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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Center of mass detection via an active pixel sensor

An imaging system for identifying the location of the center of mass (COM) in an image. In one aspect, an imaging system includes a plurality of photosensitive elements arranged in a matrix. A center of mass circuit coupled to the photosensitive elements includes a resistive network and a normalization circuit including at least one bipolar transistor. The center of mass circuit identifies a center of mass location in the matrix and includes: a row circuit, where the row circuit identifies a center of mass row value in each row of the matrix and identifies a row intensity for each row; a horizontal circuit, where the horizontal circuit identifies a center of mass horizontal value; and a vertical circuit, where the vertical circuit identifies a center of mass vertical value. The horizontal and vertical center of mass values indicate the coordinates of the center of mass location for the image.

Yadid-Pecht, Orly↗
NASA NTRS2014

Design and Development of the SMAP Microwave Radiometer Electronics

The SMAP microwave radiometer will measure land surface brightness temperature at L-band (1413 MHz) in the presence of radio frequency interference (RFI) for soil moisture remote sensing. The radiometer design was driven by the requirements to incorporate internal calibration, to operate synchronously with the SMAP radar, and to mitigate the deleterious effects of RFI. The system design includes a highly linear super-heterodyne microwave receiver with internal reference loads and noise sources for calibration and an innovative digital signal processor and detection system. The front-end comprises a coaxial cable-based feed network, with a pair of diplexers and a coupled noise source, and radiometer front-end (RFE) box. Internal calibration is provided by reference switches and a common noise source inside the RFE. The RF back-end (RBE) downconverts the 1413 MHz channel to an intermediate frequency (IF) of 120 MHz. The IF signals are then sampled and quantized by high-speed analog-to-digital converters in the radiometer digital electronics (RDE) box. The RBE local oscillator and RDE sampling clocks are phase-locked to a common reference to ensure coherency between the signals. The RDE performs additional filtering, sub-band channelization, cross-correlation for measuring third and fourth Stokes parameters, and detection and integration of the first four raw moments of the signals. These data are packetized and sent to the ground for calibration and further processing. Here we discuss the novel features of the radiometer hardware particularly those influenced by the need to mitigate RFI.

radio frequency interference↗
NASA NTRS2015

A Module for Assimilating Hyperspectral Infrared Retrieved Profiles into the Gridpoint Statistical Interpolation System for Unique Forecasting Applications

Hyperspectral infrared sounder radiance data are assimilated into operational modeling systems however the process is computationally expensive and only approximately 1% of available data are assimilated due to data thinning as well as the fact that radiances are restricted to cloud-free fields of view. In contrast, the number of hyperspectral infrared profiles assimilated is much higher since the retrieved profiles can be assimilated in some partly cloudy scenes due to profile coupling other data, such as microwave or neural networks, as first guesses to the retrieval process. As the operational data assimilation community attempts to assimilate cloud-affected radiances, it is possible that the use of retrieved profiles might offer an alternative methodology that is less complex and more computationally efficient to solve this problem. The NASA Short-term Prediction Research and Transition (SPoRT) Center has assimilated hyperspectral infrared retrieved profiles into Weather Research and Forecasting Model (WRF) simulations using the Gridpoint Statistical Interpolation (GSI) System. Early research at SPoRT demonstrated improved initial conditions when assimilating Atmospheric Infrared Sounder (AIRS) thermodynamic profiles into WRF (using WRF-Var and assigning more appropriate error weighting to the profiles) to improve regional analysis and heavy precipitation forecasts. Successful early work has led to more recent research utilizing WRF and GSI for applications including the assimilation of AIRS profiles to improve WRF forecasts of atmospheric rivers and assimilation of AIRS, Cross-track Infrared and Microwave Sounding Suite (CrIMSS), and Infrared Atmospheric Sounding Interferometer (IASI) profiles to improve model representation of tropopause folds and associated non-convective wind events. Although more hyperspectral infrared retrieved profiles can be assimilated into model forecasts, one disadvantage is the retrieved profiles have traditionally been assigned the same error values as the rawinsonde observations when assimilated with GSI. Typically, satellitederived profile errors are larger and more difficult to quantify than traditional rawinsonde observations (especially in the boundary layer), so it is important to appropriately assign observation errors within GSI to eliminate potential spurious innovations and analysis increments that can sometimes arise when using retrieved profiles. The goal of this study is to describe modifications to the GSI source code to more appropriately assimilate hyperspectral infrared retrieved profiles and outline preliminary results that show the differences between a model simulation that assimilated the profiles as rawinsonde observations and one that assimilated the profiles in a module with the appropriate error values.

