SEARCH · Search NASA

Results for “Performance”

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.

SourceResearch connectionFrom yearThrough year

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

At least 649 records · Page 36

All sources NASA NTRS DOE OSTI

NASA NTRS2016

An Approach for Performance Based Glove Mobility Requirements

The Space Suit Assembly (SSA) Development Team at NASA Johnson Space Center has invested heavily in the advancement of rear-entry planetary exploration suit design but largely deferred development of extravehicular activity (EVA) glove designs, and accepted the risk of using the current flight gloves, Phase VI, for exploration missions. However, as design reference missions mature, the risks of using heritage hardware have highlighted the need for developing robust new glove technologies. To address the technology gap, the NASA Space Technology Mission Directorate's Game-Changing Development Program provided start-up funding for the High Performance EVA Glove (HPEG) Element as part of the Next Generation Life Support (NGLS) Project in the fall of 2013. The overarching goal of the HPEG Element is to develop a robust glove design that increases human performance during EVA and creates pathway for implementation of emergent technologies, with specific aims of increasing pressurized mobility to 60% of barehanded capability, increasing the durability in on-pristine environments, and decreasing the potential of gloves to cause injury during use. The HPEG Element focused initial efforts on developing quantifiable and repeatable methodologies for assessing glove performance with respect to mobility, injury potential, thermal conductivity, and abrasion resistance. The team used these methodologies to establish requirements against which emerging technologies and glove designs can be assessed at both the component and assembly levels. The mobility performance testing methodology was an early focus for the HPEG team as it stems from collaborations between the SSA Development team and the JSC Anthropometry and Biomechanics Facility (ABF) that began investigating new methods for suited mobility and fit early in the Constellation Program. The combined HPEG and ABF team used lessons learned from the previous efforts as well as additional reviews of methodologies in physical and occupational therapy arenas to develop a protocol that assesses gloved range of motion, strength, dexterity, tactility, and fit in comparative quantitative terms and also provides qualitative insight to direct hardware design iterations. The protocol was evaluated using five experienced test subjects wearing the EMU pressurized to 4.3psid with three different glove configurations. The results of the testing are presented to illustrate where the protocol is and is not valid for benchmark comparisons. The process for requirements development based upon the results is also presented along with suggested performance values for the High Performance EVA Gloves currently in development.

Aitchison, Lindsay↗

NASA NTRS2015

Conflict Detection Performance Analysis for Function Allocation Using Time-Shifted Recorded Traffic Data

The performance of the conflict detection function in a separation assurance system is dependent on the content and quality of the data available to perform that function. Specifically, data quality and data content available to the conflict detection function have a direct impact on the accuracy of the prediction of an aircraft's future state or trajectory, which, in turn, impacts the ability to successfully anticipate potential losses of separation (detect future conflicts). Consequently, other separation assurance functions that rely on the conflict detection function - namely, conflict resolution - are prone to negative performance impacts. The many possible allocations and implementations of the conflict detection function between centralized and distributed systems drive the need to understand the key relationships that impact conflict detection performance, with respect to differences in data available. This paper presents the preliminary results of an analysis technique developed to investigate the impacts of data quality and data content on conflict detection performance. Flight track data recorded from a day of the National Airspace System is time-shifted to create conflicts not present in the un-shifted data. A methodology is used to smooth and filter the recorded data to eliminate sensor fusion noise, data drop-outs and other anomalies in the data. The metrics used to characterize conflict detection performance are presented and a set of preliminary results is discussed.

Guerreiro, Nelson M.↗

NASA NTRS2016

An Integrated Gate Turnaround Management Concept Leveraging Big Data Analytics for NAS Performance Improvements

