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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Solar power satellite system definition study. Volume 1: Executive summary

Configuration concepts, option sizes, and systems definitions study design evolutions are reviewed. The main features of the present reference design silicon solar cell solar power satellite are described, as well as the provisions for space construction and support systems. The principal study accomplishments and conclusions are summarized according to the following tasks: (1) baseline critique; (2) construction and maintenance; (3) industrial complex needs, cost estimates, and production capacity; (4) launch complex requirements at KSC or at an offshore facility; (5) integration of the SPS/ground power network; (6) technology advancement and development; (7) costs and schedules; and (8) exploratory technology: laser annealing of solar cells degraded by proton irradiation, and a fiber-optic phase distribution link at 980 MHz.

Source record↗
NASA NTRS1987

Simulation of an array-based neural net model

Research in cognitive science suggests that much of cognition involves the rapid manipulation of complex data structures. However, it is very unclear how this could be realized in neural networks or connectionist systems. A core question is: how could the interconnectivity of items in an abstract-level data structure be neurally encoded? The answer appeals mainly to positional relationships between activity patterns within neural arrays, rather than directly to neural connections in the traditional way. The new method was initially devised to account for abstract symbolic data structures, but it also supports cognitively useful spatial analogue, image-like representations. As the neural model is based on massive, uniform, parallel computations over 2D arrays, the massively parallel processor is a convenient tool for simulation work, although there are complications in using the machine to the fullest advantage. An MPP Pascal simulation program for a small pilot version of the model is running.

Barnden, John A.↗
NASA NTRS2008

A Three-Dimensional Variational Data Assimilation Scheme for the Regional Ocean Modeling System: Implementation and Basic Experiments

A three-dimensional variational data assimilation scheme for the Regional Ocean Modeling System (ROMS), named ROMS3DVAR, has been described in the work of Li et al. (2008). In this paper, ROMS3DVAR is applied to the central California coastal region, an area characterized by inhomogeneity and anisotropy, as well as by dynamically unbalanced flows. A method for estimating the model error variances from limited observations is presented, and the construction of the inhomogeneous and anisotropic error correlations based on the Kronecker product is demonstrated. A set of single observation experiments illustrates the inhomogeneous and anisotropic error correlations and weak dynamic constraints used. Results are presented from the assimilation of data gathered during the Autonomous Ocean Sampling Network (AOSN) experiment during August 2003. The results show that ROMS3DVAR is capable of reproducing complex flows associated with upwelling and relaxation, as well as the rapid transitions between them. Some difficulties encountered during the experiment are also discussed.

