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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.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

The Impact of Pictorial Display on Operator Learning and Performance

The effects of pictorially displayed information on human learning and performance of a simple control task were investigated. The controlled system was a harmonic oscillator and the system response was displayed to subjects as either an animated pendulum or a horizontally moving dot. Results indicated that the pendulum display did not effect performance scores but did significantly effect the learning processes of individual operators. The subjects with the pendulum display demonstrated more vertical internal models early in the experiment and the manner in which their internal models were tuned with practice showed increased variability between subjects.

Miller, R. A.↗
NASA NTRS1989

Machine learning in motion control

The existing methodologies for robot programming originate primarily from robotic applications to manufacturing, where uncertainties of the robots and their task environment may be minimized by repeated off-line modeling and identification. In space application of robots, however, a higher degree of automation is required for robot programming because of the desire of minimizing the human intervention. We discuss a new paradigm of robotic programming which is based on the concept of machine learning. The goal is to let robots practice tasks by themselves and the operational data are used to automatically improve their motion performance. The underlying mathematical problem is to solve the problem of dynamical inverse by iterative methods. One of the key questions is how to ensure the convergence of the iterative process. There have been a few small steps taken into this important approach to robot programming. We give a representative result on the convergence problem.

Su, Renjeng↗
NASA NTRS1997

Demonstration of a Safety Analysis on a Complex System

For the past 17 years, Professor Leveson and her graduate students have been developing a theoretical foundation for safety in complex systems and building a methodology upon that foundation. The methodology includes special management structures and procedures, system hazard analyses, software hazard analysis, requirements modeling and analysis for completeness and safety, special software design techniques including the design of human-machine interaction, verification, operational feedback, and change analysis. The Safeware methodology is based on system safety techniques that are extended to deal with software and human error. Automation is used to enhance our ability to cope with complex systems. Identification, classification, and evaluation of hazards is done using modeling and analysis. To be effective, the models and analysis tools must consider the hardware, software, and human components in these systems. They also need to include a variety of analysis techniques and orthogonal approaches: There exists no single safety analysis or evaluation technique that can handle all aspects of complex systems. Applying only one or two may make us feel satisfied, but will produce limited results. We report here on a demonstration, performed as part of a contract with NASA Langley Research Center, of the Safeware methodology on the Center-TRACON Automation System (CTAS) portion of the air traffic control (ATC) system and procedures currently employed at the Dallas/Fort Worth (DFW) TRACON (Terminal Radar Approach CONtrol). CTAS is an automated system to assist controllers in handling arrival traffic in the DFW area. Safety is a system property, not a component property, so our safety analysis considers the entire system and not simply the automated components. Because safety analysis of a complex system is an interdisciplinary effort, our team included system engineers, software engineers, human factors experts, and cognitive psychologists.

Leveson, Nancy↗
NASA NTRS2005

Proposal to Simultaneously Profile Wind and CO2 on Earth and Mars With 2-micron Pulsed Lidar Technologies