"Gate Turnaround" plays a key role in the National Air Space (NAS) gate-to-gate performance by receiving aircraft when they reach their destination airport, and delivering aircraft into the NAS upon departing from the gate and subsequent takeoff. The time spent at the gate in meeting the planned departure time is influenced by many factors and often with considerable uncertainties. Uncertainties such as weather, early or late arrivals, disembarking and boarding passengers, unloading/reloading cargo, aircraft logistics/maintenance services and ground handling, traffic in ramp and movement areas for taxi-in and taxi-out, and departure queue management for takeoff are likely encountered on the daily basis. The Integrated Gate Turnaround Management (IGTM) concept is leveraging relevant historical data to support optimization of the gate operations, which include arrival, at the gate, departure based on constraints (e.g., available gates at the arrival, ground crew and equipment for the gate turnaround, and over capacity demand upon departure), and collaborative decision-making. The IGTM concept provides effective information services and decision tools to the stakeholders, such as airline dispatchers, gate agents, airport operators, ramp controllers, and air traffic control (ATC) traffic managers and ground controllers to mitigate uncertainties arising from both nominal and off-nominal airport gate operations. IGTM will provide NAS stakeholders customized decision making tools through a User Interface (UI) by leveraging historical data (Big Data), net-enabled Air Traffic Management (ATM) live data, and analytics according to dependencies among NAS parameters for the stakeholders to manage and optimize the NAS performance in the gate turnaround domain. The application will give stakeholders predictable results based on the past and current NAS performance according to selected decision trees through the UI. The predictable results are generated based on analysis of the unique airport attributes (e.g., runway, taxiway, terminal, and gate configurations and tenants), and combined statistics from past data and live data based on a specific set of ATM concept-of-operations (ConOps) and operational parameters via systems analysis using an analytic network learning model. The IGTM tool will then bound the uncertainties that arise from nominal and off-nominal operational conditions with direct assessment of the gate turnaround status and the impact of a certain operational decision on the NAS performance, and provide a set of recommended actions to optimize the NAS performance by allowing stakeholders to take mitigation actions to reduce uncertainty and time deviation of planned operational events. An IGTM prototype was developed at NASA Ames Simulation Laboratories (SimLabs) to demonstrate the benefits and applicability of the concept. A data network, using the System Wide Information Management (SWIM)-like messaging application using the ActiveMQ message service, was connected to the simulated data warehouse, scheduled flight plans, a fast-time airport simulator, and a graphic UI. A fast-time simulation was integrated with the data warehouse or Big Data/Analytics (BAI), scheduled flight plans from Aeronautical Operational Control AOC, IGTM Controller, and a UI via a SWIM-like data messaging network using the ActiveMQ message service, illustrated in Figure 1, to demonstrate selected use-cases showing the benefits of the IGTM concept on the NAS performance.

Efficent ATM systems↗

NASA NTRS2016

Utilizing Commercial Hardware and Open Source Computer Vision Software to Perform Motion Capture for Reduced Gravity Flight

Long duration space travel to Mars or to an asteroid will expose astronauts to extended periods of reduced gravity. Since gravity is not present to aid loading, astronauts will use resistive and aerobic exercise regimes for the duration of the space flight to minimize the loss of bone density, muscle mass and aerobic capacity that occurs during exposure to a reduced gravity environment. Unlike the International Space Station (ISS), the area available for an exercise device in the next generation of spacecraft is limited. Therefore, compact resistance exercise device prototypes are being developed. The NASA Digital Astronaut Project (DAP) is supporting the Advanced Exercise Concepts (AEC) Project, Exercise Physiology and Countermeasures (ExPC) project and the National Space Biomedical Research Institute (NSBRI) funded researchers by developing computational models of exercising with these new advanced exercise device concepts. To perform validation of these models and to support the Advanced Exercise Concepts Project, several candidate devices have been flown onboard NASAs Reduced Gravity Aircraft. In terrestrial laboratories, researchers typically have available to them motion capture systems for the measurement of subject kinematics. Onboard the parabolic flight aircraft it is not practical to utilize the traditional motion capture systems due to the large working volume they require and their relatively high replacement cost if damaged. To support measuring kinematics on board parabolic aircraft, a motion capture system is being developed utilizing open source computer vision code with commercial off the shelf (COTS) video camera hardware. While the systems accuracy is lower than lab setups, it provides a means to produce quantitative comparison motion capture kinematic data. Additionally, data such as required exercise volume for small spaces such as the Orion capsule can be determined. METHODS: OpenCV is an open source computer vision library that provides the ability to perform multi-camera 3 dimensional reconstruction. Utilizing OpenCV, via the Python programming language, a set of tools has been developed to perform motion capture in confined spaces using commercial cameras. Four Sony Video Cameras were intrinsically calibrated prior to flight. Intrinsic calibration provides a set of camera specific parameters to remove geometric distortion of the lens and sensor (specific to each individual camera). A set of high contrast markers were placed on the exercising subject (safety also necessitated that they be soft in case they become detached during parabolic flight); small yarn balls were used. Extrinsic calibration, the determination of camera location and orientation parameters, is performed using fixed landmark markers shared by the camera scenes. Additionally a wand calibration, the sweeping of the camera scenes simultaneously, was also performed. Techniques have been developed to perform intrinsic calibration, extrinsic calibration, isolation of the markers in the scene, calculation of marker 2D centroids, and 3D reconstruction from multiple cameras. These methods have been tested in the laboratory side-by-side comparison to a traditional motion capture system and also on a parabolic flight.

