applied driving potentials. The results are expressed in terms of effective transport coefficients. Experiments have been performed using a variety of solutes, and results generated are that are in agreement with published transport coefficient values.

Mason, Larry W.↗

NASA NTRS2006

Vivid Motor Imagery as an Adaptation Method for Head Turns on a Short-Arm Centrifuge

Artificial gravity (AG) has been proposed as a potential countermeasure to the debilitating physiological effects of long duration space flight. The most economical means of implementing AG may be through the use of a short-radius (2m or less) centrifuge. For such a device to produce gravitational forces comparable to those on earth requires rotation rates in excess of 20 revolutions per minute (rpm). Head turns made out of the plane of rotation at these rates, as may be necessary if exercise is combined with AG, result in cross-coupled stimuli (CCS) that cause adverse side effects including motion sickness, illusory sensations of motion, and inappropriate eye movements. Recent studies indicate that people can adapt to CCS and reduce these side effects by making multiple head turns during centrifuge sessions conducted over consecutive days. However, about 25% of the volunteers for these studies have difficulty tolerating the CCS adaptation paradigm and often drop out due to motion sickness symptoms. The goal of this investigation was to determine whether vivid motor imagery could be used as a pseudostimulus for adapting subjects to this unique environment. Twenty four healthy human subjects (14 males, 10 females), ranging in age from 21 to 48 years (mean 33, sd 7 years) took part in this study. The experimental stimuli were produced using the NASA JSC short-arm centrifuge (SAC). Subjects were oriented supinely on this device with the nose pointed toward the ceiling and head centered on the axis of rotation. Thus, centrifuge rotation was in the body roll plane. After ramp-up the SAC rotated clockwise at a constant rate of 23 rpm, producing a centrifugal force of approximately 1 g at the feet. Semicircular canal CCS were produced by having subjects make yaw head turns from the nose up (NU) position to the right ear down (RED) position and from RED to NU. Each head turn was completed in about one second, and a 30 second recovery period separated consecutive head movements. Participants were randomly assigned to one of three groups (n=8 per group): physical adapters (PA), mental adapters (MA), or a control group (CG). Each subject participated in a one hour test session on each of three consecutive days. Each test session consisted of an initial (preadaptation) period during which the subject performed six CCS maneuvers in the dark, followed by an adaptation period with internal lighting on the centrifuge, and a final (postadaptation) period during which six more CCS maneuvers were performed in the dark. For the PA group, the adaptation period consisted of performing 30 additional CCS maneuvers in the light. For the MA and CG group the centrifuge was ramped down to 0 rpm after the pre-adaptation period and ramped back up to 23 rpm before the post-adaptation period. For the both of these groups, the adaptation period consisted of making 30 CCS maneuvers in the light with the centrifuge stationary (so no cross-coupling occurred). MA group subjects were instructed to vividly imagine the provocative sensations produced by the preadaptation CCS maneuvers in terms of magnitude, duration, and direction of illusory body tilt, as well as any accompanying levels of motion sickness. CG group subjects were asked to answer low imagery content questions (trivial pursuit) during each adaptation period head turn. During the 30 second recovery following each head turn, psychophysical data were collected including self reports of motion sickness, magnitude and direction estimates of illusory body tilt, and the overall duration of these sensations. A multilevel mixed effects linear regression analysis performed on all response variables indicated that all three groups experienced some psychophysical adaptation across the three test sessions. For illusory tilt magnitude, the PA group exhibited the most overall adaptation, followed by the MA group, and the CG group. The slopes of these adaptation trajectories by group over day were significantly diffent from one another. For the perceived duration of sensations, the CG group again exhibited the least amount of adaptation. However, the rates of adaptation of the PA and the MA groups were indistinguishable, suggesting that the imagined pseudostimulus appeared to be just as effective a means of adaptation as the actual stimulus. The MA group's rate of adaptation to motion sickness symptoms was also comparable to the PA group. The use of vivid motor imagery may be an effective method for adapting to the illusory sensations and motion sickness symptoms produced by cross-coupled stimuli. For space-based AG applications, this technique may prove quite useful in retaining astronauts considered highly susceptible to motion sickness as it reduces the number of actual CCS required to attain adaptation.

