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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Integrated Dispatching and Charging Management of an Autonomous Electric Vehicle Ride-Hailing Fleet

Electrification and autonomous driving are two important trends in transportation systems. The convergence of these two technologies will introduce opportunities to improve transportation systems' operation and energy efficiency. One potential application is the commercial ride-hailing fleet with autonomous electric vehicles (AEVs). In order to harvest promising benefits from introducing AEVs into ride-hailing fleets, some unavoidable challenges will need to be resolved to ensure the fleets operates functionally and efficiently. This paper discusses the challenges of dispatching AEVs and their interactions with charging infrastructure. An integrated decision making framework for dispatching and charging has been designed using a system optimization approach to study the AEVs' management within the period when they drop off passengers and pick up the next passengers. Its potential fleet-wide benefits have been illustrated by comparing operations under a heuristic approach. A simulation platform has been designed to test different decision making strategies for the ride-hailing AEV fleets operational performance. Using this platform, detailed case studies have been performed with different fleet sizes, dispatching strategies, and charging infrastructure network settings. Comprehensive analyses from various aspects have been conducted to understand the AEVs' fleet operation performance, (e.g., zero occupancy vehicle miles traveled, successfully served ratio of ride-hailing requests, fleet vehicle charging downtime, and charging infrastructure utilization). Results have provided a deep understandings on operation's dynamics under various fleet system configurations and also have demonstrated advantages of the optimization-based approach for the AEV fleet management. Studies in this paper inform better designs on the future of sophisticated management strategies and charging infrastructure to support ride-hailing AEV fleet operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1996

Automated Operations for Galileo Communications

Following the deployment failure of Galileo's high gain antenna, the downlink had to be redesigned so as to effectively use the low gain antenna. The downlink was redesigned to maximize the data return and increase the reliability which required the reconfiguration of the onboard software and the deep space network. The revised downlink features: data compression; antenna arraying; the recoding and reprocessing of telemetry; suppressed carrier tracking, and error-correction coding. The deep space network Galileo telemetry (DGT) subsystem was developed and deployed at three sites in Australia, Spain and the U.S. The DGT was designed as an automated system that continuously monitors and adjusts its parameters and environment in response to either pre-loaded sequences or changes in the internal status.

Statman, Joseph I.↗
DOE OSTI2023

Reconstruction of fast neutron direction in segmented organic detectors using deep learning

A method for reconstructing the direction of a fast neutron source using a segmented organic scintillator-based detector and deep learning model is proposed and analyzed. Here, the model is based on recurrent neural network, which can be trained by a sequence of data obtained from an event recorded in the detector and suitably pre-processed. The performance of deep learning-based model is compared with the conventional double-scatter detection algorithm in reconstructing the direction of a fast neutron source. With the deep learning model, the uncertainty in source direction of 0.301 rad is achieved with 100 neutron detection events in a segmented cubic organic scintillator detector with a side length of 46 mm. To reconstruct the source direction with the same angular resolution as the double-scatter algorithm, the deep learning method requires 75% fewer events. Application of this method could augment the operation of segmented detectors operated in the neutron scatter camera configuration for applications such as special nuclear material detection.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1966

Venus Station operations

Venus station transmitting and receiving systems during Mariner IV tracking

MARINER IV SPACE PROBE↗
NASA NTRSDate not supplied

Deep Space navigation for the BioSentinel spacecraft science orbit

BioSentinel is an astrobiology small spacecraft mission. The payload consists of two parts, the first has optical and microfluidics sensors, and the second is a Linear Energy Transfer spectrometer that has the objective to measure deep space radiation from events such as coronal mass ejections. The goal of the mission is to observe potential DNA damage due to the radiation in heliocentric space on the living organism Saccharomyces cerevisiae, which is a budding yeast. Two types of this living organism are included in the payload. The first is a natural type that is more radiation tolerant, while the second is a mutant strain that has a deficiency in a gene that allows DNA repair once damage occurs. The impact caused by the radiation on the DNA is compared to an identical sample aboard the International Space Station, as well as another identical sample at a laboratory on the ground. The BioSentinel mission consists of a 6U CubeSat currently ,as of January 2024, active in heliocentric orbit. The spacecraft was launched aboard the first SLS flight as part of the Artemis-I campaign in November 2022. After successful deployment from the launch vehicle, it performed a lunar flyby with an altitude of 406 km. The delta-V imparted by the flyby provided the necessary energy to achieve a heliocentric orbit, in an Earth-trailing pattern. The navigation analysis consisted of a Kalman-filter that utilized data from the Deep Space Network and the ESA Estrack network. All those antennas were needed since the Artemis-1 campaign included the deployment of several other cubesats, therefore the scheduling process required more antenna assets than usual due to simultaneous demands from various missions. The processed tracking data was later also refined with a smoother in order to obtain a more accurate solution. The type of tracking data included TCP, Sequential Range, Doppler and Range formats. The solar radiation pressure coefficient, as well as the delta-V from the deployment and the flyby were modeled to obtain suitable solutions that could decrease the position and velocity uncertainties at several steps along the mission concept of operations. The final product each time resulted in updated ephemeris files that were used by the mission and the antenna networks as the mission progressed. Once in the final science orbit, the utilized antennas are only from the DSN network and the data format is bounded to just TCP. Regular orbit determination is performed, every two weeks. The spacecraft is in a nominal well-known orbit, performing regular operations. This paper includes an analysis of the final science orbit, the techniques and procedures utilized to perform orbit determination and a description of the overall navigation campaign produced during the mission and, more specifically, during the final science operations in Deep Space.

