Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “EVA”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Development of ARGOS (Active Response Gravity Offload System) Offloading Assessments and Methodology for Lunar EVA Simulations

The Active Response Gravity Offload System (ARGOS) at NASA Johnson Space Center (JSC) is an analog environment that can offload pressurized suited subjects for various reduced gravity simulations. The suit is suspended from a robotic overhead crane by a cable connected to the suit via a gimbal with an adjustable pivot point (i.e. offload attachment). There has been increased interest in providing planetary pressurized suited training at ARGOS in preparation for lunar missions. Determination of the appropriate gimbal pivot point location for a given subject is vital for a high-fidelity functional lunar simulation. Interactions between the pivot point location and human-spacesuit center of gravity (CG) can result in righting moments that may lead to artificially stable or unrealistically challenging configurations. Changing the pivot point location is time consuming and repeated adjustment can result in loss of valuable pressurized suited time. This paper aims to share knowledge obtained from the offloading characterization efforts during pressurized suited testing at ARGOS and document the ongoing process to define an appropriate pivot point location through iterative quantitative and qualitative assessments. Human-spacesuit CG locations for the ARGOS lunar simulation were estimated using a 3D body scan and density model combined with spacesuit hardware CAD and specifications. Early pilot testing of the gimbal revealed that setting the pivot point coincident with the modeled CG location was not always possible due to the current gimbal design, and small pivot point shifts had noticeable effects on subject stability. Fourteen subjects performed a series of CG-related tasks in the Exploration Extravehicular Mobility Unit (xEMU) to assess simulation characteristics. Through iterative testing, this task list evolved to streamline the process needed to efficiently identify a suitable pivot point for a given subject. The developed methodology will be critical for pivot point selection during astronaut training in the ARGOS environment.

Sarah L. Jarvis↗
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

Evaluation of Aerobic Standards for Lunar Surface Extravehicular Activities

Introduction: As NASA prepares to return to the Moon, astronauts will need to be physically primed to successfully execute Extravehicular Activities (EVA) on the Lunar surface. Compared to past Apollo missions, Artemis missions will include EVAs of increased physical demand, frequency, intensity, and duration, thus requiring adequate fitness to successfully and safely complete mission objectives. The physical demand associated with partial gravity (g) EVAs on the Moon is expected to be greater compared to microgravity EVAs based on initial workload estimation. Currently, aerobic fitness standards for partial g EVAs are not well supported by high-fidelity data and require further research for establishing standards to protect crew health and performance during Lunar surface missions. Therefore, the aim of this investigation is to characterize metabolic data from Lunar analog simulations and in-flight crew population aerobic capacity data to validate the current NASA 3001 standard for celestial partial g aerobic fitness (aerobic capacity (VO_2pk) ≥36.5ml/kg/min). Methods: In order to evaluate aerobic fitness requirements for Lunar EVAs, the following were performed: 1) preliminary analysis of long-duration (6 hr) EVA analog simulations in the Neutral Buoyancy Laboratory (NBL) and the Active Response Gravity Offload System (ARGOS) to evaluate expected metabolic rates for 1/6 g EVAs (NBL: n=1 female; ARGOS: n=1 male) and 2) assessment of the current NASA 3001 celestial surface EVA aerobic standard (aerobic capacity (VO_2pk) ≥36.5ml/kg/min) with data from an ISS astronaut population (n=30 male + 13 female) captured before and during space flight (flight day 15). Preliminary Results: Average fractional aerobic capacity during simulated EVAs were 33%±7% VO_2pk and 23.3±7% VO_2pk in the NBL and ARGOS, respectively. This was within a previously predicted 30–40% sustainable work rate. Average metabolic rates for some tasks performed in the NBL, such as traverse (40.4% VO_2pk) and ingress (47.1% VO_2pk) were higher than the predicted sustainable work range. In ARGOS, the tasks with the greatest metabolic rates were object relocation (34.2% VO_2pk) and incapacitated crew rescue (27.0% VO_2pk). Characterization of ISS crewmember aerobic capacity determined that the average preflight VO_2pk was 42.1±5.4 ml/kg/min for females and 37.5±5.4 ml/kg/min for males. At preflight, 21.5% of crewmembers were below the 36.5 ml/kg/min in-mission aerobic standard for celestial surface EVA as outlined in NASA-STD-3001. In-flight, both female and male crewmembers experienced reductions in VO_2pk (11.7% and 10.9%, respectively), such that, during the mission, 62% of crewmembers were below the standard aerobic capacity level for celestial surface EVAs. Conclusions: Our preliminary data suggest that while average metabolic rates for simulated Lunar EVA fall within the 30–40% sustainable work range, task specific metabolic rates exceed this range and may indicate that greater fitness is necessary for more strenuous tasks expected to be performed on the Lunar surface. Additionally, deconditioning due to space flight results in most crewmembers falling below the current celestial partial g EVA standard, which may increase risk to crew health and performance and completing mission objectives for surface missions. Further research is necessary in Artemis-specific analog environments to validate the current NASA-3001 aerobic standard for celestial EVAs. Additionally, work is ongoing to validate the current NASA-3001 strength standard for celestial EVAs.

