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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

DSN Resource Scheduling

TIGRAS is client-side software, which provides tracking-station equipment planning, allocation, and scheduling services to the DSMS (Deep Space Mission System). TIGRAS provides functions for schedulers to coordinate the DSN (Deep Space Network) antenna usage time and to resolve the resource usage conflicts among tracking passes, antenna calibrations, maintenance, and system testing activities. TIGRAS provides a fully integrated multi-pane graphical user interface for all scheduling operations. This is a great improvement over the legacy VAX VMS command line user interface. TIGRAS has the capability to handle all DSN resource scheduling aspects from long-range to real time. TIGRAS assists NASA mission operations for DSN tracking of station equipment resource request processes from long-range load forecasts (ten years or longer), to midrange, short-range, and real-time (less than one week) emergency tracking plan changes. TIGRAS can be operated by NASA mission operations worldwide to make schedule requests for the DSN station equipment.

Wang, Yeou-Fang↗
NASA NTRS2014

The Use of Software Agents for Autonomous Control of a DC Space Power System

In order to enable manned deep-space missions, the spacecraft must be controlled autonomously using on-board algorithms. A control architecture is proposed to enable this autonomous operation for an spacecraft electric power system and then implemented using a highly distributed network of software agents. These agents collaborate and compete with each other in order to implement each of the control functions. A subset of this control architecture is tested against a steadystate power system simulation and found to be able to solve a constrained optimization problem with competing objectives using only local information.

autonomous control↗
NASA NTRS2005

Interference estimate around Canberra DSN Station at 2.04 GHz during Huygens release phase from Cassini

During the descent phase of the Huygens Probe released from the Cassini spacecraft and inserted at Titan, the Deep Space Network (DSN) Canberra Deep Space Station (DSS) 43 (with its 70-m antenna) is being considered as a backup station to directly receive the Huygens Probe data being transmitted at 2.04 GHz. This study provides an assessment on the interference level from the major nearby transmitters operating in this frequency band. The minimum trans-horizon attenuations are calculated using terrain topographic data and the Trans-Horizon Interference Propagation Loss (THIPL) Computing Program recently developed based on ITU-R P.452, and the calculations take into account all propagation modes under a 0.1% of time exceeded. We find that there are five terrestrial transmitters within 100 km of DSS 43. Transmitter 1 is the closest to DSS 43, and needs to be coordinated to avoid interference. The rest of the four transmitters will not interfere with DSS 43. The interference levels from these transmitters are all below the DSN protection criteria of 99.9% of time.

Deep Space Network (DSN)↗
NASA NTRSDate not supplied

Observing Supraglacial Lakes Using Deep Learning and PlanetScope Imagery

Supraglacial lakes (SGL)s result from melt water accumulation in topographic depressions on the surface of glaciers. SGLs primarily affect glacial dynamics through a positive feedback loop in which the albedo-lowering effect of SGLs can escalate surface melt leading to increases in lake extent and depth, amplifying the afore mentioned albedo-lowering effect. The implications of accelerated glacial melt include increased sea level rise and modifications to ocean primary productivity. SGLs are critical indicators of surface melt and its downstream impacts and should be monitored efficiently. In situ observations and measurements of SGLs are time consuming, cost-prohibitive and difficult to scale. Earth observation data and machine learning enable scalable monitoring of SGLs through pattern detection and quantification of lake evolution over time [1]. This work presents a model developed by training a convolutional neural network with imagery and labels from NASA Operation IceBridge and predicting SGLs in high temporal and spatial resolution PlanetScope imagery.

Supraglacial lake↗
NASA NTRS1989

Parkes radio science system design and testing for Voyager Neptune encounter

The Radio Science System installed at Parkes, Australia for the Voyager Neptune encounter was specified to meet the same stringent requirements that were imposed upon the Deep Space Network Radio Science System. The system design and test methodology employed to meet these requirements at Parkes are described, and data showing the measured performance of the system are presented. The results indicate that the system operates with a comfortable margin on the requirements. There was a minor problem with frequency-dependent spurious signals which could not be fixed before the encounter. Test results characterizing these spurious signals are included.