Berndt, Emily↗
NASA NTRS2021

Radar Observations from the Haystack Ultrawideband Satellite Imaging Radar in 2019

The NASA Orbital Debris Program Office (ODPO) relies primarily on ground-based radar measurements to characterize the distribution of small debris, down to approximately 3 mm depending upon altitude and the sensor used, in low Earth orbit (LEO). Since the early 1990’s, the Massachusetts Institute of Technology (MIT) Lincoln Laboratory (LL) has been collecting radar measurements for the NASA ODPO under agreements with the U.S. Department of Defense. The Haystack Ultrawideband Satellite Imaging Radar (HUSIR) is the primary ground-based radar sensor used by the ODPO and provides data on orbital debris down to an approximate size of 5.5 mm below 1000km altitude using the NASA size estimation model (SEM). Since orbital debris of this size are a significant risk to both human and robotic missions in LEO, the sensitivity of this radar makes it a high-value sensor. The NASA ODPO radar measurements are conducted on a continual basis for monitoring and enabling modeling of the orbital debris environment over time. HUSIR observations from 2019 are the most recent snapshot of the environment that has been measured and analyzed to date. In recent years, HUSIR measurements indicated relatively stable populations for the orbital debris objects that it is able to detect. In 2019, several interesting events happened on-orbit, including the start of large constellation deployments into LEO, as well as the Indian anti-satellite test with Microsat-R (International Designator 2019-006A, U.S. Strategic Command Space Surveillance Network catalog number 43947). Due to these events, coupled with a general increase in the number of missions and participants launching missions in recent years, continual monitoring is necessary to determine the effects of this increased activity on the orbital debris environment. This paper will explore the results of the 2019 HUSIR radar measurements, including above-average flux measurements at lower LEO altitudes and the evolution of the flux during the time of observations.

James Murray↗
NASA NTRSDate not supplied

Radar Observations from the Haystack Ultrawideband Satellite Imaging Radar in 2019

The NASA Orbital Debris Program Office (ODPO) relies primarily on ground-based radar measurements to characterize the distribution of small debris, down to approximately 3 mm depending upon altitude and the sensor used, in low Earth orbit (LEO). Since the early 1990’s, the Massachusetts Institute of Technology (MIT) Lincoln Laboratory (LL) has been collecting radar measurements for the NASA ODPO under agreements with the U.S. Department of Defense. The Haystack Ultrawideband Satellite Imaging Radar (HUSIR) is the primary ground-based radar sensor used by the ODPO and provides data on orbital debris down to an approximate size of 5.5 mm below 1000km altitude using the NASA size estimation model (SEM). Since orbital debris of this size are a significant risk to both human and robotic missions in LEO, the sensitivity of this radar makes it a high-value sensor. The NASA ODPO radar measurements are conducted on a continual basis for monitoring and enabling modeling of the orbital debris environment over time. HUSIR observations from 2019 are the most recent snapshot of the environment that has been measured and analyzed to date. In recent years, HUSIR measurements indicated relatively stable populations for the orbital debris objects that it is able to detect. In 2019, several interesting events happened on-orbit, including the start of large constellation deployments into LEO, as well as the Indian anti-satellite test with Microsat-R (International Designator 2019-006A, U.S. Strategic Command Space Surveillance Network catalog number 43947). Due to these events, coupled with a general increase in the number of missions and participants launching missions in recent years, continual monitoring is necessary to determine the effects of this increased activity on the orbital debris environment. This paper will explore the results of the 2019 HUSIR radar measurements, including above-average flux measurements at lower LEO altitudes and the evolution of the flux during the time of observations.

James Murray↗
DOE OSTI2023

Tensor renormalization group study of 3D principal chiral model

We study the three-dimensional $SU(2)$ principal chiral model (PCM) using different tensor renormalization group methods based on the triad and anisotropic decomposition of the tensor. The tensor network representation is formulated based on the character expansion of the Boltzmann weight. We compare the average action obtained using these two tensor network algorithms and confirm that the resulting critical coupling and exponent are comparable with the recent estimations from the Monte Carlo methods.