Biodynamics↗

NASA NTRS2017

GPS Receiver On-Orbit Performance for the GOES-R Spacecraft

This paper evaluates the on-orbit performance of the first civilian operational use of a Global Positioning System Receiver (GPSR) at a geostationary orbit (GEO). The GPSR is on-board the newly launched Geostationary Operational Environmental Satellite (GOES-R). GOES-R is the first of four next generation GEO weather satellites for NOAA, now in orbit GOES-R is formally identified as GOES-16. Among the pioneering technologies required to support its improved spatial, spectral and temporal resolution is a GPSR. The GOES-16 GPSR system is a new design that was mission critical and therefore received appropriate scrutiny. As ground testing of a GPSR for GEO can only be done by simulations with numerous assumptions and approximations regarding the current GPS constellation, this paper reveals what performance can be achieved in using on orbit data. Extremely accurate orbital position is achieved using GPS navigation at GEO. Performance results are shown demonstrating compliance with the1007575 meter and 6 cms radial/in-track/cross-track orbital position and velocity accuracy requirements of GOES-16. The aforementioned compliance includes station-keeping and momentum management maneuvers, contributing to no observational outages. This performance is achieved by a completely new system design consisting of a unique L1 GEOantenna, low-noise amplifier (LNA) assembly and a 12 channel GPSR capable of tracking the edge of the main beam and the side lobes of the GPS L1 signals. This paper presents the definitive answer that the GOES-16 GPSR solution exceeds all performance requirements tracking up to 12 satellites and achieving excellent carrier-to-noise density (C/N0). Additionally, these performance results show the practicality of this approach. This paper makes it clear that all future GEO Satellites should consider the addition of a GPSR in their spacecraft design, otherwise they may be sacrificing spacecraft capabilities and accuracy along with incurring increased and continual demand on ground support.

GEO GPS↗

NASA NTRS2018

Assessment and Verification of SLS Block 1-B Exploration Upper Stage State and Stage Disposal Performance

One of the SLS Navigation System's key performance requirements is a constraint on the payload system's delta-v allocation to correct for insertion errors due to vehicle state uncertainty at payload separation. The SLS navigation team has developed a Delta-Delta-V analysis approach to assess the effect on trajectory correction maneuver (TCM) design needed to correct for navigation errors. This approach differs from traditional covariance analysis based methods and makes no assumptions with regard to the propagation of the state dynamics. This allows for consideration of non-linearity in the propagation of state uncertainties. The Delta-Delta-V analysis approach re-optimizes perturbed SLS mission trajectories by varying key mission states in accordance with an assumed state error. The state error is developed from detailed vehicle 6-DOF Monte Carlo analysis or generated using covariance analysis. These perturbed trajectories are compared to a nominal trajectory to determine necessary TCM design. To implement this analysis approach, a tool set was developed which combines the functionality of a 3-DOF trajectory optimization tool, Copernicus, and a detailed 6-DOF vehicle simulation tool, Marshall Aerospace Vehicle Representation in C (MAVERIC). In addition to delta-v allocation constraints on SLS navigation performance, SLS mission requirement dictate successful upper stage disposal. Due to engine and propellant constraints, the SLS Exploration Upper Stage (EUS) must dispose into heliocentric space by means of a lunar fly-by maneuver. As with payload delta-v allocation, upper stage disposal maneuvers must place the EUS on a trajectory that maximizes the probability of achieving a heliocentric orbit post Lunar fly-by considering all sources of vehicle state uncertainty prior to the maneuver. To ensure disposal, the SLS navigation team has developed an analysis approach to derive optimal disposal guidance targets. This approach maximizes the state error covariance prior to the maneuver to develop and re-optimize a nominal disposal maneuver (DM) target that, if achieved, would maximize the potential for successful upper stage disposal. For EUS disposal analysis, a set of two tools was developed. The first considers only the nominal pre-disposal maneuver state, vehicle constraints, and an a priori estimate of the state error covariance. In the analysis, the optimal nominal disposal target is determined. This is performed by re-formulating the trajectory optimization to consider constraints on the eigenvectors of the error ellipse applied to the nominal trajectory. A bisection search methodology is implemented in the tool to refine these dispersions resulting in the maximum dispersion feasible for successful disposal via lunar fly-by. Success is defined based on the probability that the vehicle will not impact the lunar surface and will achieve a characteristic energy (C3) relative to the Earth such that it is no longer in the Earth-Moon system. The second tool propagates post-disposal maneuver states to determine the success of disposal for provided trajectory achieved states. This is performed using the optimized nominal target within the 6-DOF vehicle simulation. This paper will discuss the application of the Delta-Delta-V analysis approach for performance evaluation as well as trajectory re-optimization so as to demonstrate the system's capability in meeting performance constraints. Additionally, further discussion of the implementation of assessing disposal analysis will be provided.