Rebold, T. A.↗
NASA NTRS2005

Weak-signal Phase Calibration Strategies for Large DSN Arrays

The NASA Deep Space Network (DSN) is studying arrays of large numbers of small, mass-produced radio antennas as a cost-effective way to increase downlink sensitivity and data rates for future missions. An important issue for the operation of large arrays is the accuracy with which signals from hundreds of small antennas can be combined. This is particularly true at Ka band (32 GHz) where atmospheric phase variations can be large and rapidly changing. A number of algorithms exist to correct the phases of signals from individual antennas in the case where a spacecraft signal provides a useful signal-to-noise ratio (SNR) on time scales shorter than the atmospheric coherence time. However, for very weak spacecraft signals it will be necessary to rely on background natural radio sources to maintain array phasing. Very weak signals could result from a spacecraft emergency or by design, such as direct-to-Earth data transmissions from distant planetary atmospheric or surface probes using only low gain antennas. This paper considers the parameter space where external real-time phase calibration will be necessary, and what this requires in terms of array configuration and signal processing. The inherent limitations of this technique are also discussed.

interferometry↗
NASA NTRS2010

Path Length Fluctuations Derived from Site Testing Interferometer Data

To evaluate possible sites for NASA's proposed Ka-band antenna array, the NASA Glenn Research Center has constructed atmospheric phase monitors (APM) which directly measure the tropospheric phase stability. These instruments observe an unmodulated 20.2 GHz beacon signal broadcast from a geostationary satellite (Anik F2) and measure the phase difference between the signals received by the two antennas. Two APM's have been deployed, one at the NASA Deep Space Network (DSN) Tracking Complex in Goldstone, California, and the other at the NASA White Sands Complex, in Las Cruces, New Mexico. Two station-years of atmospheric phase fluctuation data have been collected at Goldstone since operations commenced in May 2007 and 0.5 station-years of data have been collected at White Sands since operations began February 2009. With identical instruments operating simultaneously, we can directly compare the phase stability at the two sites. Phase stability is analyzed statistically in terms of the root-mean-square (rms) of the tropospheric path length fluctuations over 10 min blocks. Correlation between surface wind speed and relative humidity with interferometer phase are discussed. For 2 years, the path length fluctuations at the DSN site in Goldstone, California, have been better than 757 micrometer (with reference to a 300 m baseline and to Zenith) for 90 percent of the time. For the 6 months of data collected at White Sands, New Mexico, the path length fluctuations have been better than 830 micrometers (with reference to a 300 m baseline and to Zenith) for 90 percent of the time. This type of data analysis, as well as many other site quality characteristics (e.g., rain attenuation, infrastructure, etc.), will be used to determine the suitability of both sites for NASA s future communication services at Ka-band using an array of antennas.

Acosta, Roberto J.↗
NASA NTRS2010

Atmospheric Compensation for Uplink Arrays via Radiometry

Uplink arrays for communications applications are gaining increased visibility within the NASA and military community due to the enhanced flexibility and reliability they provide. When compared with the conventional large, single aperture antennas currently comprising the Deep Space Network (DSN), for example, smaller aperture antenna arrays have the benefits of providing fault tolerance (reduced single-point failure), reduced maintenance cost, and enhanced capabilities such as electronic beam-steering and multi-beam operation. However, signal combining of antenna array elements spaced many wavelengths apart becomes problematic due to the inherent instability of earth's turbulent atmosphere, particularly at the frequencies of interest to the DSN (i.e., Ka-band). Degradation in the power combining of the individual elements comprising the array arises due to uncorrelated phase errors introduced as the signals propagate through the troposphere. It is well known that the fundamental source of this error is due to the inhomogeneous distribution of water vapor in the atmosphere [1]. Several techniques have been proposed to circumvent this issue, including the use of phase calibration towers and a moon bounce to generate a feedback loop which would provide a means of intermittent calibration of the system phase errors (thermal drifts, atmosphere) [2,3]. However, these techniques require repositioning of the antenna elements to perform this operation which ultimately results in reduced system availability. And, though they are sufficient for compensating for slow varying phase drifts, they are insufficient to compensate for faster varying phase errors, such as those introduced by the atmosphere. In this paper, preliminary radiometry and interferometry measurements collected by the NASA Glenn Research Center are analyzed and indicate that the use of optimized water vapor radiometers as a feedback system in a communications platform could provide the necessary atmospheric compensation technique to enhance the beamforming of uplink arrays.

