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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Validation of Design and Analysis Techniques of Tailored Composite Structures

Aeroelasticity is the relationship between the elasticity of an aircraft structure and its aerodynamics. This relationship can cause instabilities such as flutter in a wing. Engineers have long studied aeroelasticity to ensure such instabilities do not become a problem within normal operating conditions. In recent decades structural tailoring has been used to take advantage of aeroelasticity. It is possible to tailor an aircraft structure to respond favorably to multiple different flight regimes such as takeoff, landing, cruise, 2-g pull up, etc. Structures can be designed so that these responses provide an aerodynamic advantage. This research investigates the ability to design and analyze tailored structures made from filamentary composites. Specifically the accuracy of tailored composite analysis must be verified if this design technique is to become feasible. To pursue this idea, a validation experiment has been performed on a small-scale filamentary composite wing box. The box is tailored such that its cover panels induce a global bend-twist coupling under an applied load. Two types of analysis were chosen for the experiment. The first is a closed form analysis based on a theoretical model of a single cell tailored box beam and the second is a finite element analysis. The predicted results are compared with the measured data to validate the analyses. The comparison of results show that the finite element analysis is capable of predicting displacements and strains to within 10% on the small-scale structure. The closed form code is consistently able to predict the wing box bending to 25% of the measured value. This error is expected due to simplifying assumptions in the closed form analysis. Differences between the closed form code representation and the wing box specimen caused large errors in the twist prediction. The closed form analysis prediction of twist has not been validated from this test.

Jegley, Dawn C.↗
NASA NTRS1989

Effect of structured visual environments on apparent eye level

Each of 12 subjects set a binocularly viewed target to apparent eye level; the target was projected on the rear wall of an open box, the floor of which was horizontal or pitched up and down at angles of 7.5 degrees and 15 degrees. Settings of the target were systematically biased by 60% of the pitch angle when the interior of the box was illuminated, but by only 5% when the interior of the box was darkened. Within-subjects variability of the settings was less under illuminated viewing conditions than in the dark, but was independent of box pitch angle. In a second experiment, 11 subjects were tested with an illuminated pitched box, yielding biases of 53% and 49% for binocular and monocular viewing conditions, respectively. The results are discussed in terms of individual and interactive effects of optical, gravitational, and extraretinal eye-position information in determining judgements of eye level.

NASA Center ARC↗
NASA NTRS2010

Power Generator with Thermo-Differential Modules

A thermoelectric power generator consists of an oven box and a solar cooker/solar reflector unit. The solar reflector concentrates sunlight into heat and transfers the heat into the oven box via a heat pipe. The oven box unit is surrounded by five thermoelectric modules and is located at the bottom end of the solar reflector. When the heat is pumped into one side of the thermoelectric module and ejected from the opposite side at ambient temperatures, an electrical current is produced. Typical temperature accumulation in the solar reflector is approximately 200 C (392 F). The heat pipe then transfers heat into the oven box with a loss of about 40 percent. At the ambient temperature of about 20 C (68 F), the temperature differential is about 100 C (180 F) apart. Each thermoelectric module, generates about 6 watts of power. One oven box with five thermoelectric modules produces about 30 watts. The system provides power for unattended instruments in remote areas, such as space colonies and space vehicles, and in polar and other remote regions on Earth.

Saiz, John R.↗
NASA NTRS2010

Human Spaceflight Recent Conjunctions of Interest

I. During each nine-hour shift (or upon request), the Orbital Safety Analyst (OSA) at JSpOC updates the entire tracked catalog with the latest tracking data from the SSN and screens this catalog against NASA s assets. a) For ISS operations, a 72-hour advance screening is performed. b) For Shuttle orbit operations, a 36-hour advance screening is performed. c) If a vehicle is performing a maneuver during the screening period, OSA uses post-reboost-trajectory data supplied by Mission Control Center-Houston (MCC-H) for screening. II. An automated process is used to detect any conjunctions within 10 x 40 x 40 km box (centered on the vehicle) using Special Perturbation (SP) processing: a) Box dimensions are Radial x Downtrack x Crosstrack. b) Any object found within this box has the tracking tasking level increased to improve (hopefully) its uncertainty in its current and predicted orbital trajectory. c) OSA informs NASA if any object is found inside a 2 x 25 x 25 km box then creates and sends an Orbital Conjunction Message (OCM) to NASA which contains detailed information about the conjunction. d) For Shuttle, the box size used for screening and reporting is altered for special operations (day of rendezvous, launch screening, etc.).