2-micron lidar technology has been in use and under continued improvement for many years toward wind measurements. But the 2-micron wavelength region is also rich in absorption lines of CO2 (and H2O to a lesser extent) that can be exploited with the differential absorption lidar (DIAL) technique to make species concentration measurements. A coherent detection receiver offers the possibility of making combined wind and DIAL measurements with wind derived from frequency shift of the backscatter spectrum and species concentration derived from power of the backscatter spectrum. A combined wind and CO2 measurement capability is of interest for applications on both Earth and Mars. CO2 measurements in the Earth atmosphere are of importance to studies of the global carbon cycle. Data on vertically-resolved CO2 profiles over large geographical observations areas are of particular interest that could potentially be made by deploying a lidar on an aircraft or satellite. By combining CO2 concentration with wind measurements an even more useful data product could be obtained in the calculation of CO2 flux. A challenge to lidar in this application is that CO2 concentration measurements must be made with a high level of precision and accuracy to better than 1%. The Martian atmosphere also presents wind and CO2 measurement problems that could be met with a combined DIAL/Doppler lidar. CO2 concentration in this scenario would be used to calculate atmospheric density since the Martian atmosphere is composed of 95% CO2. The lack of measurements of Mars atmospheric density in the 30-60 km range, dust storm formation and movements, and horizontal wind patterns in the 0-20 km range pose significant risks to aerocapture, and entry, descent, and landing of future robotic and human Mars missions. Systematic measurement of the Mars atmospheric density and winds will be required over several Mars years, supplemented with day-of-entry operational measurements. To date, there have been 5 successful robotic landings on Mars. Atmospheric density and wind reconstruction has been performed for 3 of these entries (the two Viking landers and Mars Pathfinder). At present, all Mars atmospheric density and wind models have these 3 entries (at widely scattered positions and seasons) as their basis, supplemented by coarse orbital measurements of atmospheric opacity and temperature. This lack of data leads to a large uncertainty in prediction of the Mars atmospheric density and winds in the altitude regime where deceleration of landers will occur. This uncertainty will have a dramatically large impact on mass, cost and risk. The precision and accuracy for application to Mars is not as stringent as Earth, but Mars does pose a challenge in needing a high level of wavelength stability and control in order to reference wavelength to the narrow linewidths found in the low atmospheric pressure of Mars, as illustrated in Figure 1.

Singh, Upendra N.↗
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↗
DOE OSTI2018

OPTIMIZATION OF FAST FISSION GAS RELEASE MODEL PARAMETERS USING MACHINE LEARNING ACCELERATED EVOLUTIONARY ALGORITHMS

Fission gas release models used in FRAPCON and FAST are based on equations that describe the physical transport phenomena that control release from the fuel pellet to the rod void volume of fission gas produced during irradiation. Although these models are physically based, many of the parameters that control the release such as the impact of burnup on diffusion and bubble formation and saturation are critical to the accurate prediction of fission gas release, but cannot be measured in any quantitative way. To overcome this problem, these parameters have been empirically derived to provide a best fit to the available data that includes rod puncture data and electron probe microanalysis (EMPA) and X-ray fluorescence (XRF) data of radial location of residual gas within the pellet. In general, a heuristic approach for deriving these parameters via practical implementation of expert analyses on just a few dependent variables has been shown to perform quite well in most situations. In this paper, we present an approach for applying machine learning to expedite model development. This approach is based on developing a deep artificial neural network which describes FRAPCON’s fission gas release models, and optimizes parameters by a differential evaluation algorithm. This approach allows us to quickly and accurately tune physical models based on expert judgment, and works as a human-in-the-loop approach, to assist the modeler in identifying and addressing regions of high uncertainty in a multi-parameter space. Results of the updated fission gas release model will be shown for all the FAST assessment data.

Johns, Jesse M.↗
DOE OSTI2019

Design and Synthesis of Selective Phosphodiesterase 4D (PDE4D) Allosteric Inhibitors for the Treatment of Fragile X Syndrome and Other Brain Disorders

Novel pyridine- and pyrimidine-based allosteric inhibitors are reported that achieve PDE4D subtype selectivity through recognition of a single amino acid difference on a key regulatory domain, known as UCR2, that opens and closes over the catalytic site for cAMP hydrolysis. The design and optimization of lead compounds was based on iterative analysis of X-ray crystal structures combined with metabolite identification. Selectivity for the activated, dimeric form of PDE4D provided potent memory enhancing effects in a mouse model of novel object recognition with improved tolerability and reduced vascular toxicity over earlier PDE4 inhibitors that lack subtype selectivity. The lead compound, 28 (BPN14770), has entered midstage, human phase 2 clinical trials for the treatment of Fragile X Syndrome.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1999

On-Line Analysis of Physiologic and Neurobehavioral Variables During Long-Duration Space Missions