Patrick, Sean↗

NASA NTRS2018

Landsat 9 TIRS-2 Performance Results Based on Subsystem-Level Testing

Landsat 9 is the next in the series of Landsat satellites and has a complement of two pushbroom imagers: Operational Land Imager-2 (OLI-2) that samples the solar reflective spectrum with nine channels and Thermal Infrared Sensor-2 (TIRS-2) samples the thermal infrared spectrum with two channels. The first builds of these sensors, OLI and TIRS, were launched on Landsat 8 in 2013 and Landsat 9 is expected to launch in December 2020. TIRS-2 is designed and built to continue the Landsat data record and satisfy the needs of the remote sensing community. There are two sets of requirements considered for planning the component, subsystem and instrument level tests for TIRS-2: performance requirements and Special Calibration Test Requirements (SCTR). The performance requirements specify key spectral, spatial, radiometric, and operational parameters of TIRS-2 while the SCTRs specify parameters of how the instrument is tested. Several requirements can only be verified at the instrument level, but many performance metrics can be assessed earlier in prelaunch testing at the subsystem level. A test program called TIRS Imaging Performance and Cryoshell Evaluation (TIPCE) was developed to characterize TIRS-2 spectral, spatial, and scattered-light rejection performance at the telescope and detector subsystem level. There were three thermal vacuum campaigns in TIPCE that occurred from November 2017 to March 2018. This work shows results of TIPCE data analysis which provide confidence that key requirements will be met at instrument level with a few minor waivers. A full complement of performance testing will be done at the TIRS-2 instrument level for final verification in late 2018 through Spring 2019.

scatter↗

NASA NTRS2019

Human Performance Contributions to Safety in Commercial Aviation

Every day in aviation, pilots, air traffic controllers, and other front-line personnel perform countless correct judgments and actions in a variety of operational environments. These judgments and actions are often the difference between an accident and a non-event. Ironically, data on these behaviors are rarely collected or analyzed. Data-driven decisions about safety management and design of safety-critical systems are limited by the available data, which influence how decision makers characterize problems and identify solutions. Large volumes of data are collected on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that result in routine successful outcomes, safety management and system design decisions are based on a small sample of nonrepresentative safety data. This assessment aimed to find and document “safety successes” made possible by human operators. With many Aeronautics Research Mission Directorate (ARMD) Programs and Projects focusing on increased automation and autonomy and decreased human involvement, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant risk — a risk that has not been recognized to date. Without understanding how humans contribute to safety, any estimate of predicted safety of autonomous capabilities is incomplete and inherently suspect. Furthermore, understanding the ways in which humans contribute to safety can promote strategic interactions among safety technologies, functions, procedures and the people using them. Without this understanding, the full benefits of an integrated, optimized human/technology or autonomous system will not be realized. Historically, safety has been consistently defined in terms of the occurrence of accidents or recognized risks (i.e., in terms of things that go wrong). These adverse outcomes are explained by identifying their causes, and safety is restored by eliminating or mitigating these causes. An alternative to this approach is to focus on what goes right and identify how to replicate that process. Focusing on the rare cases of failures attributed to “human error” provides little information about why human performance routinely prevents adverse events. Hollnagel has proposed that things go right because people continuously adjust their work to match their operating conditions. These adjustments become increasingly important as systems continue to grow in complexity. Thus, the definition of safety should reflect not only “avoiding things that go wrong” but “ensuring that things go right.” The basis for safety management requires developing an understanding of everyday activities. However, few mechanisms to monitor everyday work exist in the aviation domain, which limits opportunities to learn how designs function in reality. This concept of safety thinking and safety management is reflected in the emerging field of resilience engineering. According to Hollnagel, a system is resilient if it can sustain required operations under expected and unexpected conditions by adjusting its functioning prior to, during, or following changes, disturbances, and opportunities. To explore “positive” behaviors that contribute to resilient performance in commercial aviation, the assessment team examined a range of existing sources of data about pilot and air traffic control (ATC) tower controller performance, including subjective interviews with domain experts and objective aircraft flight data records. These data were used to identify strategies that support resilient performance, methods for exploring and refining those strategies in existing data, and proposed methods for capturing and analyzing new data.