Browns, Ansley C.↗
NASA NTRS2013

Thermal Infrared Sensor (TIRS) Instrument Thermal Subsystem Design and Lessons Learned

The Thermal Infrared Sensor (TIRS) is one of two instruments on the Landsat Data Continuity Mission (LDCM), which is scheduled to launch in February of 2013. The TIRS instrument was officially added to the mission later in the flow, which led to a highly aggressive schedule that became one of the main drivers during instrument development. The thermal subsystem design of the TIRS Sensor Unit is comprised of five thermal zones which range in temperature from less than 43 Kelvin to 330 Kelvin. Most zones are proportional heater controlled, and all are within a volume of 35 cu.ft. A two-stage cryocooler is used to cool the "cold stage" including three QWIP detectors to less than 43 Kelvin, and cool the "warm stage" to 105 Kelvin. The excess power dissipation from the cryocooler is rejected via ammonia transport heat pipes to a dedicated Cryocooler Radiator with embedded ammonia heat pipes. The cryogenic subsystem includes a series of shells used to radiatively and conductively isolate the cold stage from the warmer surroundings. The Optical System (telescope) is passively cooled to 180-190 Kelvin using a "thermal link" (comprised of a Flexible Conductive Thermal Strap and an APG Bar) which couples the telescope stage to a dedicated radiator with embedded ethane heat pipes. The Scene Select Mechanism, which is responsible for moving the Scene Select Mirror to three distinct positions (including Nadir, Space, and On-board Black Body Calibrator pointing), runs nominally at 278 Kelvin and is thermally isolated from the cryogenic thermal zones. The On-board Black Body Calibrator requires a dedicated radiator which allows for a temperature range of 260-330 Kelvin at the Source. The detectors are powered by the FPE Box, which is mounted to the nadir external surface of the composite honeycomb structure. There are two additional electronics boxes which are wet-mounted directly to the spacecraft shear panel, the Main Electronics Box and Cryocooler Electronics Box; thermal control of these boxes is the responsibility of Orbital Sciences Corporation, the spacecraft developer. The TIRS thermal subsystem design was successfully verified during months of testing campaign, from component & subsystem level to two instrument-level thermal vacuum tests. The Instrument, despite an aggressive schedule, was delivered to the spacecraft vendor in February of 2012 and is currently undergoing the final stages of spacecraft environmental testing in preparation for launch.

Otero, Veronica↗
NASA NTRS2014

EVA Glove Sensor Feasbility II Abstract

The main objectives for the glove project include taking various measurements from human subjects during and after they perform different tasks in the glove box, acquiring data from these tests and determining the accuracy of these results, interpreting and analyzing this data, and using the data to better understand how hand injuries are caused during EVAs.1 Some of these measurements include force readings, temperature readings, and micro-circulatory blood flow.1 The three glove conditions tested were ungloved (a comfort glove was worn to house the sensors), Series 4000, and Phase VI. The general approach/procedure for the glove sensor feasibility project is as follows: 1. Prepare test subject for testing. This includes attaching numerous sensors (approximately 50) to the test subject, wiring, and weaving the sensors and wires in the glove which helps to keep everything together. This also includes recording baseline moisture data using the Vapometer and MoistSense. 2. Pressurizing the glove box. Once the glove box is pressurized to the desired pressure (4.3 psid), testing can begin. 3. Testing. The test subject will perform a series of tests, some of which include pinching a load cell, making a fist, pushing down on a force plate, and picking up metal pegs, rotating them 90 degrees, and placing them back in the peg board. 4. Post glove box testing data collection. After the data is collected from inside the glove box, the Vapometer and MoistSense device will be used to collect moisture data from the subject's hand. 5. Survey. At the conclusion of testing, he/she will complete a survey that asks questions pertaining to comfort/discomfort levels of the glove, glove sizing, as well as offering any additional feedback.

