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At least 379 records · Page 21

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NASA NTRS2021

A Mathematical Analysis of an Example Delay Tolerant Network using the Theory of Sheaves

NASA’s High-Data Rate Architecture (HiDRA) project is working towards a general yet practical toolkit and knowledge base to help usher in the era of new technologies for space systems communications, such as optical links. The High-Rate Delay Tolerant Networking (HDTN) implementation falls under the umbrellas of both the toolkit and the knowledge base, as its advancements illuminate more general areas of Delay Tolerant Networking (DTN) that need growth. The goal of this paper is to explore the usage of particular mathematical machineries, namely temporal flow networks and sheaves, to identify fundamental, underlying structures in DTN for space systems. Satellites, space assets, ground stations, etc. give rise to a disconnected network, and it is the goal of DTN to glue disparate links together into a cohesive system, that is, a network. Depending on a given link, the latencies might be beyond that which the Transmission Control Protocol (TCP) can handle, and contact times might have one-way light times in excess of minute (sometimes significantly longer). Some links might be periodic (say, due to orbital mechanics) or they might not be. This diversity has made it difficult to probe the underlying structure. An immediate consequence is that DTNs in practice today are controlled by globally distributed contact plans (schedules), which are the input to the contact graph routing (CGR) algorithm. While this is effective for smaller networks, it will be very difficult to scale for future networks. Deeper and more rigorous theory is needed to bring DTN to the next evolutionary step. To this end, this paper introduces and suggests a mathematical framework for DTN, and applies it to a space network that is simulated using an orbital analysis toolkit. The tag-line for the structure known as sheaves is that they are the mathematically precise way of gluing local data together into unique, global data. If we consider routing, we see that networking is a “sheafy” science. We then discuss a simplified sheaf model, known as the cellular sheaf. The sheaf-theoretic analysis is presented and discussed, as it is hoped that this and related papers will help form the primordial ooze of DTN theory. Finally there is a section of future work suggesting follow-on research.

Delay Tolerant Networking↗

DOE OSTI2022

Calcium silicate hydrate colloid at different humidities: Microstructure, deformation mechanism, and mechanical properties

Calcium silicate hydrate (C-S-H) is the main hydration product of Portland cement, while fundamental questions persist regarding its colloidal structure and water content. In this work, we developed a full atomistic C-S-H colloid model and investigated the physical and mechanical properties at different gel water concentrations. Water confined in nanometer gel pores degrades the mechanical properties of C-S-H because of its hydrolysis effect. A “ductile to brittle” transition was observed in the C-S-H colloid model with increasing gel water contents, evolving from transgranular failure to intergranular fracture. The gel water confined in nanometer-sized pores displays glassy characteristics, and it acts as a lubricant and facilitates structure distortions during the deformation. We elucidated the topology effect of the hydrogen bond network on the evolution of the viscoelasticity of C-S-H at different humidities. In conclusion, this work sheds light on the role of water in C-S-H colloids from nanostructure perspectives.

36 MATERIALS SCIENCE↗

DOE OSTI2024

3D Printing of Functional Hydrogel Devices for Screenings of Membrane Permeability and Selectivity

Developing a fundamental understanding of the effects of varying ligand chemistries on mass transport rates is key to designing membranes with solute-specific selectivity. While permeation cells offer a robust method to characterize membrane performance, they are limited to assessing a single membrane chemistry or salt solution per test. As a result, investigating the effects of varying ligand chemistries on membrane performance can be a tedious process, involving both the preparation of multiple samples and numerous, time-consuming permeation tests. This study uses digital light processing (DLP) 3D printing to fabricate a millifluidic flow-based permeation device made from a hydrogel active ester network that can be easily functionalized with ion-selective ligands. Without the need for bonding or assembly steps, ligands can be introduced and tested in the permeation device by simply injecting a small volume of a ligand solution. Various salt concentrations and molecular species can be cycled through a single device by switching the solution feeding into the salt reservoir, thereby reducing the number of samples needed for permeability and selectivity screenings. This research sets the groundwork for formulation development and postprocessing methods to 3D-print functional millifluidic devices capable of assessing solute selectivity in membranes and polymer adsorbents for aqueous separations. In this work, comparable salt permeability trends were observed with both 3D-printed devices and traditional assays. Devices were functionalized with an imidazole ligand to investigate salt permeability and selectivity of monovalent and divalent salts. Measurements showed increasing permeability for monovalent salts (NaCl) relative to divalent salts (MgCl 2 , CuCl 2 ) in functionalized membranes, with higher monovalent/divalent selectivity at increasing imidazole grafting densities. Here, the methods and findings described here represent a step toward developing higher-throughput methods with 3D-printed devices for screening the effects of ligand chemistry on mass transport rates in membrane materials.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Smart quantum statistical imaging beyond the Abbe-Rayleigh criterion