The goal of this project is to develop reliable statistical algorithms for on-line analysis of physiologic and neurobehavioral variables monitored during long-duration space missions. Maintenance of physiologic and neurobehavioral homeostasis during long-duration space missions is crucial for ensuring optimal crew performance. If countermeasures are not applied, alterations in homeostasis will occur in nearly all-physiologic systems. During such missions data from most of these systems will be either continually and/or continuously monitored. Therefore, if these data can be analyzed as they are acquired and the status of these systems can be continually assessed, then once alterations are detected, appropriate countermeasures can be applied to correct them. One of the most important physiologic systems in which to maintain homeostasis during long-duration missions is the circadian system. To detect and treat alterations in circadian physiology during long duration space missions requires development of: 1) a ground-based protocol to assess the status of the circadian system under the light-dark environment in which crews in space will typically work; and 2) appropriate statistical methods to make this assessment. The protocol in Project 1, Circadian Entrainment, Sleep-Wake Regulation and Neurobehavioral will study human volunteers under the simulated light-dark environment of long-duration space missions. Therefore, we propose to develop statistical models to characterize in near real time circadian and neurobehavioral physiology under these conditions. The specific aims of this project are to test the hypotheses that: 1) Dynamic statistical methods based on the Kronauer model of the human circadian system can be developed to estimate circadian phase, period, amplitude from core-temperature data collected under simulated light- dark conditions of long-duration space missions. 2) Analytic formulae and numerical algorithms can be developed to compute the error in the estimates of circadian phase, period and amplitude determined from the data in Specific Aim 1. 3) Statistical models can detect reliably in near real- time (daily) significant alternations in the circadian physiology of individual subjects by analyzing the circadian and neurobehavioral data collected in Project 1. 4) Criteria can be developed using the Kronauer model and the recently developed Jewett model of cognitive -performance and subjective alertness to define altered circadian and neurobehavioral physiology and to set conditions for immediate administration of countermeasures.

Brown, Emery N.↗
NASA NTRSDate not supplied

Countermeasures for Mitigation of Sensorimotor Decrements Following Head-Down Bed Rest

BACKGROUND Astronauts experience postflight disturbances in postural and locomotor control due to sensorimotor adaptations during spaceflight. These alterations may have adverse consequences if a rapid egress is required after landing. Although exercise is partially effective for mitigating cardiovascular and muscular deconditioning, additional countermeasures are needed to further preserve sensorimotor function for exploration missions. We have identified proprioceptive training and electrical muscle stimulation (EMS) as promising in-flight countermeasures. Since prolonged head down bed rest (HDBR) is a spaceflight analog for body unloading and causes postural and locomotor control decrements that parallel those observed after spaceflight, it can be used to accelerate the development of these countermeasures. METHODS This study will determine the effects of proprioceptive training and EMS on functional task performance and sensorimotor function following 60 days of 6° HDBR. Subjects will be randomly assigned to one of four groups: 1) an EMS arm, 2) a proprioceptive training arm, 3) an exercise plus proprioceptive training arm, and 4) a control arm. The EMS countermeasure will include daily bilateral stimulation of the quadriceps femoris muscle (30 minutes per session). Proprioceptive training will be performed three days per week (20 minutes per session) consisting of body-loaded postural tasks in the horizontal position on an air bearing sled. Exercise training will mimic current protocols used on the International Space Station, but treadmill aerobic exercise will be replaced with additional cycling aerobic exercise. Primary outcome measures will include pre and post HDBR functional tests that are representative of high priority exploration mission tasks and require high demand for dynamic control of postural stability. Additional measures will be used to identify the key physiological factors contributing to countermeasure benefits. Given the constrained samples size, a Bayesian modelling approach will be used to quantify the probability that there is an effect of a given magnitude. COUNTERMEASURE UPDATES Proprioceptive countermeasure design enhancements and human in the loop pilot testing continued through the Crew Health Countermeasures (CHC) Systems Capability Leadership Team (SCLT). The primary goals of this work were to enhance the visual feedback system’s capabilities and develop a proprioceptive training program for 60 days of HDBR. Six healthy non-astronaut volunteers participated in four pilot training sessions to systematically examine how each training variable (e.g., axial load, foot placement, and software profile) affects the overall proprioceptive challenge. The resulting training program will maintain an appropriate challenge during 60 days of HDBR by progressively decreasing the subject’s base of support, increasing tilt board target distances, and increasing axial loads using both subjective verbal feedback and objective performance data. RELEVANCE The deliverable from this project will be proof-of-concept sensorimotor countermeasure designs for functional task performance with full assessment of efficacy in a spaceflight analog. If the countermeasures are effective, they will be translated for validation with the suite of operationally implemented in-flight countermeasures.