Nessel, James A.↗
NASA NTRS2010

MaROS Strategic Relay Planning and Coordination Interfaces

The Mars Relay Operations Service (MaROS) is designed to provide planning and analysis tools in support of ongoing Mars Network relay operations. Strategic relay planning requires coordination between lander and orbiter mission ground data system (GDS) teams to schedule and execute relay communications passes. MaROS centralizes this process, correlating all data relevant to relay coordination to provide a cohesive picture of the relay state. Service users interact with the system through thin-layer command line and web user interface client applications. Users provide and utilize data such as lander view periods of orbiters, Deep Space Network (DSN) antenna tracks, and reports of relay pass performance. Users upload and download relevant relay data via formally defined and documented file structures including some described in Extensible Markup Language (XML). Clients interface with the system via an http-based Representational State Transfer (ReST) pattern using Javascript Object Notation (JSON) formats. This paper will provide a general overview of the service architecture and detail the software interfaces and considerations for interface design.

Allard, Daniel A.↗
DOE OSTI2022

A gradient-based deep neural network model for simulating multiphase flow in porous media

We report simulation of multiphase flow in porous media is crucial for the effective management of subsurface energy and environment-related activities. The numerical simulators used for modeling such processes rely on spatial and temporal discretization of the governing mass and energy balance partial-differential equations (PDEs) into algebraic systems via finite-difference/volume/element methods. These simulators usually require dedicated software development and maintenance, and suffer low efficiency from a runtime and memory standpoint for problems with multi-scale heterogeneity, coupled-physics processes or fluids with complex phase behavior. Therefore, developing cost-effective, data-driven models can become a practical choice, and in this work, we choose deep learning approaches as they can handle high dimensional data and accurately predict state variables with strong nonlinearity. In this paper, we describe a gradient-based deep neural network (GDNN) constrained by the physics related to multiphase flow in porous media. We tackle the nonlinearity of flow in porous media induced by rock heterogeneity, fluid properties, and fluid-rock interactions by decomposing the nonlinear PDEs into a dictionary of elementary differential operators. We use a combination of operators to handle rock spatial heterogeneity and fluid flow by advection. Since the augmented differential operators are inherently related to the physics of fluid flow, we treat them as first principles prior knowledge to regularize the GDNN training. We use the example of pressure management at geologic CO 2 storage sites, where CO 2 is injected in saline aquifers and brine is produced, and apply GDNN to construct a predictive model that is trained with physics-based simulation data and emulates the physics process. We demonstrate that GDNN can effectively predict the nonlinear patterns of subsurface responses, including the temporal and spatial evolution of the pressure and saturation plumes. We also successfully extend the GDNN to convolutional neural network (CNN), namely gradient-based CNN (GCNN), and validate its capability to improve the prediction accuracy. GDNN has great potential to tackle challenging problems that are governed by highly nonlinear physics and enable the development of data-driven models with higher fidelity.