California coastal ocean↗
NASA NTRS2009

NASA Tech Briefs, December 2009

Topics include: A Deep Space Network Portable Radio Science Receiver; Detecting Phase Boundaries in Hard-Sphere Suspensions; Low-Complexity Lossless and Near-Lossless Data Compression Technique for Multispectral Imagery; Very-Long-Distance Remote Hearing and Vibrometry; Using GPS to Detect Imminent Tsunamis; Stream Flow Prediction by Remote Sensing and Genetic Programming; Pilotless Frame Synchronization Using LDPC Code Constraints; Radiometer on a Chip; Measuring Luminescence Lifetime With Help of a DSP; Modulation Based on Probability Density Functions; Ku Telemetry Modulator for Suborbital Vehicles; Photonic Links for High-Performance Arraying of Antennas; Reconfigurable, Bi-Directional Flexfet Level Shifter for Low-Power, Rad-Hard Integration; Hardware-Efficient Monitoring of I/O Signals; Video System for Viewing From a Remote or Windowless Cockpit; Spacesuit Data Display and Management System; IEEE 1394 Hub With Fault Containment; Compact, Miniature MMIC Receiver Modules for an MMIC Array Spectrograph; Waveguide Transition for Submillimeter-Wave MMICs; Magnetic-Field-Tunable Superconducting Rectifier; Bonded Invar Clip Removal Using Foil Heaters; Fabricating Radial Groove Gratings Using Projection Photolithography; Gratings Fabricated on Flat Surfaces and Reproduced on Non-Flat Substrates; Method for Measuring the Volume-Scattering Function of Water; Method of Heating a Foam-Based Catalyst Bed; Small Deflection Energy Analyzer for Energy and Angular Distributions; Polymeric Bladder for Storing Liquid Oxygen; Pyrotechnic Simulator/Stray-Voltage Detector; Inventions Utilizing Microfluidics and Colloidal Particles; RuO2 Thermometer for Ultra-Low Temperatures; Ultra-Compact, High-Resolution LADAR System for 3D Imaging; Dual-Channel Multi-Purpose Telescope; Objective Lens Optimized for Wavefront Delivery, Pupil Imaging, and Pupil Ghosting; CMOS Camera Array With Onboard Memory; Quickly Approximating the Distance Between Two Objects; Processing Images of Craters for Spacecraft Navigation; Adaptive Morphological Feature-Based Object Classifier for a Color Imaging System; Rover Slip Validation and Prediction Algorithm; Safety and Quality Training Simulator; Supply-Chain Optimization Template; Algorithm for Computing Particle/Surface Interactions; Cryogenic Pupil Alignment Test Architecture for Aberrated Pupil Images; and Thermal Transport Model for Heat Sink Design.

Source record↗
NASA NTRS2019

AladynPi – Adaptive Neural Network Molecular Dynamics Simulation Code with Physically Informed Potential: Computational Materials Mini-Application

This report provides an overview and description of commands used in the Computational Materials mini-application, AladynPi. AladynPi is an extension of a previously released mini-application, Aladyn (https://github.com/nasa/aladyn; Yamakov, V.I., and Glaessgen, E.H., NASA/TM-2018-220104). Aladyn and AladynPi are basic molecular dynamics codes written in FORTRAN 2003, which are 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 method. An input for the ANN is a set of structure coefficients, characterizing the local atomic environment of each atom, for which the atomic energy is obtained in the ANN inference process. In Aladyn, the ANN gives directly the energy of interatomic interactions. In AladynPi, the ANN gives optimized parameters for a predefined empirical function, known as bond-order-potential (BOP). The parameterized BOP function is then used to calculate the energy. AladynPi code is being released to serve as a training testbed for students and professors in academia to explore possible optimization algorithms for parallel computing on multicore central processing unit (CPU) computers or computers utilizing manycore architectures based on graphic processing units (GPUs). The effort is supported by the High Performance Computing incubator (HPCi) project at NASA Langley Research Center.

Yamakov, Vesselin I.↗
NASA NTRS2020

GeneLab

GeneLab collects and enables analysis of spaceflight and ground-based spaceflight simulation genomic data, RNA and protein expression, and metabolic profiles. It interfaces with other existing databases containing spaceflight omic data. The 2011 National Research Council (NRC) Decadal Survey on NASA Life and Physical Sciences called for increased opportunities for multi-investigator spaceflight opportunities and greater use of genomic approaches to meet the needs of NASA researchers. To address these recommendations of the NRC Decadal Survey, the Space Life and Physical Sciences Research and Applications Division of NASA's Human Exploration and Operations Mission Directorate has initiated a transition to an Open Science architecture to increase research opportunities, and has developed the GeneLab Platform based on highly leveraged and integrated bioinformatics analytics. GeneLab is an interactive, open-access resource where scientists can upload, download, store, search, share, transfer, and analyze omics data from spaceflight and corresponding analogue experiments. Users can explore GeneLab datasets in the Data Repository, analyze data using the Analysis Platform, visualize high-order data and create collaborative projects using the Collaborative Workspace. Our primary goal is to maximize the utilization of the valuable biological research conducted aboard the International Space Station (ISS) by collecting genomic, transcriptomic, proteomic, and metabolomics data known as “omics”. By providing a portal linking processed data to flight parameters, GeneLab enables exploration of the molecular network responses of terrestrial biology to the space environment. This allows researchers to understand the complex responses of biological systems to the space environment. This technology development activity was transferred from the Human Exploration and Operations Mission Directorate to the Science Mission Directorate Division of Biological and Physical Sciences (BPS) in October 2020.