BioSentinel↗
DOE OSTI2025

Applying Deep Learning for Wildfire Identification: Economical and Accessible Solutions Leveraging Small Datasets

Wildfires significantly impact human health, air quality, visibility, weather, and climate change and cause substantial economic losses. While state and county-operated air quality monitors provide critical insights during wildfires, they are not available in all regions. This highlights the need for affordable, accessible tools that allow the general public to assess air quality impacts. In this study, we apply machine learning with deep neural networks to diagnose air quality rapidly from sky images taken at the Pacific Northwest National Laboratory in Richland, WA, USA. Using a convolutional neural network (CNN) framework, we trained a deep learning model to classify air quality indices based on sky images. By leveraging transfer learning, our approach fine-tunes a pre-trained model on a small dataset of sky images, significantly reducing training time while maintaining high accuracy. Our results demonstrate the potential of deep learning to provide rapid air quality diagnostics during wildfire episodes, offering early warnings to the public and enabling timely mitigation strategies, particularly for vulnerable populations. Additionally, we show that lower respiratory infections pose the highest health risk during acute smoke exposures. Reactive oxygen species (ROS) from wildfire particles further exacerbate health risks by triggering inflammation and other adverse effects.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

Orbit determination support for Hiten's aerobraking in the Earth's atmosphere

Two passes of the ISAS (Japan's Institute of Space and Astronautical Science) Hiten spacecraft through the Earth's atmosphere, at perigee altitudes of 125 km and 120 km, during Mar. 1991 marked the first aerobraking technology demonstrations for an object in cis-lunar orbit traveling at near Earth escape velocity. Prediction and control of perigee altitude to better than 1 km was desired to assure spacecraft survival. Covariance analysis provided confidence that prediction accuracy better than 200 m for support of final trim maneuver design was achievable with NASA DSN (Deep Space Network) tracking support. ISAS used orbit determination results in their decisions to cancel final trim maneuvers. Post flight reconstructions, marking the first combined use of DSN and ISAS tracking data, yielded perigee altitude solutions which agree with the near real time mission operations support predictions to better than 50 m.

Efron, L.↗
NASA NTRS1992

Systems analysis for ground-based optical navigation

Deep-space telecommunications systems will eventually operate at visible or near-infrared regions to provide increased information return from interplanetary spacecraft. This would require an onboard laser transponder in place of (or in addition to) the usual microwave transponder, as well as a network of ground-based and/or space-based optical observing stations. This article examines the expected navigation systems to meet these requirements. Special emphasis is given to optical astrometric (angular) measurements of stars, solar system target bodies, and (when available) laser-bearing spacecraft, since these observations can potentially provide the locations of both spacecraft and target bodies. The role of astrometry in the navigation system and the development options for astrometric observing systems are also discussed.

Null, G. W.↗
NASA NTRS1993

Networks Technology Conference

The papers included in these proceedings represent the most interesting and current topics being pursued by personnel at GSFC's Networks Division and supporting contractors involved in Space, Ground, and Deep Space Network (DSN) technical work. Although 29 papers are represented in the proceedings, only 12 were presented at the conference because of space and time limitations. The proceedings are organized according to five principal technical areas of interest to the Networks Division: Project Management; Network Operations; Network Control, Scheduling, and Monitoring; Modeling and Simulation; and Telecommunications Engineering.