N.C. Strock↗
NASA NTRS2021

Chapter 5: Extravehicular Activity Metabolic Rate Model: Metabolic Rate Estimated from Heart Rate

In-flight monitoring of crew metabolic rates during extravehicular activity (EVA) provides crucial information in mitigating injury. The purpose of this study was to investigate the relationship of crewmember heart rate (HR) and metabolic rate (MR) during EVA operations to develop a predictive linear model. HR and MR data was collected from 132 EVAs from Shuttle and International Space Station (ISS) missions. MR was collected every 2-min from portable life support system delta oxygen decay, while HR was collected every 20-sec via electrocardiogram. HR was down sampled to every 2-min to match MR during EVA for evaluation. Further, a new metric was observed from direct relations between metabolic rate with HR over EVA time (MR/HR) measured as a BTU/beat. A range of BTU/beat was collected as a conversion scale between MR and HR at different EVA workloads categorized by increased MR. Both HR and MR values were observed to decrease through the duration of EVA. Similarly, HR and MR slopes decreased at start of EVA compared to end of EVA. MR/HR values were used to predict MR from HR over the entire duration of EVA with root mean square error less than 200 BTU/Hr. Additionally, MR was predicted based on HR values during EVA via a calculated simple linear regression. A regression equation was found for each EVA drawing relations between HR and MR (F (2923.84) and P<0.0001) with an R(sup 2) value of 0.402. Individual crew regressions improved prediction and R(sup 2) to greater than 0.82. Two models are presented to determine metabolic rate from heart rate during EVA. Results draw correlations for heart rate and metabolic rate fluctuations during EVA for individualized crew predictions during future operations. The linear models correlate to Apollo prediction data during historic EVAs.

Metabolic rate predictions↗
NASA NTRSDate not supplied

An Optimization Approach to Support Science Decision Making for Lunar Surface Exploration