GeneLab↗
NASA NTRS2022

Aerosol Models from the AERONET Database: Application to Surface Reflectance Validation Satellite Data

Aerosols play a critical role in radiative transfer within the atmosphere, and they have a significant impact on climate change. In this paper, we propose and implement a framework for developing an aerosol model using their microphysical properties. Such microphysical properties as the size distribution, the complex refractive index, and the percentage of sphericity are derived from the global AERosol RObotic NETwork (AERONET). These measurements, however, are typically retrieved when almucantar measurement procedures are performed (i.e., early mornings and late afternoons with clear sky) and might not have a temporal correspondence to a satellite overpass time, so a valid validation of satellite-derived products cannot be carried out. To address this problem of temporal inconsistency of satellite and ground-based measurements, we developed an approach to retrieve these microphysical properties (and the corresponding aerosol model) using the optical thickness at 440 nm, τ440, and the Ångström coefficient between 440 and 870 nm, α440–870. Such aerosol models were developed for 851 AERONET sites within the last 28 years. Obtained results suggest that empirically microphysical properties can be retrieved with uncertainties of up to 23 %. An exception is the imaginary part of the refractive index ni, for which the derived uncertainties reach up to 38 %. These specific parametric models of aerosol can be used for the studies when retrieval of microphysical properties is required as well as validation of satellite-derived products over land. Specifically, we demonstrate the usefulness of the aerosol models to validate surface reflectance records over land derived from optical remote sensing sensors. We then quantify the propagation of uncertainties in the surface reflectance due to uncertainties with the aerosol model retrieval that is used as a reference from radiative transfer simulations. Results indicate that individual aerosol microphysical properties can impact uncertainties in surface reflectance retrievals between 3.5 × 10(exp -5) to 1 × 10(exp -3) (in reflectance units). The overall impact of microphysical properties combined yields an overall uncertainty in surface reflectance <0.004 (in reflectance units). That corresponds, for example, to 1 to 3 % of the retrieved surface reflectance in the red spectral band (620–670 nm) by the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. These uncertainty values are well below the specification (0.005 + 0.05ρ; ρ is the retrieved surface reflectance) used for the MODIS atmospheric correction.

Jean-Claude Roger↗
NASA NTRSDate not supplied

m:N Operations High Complexity Simulation

The Human Autonomy Teaming Laboratory at NASA Ames Research Center (ARC) is planning to conduct an experiment investigating system-level effects operational vertiport disruptions on Urban Air Mobility (UAM) traffic in a simulated vertiport network in Denver, CO. The simulation will involve two parties, one at NASA ARC and another at NASA Langley Research Center (LaRC), each playing the role of a UAM Operator. This study, referred to as the "High Complexity Sim," features a 3-by-2-by-2 mixed subjects design. The variables to be manipulated are the scale of the disruption (Disruption, three levels: Small, Medium, and Large, affecting 1, 2, and 3+, vertiports, respectively), m:N ratio (Ratio, two levels: 2:6 and 2:12), and the assumption of UAM corridors (Corridors, two levels: With and Without). Local and system-wide capacity (i.e., throughput), efficiency (scale and number of flight delays, filed-vs-flown flight times), and human performance metrics (workload, situation awareness, heart rate/heart rate variability, eye gaze/fixation and saccades, stress) will be measured. This presentation details the experimental design and planned timeline for the study.

multi-vehicle↗
NASA NTRS1993

PILOT: An intelligent distributed operations support system

The Real-Time Data System (RTDS) project is exploring the application of advanced technologies to the real-time flight operations environment of the Mission Control Centers at NASA's Johnson Space Center. The system, based on a network of engineering workstations, provides services such as delivery of real time telemetry data to flight control applications. To automate the operation of this complex distributed environment, a facility called PILOT (Process Integrity Level and Operation Tracker) is being developed. PILOT comprises a set of distributed agents cooperating with a rule-based expert system; together they monitor process operation and data flows throughout the RTDS network. The goal of PILOT is to provide unattended management and automated operation under user control.

