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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 1,099 records · Page 61

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NASA NTRS2001

New tracking implementation in the Deep Space Network

As part of the Network Simplification Project, the tracking system of the Deep Space Network is being upgraded. This upgrade replaces the discrete logic sequential ranging system with a system that is based on commercial Digital Signal Processor boards. The new implementation allows both sequential and pseudo-noise types of ranging. The other major change is a modernization of the data formatting. Previously, there were several types of interfaces, delivering both intermediate data and processed data (called 'observables'). All of these interfaces were bit-packed blocks, which do not allow for easy expansion, and many of these interfaces required knowledge of the specific hardware implementations. The new interface supports four classes of data: raw (direct from the measuring equipment), derived (the observable data), interferometric (multiple antenna measurements), and filtered (data whose values depend on multiple measurements). All of the measurements are reported at the sky frequency or phase level, so that no knowledge of the actual hardware is required. The data is formatted into Standard Formatted Data Units, as defined by the Consultative Committee for Space Data Systems, so that expansion and cross-center usage is greatly enhanced.

ranging↗

NASA NTRS2016

Forecasting the Northern African Dust Outbreak Towards Europe in April 2011: A Model Intercomparison

In the framework of the World Meteorological Organisation's Sand and Dust Storm Warning Advisory and Assessment System, we evaluated the predictions of five state-of-the-art dust forecast models during an intense Saharan dust outbreak affecting western and northern Europe in April 2011. We assessed the capacity of the models to predict the evolution of the dust cloud with lead times of up to 72 hours using observations of aerosol optical depth (AOD) from the AErosol RObotic NETwork (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) and dust surface concentrations from a ground-based measurement network. In addition, the predicted vertical dust distribution was evaluated with vertical extinction profiles from the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP). To assess the diversity in forecast capability among the models, the analysis was extended to wind field (both surface and profile), synoptic conditions, emissions and deposition fluxes. Models predict the onset and evolution of the AOD for all analysed lead times. On average, differences among the models are larger than differences among lead times for each individual model. In spite of large differences in emission and deposition, the models present comparable skill for AOD. In general, models are better in predicting AOD than near-surface dust concentration over the Iberian Peninsula. Models tend to underestimate the long-range transport towards northern Europe. Our analysis suggests that this is partly due to difficulties in simulating the vertical distribution dust and horizontal wind. Differences in the size distribution and wet scavenging efficiency may also account for model diversity in long-range transport.

dust storms↗

NASA NTRS2023

Star-Exoplanet Interactions: A Growing Interdisciplinary Field in Heliophysics

Traditionally, heliophysics is characterized as the study of the near-Earth space environment, where plasmas and neutral gases originating from the Earth, the Sun, and other solar system bodies interact in ways that are detectable only through in-situ or close-range (usually within ∼10 AU) remote sensing. As a result, heliophysics has data from the space environment around a handful of solar system objects, in particular the Sun and Earth. Comparatively, astrophysics has data from an extensive array of objects, but is more limited in temporal, spatial, and wavelength information from any individual object. Thus, our understanding of planetary space environments as a complex, multi-dimensional network of specific interacting systems may in the past have seemed to have little to do with the highly diverse space environments detected through astrophysical methods. Recent technological advances have begun to bridge this divide. Exoplanetary studies are opening up avenues to study planetary environments beyond our solar system, with missions like Kepler, TESS, and JWST, along with increasing capabilities of ground-based observations. At the same time, heliophysics studies are pushing beyond the boundaries of our heliosphere with Voyager, IBEX, and the future IMAP mission. The interdisciplinary field of star-exoplanet interactions is a critical, growing area of study that enriches heliophysics. A multidisciplinary approach to heliophysics enables us to better understand universal processes that operate in diverse environments, as well as the evolution of our solar system and extreme space weather. The expertise, data, theory, and modeling tools developed by heliophysicists are crucial in understanding the space environments of exoplanets, their host stars, and their potential habitability. The mutual benefit that heliophysics and exoplanetary studies offer each other depends on strong, continuing solar system-focused and Earth-focused heliophysics studies. The heliophysics discipline requires new targeted funding to support inter-divisional opportunities, including small multi-disciplinary research projects, large collaborative research teams, and observations targeting the heliophysics of planetary and exoplanet systems. Here we discuss areas of heliophysics-relevant exoplanetary research, observational opportunities and challenges, and ways to promote the inclusion of heliophysics within the wider exoplanetary community.