Null, Cynthia H.↗

NASA NTRS2019

High-Performance Computing Optimization for Aladyn – Adaptive Neural Network Molecular Dynamics Mini-Application

This report provides a description and performance evaluation of the optimization techniques for high performance computing (HPC) implementation of the open source Computational Materials mini-application Aladyn (https://github.com/nasa/aladyn). Aladyn is a basic molecular dynamics code written in FORTRAN 2003, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. While achieving orders of magnitude faster computational performance than DFT, the ANN-based approach was still very computationally demanding compared to the conventional approach of using empirically fitted energy functions. After its initial development, Aladyn was evaluated and optimized by experts at the NASA Advanced Supercomputing (NAS) division to exploit modern supercomputer architectures. The code has been optimized for execution on multicore central processing units (CPUs), including Intel® Skylake microarchitecture, and on graphic accelerators, such as Nvidia® V100 graphic processing units (GPUs), using Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) programming interfaces. The optimization achieved a speedup of 4.7 times the baseline version on CPU performance and an additional 2.4 times on CPU+GPU performance. Atomistic computer simulations are a fundamental tool in materials research to model material properties form physics-based first principles. Atomic interaction, governed by Quantum Mechanics (QM) require sophisticated and highly computationally demanding mathematical models to calculate [1]. Classical methods use approximate functional forms, empirically fitted through a set of variable parameters to emulate atomic energies as direct functions of atomic coordinates [2]. While empirical potentials are computationally much simpler, allowing simulations of large-scale systems of up to a trillion (1012) atoms [3], they are substantially less accurate compared to quantum calculations and applicable only to very specific atomic configurations or predefined crystallographic phases. A recently suggested approach is to use heuristic machine learning methods [4], such as those based on Adaptive Neural Networks (ANNs) to predict atomic energies, after being trained on a sufficiently large database of QM-calculated structures [5,6]. This approach reduces significantly the computational complexity, allowing for simulations of orders of magnitude larger systems compared to QM-based methods without compromising accuracy. Still, compared to classical methods using empirical energy functions, ANN methods remain two- to three orders of magnitude more computationally demanding. Hence, the computational cost of simulations, together with the need for extensive training of ANNs, still makes the practical implementation of ANN-based methods quite challenging. The purpose of the Aladyn mini-application software [7], available as open source at https://github.com/nasa/aladyn, is to be a testbed for exploring possible optimization strategies to develop highly scalable parallel algorithms for ANN-based atomistic simulations. Aladyn is aimed at utilizing the architecture of the high-end modern highperformance computing (HPC) hardware based on multicore central processing units (CPUs) equipped with graphic processing unit (GPU) accelerators. Specifically, the goal is to optimize the performance on a single HPC compute node, before implementing scaling to multi-node parallelization using message passing interface (MPI). At the same time, the open source code of Aladyn can serve as a training model for students and professors in academia.

Yamakov, Vesselin I.↗

NASA NTRS2019

A Performance Analysis of Folding Conformal Propeller Blade Designs

NASA’s X-57 Maxwell flight demonstrator has a high-lift system that includes 12 fixed- pitch high-lift propellers located upstream of the wing leading edge for lift augmentation at low speeds. These high-lift propellers are only required at low speeds, and to reduce drag, the propeller blades are folded conformally along the nacelles at other operating conditions. The method of designing the high-lift blades permits several variations of blade cross-section placement along the nacelle surface and a comparative performance analysis was needed to determine if any particular design showed significant benefits. We analyzed the performance of three conformal high-lift propeller designs and compared them to that of a non-conformal baseline propeller to establish both the benefit of stowable blades and the value of each variation. In this study, we first performed a drag analysis of each design in the stowed configuration at the X-57 cruise speed and altitude to determine the drag benefits of each conforming method. Then, among blade designs we compared the thrust, power, and lift for a given input shaft speed to establish any performance losses from the baseline. This analysis shows that the conformal blade designs do not have any appreciable performance losses compared to the baseline blades. Moreover, although the drag in the cruise condition is significantly less than for the non-folding baseline, the drag benefits of each conforming blade approach are similar and the value of each approach largely depends on the ease of integration into the nacelle. This paper presents the results of these studies and discusses the benefits and drawbacks of implementing the conformal blade designs. Specifically, we demonstrate that folding, conformal propeller blades contribute significantly less to cruise drag when compared to windmilling, with an increase relative to a. We also show a less than 1% difference in performance formal, folding propellers and the non-conforming baseline propeller.