Melone, Kate↗
NASA NTRS2019

Bounding Methods for Heterogeneous Lidar-derived Navigational Geofences

Safe Unmanned Aerial Vehicle (UAV) operations near the ground require navigation methods that avoid fixed obstacles such as buildings, power lines and trees. Aerial lidar surveys of ground structures are available with the precision and accuracy to geolocate obstacles, but the high volume of raw survey data can exceed the compute power of onboard processors and the rendering ability of ground-based flight planning maps. Representing ground structures with bounding polyhedra instead of point clouds greatly reduces the data size and can enable effective obstacle avoidance, as long as the bounding geometry envelopes the structures with high spatial fidelity. This report describes in detail four methods to compute bounding geometries of ground obstacles from lidar point clouds. The four methods are: 1) 2.5D Maximum Elevation Box, 2) 2.5D Ground Map Extrusion, 3) 3D Bounding Cylinder, and 4) 3D Bounding Box. The methods are applied to five point cloud datasets from lidar surveys of UAV flight research sites in Georgia and Virginia with an average point spacing that ranges from 0.1m to 0.6m. The methods are assessed using survey areas with geometrically heterogeneous ground structures: buildings, vegetation, power lines, and sub-meter structures such as road signs and guy wires. The 2.5D Maximum Elevation Box method is useful for simple structures. The 2.5D Ground Map Extrusion method efficiently encloses vegetation, but requires handdrawn ground footprints. The 3D Bounding Cylinder method excels at enclosing linear structures such as power lines and fences. The 3D Bounding Box method excels at enclosing planar structures such as buildings. The methods are compared on the basis of data compression and boundary fidelity on selected areas. The 2.5D methods yield the highest data compression but the polyhedra produced by them enclose significant amounts of empty space. Boundary fidelity is superior for the 3D methods, though this fidelity comes at the cost of a roughly thirtyfold lower data compression ratio than the 2.5D Maximum Elevation Box method. A mix of these output geometries is proposed for autonomous UAV navigation with limited on-board computing. Both the accuracy and spatial detail of emerging satellite-based survey technology lower than that of aerial lidar scanning survey technology. Sub-meter structures and thin linear structures are not reliably mapped at present by satellite-based surveys.

Moore, Andrew J.↗
NASA NTRSDate not supplied

A comparison between CERES TOA Radiative Fluxes and Airborne Radiative Flux Measurements from ARISE