heliophysics↗

NASA NTRSDate not supplied

Improving Modeled Snow Mass in GEOS with Snow Cover Fraction Assimilation

The NASA Goddard Earth Observing System (GEOS) Forward Processing (FP) global weather analysis often considerably underestimates near-surface air temperature during the spring snow melt season, which can be caused by excessive simulated snow cover. To mitigate such errors, we developed an empirical snow cover fraction (SCF) analysis to assimilate SCF observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) into the GEOS Catchment land surface model. Results obtained with a land-only version of the system indicate that the SCF analysis reduces errors in snow mass and snow depth by ~10% when compared to in situ measurements from the Snow Telemetry (SNOTEL), Canadian historical Snow Water Equivalent dataset (CanSWE), and Global Historical Climatology Network - Daily (GHCN-Daily) networks.

Rolf Reichle↗

NASA NTRS2002

Lidar system for investigation of aerosols and cirrus clouds near the equator

In the current environment of concern over global warming, climate change, and ozone depletion/recovery, measurements in the tropics, within 5 degrees of the equator are limited and/or sporadic. Clearly, routine observations in this region would be of great benefit to many programs including those dealing with atmospheric chemistry, dynamics and radiation, and validation of satellite instruments. Such observations represent the primary goals of the Network for the Detection of Stratospheric Change (NDSC).

atmosphere↗

NASA NTRS2003

System Identification of X-33 Neural Network

Modern flight control research has improved spacecraft survivability as its goal. To this end we need to have a failure detection system on board. In case the spacecraft is performing imperfectly, reconfiguration of control is needed. For that purpose we need to have parameter identification of spacecraft dynamics. Parameter identification of a system is called system identification. We treat the system as a black box which receives some inputs that lead to some outputs. The question is: what kind of parameters for a particular black box can correlate the observed inputs and outputs? Can these parameters help us to predict the outputs for a new given set of inputs? This is the basic problem of system identification. The X33 was supposed to have the onboard capability of evaluating the current performance and if needed to take the corrective measures to adapt to desired performance. The X33 is comprised of both rocket and aircraft vehicle design characteristics and requires, in general, analytical methods for evaluating its flight performance. Its flight consists of four phases: ascent, transition, entry and TAEM (Terminal Area Energy Management). It spends about 200 seconds in ascent phase, reaching an altitude of about 180,000 feet and a speed of about 10 to 15 Mach. During the transition phase which lasts only about 30 seconds, its altitude may increase to about 190,000 feet but its speed is reduced to about 9 Mach. At the beginning of this phase, the Main Engine is Cut Off (MECO) and the control is reconfigured with the help of aerosurfaces (four elevons, two flaps and two rudders) and reaction control system (RCS). The entry phase brings down the altitude of X33 to about 90,000 feet and its speed to about Mach 3. It spends about 250 seconds in this phase. Main engine is still cut off and the vehicle is controlled by complex maneuvers of aerosurfaces. The last phase TAEM lasts for about 450 seconds and the altitude and speed, both are reduced to zero. The present attempt, as a start, focuses only on the entry phase. Since the main engine remains cut off in this phase, there is no thrust acting on the system. This considerably simplifies the equations of motion. We introduce another simplification by assuming the system to be linear after some non-linearities are removed analytically from our consideration. Under these assumptions, the problem could be solved by Classical Statistics by employing the least sum of squares approach. Instead we chose to use the Neural Network method. This method has many advantages. It is modern, more efficient, can be adapted to work even when the assumptions are diluted. In fact, Neural Networks try to model the human brain and are capable of pattern recognition.

Aggarwal, Shiv↗

DOE OSTI2023

Unlocking Autonomous Telescopes through Reinforcement Learning: An Offline Framework and Insights from a Case Study