42 ENGINEERING↗
DOE OSTI2024

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗
DOE OSTI2026

Modeling Multi-View Impedance-Based Cross-Geometry SOH Estimator for Li-ion Batteries

Abstract: Accurately estimating battery’s State of Health (SOH) remains challenging when models must generalize across cell designs and operating conditions. Most Electrochemical Impedance Spectroscopy (EIS)-based approaches either (i) hand-engineer a few Nyquist-plot features for shallow models—fast but does not generalize across geometries—or (ii) learn directly from Nyquist plots with deep networks, which removes manual feature extraction, yet still limited to a single plot type. As a result, cross-geometry robustness and deployability on constrained Internet of Things (IoT) devices remain open problems. We propose a compact Convolutional Neural Network (CNN) (∼ 10k parameters) that takes multi-representation EIS inputs—Nyquist (real/imaginary) and phase–magnitude (|Z|/ϕ) stacked as four channels, so the model can learn complementary degradation signatures while remaining small enough for fast inference. We build a dataset from cyclic aging of two geometries (LG INR18650MJ1 cylindrical cells and LIR2032 coin cells), acquire EIS every ten cycles from 10 kHz to 10 mHz (10 points/decade), and evaluate with leave-one-cell-out testing strategy. We further study fusion vs. single-representation inputs and assess feasibility for on-device deployment (e.g., NVIDIA Jetson device). The results show that training on multiple EIS representations improves SOH estimation accuracy and cross-geometry generalization compared to single-representation models, which uses only Nyquist or phase–magnitude plots. This design targets accurate, generalizable SOH prediction without manual feature engineering while enabling practical real-time use.

Bakr, Ahmed [The University of Alabama (UA)]↗
NASA NTRS1995

The Telecommunications and Data Acquisition Progress Report 42-123

The progress of research programs monitored by the Jet Propulsion Laboratory's Telecommunications and Mission Operations Directorate (TMOD) are presented in this quarterly document. Areas monitored include space communications, radio navigation, radio science, ground-based radio and radar astronomy, information systems, and all other communication and research technology activities for the Deep Space Network (DSN).

Yuen, Joseph H.↗
NASA NTRS1996

ARTSN: An Automated Real-Time Spacecraft Navigation System

As part of the Deep Space Network (DSN) advanced technology program an effort is underway to design a filter to automate the deep space navigation process.The automated real-time spacecraft navigation (ARTSN) filter task is based on a prototype consisting of a FORTRAN77 package operating on an HP-9000/700 workstation running HP-UX 9.05. This will be converted to C, and maintained as the operational version. The processing tasks required are: (1) read a measurement, (2) integrate the spacecraft state to the current measurement time, (3) compute the observable based on the integrated state, and (4) incorporate the measurement information into the state using an extended Kalman filter. This filter processes radiometric data collected by the DSN. The dynamic (force) models currently include point mass gravitational terms for all planets, the Sun and Moon, solar radiation pressure, finite maneuvers, and attitude maintenance activity modeled quadratically. In addition, observable errors due to troposphere are included. Further data types, force and observable models will be ncluded to enhance the accuracy of the models and the capability of the package. The heart of the ARSTSN is a currently available continuous-discrete extended Kalman filter. Simulated data used to test the implementation at various stages of development and the results from processing actual mission data are presented.

Burkhart, P. Daniel↗
NASA NTRS2001

The Mars Orbiter Altimeter (MOLA) Investigation of the Shape and Topography of Mars

The Mars Orbiter Laser Altimeter (MOLA) is an instrument on the Mars Global Surveyor (MGS) spacecraft that has been orbiting Mars since September 1997. After some preliminary observations in Sept/Oct, 1997 and in the spring and summer of 1998, the MGS spacecraft entered its mapping orbit of 400 km above the surface of Mars in February 1999. MGS began a 2 year program of systematically mapping the planet with a camera (MOC), thermal emission spectrometer (TES), magnetometer (MAG), laser altimeter (MOLA), and a radio science investigation for gravity and radio occultations. MOLA has a 48mJ, 1064 nrn ND:YAG, diode pumped laser with a 8 nanosecond pulse width, a pulse rate of 10 Hz, and a range precision of less than 40 cm. MOLA has been operating almost continuously for over two years and obtained over 600 million measurements of the radius of Mars. Using very precise orbits for the MGS spacecraft derived from the Doppler and range tracking of MGS by the Deep Space Network a topographical map of Mars has been developed with an average radial accuracy of a meter and a horizontal accuracy of 100 meters. This topographical map has revealed a new Mars, a planet with some of the flattest areas in the solar system and one of the largest impact basins. MOLA has revealed clear evidence of the effect of past fluid action on the surface and found icecaps that contain as much water ice today as the icecap of Greenland.