The wave nature of light imposes limits on the resolution of optical imaging systems. For over a century, the Abbe-Rayleigh criterion has been utilized to assess the spatial resolution limits of imaging instruments. Recently, there has been interest in using spatial projective measurements to enhance the resolution of imaging systems. Unfortunately, these schemes require a priori information regarding the coherence properties of “unknown” light beams and impose stringent alignment conditions. Here, we introduce a smart quantum camera for superresolving imaging that exploits the self-learning features of artificial intelligence to identify the statistical fluctuations of unknown mixtures of light sources at each pixel. This is achieved through a universal quantum model that enables the design of artificial neural networks for the identification of photon fluctuations. Our protocol overcomes limitations of existing superresolution schemes based on spatial mode projections, and consequently provides alternative methods for microscopy, remote sensing, and astronomy.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

NASA NTRS1982

Dynamics and mechanism of cavitation erosion on perspex and epoxy resins tested in a rotating disk device

The cavitation erosion behavior including the initiation, dynamics and mechanism of damage process on perspex and epoxy resin specimens tested in a rotating disk device were discussed with respect to exposure time. The inception of erosion always took place at the location nearest to the center of rotation of the disk. Subsequently, as exposure time increased, erosion initiated at other locations as well. Light optical photographs and scanning electron micrographs clearly indicate that most of the material loss appears to occur form the networks of cracks due to their interaction and pits indicate particle debris. The optical degradation (loss of transmittance) on perspex was observed to be more on the rear side than on the front side.

Rao, P. V.↗

NASA NTRS1983

Cavitation erosion characteristics of poly(methyl methacrylate) in a rotating disk device

Veerabhadra Rao, P.↗

NASA NTRS1978

Laserfax

The photo shows an Associated Press technician receiving a photograph prepared by means of a high speed, high resolution facsimile system known as Laserfax, so called because it employs a laser beam to scan the photo being sent. Laser scanning data is converted to electrical signals and transmitted to news bureaus within the Associated Press network via microwave radio or telephone lines. At the other end, another laser sweeps reconverted imagery onto a special, light-sensitive, dry silver paper. The paper is then heat processed to reproduce the photograph. Manufactured by Harris Electronics Systems Inc., Melbourne, Florida, Laserfax incorporates NASA thermal control coating technology developed by Jet Propulsion Laboratory for use on spacecraft antennas; the coatings block absorption of solar radiation which would interfere with the antenna's proper operation. Harris Electronics Systems, originally Radiation, Inc., reused the NASA technology in development of Laserfax. A NASA technical support package provided information about the thermal properties that could be expected of various coatings for the facsimile system's paper rollers; this information served as a design base for the infrared heat source used in developing the photo. Although relatively new, Laserfax is in extensive service. It is manufactured in several models. One of them is the type used by the Associated Press, its news bureaus and affiliated newspapers, radio and television stations. Other models have been sold in large numbers to law enforcement agencies for transmitting high quality fingerprints and identification photos. Laserfax is also in service with the National Weather Service, which converts satellite imagery to weather photographs and relays them to local weather stations, television stations, newspapers and airlines.

Source record↗

NASA NTRS2016

Spaceborne Remote Sensing of Aerosol Type: Global Distribution, Model Evaluation and Translation into Chemical Speciation