Optimizing observational astronomy campaigns is becoming a complex and expensive task for next-generation telescopes, where manual planning of observations may tend to reach suboptimal results in terms of optimization.Reinforcement Learning (RL) has been well-demonstrated as a valuable approach for training autonomous systems, and it may provide the basis for self-driving telescopes capable of scanning the sky and optimizing the scheduling for astronomy campaigns.We have developed a framework for the optimization of telescope scheduling using RL techniques, based on a dataset containing data on a discrete set of sky locations that the telescope should visit, and a reward metric. We compared several RL algorithms applied to an offline simulation dataset based at the Stone Edge Observatory, considering a discrete set of sky locations to visit and using “t-effective” as a reward metric, a measure of the quality of the data.Deep Q-Networks (DQNs), belonging to the class of value-based methods, have shown remarkable success in the optimization of astronomical observations in our dataset. In the full environment, the average reward value in each state was found to be 92%±5% of the maximum possible reward, while on the test set it resulted in 87%±9% of the maximum possible reward.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Matrix Completion for Improved Observability in Low-Voltage Distribution Grids

This paper considers the problem of recovering missing entries in a partially observed matrix from relatively few measurements (i.e., the so-called matrix completion problem) with the aim of increasing the presently limited observability of low-voltage distribution grids. To this end, the partially observed matrix is formed using scarce voltage magnitude measurements while accounting for their spatial information. Voltage readings are assumed to be collected from distribution utility sensors and/or geographically-distributed cable television network sensors located in immediate proximity to distribution grid nodes. A matrix completion approach built on the parameter-less singular value shrinkage technique is used to estimate voltage magnitudes at otherwise non-observable low-voltage nodes using a small number of single- or multiple-snapshot data. The effectiveness of the proposed approach is demonstrated using a U.S.-style distribution test system from the synthetic SMART- DS data set under very low- to moderate-observability conditions.

low-rank matrix completion↗

DOE OSTI2026

Grid Topology Discovery Algorithm Evaluation of Suitability for Utility Deployment (CRADA 606 Final Report)

This work presents the results of a field-informed demonstration aimed at evaluating the practical suitability of a topology discovery algorithm for utility environments. We demonstrated an algorithm that uses a graph-theory-informed state estimation approach for model selection. In collaboration with Survalent and Peninsula Light Co., the algorithm was applied to real feeder models and field measurements from supervisory control and data acquisition (SCADA) and advanced metering infrastructure (AMI) systems to identify the operational topology of a power distribution system. The demonstration assessed the algorithm’s performance under realistic data conditions, including sparse and noisy measurements, and examined its ability to identify the most likely network configurations. The results confirmed that the approach can effectively narrow down feasible topologies, providing operators with improved situational awareness of network status. Key lessons learned emphasize the need for systematic data validation and strategic sensor placement to enhance observability. These insights inform future deployment strategies and guide refinements for broader adoption in utility operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1995

The Telecommunications and Data Acquisition Report

This quarterly publiction provides archival reports on developments in programs managed by JPL Telecommunications and Mission Operations Directorate (TMOD), which now includes the former communications and Data Acquisition (TDA) Office. In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DSN) in planning, supporting research and technology, implementation, and operations. Also included are standards activity at JPL for space data and information systems and reimbursable DSN work performed for other space agencies through NASA. The Orbital Debris Radar Program, funded by the Office of Space Systems Development, makes use of the planetary radar capability when the antennas are configured at science instruments making direct observations of planets, their satellites, and asteroids of our solar system.

Yuen, Joseph H.↗

NASA NTRS1999

The Evolution of Dust in the Multiphase ISM: Grain Destruction Processes

This proposal covered year one of a long term project in which we acquired the necessary hardware and softwaxe needed to calculate grain destruction processes in the interstellar medium (ISM). The long term goal of this research is to develop a model for the dust evolution in the ISM capable of explaining observations of elemental depletions, the grain size distribution, and the emission characteristics of the ISM from the X-ray through the FIR. We purchased a SUN Ultra 10 workstation and peripheral devices including an Exabyte Tape drive, HP Laser Printer, and Seagate External Hard Disk. The PI installed the hardware and Solaris operating system on the workstation and integrated the hardware into the network. Software was also purchased to enable connections to the workstation from a PC (Hummingbird Exceed). Additional freeware required to carry out the proposed program was installed on the system including compilers (g77, gcc, g++), editors (emacs), a markup language (LaTeX), and display programs (WIP, XV, SAOtng). We have also successfully modified the required plot files to work with our system which display the results of grain processing.