Newby, N. J.↗

NASA NTRSDate not supplied

Assessments of Physiology and Cognition in Hybrid-Reality Environments (APACHE)

NASA is planning to return to the Moon in the mid-2020s as a stepping stone to Mars missions in the 2030s. Spacewalks, or extravehicular activities (EVAs), performed on the Moon and Mars will differ in a variety of ways from those that have been performed in decades past. NASA has identified multiple risks to human health and performance associated with a crewed mission to Mars, especially those associated with exploration EVAs which are expected to be a primary mission activity. Crew may be expected to conduct up to 24 hours of EVA per person per week, where the likelihood of injury and/or mental mistakes are increased compared to ground-based training or current microgravity EVAs and the consequences of which can be catastrophic. Current test environments for exploration EVA research and technology development are large, costly facilities that are limited in their availability or capabilities. Spacesuit testing in a reduced gravity environment such as NASA’s Neutral Buoyancy Laboratory, while a good representation of the crew’s physical workload during exploration EVAs, typically has small datasets and is difficult to integrate physiological sensors or other types of crew performance measures. Meanwhile, scientific field-based testing such as NASA’s Desert Research and Technology Studies offers an operationally relevant environment for exploration EVAs, particularly for cognitive workload, but is also limited by small datasets, lack of a pressurized spacesuit, and obtrusive measures. The limitations of current analogs for exploration EVAs identify a need for a new test environment that can approximate both the physical and cognitive demands associated with exploration EVAs to enable rapid, controlled, and repeatable evaluations of human health and performance risks of exploration missions. In response, the Human Physiology, Performance, Protection, and Operations Laboratory (H-3PO) at NASA Johnson Space Center has developed a hybrid reality exploration EVA analog named the Assessments of Physiology And Cognition in Hybrid-reality Environments (APACHE) to address these limitations using a combination of virtual, physical, and hybrid reality techniques. The APACHE facility resides at NASA Johnson Space Center and serves as a large “sandbox” for EVA research and simulation. At its center is a roughly 15x20ft space surrounded by a 14” tall sandbox partially filled with lunar regolith simulant to emulate the physical feeling of walking on a planetary surface and to allow for simulated geology operations. Nearby, a curved passive treadmill (Skillmill Connect, Technogym, Fairfield, NJ) and an omnidirectional treadmill (Infinadeck, Infinadeck, Rocklin, CA) are included to enable exploration of these large virtual environments while also imposing the physical demands, representative timelines, and cognitive burdens required to navigate and traverse these distances during exploration EVA. A 6DOF motion platform is used to simulate rover operations and supports various human performance evaluations and associated risks. Lastly, APACHE can support two extravehicular (EV) crewmembers working in tandem. A computer workstation is located nearby and also supports an intravehicular (IV) crewmember as part of a full mission simulation. The IV crewmember has direct video and audio communication with the EV crew in VR to provide operational and procedural support. The software used in APACHE was created by the JSC Engineering Directorate, in partnership with Buendea, powered by a custom Unreal Engine 5 (UE5.3, Epic Games) project. APACHE currently utilizes the HTC Vive Pro Eye in a wireless configuration for VR simulations. There are two virtual environments that subjects can explore within APACHE, a Lunar and Martian surface. The virtual Lunar surface was created from LIDAR data of the Lunar South Pole to create roughly 16 sq km of explorable terrain. The virtual Martian surface contains roughly 400 sq km of explorable terrain derived from Mars Reconnaissance Orbiter LIDAR data of the Jezero Crater. The immersion and related cognitive burdens of conducting a planetary EVA is simulated through a series of EVA-relevant tasks performed in the VR environment, using these high-fidelity visual representations. Additionally, APACHE includes biosensor driven informatics, such as real-time heart rate monitoring and/or derived values from model simulations, for active monitoring by the EV crew and added cognitive demand. A “Wizard of Oz” control panel enables test operators to activate contingency events such as simulated spacesuit malfunctions, loss of communications, and/or limited visibility. Embedded performance measures such as accuracy, completeness, and execution time have been developed for various exploration tasks to objectively quantify crew performance during an EVA and compare impacts to performance when different environmental stressors, both physical and cognitive, are added to or removed from the simulation. Additionally, validated cognitive and operational performance measures such as the Digit Symbol Substitution Task have been recreated and embedded in VR for direct and relatively unobtrusive measurement of motor perception. The APACHE environment currently supports multiple research studies at NASA. Examples include the CHAPEA project, a series of simulated year-long missions on Mars by a 4-person crew; and the CO2 Contingency Walk Back Study, an investigation of elevated CO2 exposure on crew performance during a contingency EVA scenario. APACHE also provides a test environment to support the development of the Crew State and Risk Model, which is a collection of individualized, mathematical models of crew physical and cognitive state; and the Personalized EVA Informatics and Decision Support system, an operational tool for flight controllers, and eventually a self-reliant Martian crew, to make biomedically-informed decisions in real-time to optimize the EVA planning and execution with respect to crew health and performance. Some technical challenges associated with developing the APACHE environment, as well as current limitations, include VR limitless natural walking with a hybrid spacesuit simulator, optimizing performance for wireless PC VR streaming while maintaining a high degree of visual fidelity, and the integration of various physiological (metabolic masks) and psychometric (eye tracking) sensors with the VR headset.

