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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 OSTI2018

Decision support system for outage management and automated crew dispatch

A decision support system is provided for utility operations to assist with crew dispatch and restoration activities following the occurrence of a disturbance in a multiphase power distribution network, by providing a real-time visualization of possible location(s). The system covers faults that occur on fuse-protected laterals. The system uses real-time data from intelligent electronics devices coupled with other data sources such as static feeder maps to provide a complete picture of the disturbance event, guiding the utility crew to the most probable location(s). This information is provided in real-time, reducing restoration time and avoiding more costly and laborious fault location finding practices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Capacitance-Based Dimensional Change Sensors for In-Pile Materials Measurements

Advanced fuel compositions, such as accident tolerate fuels (ATF), are an active area of developing in the nuclear power industry. The long-term performance of these newly developed fuels is estimated through physics-based simulation models of irradiation-, temperature-, pressure-, etc.-induced material degradation. As these fuels are deployed in test reactors, measurement and characterization of the fuel pin evolution is used to validate prediction models. In-pile material evolution parameters, such as fuel rod pressurization, fuel stack and cladding elongation, and cladding diameter, are commonly measured using a linear voltage differential transformer (LVDT). However, LVDTs are bulky and limited to lower (350-500C) temperature operation. The high power density and small size of most experimental positions in high performance research reactors used for accelerated materials irradiation studies generally precludes the use of LVDTs in these reactors. There is a critical need for sensors that provide real-time data regarding material evolution under highly accelerated irradiation. These sensors would ideally have a small profile and the ability to withstand irradiation at extremely high dose rates and temperatures for extended periods of time. A capacitance-based sensor is currently under development at the University of Tennessee to provide a direct measurement of in-pile dimensional change during irradiation. Sensor response was simulated using AutoCAD Electromagnetic field simulator (EMS) for a variety of sensor materials and configurations and fuel pin swelling conditions. Initial results of these simulations are summarized and areas of ongoing research and development are discussed.

Naughton, Tyler↗
DOE OSTI2022

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

Abstract In droplet-on-demand liquid metal jetting (DoD-LMJ) additive manufacturing, complex physical interactions govern the droplet characteristics, such as size, velocity, and shape. These droplet characteristics, in turn, determine the functional quality of the printed parts. Hence, to ensure repeatable and reliable part quality it is necessary to monitor and control the droplet characteristics. Existing approaches for in-situ monitoring of droplet behavior in DoD-LMJ rely on high-speed imaging sensors. The resulting high volume of droplet images acquired is computationally demanding to analyze and hinders real-time control of the process. To overcome this challenge, the objective of this work is to use time series data acquired from an in-process millimeter-wave sensor for predicting the size, velocity, and shape characteristics of droplets in DoD-LMJ process. As opposed to high-speed imaging, this sensor produces data-efficient time series signatures that allows rapid, real-time process monitoring. We devise machine learning models that use the millimeter-wave sensor data to predict the droplet characteristics. Specifically, we developed multilayer perceptron-based non-linear autoregressive models to predict the size and velocity of droplets. Likewise, a supervised machine learning model was trained to classify the droplet shape using the frequency spectrum information contained in the millimeter-wave sensor signatures. High-speed imaging data served as ground truth for model training and validation. These models captured the droplet characteristics with a statistical fidelity exceeding 90%, and vastly outperformed conventional statistical modeling approaches. Thus, this work achieves a practically viable sensing approach for real-time quality monitoring of the DoD-LMJ process, in lieu of the existing data-intensive image-based techniques.