Wolfire, Mark↗

NASA NTRS2007

Ikhana: A NASA Unmanned Aerial System Supporting Long-Duration Earth Science Missions

This viewgraph presentation reviews Ikhana's project goals: (1) Develop an airborne platform to conduct Earth observation and atmospheric sampling science missions both nationally and internationally, (2) develop and demonstrate technologies that improve the capability of UAVs to conduct science collection missions, (3) develop technologies that improve manned and unmanned aircraft systems, and (4) support important national UAV development activities. The criteria that guided the selection of the aircraft are listed. The payload areas on Ikhana are shown and the network that connects the systems are also reviewed. The data recorder is shown. Also the diagram of the Airborne Research Test System (ARTS) is reviewed. The Mobile Ground Control Station and the Mobile Ku SatCom Antenna are also shown and described.

Cobleigh, Brent R.↗

NASA NTRSDate not supplied

The Completion of a Geosynchronous Earth Orbit Survey with the Eugene Stansbery-Meter Class Autonomous Telescope

The Eugene Stansbery-Meter Class Autonomous Telescope (ES-MCAT) is the primary optical sensor used by the NASA Orbital Debris Program Office (ODPO) to statistically characterize the geosynchronous Earth orbit (GEO) debris environment and support future Orbital Debris Engineering Model (ORDEM) releases. The ES-MCAT completed its first optical survey of the GEO region from 2020 to 2022. The primary goal of this survey was to autonomously collect and process GEO data with calculated photometric and astrometric uncertainties. A pointing plan was developed to provide uniform sampling within the region of interest (ROI) while accounting for predicted downtime due to insufficient observing conditions. Detections are autonomously correlated to the Space Surveillance Network (SSN) catalog to determine if objects are correlated targets (CTs) or uncorrelated targets (UCTs), the latter of which are of interest for modeling the GEO orbital debris environment. To assess the size detection sensitivity over time and monitor the general performance of the telescope’s optics and software, the optical throughput of the system and limiting magnitudes are evaluated on a routine basis. While the telescope’s ability to operate autonomously and remotely allowed for the GEO survey to continue throughout the COVID-19 pandemic, travel restrictions hampered routine cleaning of the optics during this time, and the primary mirror degraded enough to require recoating. The mirror was removed in 2022, concluding the first GEO survey. The primary mirror received a new coating designed to be more robust against the harsh environment surrounding the ES-MCAT’s location on Ascension Island, accounting for experience gained during operations over the first GEO survey. In early 2023, the recoated primary mirror was reinstalled, and the second GEO survey was initiated. The primary goal of the second GEO survey is to characterize the evolving GEO debris environment with updated optics, software, and pointing strategies while allowing for the inclusion of non-GEO regimes or those that are outside of the ROI. While the pointing method implemented in the first survey allowed for adequate coverage of the ROI over two years, it has been improved to include pointings that avoid the Moon’s position and the galactic plane to reduce software processing time and maximize the detection capabilities of fainter objects. This method also accounts for the changing weather patterns throughout the year and reduces coverage gaps in the ROI. Provided the success of the first two-year GEO survey using autonomous operations, the ODPO is actively collaborating with the United States Space Force (USSF) to make the ES-MCAT a contributing sensor to the SSN. This paper presents results from the first GEO survey including magnitude distributions and orbital parameters for CTs and UCTs. Details are provided for the automated processing pipeline and the optical system throughput for the previous and current primary mirror coatings. In addition, an updated strategy for the second GEO survey to optimize coverage over the ROI is discussed, as are preliminary results from the ongoing second survey.

Corbin Cruz↗

NASA NTRSDate not supplied

The Completion of a Geosynchronous Earth Orbit Survey with the Eugene Stansbery-Meter Class Autonomous Telescope

Corbin Cruz↗

DOE OSTI2021

Core-flood Effluent and Shale Surface Chemistries in Predicting Interaction between Shale, Brine, and Reactive Fluid