Litherland, Brandon L.↗

NASA NTRSDate not supplied

Analytical and Experimental Demonstration of an Alternate Mixing Performance Metric for High-Speed Fuel Mixing Studies

To experimentally assess the fuel/air mixing performance of high-speed fuel injectors, one-dimensional metrics that quantify the degree of mixing completeness downstream of the fuel injection location are required. The most accurate assessment of mixing performance is achieved with the mixing efficiency parameter. In order to experimentally determine the mixing efficiency parameter, the spatial distributions of both mass flux and fuel mass fraction must be measured. In-stream gas sampling techniques are commonly used to measure the fuel mass fraction distribution; however, the mass flux distribution is not easily determined because it requires the measurement of three independent aerothermodynamic variables in addition to the gas composition. Therefore, to experimentally determine the mixing efficiency parameter, the spatial distributions of four independent properties must be measured, with each property generally requiring its own unique probe. Because of this difficulty, it is commonly assumed that alternate metrics, which rely solely on the fuel distribution, are good indicators of mixing performance. However, since these alternate metrics do not provide a mass flux-weighted measure of mixing completeness, they can lead to incorrect conclusions being drawn about the mixing performance of the studied fuel injector configuration. Recognizing this shortcoming, this work proposes two new alternate mixing performance metrics that are easier to obtain than the mixing efficiency parameter. The analytical development of the new metrics, as well as their application to relevant CFD and experimental data of high-speed fuel injector configurations, is presented in this work. For two different experiments, the new metrics are shown to provide an excellent representation of the true mass flux-weighted mixing performance, unlike the traditionally-used alternatives. The results presented herein suggest that the new metrics can serve as accurate surrogates for mixing efficiency in future high-speed fuel/air mixing studies.

Cody R Ground↗

NASA NTRSDate not supplied

More / All Electric Vertical Take-Off and Landing (VTOL) Vehicle Sensitivities to Propulsion and Power Performance

Battery power and energy density are important parameters for the emerging concepts for more / all-electric vehicles. Electric propulsion and power system performance is also important. To better understand how electric propulsion and power systems component performance influences overall vehicle design, a sensitivity assessment was performed noting changes in vehicle gross weight and energy usage. Updated versions of the Revolutionary Vertical lift Technology (RVLT) Project vertical take-off and landing (VTOL) urban air mobility (UAM) reference vehicles and missions were used. NASA electric vehicle studies are discussed which were used to help select the range of electric propulsion and power system performance parameters used in this assessment. Thermal management systems (TMS) considerations are also important; new and innovative power management and distribution systems can reduce electric system weight and losses, reducing thermal management constraints often imposed by electric systems modest maximum use temperatures. Vehicles with higher disk loadings (smaller rotors) require higher power levels per unit weight for VTOL operations, which make them more sensitive to electric system weights and efficiencies. Battery, all-electric vehicles show different sensitivities to component performance than turboelectric or hybrids systems. Battery, all-electric propulsion systems may increase vehicle weight and size, but still results in lower mission energy usage than their hydrocarbon-fueled versions. Significant vehicle weight growth to electric propulsion and power system power-to-weight reductions also occurs at different levels among the various concepts. From these results, one can more readily identify required component performance levels, potential component choices or, research and development paths.

Electric Propulsion Systems (Aircraft)↗

NASA NTRSDate not supplied

Science Performance Comparison Between a Spaceborne HSRL and CALIOP

NASA operates airborne and spaceborne lidar systems to answer aerosol and cloud related science questions. NASA Langley Research Center has extensive experience operating lidar systems in both regimes. These include High Spectral Resolution Lidar (HSRL) systems, which have been operating on airborne systems,and CALIOP, the spaceborne elastic backscatter lidar system on board CALIPSO. In support of NASA’s ACCP Study Plan to address the Aerosol (A) and Cloud, Convection, and Precipitation (CCP) Designated Observables called out in the 2017 Earth Science Decadal Survey, LaRC is using lidar simulation tools to evaluate the performance of spaceborne systems using both the HSRL and elastic backscatter techniques. The LaRC high-fidelity simulator tool models both HSRL and elastic backscatter lidar systems by modeling the effects of the instrument specifications and producing backscatter signals generated from molecules, aerosols, clouds, ocean surface, and ocean subsurface. It derives the solar background signals from the scene specific aerosol and cloud characteristics, surface type, and sun elevation. The tool models both random and systematic uncertainties in the retrieved geophysical parameters. In this study, we will present simulated results that compare and contrast the performance of spaceborne HSRL systems to the performance of CALIOP. As recommended by the Decadal Study, ACCP is seeking advances over performance that has been achieved by A-Train. This study will provide a description of the HSRL and elastic backscatter techniques and demonstrate how and why the performance of these HSRL systems exceeds the performance of CALIOP.