It is essential to evaluate and refine aerosol classification methods applied to passive satellite remote sensing. We have developed an aerosol classification algorithm (called Specified Clustering and Mahalanobis Classification, SCMC) that assigns an aerosol type to multi-parameter retrievals by spaceborne, airborne or ground-based passive remote sensing instruments [1]. The aerosol types identified by our scheme are pure dust, polluted dust, urban-industrialdeveloped economy, urban-industrialdeveloping economy, dark biomass smoke, light biomass smoke and pure marine. We apply the SCMC method to inversions from the ground-based AErosol RObotic NETwork (AERONET [2]) and retrievals from the space-borne Polarization and Directionality of Earths Reflectances instrument (POLDER, [3]). The POLDER retrievals that we use differ from the standard POLDER retrievals [4] as they make full use of multi-angle, multispectral polarimetric data [5]. We analyze agreement in the aerosol types inferred from both AERONET and POLDER and evaluate GEOS-Chem [6] simulations over the globe. Finally, we use in-situ observations from the SEAC4RS airborne field experiment to bridge the gap between remote sensing-inferred qualitative SCMC aerosol types and their corresponding quantitative chemical speciation. We apply the SCMC method to airborne in-situ observations from the NASA Langley Aerosol Research Group Experiment (LARGE, [7]) and the Differential Aerosol Sizing and Hygroscopicity Spectrometer Probe (DASH-SP, [8]) instruments; we then relate each coarsely defined SCMC type to a sum of percentage of individual aerosol species, using in-situ observations from the Particle Analysis by Laser Mass Spectrometry (PALMS, [9]), the Soluble Acidic Gases and Aerosol (SAGA, [10]), and the High - Resolution Time - of - Flight Aerosol Mass Spectrometer (HR ToF AMS, [11]).

airborne↗

NASA NTRSDate not supplied

Implementation of Machine Learning Methods for Crater-Based Navigation

Terrain Relative Navigation methods require surface feature detectors to gain information from images used to improve on-board state estimates. This paper presents the development of a crater detection method based on Machine Learning that can extract data from optical images with different crater shapes and sizes, under varying lighting conditions. This work includes an automated capability for generating labeled training data and iterative testing of the neural network-based crater detector. Preliminary results are included to quantify the detector’s accuracy compared to a known crater catalog, given a set of real lunar images from the Lunar Reconnaissance Orbiter.

Sofia G Catalan↗

NASA NTRS2020

The First Habitable-zone Earth-sized Planet from TESS. II. Spitzer Confirms TOI-700 d

We present Spitzer 4.5 μm observations of the transit of TOI-700 d, a habitable-zone Earth-sized planet in a multiplanet system transiting a nearby M-dwarf star (TIC 150428135, 2MASS J06282325–6534456). TOI-700 d has a radius of 1.144 (+0.062, -0.061)Rꚛ and orbits within its host star’s conservative habitable zone with a period of 37.42 days (T(eq)~269 K). TOI-700 also hosts two small inner planets (R(b)=1.037(+0.065, -0.064) Rꚛ and R(c)=2.65(+0.16,-0.15) Rꚛ with periods of 9.98 and 16.05 days, respectively. Our Spitzer observations confirm the Transiting Exoplanet Survey Satellite (TESS) detection of TOI-700 d and remove any remaining doubt that it is a genuine planet. We analyze the Spitzer light curve combined with the 11 sectors of TESS observations and a transit of TOI-700 c from the LCOGT network to determine the full system parameters. Although studying the atmosphere of TOI-700 d is not likely feasible with upcoming facilities, it may be possible to measure the mass of TOI-700 d using state-of-the-art radial velocity (RV) instruments (expected RV semiamplitude of ∼70 cm/s).

Joseph E. Rodriguez↗

DOE OSTI2024

Fast Photoactuation Driven by Supramolecular Polymers Integrated into Covalent Networks

Abstract The design of robotic soft matter capable of emulating the complex movements of living organisms such as mechanical actuation, shape transformation, and autonomous translation remains a grand challenge in soft materials science. Functionalized hydrogels are excellent candidates for such materials since they can operate in water and are highly responsive to their environment, but their response times can be slow. This work investigates fast photoactuation of hybrid bonding hydrogels composed of peptide amphiphile (PA) supramolecular nanofibers bonded covalently to merocyanine‐based (MCH + ) photoresponsive networks. By incorporating ionizable acrylic acid (AA) co‐monomers in these networks, photoactuation at nearly neutral pH is observed, which in turn enables a new mechanism to accelerate the response by triggering the bundling of supramolecular nanofibers by rapid proton exchange reactions. Furthermore, this rapid response and its consequent large shape transformations lead to hydrogels capable of spontaneously tracking external light sources inspired by pedicellariae, defensive organs present in echinoderms like the starfish and the sea urchin. This work suggests that hybrid bonding polymers (HBPs), which leverage the interplay between supramolecular assemblies and covalent networks, offer novel strategies to design rapidly actuating soft robotic materials.