Uncertainty in top-of-atmosphere (TOA) radiation fluxes observations are larger in the Arctic than in other regions. These uncertainties are due to the low sun angles and the highly reflective, anisotropic, and heterogeneous surface conditions. Quantifying, attributing, and reducing Arctic TOA radiative flux uncertainty enables a better understanding of the rapidly changing Arctic. To advance this goal, we compare the Cloud and Earth’s Radiant Energy System (CERES) TOA radiative fluxes with Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) campaign measurements collected in September 2014. We compare CERES TOA and aircraft radiative flux measurements using two complementary approaches: grid box average fluxes and instantaneously matched footprints. The grid box mean flux comparison indicates an agreement between CERES and aircraft measurements within 2σ uncertainty (calibration and inversion) in the longwave for all five grid boxes and for four-of-five grid boxes in the shortwave; shortwave and longwave mean differences are -7.9 and +2.3 Wm 2, respectively. The comparison of 36 40 instantaneously matched footprints with aircraft measurements reveals mean differences of -120.25 and -1.00.4 Wm 2 in the shortwave and longwave, respectively. To further explore the persistent negative difference in the shortwave, Wwe further quantify the effects of temporal and spatial sampling differences, angular distribution models, and scene identification to CERES-aircraft differences. Our analysis indicates that sampling differences (including scene evolution) account for an additional 1.8 and 1.7% uncertainty in the shortwave and longwave, respectively and , but indicates no bias. After accounting for this sampling uncertainty, all CERES-aircraft grid box mean fluxes agree within 2σ uncertainty. Scene identification errors due to sea ice concentration data set differences exhibit no bias in the shortwave flux difference and indicate the possibility of substantial differences in the CERES fluxes in specific cases with large spatial heterogeneity. Considering the instantaneously matched footprints, we find that the angular distribution models account may account for up to 7.3 Wm-2 of the persistent CERES-aircraft shortwave flux difference due to systematic differences in the anisotropy for sea ice partly cloudy scenes. Additional analysis using a special programmable scan model with the CERES FM2 instrument suggests a significant view zenith angle dependence of the CERES fluxes for sea ice partly cloudy scenes where shortwave fluxes systematically decrease with increasing view zenith angle; no dependence is found for other scene types. We conclude that (1) spatial heterogeneity and scene temporal evolution substantially limit our ability to use aircraft measurements to place strong constraints on CERES TOA fluxes and (2) that the representation of anisotropy in sea ice partly cloudy scenes is likely a significant factor contributing to the persistent negative CERES-aircraft shortwave flux difference in this comparison and require additional data to analysis fully quantify the potential bias.

Patrick C. Taylor↗
NASA NTRSDate not supplied

Radiative Flux Measurements from ARISE: A Comparison with CERES Top-of-Atmosphere Radiative Fluxes

Uncertainty in top-of-atmosphere (TOA) radiation fluxes observations are larger in the Arctic than in other regions. These uncertainties are due to the low sun angles and the highly reflective, anisotropic, and heterogeneous surface conditions. Quantifying, attributing, and reducing Arctic TOA radiative flux uncertainty enables a better understanding of the rapidly changing Arctic. To advance this goal, we compare the Cloud and Earth’s Radiant Energy System (CERES) TOA radiative fluxes with Arctic Radiation-IceBridge Sea and Ice Experiment (ARISE) campaign measurements collected in September 2014. We compare CERES TOA and aircraft radiative flux measurements using two complementary approaches: grid box average fluxes and instantaneously matched footprints. The grid box mean flux comparison indicates an agreement between CERES and aircraft measurements within 2 uncertainty (calibration and inversion) in the longwave for all five grid boxes and for four-of-five grid boxes in the shortwave; shortwave and longwave mean differences are -7.9 and +2.3 Wm‑2, respectively. The comparison of 36 instantaneously matched footprints with aircraft measurements reveals mean differences of -10.5 and 0.4 Wm‑2 in the shortwave and longwave, respectively. To further explore the persistent negative difference in the shortwave, we further quantify the effects of temporal and spatial sampling differences, angular distribution models, and scene identification to CERES-aircraft differences. Our analysis indicates that sampling differences (including scene evolution) account for an additional 1.8 and 1.7% uncertainty in the shortwave and longwave, respectively and indicates no bias. After accounting for this sampling uncertainty, all CERES-aircraft grid box mean fluxes agree within 2 uncertainty. Scene identification errors due to sea ice concentration data set differences exhibit no bias in the shortwave flux difference and indicate the possibility of substantial differences in the CERES fluxes in specific cases with large spatial heterogeneity. Considering the instantaneously matched footprints, we find that the angular distribution models account may account for up to ‑7.3 Wm-2 of the persistent CERES-aircraft shortwave flux difference due to systematic differences in the anisotropy for sea ice partly cloudy scenes. Additional analysis using a special scan model with the CERES FM2 instrument suggests a significant view zenith angle dependence of the CERES fluxes for sea ice partly cloudy scenes where shortwave fluxes systematically decrease with increasing view zenith angle; no dependence is found for other scene types. We conclude that (1) spatial heterogeneity and scene temporal evolution substantially limit our ability to use aircraft measurements to place strong constraints on CERES TOA fluxes and (2) that the representation of anisotropy in sea ice partly cloudy scenes is a significant factor contributing to the persistent negative CERES-aircraft shortwave flux difference in this comparison and require additional data to analysis fully quantify the potential bias.