Akiyama, Shinichiro↗
DOE OSTI2022

Low-Temperature Competing Magnetic Energy Scales in the Topological Ferrimagnet TbMn 6 Sn 6

TbMn 6 Sn 6 is a metallic ferrimagnet displaying signatures of both topological electrons and topological magnons arising from ferromagnetism and spin-orbit coupling within its Mn kagome layers. Inelastic neutron scattering measurements find strong ferromagnetic (FM) interactions within the Mn kagome layer and reveal a magnetic bandwidth of ~230 meV. The low-energy magnetic excitations are characterized by strong FM Mn-Mn and antiferromagnetic (AFM) Mn-Tb interlayer magnetic couplings. We observe weaker, competing long-range FM and AFM Mn-Mn interlayer interactions similar to those driving helical magnetism in the YMn 6 Sn 6 system. Combined with density-functional theory calculations, we find that competing Mn-Mn interlayer magnetic interactions occur in all RMn 6 Sn 6 compounds with R=Y, Gd-Lu, resulting in magnetic instabilities and tunability when Mn-R interactions are weak. In the case of TbMn 6 Sn 6 , strong AFM Mn-Tb coupling ensures a highly stable three-dimensional ferrimagnetic network.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Employing Interacting Qubits for Distributed Microgrid Control

To empower flexible and scalable operations, distributed control of multi-inverter microgrids, based on classical communication networks among distributed energy resources, has attracted considerable attention as it can guarantee synchronization and provide suitable remedies to the problem of improper power sharing. Notwithstanding this, resilience of the current schemes on classical communication makes microgrids vulnerable to cyber attacks. Inspired by recent revolutionary breakthroughs in quantum communication, in this paper, we devise a novel synchronization mechanism. We extend the synchronization framework utilized in distributed control algorithms to networks of quantum systems by generating pinning terms and coupling mechanism for the new synchronization rule via exploiting proper quantum jump operators and observables, and show that the quantum system will converge to a time-variant target state. Our devised quantum distributed controller (QDC) gives rise to a novel quantum communication scheme for distributed control of microgrids and enables microgrids to exploit the state-of-the-art quantum communication frameworks as communication infrastructure. Finally, test results on two representative AC and DC networked microgrids validate the efficacy and universality of the quantum distributed control.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Petroleum pollution changes microbial diversity and network complexity of soil profile in an oil refinery

Petroleum pollution resulting from spills and leakages in oil refinery areas has been a significant environmental concern for decades. Despite this, the effects of petroleum pollutants on soil microbial communities and their potential for pollutant biodegradation still required further investigation. In this study, we collected 75 soil samples from 0 to 5 m depths of 15 soil profiles in an abandoned refinery to analyze the effect of petroleum pollution on soil microbial diversity, community structure, and network co-occurrence patterns. Our results suggested soil microbial a-diversity decreased under high C10–C40 levels, coupled with significant changes in the community structure of soil profiles. However, soil microbial network complexity increased with petroleum pollution levels, suggesting more complex microbial potential interactions. A module specific for methane and methyl oxidation was also found under high C10–C40 levels of the soil profile, indicating stronger methanotrophic and methylotrophic metabolic activities at the heavily polluted soil profile. The increased network complexity observed may be due to more metabolic pathways and processes, as well as increased microbial interactions during these processes. These findings highlight the importance of considering both microbial diversity and network complexity in assessing the effects of petroleum pollution on soil ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

A seven patch hexagonal CP subarray with a CPW/stripline feed network

A seven element microstrip subarray of hexagonal geometry were designed and tested at S-band frequencies. The microstrip patch antenna is excited at a single feed position using a direct probe type connection to generate a circularly polarized wave. The RF power is coupled to these feed positions by a novel coplanar waveguide/stripline feeding network. The subarray architecture and the feed network are described in detail. The measured results include the feed network characterization as well as the radiation patterns of the subarray.