Cezan, S. Doruk↗

DOE OSTI2020

One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks

High-temperature advanced reactors under development, such as sodium fast reactors (SFR) and molten salt cooled reactors (MSCR), are expected to offer lower levelized cost of energy (LCOE) compared to existing light water reactor (LWR’s). In the existing light water reactors (LWR’s), operation and maintenance (O&M) expenses constitute the largest fraction of the total operating cost. Some of the O&M costs are related maintenance of sensors which can fail due to exposure to harsh environment in a reactor. The O&M costs of Advanced Reactor (AR)’s are expected to constitute a significant fraction of the total cost as well, because of high temperature and radiation level in AR are likely to cause material fatigue and premature failure of sensors and components. The O&M costs in AR’s could be reduced through integration of advanced informatics of performance-related sensors into a digital twin designed for reactor monitoring. For example, machine learning (ML) could be employed for real-time validation and correction of performance-related sensors, and reducing the number of performance-related physical sensor units through virtual sensing. As part of the effort, we investigate real-time validation of thermal hydraulic sensors through one-step ahead forecasting of sensor values using long short-term memory (LSTM) recurrent neural networks (RNN). The sensors are installed in a flow loop containing a thermal mixing tee, which is a common experimental model to study thermal fatigue in a thermal hydraulic loop. In addition, nonlinear transients generated in a thermal mixing tee constitute a good challenge data set for training and validation of ML algorithms. Sensors in this study include thermocouples, flow meters, and optical fibers for distributed temperature sensing. In one experiment, measurement data sets were obtained for a loop was filled with water, and in another experiment, measurements were performed on a loop filled with liquid metal Galinstan. We have also conducted preliminary investigation of one-step ahead prediction of fiber optics-based distributed temperature sensing with LSTM networks. In predicting fiber-based temperature measurements, we treated each gauge pitch of the fiber as an independent sensor. Accuracy of one-step ahead forecasting was estimated by calculating root mean square error (RMSE) for the test segment of time series of each sensor. RMSE’s for temperature sensors in water loop were, for the most part, lower than for the same sensors in Galinstan loop. The RMSE’s for flow meters were similar for both loops. The RMSE’s for distributed temperature measured with the fiber optic sensor were similar to those of the point sensors. Results of this study demonstrated the capability of LSTM one-step ahead forecasting with RMSE comparable to uncertainty in sensor measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Machine learning-enhanced hybrid modeling approach for better identification of a building thermal network model and improved prediction

The gray-box modeling approach, which uses a semi-physical thermal network model, has been widely used in building prediction applications, such as model predictive control (MPC). However, unmeasured disturbances, such as occupants, lighting, and in/exfiltration loads, make it challenging to apply this approach to practical buildings. In this word, we propose a hybrid modeling approach that integrates the gray-box model with a model for unmeasured disturbance. After reviewing several system identification approaches, we systematically designed the unmeasured disturbance model with a model selection process based on statistical tests to make it robust. We generated data based on the building model calibrated by real operational data and then trained the hybrid model for two different weather conditions. The hybrid model approach demonstrates an RMSE reduction of approximately 0.2–0.9 °C and 0.3–2 °C on 1-day ahead temperature prediction compared to the Conventional approach for mild (Berkeley, CA) and cold (Chicago, IL) climates, respectively. In addition, this approach was applied to experimental data obtained from the laboratory building to be used for the MPC application, showing superior prediction performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1972

Research study on stellar X-ray imaging experiment, volume 1

The use of microchannel plates as focal plane readout devices and the evaluation of mirrors for X-ray telescopes applied to stellar X-ray imaging is discussed. The microchannel plate outputs were either imaged on a phosphor screen which was viewed by a low light level vidicon or on a wire array which was read out by digitally processing the output of a charge division network attached to the wires. A service life test which was conducted on two image intensifiers is described.

Wilson, H. H.↗

NASA NTRS1992

Active member bridge feedback control for damping augmentation

An active damping augmentation approach using active members in a structural system is described. The problem of maximizing the vibration damping in a lightly damped structural system is considered using the analogy of impedance matching between the load and source impedances in an electrical network. The proposed active damping augmentation approach therefore consists of finding the desired active member impedances that maximize the vibration damping, and designing a feedback control in order to achieve desired active member impedances. This study uses a bridge feedback concept that feeds back a combination of signals from sensors of the axial force and relative velocity across the active member to realize the desired active member impedance. The proposed active damping augmentation approach and bridge feedback concept were demonstrated on a three-longeron softly suspended truss structure.