Introduction: Scientific exploration is one of the three pillars of NASA’s Moon2Mars architecture, with crew surface extra vehicular activities (EVA) serving a critical enabling function. Development of surface EVA operational planning and execution, specifically integrating science and flight control teams (FCT), is currently being explored through analog scenarios. This integration, exercised, for example, through the Joint EVA and Hu-man Surface Mobility Test Team (JETT), allows for science input on EVA activities in near real-time through a Science Evaluation Room (SER), or Arte-mis science backroom, which integrates with the broader FCT through the Science Officer. The SER works within the FCT to support dynamic EVA planning in response to changes in operational constraints as well as science opportunities and re-prioritization, increasing the mission science return and accelerating the accomplishment of the Moon2Mars science objectives. The SER works within the FCT to provide recommendations to traverse execution in near real-time. One challenge is the requirement to deliver SER inputs to the FCT on operationally relevant timelines. Failure to do so may result in suboptimal execution of science exploration EVAs or even loss of key science objectives. To close this gap, we present a network optimization tool to allow the SER to provide rapid input to the FCT in response to changes in operational constraints or science opportunities. Inputs are predicated on approved science objectives, and clear rationale must be provided to the FCT for any requested change. Accordingly, this tool incorporates the Science Traceability Matrix (STM), SER prioritization scheme, and station characterization and action planning with operational constraints such as duration, traverse speed, and distance to maximize science objectives based on SER priorities, consistent with FCT operational requirements. Method: As a proof of concept, we used an existing linear programing software package used to simulate optimal routes through cellular metabolism. We built a Demonstrative Model with three STM objectives and four stations on a region of the Moon. The objectives were given an arbitrary prioritization and mapped to the stations through four possible crew actions. (Figs. 1 and 2). This station to STM mapping is consistent with the method used by the JETT5 Science Team to develop analog surface EVA science planning. We used a grid system with the landing site at the origin and the four stations placed across the positive x,y quadrant. Actions were assigned to each station and the accomplishment of those actions resulted in a numerical “reward” based on the ability of that action to achieve science objectives. The aggregate reward from each individual STM objective contributes to a global score (Science Yield), weighted by its priority. Operational constraints included a requirement to start and end at the landing site, 5 minutes each for initial station characterization and “clean up,” and variable total EVA time, traverse rate (fixed to 0.5 meters per second in our example), and time to perform each action (10, 5, 7, and 15 min for actions 1, 2, 3, and 4, respectively). Additional constraints and variables will be added in the future (e.g., sample mass, number of stations, traverse route constraints, illumination). Optimization. We converted the connections (arcs) between these stations (nodes) into a mixed integer linear programming optimization problem (arcs = constraints, nodes = variables) with the objective to maximize Science Yield. For any action, the Science Yield is equal to the relevance of that action to an STM objective [3, 2, and 1 point(s) for High, Med., and Low relevance, respectively], multiplied by the STM Objective Priority [3, 2, and 1 point(s) for High, Med., and Low priority, respectively]. This resulted in a model that computes the optimal station and action combination to maximize the Science Yield. These weightings can be adjusted by the SER as desired. Results: We explored three test cases for the Demonstrative Model. First, we set the maximum EVA duration to 120 minutes and computed the optimal route (Fig. 3A). The model suggested per-forming Actions 1 and 2 at Station P01, followed by Actions 1 and 2 at Station P02, and finally Actions 1 and 3 at Station P04 before returning to the Landing Site. Second, we adjusted the STM Objective Priori-ty order and computed the new optimal route (Fig. 3B). Under this situation, the model suggested per-forming all Actions at Station P02 followed by all Actions at Station P03. The previous test cases were relevant to SER planning activities. Next, we explored providing mid-EVA replanning input to the FCT. Scenario: While executing the Route in Fig. 3A the crew finishes at Station P01 and FCT decides that the EVA needs to finish in 45 minutes back at the Landing Site. FCT asks SER to recommend changes to the plan to accommodate this operation-al change. Using the model and incorporating these new constraints (start at Station P01, max. time of 45 min), the model suggested performing Actions 2 and 4 at Station P03 (Fig. 4), requiring 41 minutes to complete and return to the Landing Site. Interestingly, Station 3 was not part of the original route. Using the model, we determined the EVA would need 66 minutes, instead of 45, in order for the original Station P04 to yield a larger Science Yield than Station P03. The parametrization and simulation was per-formed in less than a minute, demonstrating the operational relevance of the approach. Future Efforts: The results from the Demonstrative Model suggest this tool can accelerate SER decision making on operationally relevant timelines. Use in analog activities, such as JETT5 or follow-ons, which have over a dozen stations for a crew to explore and over a dozen actions per station, will provide needed validation of the utility of this tool for planning EVAs, replanning mid-EVA, or planning follow-on EVAs based on previous results. Further integration with FCT execution monitoring tools may provide additional efficiency gains, al-lowing rapid and iterative exploration of operation-al and science decision space by the FCT and SER.

Science Operations↗
NASA NTRS2010

The Evolution of Extravehicular Activity Operations to Lunar Exploration Based on Operational Lessons Learned During 2009 NASA Desert RATS Field Testing

This paper will present options to evolutionary changes in several philosophical areas of extravehicular activity (EVA) operations. These areas will include single person verses team EVAs; various loss of communications scenarios (with Mission Control, between suited crew, suited crew to rover crew, and rover crew A to rover crew B); EVA termination and abort time requirements; incapacitated crew ingress time requirements; autonomous crew operations during loss of signal periods including crew decisions on EVA execution (including decision for single verses team EVA). Additionally, suggestions as to the evolution of the make-up of the EVA flight control team from the current standard will be presented. With respect to the flight control team, the major areas of EVA flight control, EVA Systems and EVA Tasks, will be reviewed, and suggested evolutions of each will be presented. Currently both areas receive real-time information, and provide immediate feedback during EVAs as well as spacesuit (extravehicular mobility unit - EMU) maintenance and servicing periods. With respect to the tasks being performed, either EMU servicing and maintenance, or the specific EVA tasks, daily revising of plans will need to be able to be smoothly implemented to account for unforeseen situations and findings. Many of the presented ideas are a result of lessons learned by the NASA Johnson Space Center Mission Operations Directorate operations team support during the 2009 NASA Desert Research and Technology Studies (Desert RATS). It is important that the philosophy of both EVA crew operations and flight control be examined now, so that, where required, adjustments can be made to a next generation EMU and EVA equipment that will complement the anticipated needs of both the EVA flight control team and the crews.

Bell, Ernest R., Jr.↗