Simons, R. N.↗
DOE OSTI2021

Local Explosion Detection and Infrasound Localization by Reverse Time Migration Using 3-D Finite-Difference Wave Propagation

Infrasound data are routinely used to detect and locate volcanic and other explosions, using both arrays and single sensor networks. However, at local distances (< km) topography often complicates acoustic propagation, resulting in inaccurate acoustic travel times leading to biased source locations when assuming straight-line propagation. Here we present a new method, termed Reverse Time Migration-Finite-Difference Time Domain (RTM-FDTD), that integrates numerical modeling into the standard RTM back-projection process. Travel time information is computed across the entire potential source grid via FDTD modeling to incorporate the effects of topography. The waveforms are then back-projected and stacked at each grid point, with the stack maximum corresponding to the likely source. We apply our method to three volcanoes with different network configurations, source-receiver distances, and topography. At Yasur Volcano, Vanuatu, RTM-FDTD locates explosions within ~20 m of the source and differentiates between multiple vents. RTM-FDTD produces a more accurate location for the two Yasur subcraters than standard RTM and doubles the number of detected events. At Sakurajima Volcano, Japan, RTM-FDTD locates the source within 50 m of the active vent despite notable topographic blocking. The RTM-FDTD location is similar to that from the Time Reversal Mirror method, but is more computationally efficient. Lastly, at Shishaldin Volcano, Alaska, RTM and RTM-FDTD both produce realistic source locations (<50 m) for ground-coupled airwaves recorded on a four-station seismic network. We show that RTM is an effective method to detect and locate infrasonic sources across a variety of scenarios, and by integrating numerical modeling, RTM-FDTD produces more accurate source locations and increases the detection capability.

58 GEOSCIENCES↗
DOE OSTI2023

New trends in photonic switching and optical networking architectures for data centers and computing systems [Invited]

The rapid increases in data traffic coupled with user preferences are driving the data center and computing system service providers to offer energy-efficient, intelligent, flexible, cost-effective, high-capacity, and low-latency data services without added complexity to the users. Disaggregated heterogeneous reconfigurable computing systems realized by photonic switching and interconnects can enhance throughput and energy efficiency for artificial intelligence/machine learning (AI/ML) workloads, especially when aided by the AI/ML-enhanced control plane. Photonic switching and new optical networking architectures are expected to solve many of these challenging problems. This paper discusses new trends in photonic switching and optical network architectures for future data centers and computing systems summarized as follows: (1) flat reconfigurable disaggregated computing enabled by high-radix photonic switching and interconnects in data centers; (2) chiplet-based computing architectures empowered by embedded photonics toward heterogeneous reconfigurable computing; (3) nanosecond-scale photonic switching in data centers and computing systems; (4) AI/ML in self-driving, application-aware, and situation-aware data centers; (5) the emergence of flexible networking for cloud computing, edge computing, and split computing, as well as flexible networking for 5G/6G RF-optical networks; and (6) the deployment of embedded co-designed silicon photonics being considered for future data centers.

Yoo, S. J. Ben (ORCID:0000000274201871)↗
DOE OSTI2024

Isolation of individual Er quantum emitters in anatase TiO 2 on Si photonics

Defects and dopant atoms in solid state materials are a promising platform for realizing single photon sources and quantum memories, which are the basic building blocks of quantum repeaters needed for long distance quantum networks. In particular, trivalent erbium (Er 3+ ) is of interest because it couples C-band telecom optical transitions with a spin-based memory platform. In order to produce quantum repeaters at the scale required for quantum networks it is imperative to integrate these necessary building blocks with mature and scalable semiconductor processes. Here, in this work, we demonstrate the optical isolation of single Er 3+ ions in CMOS-compatible titanium dioxide (TiO 2 ) thin films monolithically integrated on a silicon-on-insulator photonics platform. Our results demonstrate an initial step toward the realization of a monolithically integrated and scalable quantum photonics package based on Er 3+ doped thin films.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2005

Design Sensitivity for a Subsonic Aircraft Predicted by Neural Network and Regression Models

A preliminary methodology was obtained for the design optimization of a subsonic aircraft by coupling NASA Langley Research Center s Flight Optimization System (FLOPS) with NASA Glenn Research Center s design optimization testbed (COMETBOARDS with regression and neural network analysis approximators). The aircraft modeled can carry 200 passengers at a cruise speed of Mach 0.85 over a range of 2500 n mi and can operate on standard 6000-ft takeoff and landing runways. The design simulation was extended to evaluate the optimal airframe and engine parameters for the subsonic aircraft to operate on nonstandard runways. Regression and neural network approximators were used to examine aircraft operation on runways ranging in length from 4500 to 7500 ft.