Patrick C Taylor↗
DOE OSTI2026

A Data-Driven Method for Modeling Creep-Fatigue Stress- Strain Behavior Using Neural ODEs

In this paper, we introduce a data-driven machine learning approach for modeling one-dimensional stress–strain behavior under cyclic loading, utilizing experimental data from the nickel-based Alloy 617. The study employs uniaxial creep–fatigue test data acquired under various loading histories and compares two distinct neural network-based ODE models. The first model, known as the black-box model, comprehensively describes the strain–stress relationship using a Neural ODE equation. To interpret this black-box model, we apply the Sparse Identification of Nonlinear Dynamical Systems (SINDy) technique, transforming the black-box model into an equation-based model using symbolic regression. The second model, the Neural flow rule model, incorporates Hooke’s Law for the linear elastic component, with the nonlinear part characterized by a Neural ODE. Both models are trained with experimental data to accurately reflect the observed stress–strain behavior. We conduct a detailed comparison with the standard Chaboche model, which includes three back stresses. Our results demonstrate that the neural network-based ODE models precisely capture the experimental creep–fatigue mechanical behavior, exceeding the standard Chaboche model’s accuracy. Furthermore, an interpretable model derived from the black-box neural ODE model through symbolic regression achieves accuracy comparable to the Chaboche model, enhancing its interpretability. The results highlight the potential of neural network-based ODE models to depict complex creep–fatigue behavior, eliminating the necessity for experts to define a specific, material-focused model form.

creep-fatigue↗
NASA NTRS1979

New hard X-ray sources observed with HEAO A-2

A search for new hard X-ray sources using data from the first complete view of the sky with the HEAO A-2 experiment has discovered 47 new sources, detected seven sources recently discovered with other experiments, and significantly reduced the size of the error boxes for six previously known sources. Intensities and error boxes are given for each of these sources; identifications are suggested when an error box contains an object similar to known X-ray sources. The new identifications consist of seven type 1 Seyfert galaxies, including two whose Seyfert characteristics were discovered as a result of their location in an X-ray error box; one intermediate Seyfert galaxy; three Abell clusters; five N galaxies; two bursting radio sources; and an additional three nearby galaxies with bright nuclei and narrow emission lines.

Marshall, F. E.↗
NASA NTRS1982

Air Bag Applies Uniform Bonding Pressure

Air-bag box applies constant uniform pressure to tiles and other objects undergoing adhesive bonding. Box is basically a compliant clamp with adjustable force and position. Can be used on irregular surfaces as well as on flat ones. Pressurized air is fed to bag through a tube so that it expands, filling the box and pressing against work. Bag adopts a contour that accommodates surface under open side of box.

Gillespie, C. A.↗
NASA NTRS1980

Aircraft wing weight build-up methodology with modification for materials and construction techniques

An aircraft wing weight estimating method based on a component buildup technique is described. A simplified analytically derived beam model, modified by a regression analysis, is used to estimate the wing box weight, utilizing a data base of 50 actual airplane wing weights. Factors representing materials and methods of construction were derived and incorporated into the basic wing box equations. Weight penalties to the wing box for fuel, engines, landing gear, stores and fold or pivot are also included. Methods for estimating the weight of additional items (secondary structure, control surfaces) have the option of using details available at the design stage (i.e., wing box area, flap area) or default values based on actual aircraft from the data base.

