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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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At least 163 records · Page 9

Accuracy of Center of Pressure Determination via Motion Capture

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

C A Bell↗

Extraction of Doppler Observables from Open-Loop Recordings for the Juno Radio Science Investigation

The goal of the Juno Gravity Science investigation is to estimate the gravitational field of Jupiter by measurement of the spacecraft velocity during periods of closest approach. Velocity is measured by the Doppler shift of dual X- and Ka-band radio links between the Juno spacecraft, in orbit around Jupiter, and the DSS-25 antenna of the Deep Space Network (DSN). During times of closest-approach, Juno experiences large dynamic ranges caused by the orbital dynamics and spin signatures caused by the spin-stabilized spacecraft that are detectable by the receivers at DSS-25. Open-loop recordings of received voltages are processed to compute Doppler observables utilized in the estimation of the gravity field. Presented is a method to process open-loop data collected by the DSN to compensate for the spin signature of the spacecraft, removal of artifacts from Doppler observables caused by the high dynamic environment, and improve performance of the digital phase-locked loop utilized in the data processing.

Buccino, Dustin R.↗

Acquisition of a Biomedical Database of Acute Responses to Space Flight during Commercial Personal Suborbital Flights

There is currently too little reproducible data for a scientifically valid understanding of the initial responses of a diverse human population to weightlessness and other space flight factors. Astronauts on orbital space flights to date have been extremely healthy and fit, unlike the general human population. Data collection opportunities during the earliest phases of space flights to date, when the most dynamic responses may occur in response to abrupt transitions in acceleration loads, have been limited by operational restrictions on our ability to encumber the astronauts with even minimal monitoring instrumentation. The era of commercial personal suborbital space flights promises the availability of a large (perhaps hundreds per year), diverse population of potential participants with a vested interest in their own responses to space flight factors, and a number of flight providers interested in documenting and demonstrating the attractiveness and safety of the experience they are offering. Voluntary participation by even a fraction of the flying population in a uniform set of unobtrusive biomedical data collections would provide a database enabling statistical analyses of a variety of acute responses to a standardized space flight environment. This will benefit both the space life sciences discipline and the general state of human knowledge.

Charles, John B.↗

Rotary Balance Wind Tunnel Testing for the FASER Flight Research Aircraft

Flight dynamics research was conducted to collect and analyze rotary balance wind tunnel test data in order to improve the aerodynamic simulation and modeling of a low-cost small unmanned aircraft called FASER (Free-flying Aircraft for Sub-scale Experimental Research). The impetus for using FASER was to provide risk and cost reduction for flight testing of more expensive aircraft and assist in the improvement of wind tunnel and flight test techniques, and control laws. The FASER research aircraft has the benefit of allowing wind tunnel and flight tests to be conducted on the same model, improving correlation between wind tunnel, flight, and simulation data. Prior wind tunnel tests include a static force and moment test, including power effects, and a roll and yaw damping forced oscillation test. Rotary balance testing allows for the calculation of aircraft rotary derivatives and the prediction of steady-state spins. The rotary balance wind tunnel test was conducted in the NASA Langley Research Center (LaRC) 20-Foot Vertical Spin Tunnel (VST). Rotary balance testing includes runs for a set of given angular rotation rates at a range of angles of attack and sideslip angles in order to fully characterize the aircraft rotary dynamics. Tests were performed at angles of attack from 0 to 50 degrees, sideslip angles of -5 to 10 degrees, and non-dimensional spin rates from -0.5 to 0.5. The effects of pro-spin elevator and rudder deflection and pro- and anti-spin elevator, rudder, and aileron deflection were examined. The data are presented to illustrate the functional dependence of the forces and moments on angle of attack, sideslip angle, and angular rate for the rotary contributions to the forces and moments. Further investigation is necessary to fully characterize the control effectors. The data were also used with a steady state spin prediction tool that did not predict an equilibrium spin mode.