Kathleen A Powell↗

NASA NTRSDate not supplied

More / All Electric Vertical Take-Off and Landing (VTOL) Vehicle Sensitivities to Propulsion and Power Performance

electric propulsion systems (aircraft) mission ana↗

NASA NTRSDate not supplied

Astronauts' Performance: The Retention and Transfer of Training

The space environment imposes on the astronaut crew significant physiological, psycho-social, and cognitive loads that can not be replicated on the ground. These loads likely impact crew performance. To date, no systematic data collection has taken place to understand the effects of such loads on crew members’ ability to retain trained knowledge and skills, and to transfer such knowledge and skills to novel situations. The research described here was originally designed to be the first such study to systematically collect data on the effects of long duration space flight on training retention and transfer. Because current theories of retention and transfer are based on results obtained in university laboratories using undergraduate students as research participants, and because crew time in space is very expensive, this study was designed to compare the performance of 4 groups of subjects: crew members in space, crew members on the ground, crew-like subjects, and university undergraduate students. Results from the ground-phase of the study reported here demonstrate that crew members’ performance under cognitive load can not be predicted from the performance of university undergraduate students. It is still an open question the extent to which crew members’ cognitive performance in space can be predicted from the performance of crew members on the ground.

training↗

NASA NTRSDate not supplied

Signal and Power Integrity Design Methodology for High-Performance Flight Computing Systems

Computing capabilities of space systems have in-creased onboard performance by orders of magnitude with the use of radiation-tolerant field-programmable gate arrays (FPGA)and processors. The incorporation of signal and power integrity analysis with printed circuit board (PCB) design in reliable computing architectures for space systems has become critical to enable future mission capabilities. Developers launch high-performance processors into a breadth of orbits and missions, running varying applications that create challenges for designing reliable computing hardware. Specifically, for these designs, academic and industry research has focused on component radiation performance, fault mitigation, and reliable architectures. How-ever, other design parameters including electromagnetic interference (EMI), PCB stackup, signal integrity (SI), voltage regulator module (VRM) design, and power distribution network (PDN)are often deprioritized or disregarded as the design matures. Since these characteristics are becoming more significant in high-performance processor designs, this research presents a hardware design and analysis methodology for high-performance, space-computing systems that focuses on a holistic design approach and PDN reliability. While these challenges exist across all space hardware, the reduced PCB dimensions imposed by SmallSats and CubeSats introduce additional hurdles, specifically to VRM and decoupling design. By examining the relationship between the PDN and radiation performance, an analytical relationship is developed that incorporates Total Ionizing Dose and Single-Event Transients to ensure reliability throughout the mission duration. The presented design methodology is applied to the SpaceCube v3.0 Mini, an FPGA-based on-board science data processing system developed at NASA Goddard Space Flight Center.

Advanced avionics↗

NASA NTRSDate not supplied

Human Factors and Behavioral Performance Exploration Measures Harmonized Across HERA, NEK, and ISS: Teams Risk