We report field and laboratory observations to date indicate that the efficiency of hydraulic fracturing, as it relates to hydrocarbon recovery, depends significantly on geochemical alterations to rock surfaces that diminish accessibility by partial or total plugging of the pore and fracture networks. This is caused by mineral scale deposition such as coating of fracture surfaces with precipitates, particle migration, and/or crack closure due to dissolution under stress. In reactive flow-through experiments, mineral reactions in response to acidic fluid injection significantly reduced system porosity and core permeability. The present study focuses on changes to fluid chemistry and shale surfaces (inlet and fracture walls) resulting from shale-fluid interactions and integrating these findings for an improved estimate of transport-related consequences. The reacted shale surfaces were examined by spatially-resolved scanning electron microscopy - energy dispersive spectroscopy (SEM-EDS) analysis. Importantly, inductively coupled plasma - mass spectrometry/optical emission spectroscopy (ICP-MS/OES) was utilized to probe the chemical evolution of the core-flood effluents. The three study cores selected from the Marcellus formation represent different mineralogies and structural features. In flow-through experiments, lab-generated brine and HCl-based fracture fluid (pH=2) were injected sequentially under effective stress (up to 500 psi) at reservoir temperature (80°C). SEM-EDS results confirmed by the ICP concentration trends showed significant Fe hydroxide precipitates in clay- and pyrite-rich outcrop samples due to partial oxidation of Fe-bearing phases in the case of intrusion of low salinity water-based fluids. Porosity reduction in the MSEEL (Marcellus Shale Energy and Environmental Laboratory) carbonate-rich sample is related to compaction of cores under stress due to matrix softening with substantial dissolution, and pore-filling by hydroxides, as well as barite and salts. Despite the same fluid compositions and experimental conditions used for both MSEEL samples, barite precipitation was much more intense in the MSEEL clay-rich sample due to its greater sorption capacity and additional sulfate source as well as fissile nature with multiple lengthwise cracks. ICP tests revealed time-resolved concentration trends in produced brine and reactive fluids that in turn complemented the pre-/post-reaction SEM-EDS observations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1987

Photographic fireball networks

Long term radar observations of any meteor shower yield good data for a study of the features of its cross section structure in detail. The hourly rates of meteor echoes represent usually the basic data from which shower characteristics are derived. Unfortunately, the hourly rate does not depend only on the activity of the shower in question but also on the position of the shower radiant, on the mutual radiant antenna position, and on the parameters of the radar system. It is known that the knowledge of the response function of the radar is necessary for good interpretation of the hourly echo counts. A method of long term radar shower data analysis is discussed along with preliminary results.

Ceplecha, Z.↗

DOE OSTI2023

Lower and upper threshold limit for artificial neural network based chilled and condenser water temperatures set-point control in a chilled water system

In this study, an ANN (artificial neural network) based real-time optimized control algorithm for a chilled water cooling system was developed and applied in an actual building to analyze its cooling energy saving effects through in-situ application and actual measurements. For this purpose, the cooling tower’s CndWT (condenser water temperature) and the chiller’s ChWT (chilled water temperature) were set as system control variables. To evaluate algorithm performance, the electric consumption and the COP (coefficient of performance) were compared and analyzed when ChWT and CndWTs were controlled conventionally and controlled based on the ANN. During the analysis, unexpected abnormal data was observed due to insufficient training data and limited consideration of OWBT (outdoor air wet-bulb temperature) when determining the CndWT set-point. Therefore, it is necessary to further build training data from a wider range of conditions and to set the lower limit of CndWT set-point to at least +3.6 °C above OWBT when the OWBT is higher than 23 °C, so that further energy savings can be achieved.

42 ENGINEERING↗

DOE OSTI2023

Many-Body Potential for Simulating Self-Assembly of Polymer-Grafted Nanoparticles in a Polymer Matrix

Many-body interactions between polymer-grafted nanoparticles (NPs) play a key role in promoting their assembly into low-dimensional structures within polymer melts, even when the particles are spherical and isotropically grafted. However, capturing such interactions in simulations of NP assembly is very challenging because explicit modeling of the polymer grafts and melt chains is highly computationally expensive, even using coarse-grained models. Here, we develop a many-body potential for describing the effective interactions between spherical polymer-grafted NPs in a polymer matrix through a machine-learning approach. The approach involves using permutationally invariant polynomials to fit two- and three-body interactions derived from the potential of mean force calculations. The potential developed here reduces the computational cost by several orders of magnitude, thereby, allowing us to explore assembly behavior over large length and time scales. We show that the potential not only reproduces previously known assembled phases such as 1D strings and 2D hexagonal sheets, which generally cannot be achieved using isotropic two-body potentials, but can also help discover interesting phases such as networks, clusters, and gels. We demonstrate how each of these assembly morphologies intrinsically arises from a competition between two- and three-body interactions. Our approach for deriving many-body effective potentials can be readily extended to other colloidal systems, enabling researchers to make accurate predictions of their behavior and dissect the role of individual interaction energy terms of the overall potential in the observed behavior.

36 MATERIALS SCIENCE↗