Gaikwad, Aniruddha (ORCID:0000000285642621)↗
DOE OSTI2025

Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors

This R&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1993

The SSABLE system - Automated archive, catalog, browse and distribution of satellite data in near-real time

Historically, locating and browsing satellite data has been a cumbersome and expensive process. This has impeded the efficient and effective use of satellite data in the geosciences. SSABLE is a new interactive tool for the archive, browse, order, and distribution of satellite date based upon X Window, high bandwidth networks, and digital image rendering techniques. SSABLE provides for automatically constructing relational database queries to archived image datasets based on time, data, geographical location, and other selection criteria. SSABLE also provides a visual representation of the selected archived data for viewing on the user's X terminal. SSABLE is a near real-time system; for example, data are added to SSABLE's database within 10 min after capture. SSABLE is network and machine independent; it will run identically on any machine which satisfies the following three requirements: 1) has a bitmapped display (monochrome or greater); 2) is running the X Window system; and 3) is on a network directly reachable by the SSABLE system. SSABLE has been evaluated at over 100 international sites. Network response time in the United States and Canada varies between 4 and 7 s for browse image updates; reported transmission times to Europe and Australia typically are 20-25 s.

Simpson, James J.↗
DOE OSTI2019

A multiwavelength analysis of a collection of short-duration GRBs observed between 2012 and 2015

We explore the prompt emission and the afterglow properties of short-duration gamma-ray burst (sGRB) 130603B and another eight sGRB events during 2012-2015, observed by several multiwavelength facilities including the Gran Canarias Telescope 10.4 m telescope. Prompt emission high energy data of the events were obtained by INTEGRAL-SPI-ACS, Swift-BAT, and Fermi-GBM satellites. The prompt emission data by INTEGRAL in the energy range of 0.1-10 MeV for sGRB 130603B, sGRB 140606A, sGRB 140930B, sGRB 141212A, and sGRB 151228A do not show any signature of the extended emission or precursor activity and their spectral and temporal properties are similar to those seen in case of other short bursts. For sGRB 130603B, our new afterglow photometric data constrain the pre-jet-break temporal decay due to denser temporal coverage. For sGRB 130603B, the afterglow light curve, containing both our new and previously published photometric data is broadly consistent with the ISM afterglow model. Modeling of the host galaxies of sGRB 130603B and sGRB 141212A using the LePHARE software confirms a scenario in which the environment of the burst is undergoing moderate star formation activity. From the inclusion of our late-time data for eight other sGRBs we are able to: place tight constraints on the non-detection of the afterglow, host galaxy, or any underlying 'kilonova' emission. Our late-time afterglow observations of the sGRB 170817A/GW170817 are also reported on and compared with the sub-set of sGRBs.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2019

A Multiwave Length Analysis of a Collection of Short-duration GRBs Observed between 2012 and 2015

We investigate the prompt emission and the afterglow properties of short-duration gamma-ray burst (sGRB) 130603B and another eight sGRB events during 2012–2015, observed by several multiwavelength facilities including the Gran Canarias Telescope 10.4 m telescope. Prompt emission high energy data of the events were obtained by INTEGRAL-SPI-ACS, Swift-BAT, and Fermi-GBM satellites. The prompt emission data by INTEGRAL in the energy range of 0.1–10 MeV for sGRB 130603B, sGRB 140606A, sGRB 140930B, sGRB 141212A, and sGRB 151228A do not show any signature of the extended emission or precursor activity and their spectral and temporal properties are similar to those seen in case of other short bursts. For sGRB 130603B, our new afterglow photometric data constrain the pre-jet-break temporal decay due to denser temporal coverage. For sGRB 130603B, the afterglow light curve, containing both our new and previously published photometric data is broadly consistent with the ISM afterglow model. Modeling of the host galaxies of sGRB 130603B and sGRB 141212A using the LePHARE software supports a scenario in which the environment of the burst is undergoing moderate star formation activity. From the inclusion of our late-time data for eight other sGRBs we are able to: place tight constraints on the non-detection of the afterglow, host galaxy, or any underlying ‘kilonova’ emission. Our late-time afterglow observations of the sGRB 170817A/GW170817 are also discussed and compared with the sub-set of sGRBs.