Denham, Casey↗

Predicting the Operational Acceptability of Route Advisories

NASA envisions a future Air Traffic Management system that allows safe, efficient growth in global operations, enabled by increasing levels of automation and autonomy. In a safety-critical system, the introduction of increasing automation and autonomy has to be done in stages, making human-system integrated concepts critical in the foreseeable future. One example where this is relevant is for tools that generate more efficient flight routings or reroute advisories. If these routes are not operationally acceptable, they will be rejected by human operators, and the associated benefits will not be realized. Operational acceptance is therefore required to enable the increased efficiency and reduced workload benefits associated with these tools. In this paper, the authors develop a predictor of operational acceptability for reroute advisories. Such a capability has applications in tools that identify more efficient routings around weather and congestion and that better meet airline preferences. The capability is based on applying data mining techniques to flight plan amendment data reported by the Federal Aviation Administration and data on requested reroutes collected from a field trial of the NASA developed Dynamic Weather Routes tool, which advised efficient route changes to American Airlines dispatchers in 2014. 10-Fold cross validation was used for feature, model and parameter selection, while nested cross validation was used to validate the model. The model performed well in predicting controller acceptance or rejection of a route change as indicated by chosen performance metrics. Features identified as relevant to controller acceptance included the historical usage of the advised route, the location of the maneuver start point relative to the boundaries of the airspace sector containing the maneuver start (the maneuver start sector), the reroute deviation from the original flight plan, and the demand level in the maneuver start sector. A random forest with forty trees was the best performing of the five models evaluated in this paper.

route advisories↗

Predicting the Operational Acceptability of Route Advisories

NASA envisions a future Air Traffic Management system that allows safe, efficient growth in global operations, enabled by increasing levels of automation and autonomy. In a safety-critical system, the introduction of increasing automation and autonomy has to be done in stages, making human-system integrated concepts critical in the foreseeable future. One example where this is relevant is for tools that generate more efficient flight routings or reroute advisories. If these routes are not operationally acceptable, they will be rejected by human operators, and the associated benefits will not be realized. Operational acceptance is therefore required to enable the increased efficiency and reduced workload benefits associated with these tools. In this paper, the authors develop a predictor of operational acceptability for reroute advisories. Such a capability has applications in tools that identify more efficient routings around weather and congestion and that better meet airline preferences. The capability is based on applying data mining techniques to flight plan amendment data reported by the Federal Aviation Administration and data on requested reroutes collected from a field trial of the NASA developed Dynamic Weather Routes tool, which advised efficient route changes to American Airlines dispatchers in 2014. 10-Fold cross validation was used for feature, model and parameter selection, while nested cross validation was used to validate the model. The model performed well in predicting controller acceptance or rejection of a route change as indicated by chosen performance metrics. Features identified as relevant to controller acceptance included the historical usage of the advised route, the location of the maneuver start point relative to the boundaries of the airspace sector containing the maneuver start (the maneuver start sector), the reroute deviation from the original flight plan, and the demand level in the maneuver start sector. A random forest with forty trees was the best performing of the five models evaluated in this paper.

operational acceptability↗

Experimental and Computational Characterization of a Modified Sioutas Cascade Impactor for Respirable Radioactive Aerosols

Oak Ridge National Laboratory is collecting and characterizing aerosols released when spent nuclear fuel (SNF) rods are fractured in bending. An aerosol collection system was designed and tested to collect respirable sized (<10 μm aerodynamic diameter [AED]) particulates inside a hot cell facility. The setup is a modified version of the commercially available Sioutas cascade impactor, to which additional stages were added to expand the aerosol collection range from 2.5 to ~15 μm AED. To accommodate the additional stages and specific test conditions, the operating flow rate for aerosol collection was reduced, and testing was conducted by using pressure drop measurements, surrogate dust collection, and particle size characterization. The fluid flow distribution within the cascade and its stages was simulated in STAR-CCM+, and the stage-wise pressure drops obtained using the computational fluid dynamics model were then compared to experimental data. Lagrangian particle simulations were also performed, and stage-wise collection statistics were obtained from the simulation for comparison with the experimental data obtained using SNF-surrogate dust particles. The results provide valuable insights into the stage-wise particle collection characteristics of the modified cascade impactor and can also be used to improve the prediction accuracy of the manufacturer-determined analytical correlations.