Smith, David E.↗
NASA NTRS2014

Iris Transponder-Communications and Navigation for Deep Space

The Jet Propulsion Laboratory has developed the Iris CubeSat compatible deep space transponder for INSPIRE, the first CubeSat to deep space. Iris is 0.4 U, 0.4 kg, consumes 12.8 W, and interoperates with NASA's Deep Space Network (DSN) on X-Band frequencies (7.2 GHz uplink, 8.4 GHz downlink) for command, telemetry, and navigation. This talk discusses the Iris for INSPIRE, it's features and requirements; future developments and improvements underway; deep space and proximity operations applications for Iris; high rate earth orbit variants; and ground requirements, such as are implemented in the DSN, for deep space operations.

cubesat↗
NASA NTRS2018

CubeX: A Compact X-Ray Telescope Enables Both X-Ray Fluorescence Imaging Spectroscopy and Pulsar Timing Based Navigation

This paper describes the benefits of a miniaturized X-ray telescope payload in the context of a lunar mission. The first part describes the payload in detail, the second part summarizes a small satellite mission concept that utilizes its compact form factor and performance. The CubeX instrument can be used for both X-ray fluorescence (XRF) imaging spectroscopy and X-ray pulsar timing based navigation (XNAV). Using our recent technological advances in X-ray optics and sensors, CubeX combines high angular resolution (<1 arcminutes) Miniature Wolter-I X-ray optics (MiXO) with a common focal plane consisting of high spectral resolution (<150 eV at 1 keV) CMOS X-ray sensors and a high timing resolution (< 1 usec) SDD X-ray sensor. This novel combination of the instruments enables both XRF measurements and XNAV operations without moving parts. The high angular resolution of the MiXO opens a wide range of orbital configurations for observation. Given that performance, the instrument has unprecedented small volume (~1×1×6U), mass (<6 kg), and power (<9W) requirements and opens a wide range of applications for a variety of targets and missions including NEOs and Martian moons. In this paper we illustrate one potential application for a lunar mission concept: The elemental composition of the Moon holds keys to understanding the origin and evolution of both the Moon and the Earth. X-ray fluorescence (XRF), induced either by solar X-ray flux or energetic ions, carries decisive signatures of surface elemental composition. X-ray observations, therefore, give a unique, powerful diagnostic tool for remotely determining elemental abundances including major rock forming elements such as Mg, Al, Na, Si, Fe, and Ca. Through high-resolution XRF imaging spectroscopy, CubeX searches for small patches of elusive lower crust and mantle material excavated within and around impact craters. CubeX identifies regional compositional variations and allows straightforward comparison of elemental distributions with the surface topography from LRO and the gravity data from GRAIL. The elemental compositions of the lower crust and the mantle are sensitive to the conditions of the giant impact which led to the Moon's formation and the subsequent lunar magma ocean (LMO), and thus they are key missing pieces in understanding the formation and early evolution of the Moon. In between XRF observations, CubeX also leverages the technology of high resolution X-ray imaging and time series measurements to conduct XNAV operations and evaluate their performance. Deep space navigation is a critical issue for small planetary missions. XNAV can enable low-cost autonomous deep-space navigation, and has the potential to greatly assist, or even outperform, NASA's Deep Space Network (DSN) or ESA's European Space Tracking (ESTRACK). CubeX is designed to perform sequential observations of 3-4 millisecond pulsars (MSPs) to solve the spacecraft trajectory for absolute navigation, and explore the remote sensing capability of XNAV. In the presented mission concept, the Moon's relative proximity enables a straightforward evaluation of the XNAV performance through DSN.

x-ray spectroscopy↗