S B Pandey↗
NASA NTRS1993

The Generic Spacecraft Analyst Assistant (gensaa): a Tool for Developing Graphical Expert Systems

During numerous contacts with a satellite each day, spacecraft analysts must closely monitor real-time data. The analysts must watch for combinations of telemetry parameter values, trends, and other indications that may signify a problem or failure. As the satellites become more complex and the number of data items increases, this task is becoming increasingly difficult for humans to perform at acceptable performance levels. At NASA GSFC, fault-isolation expert systems are in operation supporting this data monitoring task. Based on the lessons learned during these initial efforts in expert system automation, a new domain-specific expert system development tool named the Generic Spacecraft Analyst Assistant (GenSAA) is being developed to facilitate the rapid development and reuse of real-time expert systems to serve as fault-isolation assistants for spacecraft analysts. Although initially domain-specific in nature, this powerful tool will readily support the development of highly graphical expert systems for data monitoring purposes throughout the space and commercial industry.

Hughes, Peter M.↗
DOE OSTI2024

Accelerating data acquisition with FPGA-based edge machine learning: a case study with LCLS-II

New scientific experiments and instruments generate vast amounts of data that need to be transferred for storage or further processing, often overwhelming traditional systems. Edge machine learning (EdgeML) addresses this challenge by integrating machine learning (ML) algorithms with edge computing, enabling real-time data processing directly at the point of data generation. EdgeML is particularly beneficial for environments where immediate decisions are required, or where bandwidth and storage are limited. In this paper, we demonstrate a high-speed configurable ML model in a fully customizable EdgeML system using a field programmable gate array (FPGA). Our demonstration focuses on an angular array of electron spectrometers, referred to as the ‘CookieBox,’ developed for the Linac Coherent Light Source II project. The EdgeML system captures 51.2 Gbps from a 6.4 GS s −1 analog to digital converter and is designed to integrate data pre-processing and ML inside an FPGA. Our implementation achieves an inference latency of 0.2 µs for the ML model, and a total latency of 0.4 µs for the complete EdgeML system, which includes pre-processing, data transmission, digitization, and ML inference. The modular design of the system allows it to be adapted for other instrumentation applications requiring low-latency data processing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

How Cooperative Engagement Programs Strengthen Sequencing Capabilities for Biosurveillance and Outbreak Response

The threat of emerging and re-emerging infectious diseases continues to be a challenge to public and global health security. Cooperative biological engagement programs act to build partnerships and collaborations between scientists and health professionals to strengthen capabilities in biosurveillance. Biosurveillance is the systematic process of detecting, reporting, and responding to especially dangerous pathogens and pathogens of pandemic potential before they become outbreaks, epidemics, and pandemics. One important tool in biosurveillance is next generation sequencing. Expensive sequencing machines, reagents, and supplies make it difficult for countries to adopt this technology. Cooperative engagement programs help by providing funding for technical assistance to strengthen sequencing capabilities. Through workshops and training, countries are able to learn sequencing and bioinformatics, and implement these tools in their biosurveillance programs. Cooperative programs have an important role in building and sustaining collaborations among institutions and countries. One of the most important pieces in fostering these collaborations is trust. Trust provides the confidence that a successful collaboration will benefit all parties involved. With sequencing, this enables the sharing of pathogen samples and sequences. Obtaining global sequencing data helps to identify unknown etiological agents, track pathogen evolution and infer transmission networks throughout the duration of a pandemic. Having sequencing technology in place for biosurveillance generates the capacity to provide real-time data to understand and respond to pandemics. We highlight the need for these programs to continue to strengthen sequencing in biosurveillance. By working together to strengthen sequencing capabilities, trust can be formed, benefitting global health in the face of biological threats.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1978

Sensor data autonomy

'Smart' sensors onboard NASA space missions will require variable data output bandwidth as they respond to phenomena of interest. An Instrument Telemetry Packet (ITP) approach has been developed which encodes experimental instrument data into an autonomous data package, along with pertinent engineering parameters and ancillary data (time, position, attitude, etc.). New requirements for onboard concentration and buffering, as well as for end-to-end error control, arise from this approach. Emphasis is placed on packet protocols compatible with the data link standard ADCCP, to enable one set of ground support equipment to readily support instrument development, launch site checkout and mission operations phases.