York, P.↗
NASA NTRS1982

Flight-test verification of a pictorial display for general aviation instrument approach

Results of flight tests of the pictorial "follow me" box display results of the simulator study. The most important item of agreement was the frequency of the vertical and lateral models of motion of the pilot-aircraft-display system, which was 0.4 rad/sec in each study. Successful short, curved, descending approaches, such as are often suggested for use with microwave landing systems, can be executed with the "follow me" box display. Variations of the value of distance from the aircraft to the box were also examined using values of 736 m, 368 m, and 184 m. The results show that successful approaches can be made with all of these values. A sharper final turn and greater precision of position control are obtained with the shorter distance. Deletion of distance measuring equipment from the system was also studies. It was shown that successful approaches can be made with no distance measures included in the system, but the values of distance from the aircraft to the box that can be used are restricted.

Adams, J. J.↗
NASA NTRS1981

X-ray and optical observations of the November 19, 1978 gamma-ray burst source region

The Nov. 19, 1978 gamma-ray burst (GRB) has a very well determined error box, 10 square arcmin (Cline et al., 1981). An 8000-sec IPC exposure with the Einstein Observatory detected, at a 3.4-sigma level, one low intensity (less than 3 x 10 to the -13th erg/sq cm per sec) X-ray source inside the error box. The probability of a serendipitous detection was 0.01. Inside the X-ray source error box there are two weak radio sources, one of them highly polarized (Hjellming and Ewald, 1981), and two 20-magnitude objects, not coincident with the radio sources visible in the ESO/SRC J and R plates. With the exception of N49, this is the first possible detection of X-ray emission inside a GRB box. Its low intensity justifies, in fact, the lack of detection for other events.

Pizzichini, G.↗
NASA NTRS1982

Cloud fluctuation statistics

A space-time statistical analysis of total outgoing infrared radiation is used to determine the gross features of day-to-day cloudiness fluctuations over the Pacific Ocean in summer and winter. IR fluctuations arise from the passage of cloudiness systems through a grid box as well as the creation and destruction of cloudiness in the box. Which process dominates depends upon the size of the box relative to the size, speed and persistence time of a typical cloudiness system. In most regions the statistical analysis yields advection speeds characteristic of 700 mb mean flow with spatial dependence resembling the 300 mb mean flow. Spatial scales less than 2000 km predominate, smaller scales having less persistence. Characteristic time scales are on the order of one or two days, even for a grid box spanning the entire North Pacific storm track. This result is remarkable in view of the much longer time scales commonly associated with atmospheric disturbances. Apparently many cloudiness systems are created and destroyed during the lifetime of a single disturbance.

Cahalan, R. F.↗
NASA NTRS1983

A time-dependent approach for calculating steady inverse boundary-layer flows with separation

An unsteady inverse boundary-layer method is developed which can be used to calculate steady flows with separation. Two versions of Keller's box method with the Mechul function formulation developed by Cebeci (1976) are employed, depending on the complexity of the flow. The regular box is employed in regions of positive streamwise velocity component u, whereas the zigzag box is employed in regions where u becomes negative (t greater than 0). The regular box with the FLARE approximation is employed when t = 0 and u becomes negative in some region across the layer. Results of calculations show that the use of a time-dependent inverse boundary-layer method in which time is used as an iteration parameter provides a good approach in improving the accuracy of the solutions obtained from the FLARE approximation.

Cebeci, T.↗
NASA NTRS1989

Magma zonation - Effects of chemical buoyancy and diffusion

Numerical simulations and scale analysis are used to assess the viability of the marginal box-filling mechanism for producing compositional zonation in magma bodies. Scale analysis and two-dimensional numerical experiments both show that box-filling occurs provided a critical ratio of compositional-to-thermal buoyancy is exceeded. This critical ratio depends on the ratio of thermal-to-chemical diffusivity; application of this result to magma bodies suggests that box-filling may occur for components with relatively high-chemical diffusivities such as water. However, box-filling will not produce significant zonation for components with small chemical diffusivities, such as silica, unless diffusive coupling increases silica diffusivity.

Spera, Frank J.↗