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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Transient Reactor Test Facility (TREAT)

TREAT operated from 1959-1994, later refurbished & resumed operation in 2017 to support fuel safety testing Zircaloy-clad graphite/fuel blocks comprise core Virtually any power history possible within ~2000 MJ core transient energy capacity From milliseconds to minutes: Pulses, Ramps, LOCA Fuel motion monitoring system “hodoscope” observes fast neutrons emitted from specimens to track fuel relocation in real time Reactor also can be a neutron source to adjacent radiography facility Experiment vehicle does everything else Safety containment, specimen environment, and instrumentation

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Measuring the Electrochemical Corrosion of 233U-doped UO2 in a Microfluidic Device

We have developed a microfluidic, ‘particle-attached microfluidic electrochemical’ device, or PAMEC, that uses microgram quantities of radioactive materials, such as uranium dioxide (UO2), rather than bulk specimens to both observe electrochemical changes and measure spectroscopic properties. The PAMEC device reduces the need for running experiments in a shielded facility and allows for in-operando, real-time investigations of electrochemical properties and morphological changes of micrograms of particles. This novel design offers a promising platform to investigate spent nuclear fuel (SNF) and other nuclear materials under various controlled condition, and to provide insights to nuclear waste management, and fuel performance

Electrochemistry↗
DOE OSTI2021

Possibility of an integrated transmission electron microscope: enabling complex in-situ experiments

Abstract Multimodal in-situ experiments are the wave of the future, as this approach will permit multispectral data collection and analysis during real-time nanoscale observation. In contrast, the evolution of technique development in the electron microscopy field has generally trended toward specialization and subsequent bifurcation into more and more niche instruments, creating a challenge for reintegration and backward compatibility for in-situ experiments on state-of-the-art microscopes. We do not believe this to be a requirement in the field; therefore, we propose an adaptive instrument that is designed to allow nearly simultaneous collection of data from aberration-corrected transmission electron microscopy (TEM), probe-corrected scanning transmission electron microscopy, ultrafast TEM, and dynamic TEM with a flexible in-situ testing chamber, where the entire instrument can be modified as future technologies are developed. The value would be to obtain a holistic understanding of the underlying physics and chemistry of the process-structure–property relationships in materials exposed to controlled extreme environments. Such a tool would permit the ability to explore, in-situ, the active reaction mechanisms in a controlled manner emulating those of real-world applications with nanometer and nanosecond resolution. If such a powerful tool is developed, it has the potential to revolutionize our materials understanding of nanoscale mechanisms and transients. Graphical Abstract

36 MATERIALS SCIENCE↗
NASA NTRS1972

The Global Rescue Alarm Net (GRAN) experiment

The OMEGA Position Location Experiment (OPLE) was performed in 1967 by the Goddard Space Flight Center in order to demonstrate a position location and data collection system. OMEGA navigation signals were received at a remote site and retransmitted via a synchronous satellite to a ground processing center where data collecting and position determination were performed. Recent technological advances have made it possible to develop an Advanced OPLE System towards a global search and rescue application. This application generated some new problem areas such as the OMEGA lane ambiguity, random access, location accuracy, real-time processing, and size and weight of the Search and Rescue Communications (SARCOM). This experiment demonstrates the feasibility of instantaneous alarm and position location by using a relatively inexpensive, battery operated, three-pound package.

Morakis, J. C.↗
DOE OSTI2023

TomocuPy – efficient GPU-based tomographic reconstruction with asynchronous data processing

Fast 3D data analysis and steering of a tomographic experiment by changing environmental conditions or acquisition parameters require fast, close to real-time, 3D reconstruction of large data volumes. Here a performance-optimized TomocuPy package is presented as a GPU alternative to the commonly used central processing unit (CPU) based TomoPy package for tomographic reconstruction. TomocuPy utilizes modern hardware capabilities to organize a 3D asynchronous reconstruction involving parallel read/write operations with storage drives, CPU–GPU data transfers, and GPU computations. In the asynchronous reconstruction, all the operations are timely overlapped to almost fully hide all data management time. Since most cameras work with less than 16-bit digital output, the memory usage and processing speed are furthermore optimized by using 16-bit floating-point arithmetic. As a result, 3D reconstruction with TomocuPy became 20–30 times faster than its multi-threaded CPU equivalent. Full reconstruction (including read/write operations and methods initialization) of a 2048 3 tomographic volume takes less than 7 s on a single Nvidia Tesla A100 and PCIe 4.0 NVMe SSD, and scales almost linearly increasing the data size. To simplify operation at synchrotron beamlines, TomocuPy provides an easy-to-use command-line interface. Efficacy of the package was demonstrated during a tomographic experiment on gas-hydrate formation in porous samples, where a steering option was implemented as a lens-changing mechanism for zooming to regions of interest.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1997