T R Macaulay↗
DOE OSTI2025

Evaluation and calibration of MERRA-2 and CAMS reanalysis for PM 2.5 in a semi-urbanized area in the south of the Amazon

Air pollution has significant implications for the climate and poses irreversible risks to human health. The Amazon region of Brazil is severely affected by biomass burning (BB) emissions, yet air quality monitoring remains highly inadequate. Given the scarcity of surface-based observations, reanalysis models have become essential tools for assessing air pollution. Although MERRA-2 and CAMS PM 2.5 products are widely utilized, their validation and comprehensive evaluation for the Amazon Basin remain limited. Here, this study assesses the performance of these products in a semi-urbanized region in the southern Amazon. The calibrated time series was employed to analyze PM 2.5 concentrations from 2003 to 2023. Our results showed satisfactory performance of both products for the 24-h averages of PM 2.5 , with linear correlations above 0.76. However, it was found that both products overestimate surface concentrations. MERRA-2 performed better, with approximately 30% lower bias than CAMS. Time series analysis showed that the study area is strongly impacted by emissions BB in the dry period, mainly in August and September. Furthermore, our findings indicate a positive trend in increasing PM 2.5 concentrations, with a notable rise observed since 2014. The average PM 2.5 levels frequently exceed the daily air quality guidelines established by the WHO in 2021. It has been estimated that the population of this region is exposed to concentrations above 15 μg.m -3 , on average, more than 30 days per year. Our results contribute to the evaluation of MERRA-2 and CAMS products for Amazon and provide a corrected estimate for surface PM 2.5 . Recent concerns about air quality and the implementation of new surface monitoring networks may improve the evaluation of reanalysis products. In the short term, the need for this information makes our assessments indispensable.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

IDAC-Bio, A Software for Internal Dosimetry Based on the New ICRP Biokinetic Models and Specific Absorbed Fractions

Radiation dosimetry is central to virtually all radiation safety applications, optimization, and research. It relates to various individuals and population groups and to miscellaneous exposure situations—including planned, existing, and emergency situations. The International Commission on Radiological Protection (ICRP) has developed a new computational framework for internal dose estimations. Important components are more detailed and improved anatomical models and more realistic biokinetic models than before. The ICRP is currently producing new organ dose and effective dose coefficients for occupational intakes of radionuclides (OIR) and environmental intakes of radionuclides (EIR), which supersede the earlier dose coefficients in Publication 68 and the Publication 72 series, respectively. However, the ICRP only publishes dose coefficients for a single acute intake of a radionuclide and for an integration period of 50 years for intake by adults and to age 70 years for intakes by pre-adults. The new software, IDAC-Bio, performs committed absorbed dose and effective dose calculations for a selectable intake scenario, e.g., for a continuous intake or an intake during x hours per day and y days per week, and for any selected integration time. The software uses the primary data and models of the ICRP biokinetic models and numerically solves the biokinetic model and calculates the absorbed doses to organs and tissues in the ICRP reference human phantoms. The software calculates absorbed dose using the nuclear decay data in ICRP publication 107. IDAC-Bio is a further development and an important addition to the internal dosimetry program IDAC-Dose2.1. Here, the results generated by the software were validated against published ICRP dose coefficients. The potential of the software is illustrated by dose calculations for a nuclear power plant worker who had been exposed to varying levels of 60 Co and who had undergone repeated whole-body measurements, and for a hypothetical member of the public subject to future releases of 148 Gd from neutron spallation in tungsten at the European Spallation Source.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Machine Learning of All Mycobacterium tuberculosis H37Rv RNA-seq Data Reveals a Structured Interplay between Metabolism, Stress Response, and Infection