Hopkins, Dale A.↗
NASA NTRSDate not supplied

Deep Neural Network Based Convergence Classification for Computational Fluid Dynamics

A supervised deep learning approach is coupled with heuristic convergence criteria to construct a classification model for detecting the completion (convergence) of computational fluid dynamics (CFD) simulations. Heuristic convergence criteria alone are not always sufficient and more complex decisions are often left to a human analyst. The proposed approach leverages heuristic convergence criteria as well as two deep neural network (DNN) models, one binary and one multi-class, to improve the efficiency and consistency of convergence classification across a wide range of flight regimes. The DNN models presented are each trained on a subset of ascent aerodynamic CFD simulations for NASA’s Space Launch System and were produced using NASA’s unstructured Navier-Stokes solver FUN3D. Individual solutions are analyzed intermittently and are classified as sufficiently converged, further iterations required, or switch from steady Reynolds Averaged Navier-Stokes (RANS) to unsteady RANS CFD based on the iterative histories of four aerodynamic coefficients. The implemented classification model is shown to produce solutions that closely correlate to solutions produced by a human analyst. This work lays groundwork for expanding the capabilities of DNNs for automating and improving more of the CFD process.

SLS↗
DOE OSTI2022

Tensor Network Path Integral Study of Dynamics in B850 LH2 Ring with Atomistically Derived Vibrations

The recently introduced multisite tensor network path integral (MS-TNPI) allows simulation of extended quantum systems coupled to dissipative media. We use MS-TNPI to simulate the exciton transport and the absorption spectrum of a B850 bacteriochlorophyll (BChl) ring. The MS-TNPI network is extended to account for the ring topology of the B850 system. Accurate molecular-dynamics-based description of the molecular vibrations and the protein scaffold is incorporated through the framework of Feynman–Vernon influence functional. To relate the present work with the excitonic picture, an exploration of the absorption spectrum is done by simulating it using approximate and topologically consistent transition dipole moment vectors. Comparison of these numerically exact MS-TNPI absorption spectra are shown with second-order cumulant approximations. Finally, the effect of temperature on both the exact and the approximate spectra is also explored.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Energetic and structural control of polyspecificity in a multidrug transporter

Multidrug efflux pumps are dynamic molecular machines that drive antibiotic resistance by harnessing ion gradients to export chemically diverse substrates. Despite their clinical importance, the molecular principles underlying multidrug promiscuity and energy efficiency remain poorly understood. Using multiparametric deep mutational scanning across eight substrates and two energy conditions, we deconvolute the contributions of substrate recognition, energetic coupling, and protein stability, providing an integrated, high-resolution view of multidrug transport. We find that substrate specificity arises from a distributed network of residues extending beyond the binding site, with mutations that reshape binding, coupling, conformational flexibility, and membrane interactions. Further, we apply a pH-based selection scheme to measure the effect of mutation on pH-dependent transport efficiency. By integrating these data, we reveal a fundamental relationship between efficiency and promiscuity: Highly efficient variants exhibit broad substrate profiles, while inefficient variants are narrower. In conclusion, these findings establish a direct link between energy coupling and polyspecificity, uncovering the biochemical logic underlying multidrug transport.

Biological Sciences↗
DOE OSTI2022

Local imaging of diamagnetism in proximity-coupled niobium nanoisland arrays on gold thin films.

In this work, we study the effect of engineered disorder on the local magnetic response of proximity-coupled superconducting island arrays by comparing scanning superconducting quantum interference device (SQUID) susceptibility measurements to a model in which we treat the system as a network of one-dimensional (1D) superconductor-normal-metal-superconductor Josephson junctions, each with a Josephson coupling energy E-J determined by the junction length or distance between islands. We find that the disordered arrays exhibit a spatially inhomogeneous diamagnetic response which, for low local applied magnetic fields, is well described by this junction network model, and we discuss these results as they relate to inhomogeneous 2D superconductors. Our model of the static magnetic response of the arrays does not fully capture the onset of nonlinearity and dissipation with increasing applied field, as these effects are associated with vortex motion due to the dynamic nature of the scanning SQUID susceptometry measurement. This work demonstrates a model 2D superconducting system with engineered disorder, and it highlights the impact of dissipation on the local magnetic properties of 2D superconductors and Josephson junction arrays.

Bishop-Van Horn, Logan↗