Rasmussen, Arthur N.↗
NASA NTRS1998

FPGA Based Reconfigurable ATM Switch Test Bed

Various issues associated with "FPGA Based Reconfigurable ATM Switch Test Bed" are presented in viewgraph form. Specific topics include: 1) Network performance evaluation; 2) traditional approaches; 3) software simulation; 4) hardware emulation; 5) test bed highlights; 6) design environment; 7) test bed architecture; 8) abstract sheared-memory switch; 9) detailed switch diagram; 10) traffic generator; 11) data collection circuit and user interface; 12) initial results; and 13) the following conclusions: Advances in FPGA make hardware emulation feasible for performance evaluation, hardware emulation can provide several orders of magnitude speed-up over software simulation; due to the complexity of hardware synthesis process, development in emulation is much more difficult than simulation and requires knowledge in both networks and digital design.

Chu, Pong P.↗
NASA NTRS2020

Composite Piezoelectric Resonator 1D Modeling with Loss

Abstract – Maximizing the benefit of applying piezoelectric materials in composite structures, which are a mix of piezoelectric and elastic elements, requires analytical modeling. In this paper we describe two main classes of resonators and how to model using network equivalent including material losses. These include extensional devices where the layer areas are parallel to the direction of wave travel as found in a variety of thickness or length mode transducers (BC. Stress T1 = T2 at boundary) and transverse mode resonators (BC. Strain S1 = S2 at boundary) where the major layer area is perpendicular to the wave direction as is found in radial mode or length thickness mode composite resonators. In order to illustrate these models we will use Mason’s equivalent circuits with elastic, dielectric and piezoelectric loss. It is noted that in order to maintain consistency with the linear equations of piezoelectricity and the wave equation care is required when applying complex loss coefficients to the models. Although these techniques are applicable to other models we use Mason’s network equivalent circuit because it is intuitive for composite modeling due to the frequency independent turns ratio and the independence of the equations describing the elastic layers from the adjoining layer properties which aids in understanding the boundary conditions to impose on the layer boundaries. We will present network models for a variety of applications with loss and show how to calculate their impedance spectra, interface stress, strain velocity and displacement spectra.

Sherrit, Stewart↗
NASA NTRS2015

Further Research on the Electrification of Pyrocumulus Clouds

Past research on pyrocumulus electrification has demonstrated that a variety of lightning types can occur, including cloud‐to‐ground (CG) flashes, sometimes of dominant positive polarity, as well as small intra‐cloud (IC) discharges in the upper levels of the pyro‐cloud. In Colorado during summer 2012, the first combined polarimetric radar, multi‐Doppler radar, and three‐dimensional lightning mapping array (LMA) observations of lightning‐producing pyrocumulus were obtained. These observations suggested that the National Lightning Detection Network (NLDN) was not sensitive enough to detect the small IC flashes that appear to be the dominant mode of lightning in these clouds. However, after an upgrade to the network in late 2012, the NLDN began detecting some of this pyrocumulus lightning. Multiple pyrocumulus clouds documented by the University of Wisconsin for various fires in 2013 and 2014 (including over the Rim, West Fork Complex, Yarnell Hill, Hardluck, and several other incidents) are examined and reported on here. This study exploits the increased‐sensitivity NLDN as well as the new nationwide U.S. network of polarimetric Next‐generation Radars (NEXRADs). These observations document the common occurrence of a polarimetric "dirty ice" signature ‐ modest reflectivities (20‐40+ dBZ), near‐zero differential reflectivity, and reduced correlation coefficient (less than 0.9) ‐ prior to the production of lightning. This signature is indicative of a mixture of ash and ice particles in the upper levels of the pyro‐cloud (less than ‐20 C), with the ice interpreted as being necessary for pyro‐cloud electrification. Pseudo‐Geostationary Lightning Mapper (GLM) data will be produced from the 2012 LMA observations, and the ability of GLM to detect small pyrocumulus ICs will be assessed. The utility of lightning and polarimetric radar for documenting rapid wildfire growth, as well as for documenting pyrocumulus impacts on the composition of the upper troposphere/lower stratosphere (UTLS), will be discussed.