BACKGROUNDThe Human Factors and Behavioral Performance Exploration Measures (HFBP-EM) suite is a set of standardized measures to assess behavioral health and performance risk related to future exploration class missions, and to support reduction of the Human Research Program’s (HRP) Behavioral Medicine (BMed), Team, Sleep, and Human Systems Integration Architecture risks. HFBP-EM were collected during Human Exploration Research Analogs (HERA) campaigns 4 (C4) and 5 (C5), and during SIRIUS 17 and 19 missions in the Russian Ground Based Experiment Complex, NEK, to document the feasibility, flexibility, and acceptability of these measures in analogsof the spaceflight environment. A subset of the HFBP-EM suite was collected during spaceflight as part HRP’s Standard Measures in Spaceflight Project. Whenever possible, the HFBP-EM protocol and measures are kept the same across studies, however, differences across research settings (e.g., experimental manipulations, mission scenarios, mission length) and implementation of the measures require the data are harmonized to ensure comparable views across missions. The purpose of our project is to develop a harmonized database of HFBP-EM data from different settings, and to summarize the trajectory of behavioral health and performance within and between research settings. In this presentation, we will summarize the harmonized dataset and the trajectory of measures related to the Team Risk, including team performance, team cohesion, team processes, and psychological safety, over time and between and within settings.METHODSWe followed best practices for data harmonization. Characteristics of each research setting were assessed for harmonization potential, and we deemed NEK-SIRIUS 17 as inappropriate due to study aims, length, and data quality. Common variables of interest were identified. Study characteristics and key variables with which datafiles could be merged were defined as “meta-data.” HFBP-EM data from all settings were processed under a common format. We then created a harmonized team-level database designed to facilitate analyses that address HRP research gaps related to HRP’s Team risk. In this database, team cohesion, processes, performance, psychological safety, and group living were operationalized as the team mean of the crew responses for each data collection (e.g., on mission day 7). Data collected on the International Space Station (ISS) included a subset of scale items administered to participants. Data collected from ISS team members within +/- 7 days of the first data collection and every following 30 +/-4 days were aggregated to the team level. We generated figures that display the mean and variability of team constructs across research settings. We also generated plots of changes in team constructs with mission day and with percentage of the mission completed. Where possible, we compared data from spaceflight analogs with data from astronauts aboard the ISS. RESULTS AND DISCUSSIONThe team-level harmonized database was structured such that each row represents data for a team on a specific mission day. Team constructs (e.g., team cohesion) were included as columns (i.e., wide format) with repeated measures across mission days given in rows (i.e., long format). The database included data from 18 crews: five, 4-person American crews in HERA C4, four 4-person American crews in HERA C5, one 6-person multinational crew in NEK-SIRIUS 19, and eight, 2–11-person multinational crews aboard the ISS. Results provide insights into mission and campaign differences in team functioning and performance. For example, between campaign differences were observed for team processes—the interdependent team actions that orchestrate taskwork in pursuit of the team’s goals [1]. Greater between-team variability on team processes was detected during HERA C4 than during C5, and a downward trend was observed over the duration of C4, but not C5 or SIRIUS 19. This may be due to the campaign-level differences such as the sleep deprivation implemented during C4. Responses on the subset of team process items administered on the ISS indicate between-crew variability more like those observed in C4 than C5 and NEK, with some, but not all ISS crews demonstrating a downward trend over time. Additional findings will be presented. REFERENCESMarks, M. A., et al (2001) Academy of Management Review, 26(3), 356-376.

S T Bell↗

NASA NTRSDate not supplied

HUMAN FACTORS AND BEHAVIORAL PERFORMANCE EXPLORATION MEASURES: ASSESSING ASTRONAUT RISK

INTRODUCTION: The Human Factors and Behavioral Performance Exploration Measures (HFBP-EM) suite is a set of standardized measures to assess behavioral health and performance risk related to future exploration class missions, and to support reduction of the Human Research Program’s (HRP) Behavioral Medicine (BMed), Team, Sleep, and Human Systems Integration Architecture (HSIA) risks. This presentation will provide an overview of the HFBP-EM program, describe its implementation across spaceflight analogs and the international space station (ISS), and discuss its applicability to audience members. TOPIC: HFBP-EM is a research program designed to develop a standard set of measures that can be used in space and space-analog research to characterize BMed, Team, Sleep, and HSIA risks. It is an ongoing research project that is used examine the validity and reliability of HFBP measures, as well as their shorter forms. It also serves as a test bed for HFBP measures being considered for the spaceflight standard measures. The suite of measures is used to test the efficacy of countermeasures. To date, HFBP-EM has been collected in Human Exploration Research Analogs campaigns 4 and 5, and the SIRIUS 19 mission in the Russian Ground Based Experiment Complex. A subset of the HFBP-EM suite was collected during spaceflight as part of HRP’s Standard Measures in Spaceflight Project. Data was collected from a total of 55 multinational astronaut and astronaut-like crewmembers (mean age: 39.5, SD = 7.6; 31% female; 91% with advanced degrees). Three broad categories of HFBP-EM measures and their relevance to HRP risks will be discussed: 1) surveys that assess team functioning (Teams risk) as well as mood and affect (Bmed risk), 2) performance-based tasks of cognitive functioning and operationally relevant performance (Bmed risk), and 3) physiological biomarkers of sleep (sleep risk) and heart rate (Bmed risk). We will provide an overview of the background of the HFBP-EM program, what the suite currently includes, and next steps in its future development. We will also discuss the application to aerospace practitioners and researchers. APPLICATION: Astronaut teams selected for future space exploration missions will face several challenges that pose significant yet still unknown risks to the behavioral health and performance of astronauts. The HFBP-EM suite provides a comprehensive assessment of behavioral health and performance in space analog and spaceflight settings. This suite can be applied to both operational and research settings to advance risk reduction research for long duration space exploration missions.

S T Bell↗