aerosol modeling↗

An EVA Suit Fatigue, Strength, and Reach Model

The number of Extra-Vehicular Activities (EVAs) performed will increase dramatically with the upcoming Space Station assembly missions. It is estimated that up to 900 EVA hours may be required to assemble the Space Station with an additional 200 hours per year for maintenance requirements. Efficient modeling tools will be essential to assist in planning these EVAS. Important components include strength and fatigue parameters, multi-body dynamics and kinematics. This project is focused on building a model of the EVA crew member encompassing all these capabilities. Phase 1, which is currently underway, involves collecting EMU suited and unsuited fatigue, strength and range of motion data, for all major joints of the body. Phase 2 involves processing the data for model input, formulating comparisons between the EMU suits and deriving generalized relationships between suited and unsuited data. Phase 3 will be formulation of a multi-body dynamics model of the EMU capable of predicting mass handling properties and integration of empirical data into the model. Phase 4 will be validation of the model with collected EMU data from the Neutral Buoyancy Laboratory at NASA/JSC. Engineers and designers will use tie EVA suit database to better understand the capabilities of the suited individuals. This knowledge will lead to better design of tools and planned operations. Mission planners can use the modeling system and view the animations and the visualizations of the various parameters, such as overall fatigue, motion, timelines, reach, and strength to streamline the timing, duration, task arrangement, personnel and overall efficiency of the EVA tasks. Suit designers can use quantifiable data at common biomechanical structure points to better analyze and compare suit performance.

Maida, James C.↗

Loading, electromyograph, and motion during exercise

A system is being developed to gather kineto-dynamic data for a study to determine the load vectors applied to bone during exercise on equipment similar to that used in space. This information will quantify bone loading for exercise countermeasures development. Decreased muscle loading and external loading of bone during weightlessness results in cancellous bone loss of 1 percent per month in the lower extremities and 2 percent per month in the calcaneous. It is hypothesized that loading bone appropriately during exercise may prevent the bone loss. The system consists of an ergometer instrumented to provide position of the pedal (foot), pedaling forces on the foot (on the sagittal plane), and force on the seat. Accelerometers attached to the limbs will provide acceleration. These data will be used as input to an analytical model of the limb to determine forces on the bones and on groups of muscles. EMG signals from activity in the muscles will also be used in conjunction with the equations of mechanics of motion to be able to discern forces exerted by specific muscles. The tasks to be carried out include: design of various mechanical components to mount transducers, specification of mechanical components, specification of position transducers, development of a scheme to control the data acquisition instruments (TEAC recorder and optical encoder board), development of a dynamic model of the limbs in motion, and development of an overall scheme for data collection analysis and presentation. At the present time, all the hardware components of the system are operational, except for a computer board to gather position data from the pedals and crank. This board, however, may be put to use by anyone with background in computer based instrumentation. The software components are not all done. Software to transfer data recorded from the EMG measurements is operational, software to drive the optical encoder card is mostly done. The equations to model the kinematics and dynamics of motion of the limbs have been developed, but they have not yet been implemented in software. Aside from the development of the hardware and software components of the system, the methodology to use accelerometers and encoders and the formulation of the appropriate equations are an important contribution to the area of biomechanics, particularly in space applications.

Figueroa, Fernando↗

S-MODE Sonde

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗

S-MODE Saildrone

These data are collected by NASA as part of the Sub-Mesoscale Ocean Dynamics Experiment (https://espo.nasa.gov/s-mode), providing observations of submesoscale (1-10 km) processes.

17 WIND ENERGY↗