Chen, Gun-Shing↗

NASA NTRS2014

The Central Engine of GRB 130831A and the Energy Breakdown of a Relativistic Explosion

Gamma-ray bursts (GRBs) are the most luminous explosions in the Universe, yet the nature and physical properties of their energy sources are far from understood. Very important clues, however, can be inferred by studying the afterglows of these events. We present optical and X-ray observations of GRB 130831A obtained by Swift, Chandra, Skynet, Reionization And Transients Infra-Red camera, Maidanak, International Scientific Optical-Observation Network, Nordic Optical Telescope, Liverpool Telescope and Gran Telescopio Canarias. This burst shows a steep drop in the X-ray light curve at asymptotically equal to 10(exp 5) s after the trigger, with a power-law decay index of alpha that is approximately 6. Such a rare behaviour cannot be explained by the standard forward shock (FS) model and indicates that the emission, up to the fast decay at 10(exp 5) s, must be of internal origin, produced by a dissipation process within an ultrarelativistic outflow. We propose that the source of such an outflow, which must produce the X-ray flux for an asymptotically equal to 1 d in the cosmological rest frame, is a newly born magnetar or black hole. After the drop, the faint X-ray afterglow continues with a much shallower decay. The optical emission, on the other hand, shows no break across the X-ray steep decrease, and the late-time decays of both the X-ray and optical are consistent. Using both the X-ray and optical data, we show that the emission after an asymptotically equal to 10(exp 5) scan be explained well by the FS model. We model our data to derive the kinetic energy of the ejecta and thus measure the efficiency of the central engine of a GRB with emission of internal origin visible for a long time. Furthermore, we break down the energy budget of this GRB into the prompt emission, the late internal dissipation, the kinetic energy of the relativistic ejecta,and compare it with the energy of the associated supernova, SN 2013 fu.

radiation mechanisms: non-thermal â€“ shock waves ↗

NASA NTRSDate not supplied

Synthetic Data Generation for 3D Mesh Prediction and Spatial Reasoning During Multi-Agent Robotic Missions

In-space assembly operations require accurate reasoning over the pose, location, and structural organization of both the autonomous agents and assembly materials. In a full six-degree-of-freedom space, an accurate understanding of the full three-dimensional structure of the object of interest greatly enriches information for pose estimation and collision planning. Current methods of predicting pose estimation require a priori understanding of the shape of the object. Additionally, visual information in the space environment is impacted by variations in contrast and illumination. Using synthetic data allows us to rapidly generate large datasets with in varying environments and lighting conditions.This work details the generation of synthetic data used to explore the use of a region-based convolutional neural networks to detect objects of interest and predict a voxel-based three-dimensional mesh in order to understand their full three-dimensional shape. This mesh provides useful spatial information during in-space assembly operations without requiring either the complexity of maintaining models over the progress of building an object or observations from multiple angles. The generated meshes are then compared to that of ground truth in order to measure its performance.

synthetic data↗

DOE OSTI2023

Low-Voltage Haze Tuning with Cellulose-Network Liquid Crystal Gels

Being key components of the building envelope, glazing products with tunable optical properties are in great demand because of their potential for boosting energy efficiency and privacy features while enabling the main function of allowing natural light indoors. However, windows and skylights with electric switching of haze and transparency are rare and often require high voltages or electric currents, as well as not fully meet the stringent technical requirements for glazing applications. Here, by introducing a predesigned gel material we describe an approach dubbed “Haze-Switch” that involves low-voltage tuning of the haze coefficient in a broad range of 2–90% while maintaining high visible-range optical transmittance. The approach is based on a nanocellulose fiber gel network infiltrated by a nematic liquid crystal, which can be switched between polydomain and monodomain spatial patterns of optical axis via a dielectric coupling between the nematic domains and the applied external electric field. By utilizing a nanocellulose network of nanofibers ~10 nm in diameter we achieve <10 V dielectric switching and <2% haze in the clear state, as needed for applications in window products. Finally, we characterize physical properties relevant to window and smart glass technologies, like the color rendering index, haze coefficient, and switching times, demonstrating that our material and envisaged products can meet the stringent requirements of the glass industry, including applications such as privacy windows, skylights, sunroofs, and daylighting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