Tasaki, Keiji K.↗
DOE OSTI2024

Deep learning based event reconstruction for cyclotron radiation emission spectroscopy

The objective of the cyclotron radiation emission spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time–frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization—may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment—a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium β - -decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

New data-driven approach to bridging power system protection gaps with deep learning

Protection is a critical function in power systems to avoid equipment damage, maintain personnel safety, and support system reliability. However, current protective relay technology cannot adequately protect equipment and personnel from effects of some events; these deficiencies are termed protection gaps. In this paper, a data-driven approach is proposed to complement traditional protection technology and distinguish fault conditions from transients caused by normal operations. A combined convolutional neural network and long short-term memory (CNN-LSTM) network is implemented to achieve data translation invariance and capture the temporal correlation of the time-series input data. As a result, the data-driven method can accurately detect system faults despite variation and noise in the input data. In addition, using the CNN-LSTM--based method avoids the complicated, manual feature extraction procedure required by many traditional data-driven methods. The effectiveness of the proposed approach is tested on two kinds of protection gaps: high-impedance faults and transformer inter-turn faults. Lastly, a transfer learning method is also proposed to address the common issue of data-driven methods for which real-world training data are scarce. Extensive study results demonstrate that the proposed approach can accurately bridge power system protection gaps.

42 ENGINEERING↗
DOE OSTI2024

The optimal use of segmentation for sampling calorimeters

One of the key design choices of any sampling calorimeter is how fine to make the longitudinal and transverse segmentation. Here, to inform this choice, we study the impact of calorimeter segmentation on energy reconstruction. To ensure that the trends are due entirely to hardware and not to a sub-optimal use of segmentation, we deploy deep neural networks to perform the reconstruction. These networks make use of all available information by representing the calorimeter as a point cloud. To demonstrate our approach, we simulate a detector similar to the forward calorimeter system intended for use in the ePIC detector, which will operate at the upcoming Electron Ion Collider. We find that for the energy estimation of isolated charged pion showers, relatively fine longitudinal segmentation is key to achieving an energy resolution that is better than 10% across the full phase space. These results provide a valuable benchmark for ongoing EIC detector optimizations and may also inform future studies involving high-granularity calorimeters in other experiments at various facilities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Simultaneous prediction of structural properties in epitaxially–grown GaN with quantum and conventional multi–output learning algorithms

Hundreds of GaN thin film crystal plasma–assisted molecular beam epitaxy synthesis experiment records spanning two decades were organized into a dataset correlating the growth experiment design parameters with discrete, binary determinations of crystallinity and surface morphology. Conventional data science techniques as well as both quantum and classical multi–output supervised machine learning algorithms were implemented to investigate the relationships between the operating parameter data and the structural figures of merit. Correlation coefficients, decision tree nodes, p–values, and SHAP values all support substrate temperature and gallium effusion cell conditions as being statistically significant for simultaneously influencing GaN crystallinity and surface morphology. Here, a conventional deep neural network learned best from the data, followed by a quantum–classical hybrid gradient boosting algorithm. When combined with calculations of uncertainty intervals based on VennAbers predictors, machine learning predictions of both structural properties show good agreement with results reported in published experimental literature.

36 MATERIALS SCIENCE↗
NASA NTRS1985

Network Information Management Subsystem

The Deep Space Network is implementing a distributed data base management system in which the data are shared among several applications and the host machines are not totally dedicated to a particular application. Since the data and resources are to be shared, the equipment must be operated carefully so that the resources are shared equitably. The current status of the project is discussed and policies, roles, and guidelines are recommended for the organizations involved in the project.

Chatburn, C. C.↗
NASA NTRS2012

Automated Performance Characterization of DSN System Frequency Stability Using Spacecraft Tracking Data

This software provides an automated capability to measure and qualify the frequency stability performance of the Deep Space Network (DSN) ground system, using daily spacecraft tracking data. The results help to verify if the DSN performance is meeting its specification, therefore ensuring commitments to flight missions; in particular, the radio science investigations. The rich set of data also helps the DSN Operations and Maintenance team to identify the trends and patterns, allowing them to identify the antennas of lower performance and implement corrective action in a timely manner. Unlike the traditional approach where the performance can only be obtained from special calibration sessions that are both time-consuming and require manual setup, the new method taps into the daily spacecraft tracking data. This new approach significantly increases the amount of data available for analysis, roughly by two orders of magnitude, making it possible to conduct trend analysis with good confidence. The software is built with automation in mind for end-to-end processing. From the inputs gathering to computation analysis and later data visualization of the results, all steps are done automatically, making the data production at near zero cost. This allows the limited engineering resource to focus on high-level assessment and to follow up with the exceptions/deviations. To make it possible to process the continual stream of daily incoming data without much effort, and to understand the results quickly, the processing needs to be automated and the data summarized at a high level. Special attention needs to be given to data gathering, input validation, handling anomalous conditions, computation, and presenting the results in a visual form that makes it easy to spot items of exception/ deviation so that further analysis can be directed and corrective actions followed.