Greene, E. P.↗
NASA NTRS2015

Enabling Earth Science Measurements with NASA UAS Capabilites

NASA's Airborne Science Program (ASP) maintains a fleet of manned and unmanned aircraft for Earth Science measurements and observations. The unmanned aircraft systems (UAS) range in size from very large (Global Hawks) to medium (SIERRA, Viking) and relatively small (DragonEye). UAS fly from very low (boundary layer) to very high altitude (stratosphere). NASA also supports science and applied science projects using UAS operated by outside companies or agencies. The aircraft and accompanying data and support systems have been used in numerous investigations. For example, Global Hawks have been used to study both hurricanes and atmospheric composition. SIERRA has been used to study ice, earthquake faults, and coral reefs. DragonEye is being used to measure volcanic emissions. As a foundation for NASA's UAS work, Altair and Ikkana not only flew wildfires in the Western US, but also provided major programs for the development of real-time data download and processing capabilities. In early 2014, an advanced L-band Synthetic Aperture Radar (SAR) also flew for the first time on Global Hawk, proving the utility of UAVSAR, which has been flying successfully on a manned aircraft. In this paper, we focus on two topics: 1) the results of a NASA program called UAS-Enabled Earth Science, in which three different science teams flew (at least) two different UAS to demonstrate platform performance, airspace integration, sensor performance, and applied science results from the data collected; 2) recent accomplishments with the high altitude, long-duration Global Hawks, especially measurements from several payload suites consisting of multiple instruments. The latest upgrades to data processing, communications, tracking and flight planning systems will also be described.

Earth Science↗
DOE OSTI2024

Structured Neural Network Modeling for Developing Digital Twins Models of Hydropower Generation Units

Dynamic modeling is a key part in the development of digital twin (DT) for dynamic systems. This is true for hydropower systems, where whole system modeling including penstock, turbine and generators, etc is important in realizing actuate modeling for the real systems. On the other hand, in response to the large variations of the power demand due to increased penetration of renewables such as wind and solar, hydropower systems are now required to operate in a large power generation range. This situation triggers the nonlinear characteristics of the generation unit with respect to its models. As such, it is imperative to use data driven modeling such as neural networks to learn the nonlinear dynamics of the hydropower generation unit. To achieve this objective, this study constructs a modeling and learning algorithm integrated with multiple structured neural network models for the modeling of turbine shaft speed, penstock pressure, and generator power output based on the generator power control setpoint, field current, and field voltage. In addition, the study uses the hydropower data from Tacoma Public Utilities to train and validate the proposed neural network algorithm. The results have shown that this structured neural network modeling approach can learn the system dynamics effectively by using the real-time data collected from the hydropower system with the desired modeling results.

Wang, Hong↗
DOE OSTI2026

Lightfall v0.0.1

Lightfall is a desktop application for synchrotron beamline instrument control, data acquisition, and live analysis at the Advanced Light Source (ALS). Built on Python and Qt, it provides a native graphical interface for operating beamline hardware, configuring and executing experimental scans, and visualizing results in real time. Key features include direct integration with EPICS control systems, a built-in electronic logbook, remote beamline access over secure tunnels, and an interprocess communication (IPC) architecture that coordinates with external analysis applications via ZMQ and EPICS process variables. This IPC approach allows Lightfall to orchestrate specialized analysis tools—including GPU-accelerated streaming correlators—without embedding them, avoiding the dependency conflicts common in monolithic scientific software platforms. Compared to prior approaches such as Xi-CAM's plugin-based architecture, Lightfall's design cleanly separates instrument control from domain-specific analysis, enabling feedback-driven acquisition where live analysis results can adjust scan parameters during an experiment. Its native Qt interface provides responsive performance for real-time data visualization that web-based alternatives struggle to match. Lightfall is designed for use by beamline scientists and staff operating synchrotron instruments at national user facilities.