Lang, Timothy J.↗
NASA NTRS2022

Deriving Severe Hail Likelihood from Satellite Observations and Model Reanalysis Parameters using a Deep Neural Network

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) series, have been observing severe convection at 15–60-minute intervals for over 40 years. When properly assessed, such a data record can be valuable in efforts of estimating severe storm risk throughout the diurnal cycle based on automated detection of patterns consistently found atop severe storms. Furthermore, environmental conditions favorable for severe weather are well-known and are thought to be represented well by modern reanalysis products. Promoting resilience against such hazards on local and global scales is a chief goal the NASA Disasters program, which seeks to encourage use of satellite observations to mitigate risk. For instance, hail is the costliest severe weather hazard across the globe in terms of insured loss, but reporting inconsistencies for hail events globally make it difficult to develop models that can quantify the risk. Satellite observation and model reanalysis taken together have the potential to, with reasonable skill and specificity, characterize environmental conditions that are favorable for hazardous weather, and thereby enable creation of hazard climatologie. Such climatologies are particularly useful over regions without extensive radar networks or storm reporting. By mapping the multivariate combination of observed cloud features and reanalysis environmental parameters/indices to United States Next Generation Weather Radar (NEXRAD) radar-estimated Maximum Expected Size of Hail (MESH) by way of a deep neural network (DNN), estimates of likelihood for potentially severe hail can be produced. Such estimates are of greater complexity and efficiency than could be performed with previous multivariate or logistic regression analyses for observed points within convective systems. Statistical distributions of convective parameters from satellite and reanalysis are shown to highlight non-severe/severe class separation for well-known hailstorm predictors, e.g., overshooting cloud top characteristics, deep-layer wind shear, mid-level stability, helicity, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN, which can efficiently produce a quantitative hail risk metric with better than 70% detection rate and under 30% false alarms. These hail classifications can then be aggregated across the satellite record to yield a hazard climatology for hail frequency and severity – knowledge of which is of particular interest to those who manage risk (e.g., insurers) and are seeking opportunities to identify hail-prone regions, particularly in developing nations. This NASA study uses satellite observations and model parameters in a DNN to perform climatological hailstorm analysis in support of catastrophe model development, with the hope of promoting risk resilience particularly in regions without adequate weather radar coverage.

Passive Remote Sensing↗
NASA NTRS1985

Cognitive network organization and cockpit automation

Attention is given to a technique for the derivation of pilot cognitive networks from empirical data, which has been successfully used to guide the redesign of the Control Display Unit that serves as the primary interface of the complex flight management system being developed by NASA's Advanced Concepts Flight Simulator program. The 'pathfinder' algorithm of Schvaneveldt et al. (1985) is used to obtain the conceptual organization of four pilots by generating a family of link-weighted networks from a set of psychological distance data derived through similarity ratings. The degree of conceptual agreement between pilots is assessed, and the means of translating a cognitive network into a menu structure are noted.