Mycobacterium tuberculosis is one of the most consequential human bacterial pathogens, posing a serious challenge to 21st century medicine. A key feature of its pathogenicity is its ability to adapt its transcriptional response to environmental stresses through its transcriptional regulatory network (TRN). While many studies have sought to characterize specific portions of the M. tuberculosis TRN, and some studies have performed system-level analysis, few have been able to provide a network-based model of the TRN that also provides the relative shifts in transcriptional regulator activity triggered by changing environments. Here, we compiled a compendium of nearly 650 publicly available, high quality M. tuberculosis RNA-sequencing data sets and applied an unsupervised machine learning method to obtain a quantitative, top-down TRN. It consists of 80 independently modulated gene sets known as “iModulons,” 41 of which correspond to known regulons. These iModulons explain 61% of the variance in the organism’s transcriptional response. We show that iModulons (i) reveal the function of poorly characterized regulons, (ii) describe the transcriptional shifts that occur during environmental changes such as shifting carbon sources, oxidative stress, and infection events, and (iii) identify intrinsic clusters of regulons that link several important metabolic systems, including lipid, cholesterol, and sulfur metabolism. This transcriptome-wide analysis of the M. tuberculosis TRN informs future research on effective ways to study and manipulate its transcriptional regulation and presents a knowledge-enhanced database of all published high-quality RNA-seq data for this organism to date.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Hypothesis testing via AI: Generating physically interpretable models of scientific data with machine learning (Full Technical Report)

Deep learning has demonstrated an exceptional ability to solve complex tasks (an engineering success); however, it has done so at the expense of the ability to generate new knowledge (a scientific failure). We propose an alternative framework—entitled Deep Symbolic Regression (DSR)—in which artificial neural networks (NNs) rapidly generate hypotheses about physical relationships among inputs. This framework bypasses the need to interpret an NN altogether, while still leveraging the representational power of deep learning. The resulting models are tractable mathematical expressions, which are inherently and readily human interpretable and can provide insights into underlying physical phenomena. Further, we fold this methodology into the scientific process by allowing the scientist to directly integrate a priori knowledge and beliefs to accelerate learning. We demonstrate this methodology on symbolic regression—the problem of rediscovering underlying expressions describing a dataset—and achieve state-of-the-art performance across a wide variety of symbolic regression problems. Further, we generalize our DSR framework to apply to the more general class of symbolic optimization problems, in which one seeks to optimize a sequence of symbols or “tokens” under a black-box reward function. Examples of other symbolic optimization problems include neural architecture search and computational antibody design. Our generalized tool, Deep Symbolic Optimization (DSO), has been demonstrated on the task of learning symbolic control policies for reinforcement learning environments, and has been adopted as an enabling capability for computational antibody design.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

Principled halftoning based on human vision models

When models of human vision adequately measure the relative quality of candidate halftonings of an image, the problem of halftoning the image becomes equivalent to the search problem of finding a halftone that optimizes the quality metric. Because of the vast number of possible halftones, and the complexity of image quality measures, this principled approach has usually been put aside in favor of fast algorithms that seem to perform well. We find that the principled approach can lead to a range of useful halftoning algorithms, as we trade off speed for quality by varying the complexity of the quality measure and the thoroughness of the search. High quality halftones can be obtained reasonably quickly, for example, by using as a measure the vector length of the error image filtered by a contrast sensitivity function, and, as the search procedure, the sequential adjustment of individual pixels to improve the quality measure. If computational resources permit, simulated annealing can find nearly optimal solutions.

Mulligan, Jeffrey B.↗
NASA NTRS1991

Objective evaluation of situation awareness for dynamic decision makers in teleoperations

Situation awareness, a current mental mode of the environment, is critical to the ability of operators to perform complex and dynamic tasks. This should be particularly true for teleoperators, who are separated from the situation they need to be aware of. The design of the man-machine interface must be guided by the goal of maintaining and enhancing situation awareness. The objective of this work has been to build a foundation upon which research in the area can proceed. A model of dynamic human decision making which is inclusive of situation awareness will be presented, along with a definition of situation awareness. A method for measuring situation awareness will also be presented as a tool for evaluating design concepts. The Situation Awareness Global Assessment Technique (SAGAT) is an objective measure of situation awareness originally developed for the fighter cockpit environment. The results of SAGAT validation efforts will be presented. Implications of this research for teleoperators and other operators of dynamic systems will be discussed.