Pandolfi, Ronald [Lawrence Berkeley National Labor↗
NASA NTRS2001

Predicting Fatigue for Isolated Joints While Wearing an Extra-Vehicular Mobility Unit (EMU)

To work outside a space craft, humans must wear a protective suit. The required suit pressurization creates additional resistance for the wearer while performing work. How much does the suit effect work and fatigue? To answer these questions, dynamic torque was collected for the shoulder, elbow and wrist for six subjects in an Extra-vehicular Mobility Unit (EMU). In order to quantify fatigue, the subjects were to exert maximum voluntary torque for five minutes or until their maximum fell below 50% of their initial maximum for three consecutive repetitions. Using the collected torque and time data, logarithmic based functions were derived to estimate torque decay to within an absolute error of 20%. These results will be used in the development of a generalized tool for prediction of maximum available torque over time for humans using the current EMU.

Maida, James C.↗
NASA NTRS2009

Design and Performance of a Spectrometer for Deployment on MISSE 7

A spectrometer for reflectance and transmission measurements of samples exposed to the space environment has been developed for deployment on the Materials on the International Space Station Experiment (MISSE) 7. The instrument incorporates a miniature commercial fiber optic coupled spectrometer with a computer control system for detector operation, sample motion and illumination. A set of three spectrometers were recently integrated on the MISSE7 platform with launch and deployment on the International Space Station scheduled for summer of this year. The instrument is one of many active experiments on the platform. The performance of the instrument prior to launch will be discussed. Data from samples measured in the laboratory will be compared to those from the instrument prior to launch. These comparisons will illustrate the capabilities of the current design. The space environment challenges many materials. When in operation on the MISSE 7 platform, the new spectrometer will provide real time data on the how the space environment affects the optical properties of thermal control paints and optical coatings. Data obtained from comparison of pre and post flight measurements on hundreds of samples exposed on previous MISSE platforms have been reported at these meetings. With the new spectrometer and the ability to correlate measured changes with time on orbit and the occurrence of both natural events and human activities, a better understanding of the processes responsible for degradation of materials in space will be possible.

Pippin, Gary↗
DOE OSTI2023

TDD LoRa and Delta Encoding in Low-Power Networks of Environmental Sensor Arrays for Temperature and Deformation Monitoring

Abstract Densely distributed sensor networks can revolutionize environmental observations by providing real-time data with an unprecedented spatiotemporal resolution. However, field deployments often pose unique challenges in terms of power provisions and wireless connectivity. We present a framework for wirelessly connected distributed sensor arrays for near-surface temperature and/or deformation monitoring. Our research focuses on a novel time division duplex implementation of the LoRa protocol, enabling battery powered base stations and avoiding collisions within the network. In order to minimize transmissions and improve battery life throughout the network, we propose a dedicated delta encoding algorithm that utilizes the spatial and temporal similarity in the acquired data sets. We implemented the developed technologies in a AA battery powered hardware platform that can be used as a wireless data logger or base station, and we conducted an assessment of the power consumption. Without data compression, the projected battery life for a data logger is 4.74 years, and a wireless base stations can last several weeks or months depending on the amount of network traffic. The delta encoding algorithm can further improve this battery life with a factor of up to 3.50. Our results demonstrate the viability of the proposed methods for low-power environmental wireless sensor networks.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

Acquisition and description of Mariner 10 television science data at Mercury

The Mariner 10 television science subsystem was an improved version of the Mariner 9 system, using 1500-mm-focal-length optics. An elaborate picture-taking sequence resulted in transmission of over 4000 frames back to earth during two flyby encounters with Mercury. These sequences utilized a real-time data rate of 117.6 kbit/s, resulting in coverage of about 75% of the lighted portion of Mercury's surface at a resolution of better than 2 km. The complete set of useful images, which amounted to about 3000 frames, was processed with three different types of digital image-processing enhancements.

Danielson, G. E., Jr.↗