Roske-Hofstrand, R. J.↗
NASA NTRS1997

Neural Network Prediction of Failure of Damaged Composite Pressure Vessels from Strain Field Data Acquired by a Computer Vision Method

This effort used a new and novel method of acquiring strains called Sub-pixel Digital Video Image Correlation (SDVIC) on impact damaged Kevlar/epoxy filament wound pressure vessels during a proof test. To predict the burst pressure, the hoop strain field distribution around the impact location from three vessels was used to train a neural network. The network was then tested on additional pressure vessels. Several variations on the network were tried. The best results were obtained using a single hidden layer. SDVIC is a fill-field non-contact computer vision technique which provides in-plane deformation and strain data over a load differential. This method was used to determine hoop and axial displacements, hoop and axial linear strains, the in-plane shear strains and rotations in the regions surrounding impact sites in filament wound pressure vessels (FWPV) during proof loading by internal pressurization. The relationship between these deformation measurement values and the remaining life of the pressure vessels, however, requires a complex theoretical model or numerical simulation. Both of these techniques are time consuming and complicated. Previous results using neural network methods had been successful in predicting the burst pressure for graphite/epoxy pressure vessels based upon acoustic emission (AE) measurements in similar tests. The neural network associates the character of the AE amplitude distribution, which depends upon the extent of impact damage, with the burst pressure. Similarly, higher amounts of impact damage are theorized to cause a higher amount of strain concentration in the damage effected zone at a given pressure and result in lower burst pressures. This relationship suggests that a neural network might be able to find an empirical relationship between the SDVIC strain field data and the burst pressure, analogous to the AE method, with greater speed and simplicity than theoretical or finite element modeling. The process of testing SDVIC neural network analysis and some encouraging preliminary results are presented in this paper. Details are given concerning the processing of SDVIC output data such that it may be used as back propagation neural network (BPNN) input data. The software written to perform this processing and the BPNN algorithm are also discussed. It will be shown that, with limited training, test results indicate an average error in burst pressure prediction of approximately six percent,

Russell, Samuel S.↗
NASA NTRSDate not supplied

Convolutional Neural Networks for Image Classification in Metal Selective Laser Meting Additive Manufacturing

Selective laser melting (SLM) is a metal additive manufacturing process that has several advantages such as the large range of metal materials that can be accommodated, 3D printing of complex shape components, the ability to adjust material properties, and cost reduction as expensive production equipment may not be required. Therefore, process monitoring is crucial in different stages of the component building. In this work, convolutional neural networks (CNNs) are investigated as a suitable technique for post-inspection of builds. The monitoring of manufactured parts was conducted by collecting computed tomography (CT) images and identifying defects. Five CNN models were implemented and tested for the classification of the CT images. The models were based on NASNetMobile and DenseNet121, and a custom built CNN model. The results of this work show that CNNs can be feasible and reliable for rapid monitoring and classification of defects in CT images from build fabrication using SLM.

Rodolfo Ledesma↗
NASA NTRS2007

Catalytic dehydrogenation of amine borane complexes

A method of generating hydrogen includes the steps of providing an amine borane (AB) complex, at least one hydrogen generation catalyst, and a solvent, and mixing these components. Hydrogen is generated. The hydrogen produced is high purity hydrogen suitable for PEM fuel cells. A hydrolytic in-situ hydrogen generator includes a first compartment that contains an amine borane (AB) complex, a second container including at least one hydrogen generation catalyst, wherein the first or second compartment includes water or other hydroxyl group containing solvent. A connecting network permits mixing contents in the first compartment with contents in the second compartment, wherein high purity hydrogen is generated upon mixing. At least one flow controller is provided for controlling a flow rate of the catalyst or AB complex.

Mohajeri, Nahid↗
NASA NTRS2009

Catalytic dehydrogenation of amine borane complexes

A method of generating hydrogen includes the steps of providing an amine borane (AB) complex, at least one hydrogen generation catalyst, and a solvent, and mixing these components Hydrogen is generated. The hydrogen produced is high purity hydrogen suitable for PEM fuel cells. A hydrolytic in-situ hydrogen generator includes a first compartment that contains an amine borane (AB) complex, a second container including at least one hydrogen generation catalyst, wherein the first or second compartment includes water or other hydroxyl group containing solvent. A connecting network permits mixing contents in the first compartment with contents in the second compartment, wherein high purity hydrogen is generated upon mixing. At least one flow controller is provided for controlling a flow rate of the catalyst or AB complex.

Mohajeri, Nahid↗