Human Performance↗

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

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↗

NASA NTRSDate not supplied

Modeling and Simulation of Tank Pressure Control using Zero-Boiloff 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 NTRS2024

Assessments of Physiology and Cognition in Hybrid-Reality Environments (APACHE) – A VR “Sandbox” for Planetary Spacewalk Research

NASA is planning to return to the Moon in the mid-2020s as a steppingstone to Mars missions in the 2030s. Spacewalks, or extravehicular activities (EVAs), performed on the Moon and Mars will differ in a variety of ways from those that have been performed in decades past. NASA has identified multiple risks to human health and performance associated with a crewed mission to Mars, especially those associated with exploration EVAs which are expected to be a primary mission activity. Crew may be expected to conduct up to 24 hours of EVA per person per week, where the likelihood of injury and/or mental mistakes are increased compared to ground-based training or current microgravity EVAs and the consequences of which can be catastrophic. Current test environments for exploration EVA research and technology development are large, costly facilities that are limited in their availability or capabilities. Spacesuit testing in a reduced gravity environment such as NASA’s Neutral Buoyancy Laboratory, while a good representation of the crew’s physical workload during exploration EVAs, typically has small datasets and is difficult to integrate physiological sensors or other types of crew performance measures. Meanwhile, scientific field-based testing such as NASA’s Desert Research and Technology Studies offers an operationally relevant environment for exploration EVAs, particularly for cognitive workload, but is also limited by small datasets, lack of a pressurized spacesuit, and obtrusive measures. The limitations of current analogs for exploration EVAs identify a need for a new test environment that can approximate both the physical and cognitive demands associated with exploration EVAs to enable rapid, controlled, and repeatable evaluations of human health and performance risks of exploration missions. In response, the Human Physiology, Performance, Protection, and Operations Laboratory (H-3PO) at NASA Johnson Space Center has developed a hybrid reality exploration EVA analog named the Assessments of Physiology And Cognition in Hybrid-reality Environments (APACHE)[ to address these limitations using a combination of virtual, physical, and hybrid reality techniques. The APACHE facility resides at NASA Johnson Space Center and serves as a large “sandbox” for EVA research and simulation. At its center is a roughly 15x20ft space surrounded by a 14” tall sandbox partially filled with lunar regolith simulant to emulate the physical feeling of walking on a planetary surface and to allow for simulated geology operations. Nearby, a curved passive treadmill (Skillmill Connect, Technogym, Fairfield, NJ) and an omnidirectional treadmill (Infinadeck, Infinadeck, Rocklin, CA) are included to enable exploration of these large virtual environments while also imposing the physical demands, representative timelines, and cognitive burdens required to navigate and traverse these distances during exploration EVA. A 6DOF motion platform is used to simulate rover operations and supports various human performance evaluations and associated risks. Lastly, APACHE can support two extravehicular (EV) crewmembers working in tandem. A computer workstation is located nearby and also supports an intravehicular (IV) crewmember as part of a full mission simulation. The IV crewmember has direct video and audio communication with the EV crew in VR to provide operational and procedural support.

Human Performance↗