Endsley, Mica R.↗
NASA NTRS2010

Full Mission Astronaut Radiation Exposure Assessments for Long Duration Lunar Surface Missions

Risk to astronauts due to ionizing radiation exposure is a primary concern for missions beyond Low Earth Orbit (LEO) and will drive mission architecture requirements, mission timelines, and operational practices. Both galactic cosmic ray (GCR) and solar particle event (SPE) environments pose a risk to astronauts for missions beyond LEO. The GCR environment, which is made up of protons and heavier ions covering a broad energy spectrum, is ever present but varies in intensity with the solar cycle, while SPEs are sporadic events, consisting primarily of protons moving outward through the solar system from the sun. The GCR environment is more penetrating and is more difficult to shield than SPE environments, but lacks the intensity to induce acute effects. Large SPEs are rare, but they could result in a lethal dose, if adequate shielding is not provided. For short missions, radiation risk is dominated by the possibility of a large SPE. Longer missions also require planning for large SPEs; adequate shielding must be provided and operational constraints must allow astronauts to move quickly to shielded locations. The dominant risk for longer missions, however, is GCR exposure, which accumulates over time and can lead to late effects such as cancer. SPE exposure, even low level SPE exposure received in heavily shielded locations, will increase this risk. In addition to GCR and SPE environments, the lunar neutron albedo resulting mainly from the interaction of GCRs with regolith will also contribute to astronaut risk. Full mission exposure assessments were performed for proposed long duration lunar surface mission scenarios. In order to accomplish these assessments, radiation shielding models were developed for a proposed lunar habitat and rover. End-to-End mission exposure assessments were performed by first calculating exposure rates for locations in the habitat, rover, and during extra-vehicular activities (EVA). Subsequently, total mission exposures were evaluated for proposed timelines. A number of computational tools and mathematical models, which have been incorporated into NASA's On-Line Tool for the Assessment of Radiation In Space (OLTARIS), were used for this study. These tools include GCR and SPE environment models, human body models, and the HZETRN space radiation transport code, which is used to calculate the transport of the charged particles and neutrons through shielding materials and human tissue. Mission exposure results, assessed in terms of effective dose, are presented for proposed timelines and recommendations are made for improved astronaut shielding and safer operational practice.

Adamczyk, Anne M.↗
NASA NTRS2017

Evaluation of a Tactical Surface Metering Tool for Charlotte Douglas International Airport via Human-in-the-Loop Simulation

NASA has been working with the FAA and aviation industry partners to develop and demonstrate new concepts and technologies that integrate arrival, departure, and surface traffic management capabilities. In March 2017, NASA conducted a human-in-the-loop (HITL) simulation for integrated surface and airspace operations, modeling Charlotte Douglas International Airport, to evaluate the operational procedures and information requirements for the tactical surface metering tool, and data exchange elements between the airline controlled ramp and ATC Tower. In this paper, we focus on the calibration of the tactical surface metering tool using various metrics measured from the HITL simulation results. Key performance metrics include gate hold times from pushback advisories, taxi-in-out times, runway throughput, and departure queue size. Subjective metrics presented in this paper include workload, situational awareness, and acceptability of the metering tool and its calibration.

Verma, Savita↗
NASA NTRSDate not supplied

Development of ARGOS 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 at various gravity levels. The suit is suspended from a robotic overhead crane by a cable connected to the suit via a gimbal with an adjustable pivot point. There has been increased interest in providing lunar 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 and functional lunar simulation. Interactions between the pivot point location and 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 significant loss of valuable pressurized suited time. This paper aims to share the 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. The 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 shifts forward and aft had noticeable effects on subject stability. Fifteen subjects performed a series of CG-related tasks in the xEMU spacesuit to assess simulation acceptability. 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 to determine pivot point selection for astronaut training in the xEMU ARGOS environment.

Sarah L. Jarvis↗