Pham, Timothy T.↗
NASA NTRS2012

Automated Performance Characterization of DSN System Frequency Stability Using Spacecraft Tracking Data

This software provides an automated capability to measure and qualify the frequency stability performance of the Deep Space Network (DSN) ground system, using daily spacecraft tracking data. The results help to verify if the DSN performance is meeting its specification, therefore ensuring commitments to flight missions; in particular, the radio science investigations. The rich set of data also helps the DSN Operations and Maintenance team to identify the trends and patterns, allowing them to identify the antennas of lower performance and implement corrective action in a timely manner. Unlike the traditional approach where the performance can only be obtained from special calibration sessions that are both time-consuming and require manual setup, the new method taps into the daily spacecraft tracking data. This new approach significantly increases the amount of data available for analysis, roughly by two orders of magnitude, making it possible to conduct trend analysis with good confidence. The software is built with automation in mind for end-to-end processing. From the inputs gathering to computation analysis and later data visualization of the results, all steps are done automatically, making the data production at near zero cost. This allows the limited engineering resource to focus on high-level assessment and to follow up with the exceptions/deviations. To make it possible to process the continual stream of daily incoming data without much effort, and to understand the results quickly, the processing needs to be automated and the data summarized at a high level. Special attention needs to be given to data gathering, input validation, handling anomalous conditions, computation, and presenting the results in a visual form that makes it easy to spot items of exception/deviation so that further analysis can be directed and corrective actions followed.

Pham, Timothy T.↗
NASA NTRS2018

Starling1 - Mission Technologies Overview

The Starling series of demonstration missions will test technologies required to achieve affordable, distributed spacecraft ("swarm") missions that: are scalable to at least 100 spacecraft for applications that include synchronized multipoint measurements; involve closely coordinated ensembles of two or more spacecraft operating as a single unit for interferometric, synthetic aperture, or similar sensor architectures; or use autonomous or semi-autonomous operation of multiple spacecraft functioning as a unit to achieve science or other mission objectives with low-cost small spacecraft.Starling1 will focus on developing technologies that enable scalability and deep space application. The mission goals include the demonstration of a Mobile Ad-hoc NETwork (MANET) through an in-space communication experiment, vision based relative navigation through the Starling Formation-flying Optical eXperiment (StarFOX), and demonstration of autonomous spacecraft reconfiguration using technologies developed by the Distributed System Autonomy (DSA) project.

Cannon, Howard↗
NASA NTRSDate not supplied

Advancing Autonomy in Distributed Space Systems: Insights From on-Orbit Testing with the Starling 1.0 Mission

Autonomous decision-making is crucial for enhancing mission effectiveness in Distributed Space Systems (DSS), particularly in multi-spacecraft operations where communication constraints and mission complexity pose challenges. The Distributed Spacecraft Autonomy (DSA) team at NASA’s Ames Research Center is advancing autonomy in DSS through five key technical areas: distributed resource and task management, reactive operations, system modeling and simulation, human-swarm interaction, and ad hoc network communications. The DSA experiment onboard the Starling 1.0 Mission showcases collaborative resource allocation for multi-point science data collection with four small spacecraft. Autonomy in decision-making is highlighted as a crucial factor for multi-spacecraft missions, enabling spacecraft to operate independently, reducing reliance on ground control. This capability is particularly significant for future deep-space missions, where communication delays and limited data transmission capacity make traditional command and control approaches impractical. This demonstration focuses on a GPS Channel Selection Experiment, leveraging emergent capabilities like "shared sampling" and "simultaneous sampling" to optimize channel selection across the spacecraft swarm. The experiment aims to capture ionospheric phenomena such as the Equatorial Ionization Anomaly and Polar Patches. The DSA system's autonomous reconfiguration ability is showcased, emphasizing its adaptability to natural phenomena without significant integration efforts. The GPS Channel Selection Experiment utilizes a dual-band GPS receiver to estimate plasma density in the ionosphere. Explorative and exploitative channel selections are employed based on the nature of observed phenomena. The performance of DSA algorithms is evaluated in terms of optimal channel allocations and responsiveness to changes in observed features. The DSA Flight Software utilizes the Core Flight System (cFS) framework, ensuring compatibility with the Starling 1.0 flight mission software. DSA showcases results from RTI’s Connext DDS Micro communication middleware, enabling message routing over the Ad-Hoc Network of Starling 1.0. This paper provides a comprehensive overview of the DSA experiment's initial results, emphasizing the advancements in autonomy for Distributed Space Systems and the successful collaboration with the Starling 1.0 mission.

Caleb Ashmore Adams↗