STS-83 Mission Highlights Resources Tape

The STS-83 mission flight crew, Cmdr. James D. Halsell Jr., Pilot Susan L. Still, Payload Cmdr. Janice E. Voss, Mission Specialists Michael L. Gernhardt and Donald A. Thomas, and Payload Specialist Gregory T. Linteris and Roger K. Crouch present an overview of their mission. The primary payload is the Microgravity Science Laboratory (MSL), which is a collection of microgravity experiments housed inside a European Spacelab Long Module (LM). MSL features 19 materials science investigations in 4 major facilities. These facilities are the Large Isothermal Furnace, the EXpedite the PRocessing of Experiments to the Space Station (EXPRESS) Rack, the Electromagnetic Containerless Processing Facility (TEMPUS), and the Coarsening in Solid-Liquid Mixtures (CSLM) Facility, the Droplet Combustion Experiment (DCE); and the Combustion Module-1 Facility. Additional technology experiments will be performed in the Middeck Glovebox (MGBX) developed by the Marshall Space Flight Center (MSFC) and the High-Packed Digital Television (HI-PAC DTV) system will be used to provide multi-channel real-time analog science video. Pre-flight, launch, and orbital footage is followed a discussion of the spaceborne experiments aboard the MSL. The end footage shows the shuttle's prelanding checkout, reentry, and landing.

Source record↗
DOE OSTI2025

Real Time implementation of Artificial Intelligence compression algorithm for High-Speed Streaming Readout signals

The new generation of high-energy physics experiments plans to acquire data in streaming mode. With this approach, it is possible to access the information of the whole detector (organized in time slices) for optimal and lossless triggering of data acquisitions. With this approach, data rates, especially in large detectors, are often very high, and the network is likely to be the bottleneck for the entire Streaming Read Out system. The aim of this work is to study the implementation of a lossy compression algorithm based on Artificial Intelligence: an Autoencoder. With Machine Learning it is possible to achieve a high compression ratio and fast inference time with only a small degradation of the signals, almost negligible for the specific application. This work explores different configurations of the Autoencoder and the implementation on different hardware. Different Autoencoder configurations are explored to find the best trade-off between compression ratio and reconstruction loss, both for signals and energy spectrum. Different hardware implementations are also explored to find the best platform to achieve real-time performance for the specific application.

Rossi, Fabio (ORCID:0009000385713885)↗
DOE OSTI2021

Real-Time Dispatch With Secondary Frequency Regulation: A Pathway to Consider Intra-Interval Fluctuations

Real-time dispatch balances the power demand with minimized operating costs. For the current dispatch model, the demand is assumed to be constant within a time interval, while the intra-interval power balance is left to frequency regulation. Based on practical experience and simulations, this behavior may lead to insufficient frequency regulation and uneconomic regulation costs considering the increase in intra-interval fluctuations caused by renewables. Here, a real-time dispatch method with secondary frequency regulation behaviors is proposed. Without changing the interval of the real-time dispatch command, the system regulation mileage and intra-interval generation adjustment are explicitly formulated in a mixed-integer optimization model. To reduce the computational burden, an efficient two-stage calculation method is proposed. With practical utility data, this article finds that the proposed method can effectively improve the system frequency performance with the subminute net load forecasting curve obtained by an off-the-shelf forecasting approach. Time-domain simulations in IEEE and practical utility systems validate the effectiveness of the proposed method in terms of the frequency performance and total operating costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2002

Monitoring the Microgravity Environment Quality On-board the International Space Station Using Soft Computing Techniques: Preliminary System Performance Results - Part 2

This paper presents the preliminary performance results of the artificial intelligence monitoring system in full operational mode using near real time acceleration data downlinked from the International Space Station. Preliminary microgravity environment characterization analysis result for the International Space Station (Increment-2), using the monitoring system is presented. Also, comparison between the system predicted performance based on ground test data for the US laboratory "Destiny" module and actual on-orbit performance, using measured acceleration data from the U.S. laboratory module of the International Space Station is presented. Finally, preliminary on-orbit disturbance magnitude levels are presented for the Experiment of Physics of Colloids in Space, which are compared with on ground test data. The ground test data for the Experiment of Physics of Colloids in Space were acquired from the Microgravity Emission Laboratory, located at the NASA Glenn Research Center, Cleveland, Ohio. The artificial intelligence was developed by the NASA Glenn Principal Investigator Microgravity Services Project to help the principal investigator teams identify the primary vibratory disturbance sources that are active, at any moment of time, on-board the International Space Station, which might impact the microgravity environment their experiments are exposed to. From the Principal Investigator Microgravity Services' web site, the principal investigator teams can monitor via a dynamic graphical display, implemented in Java, in near real time, which event(s) is/are on, such as crew activities, pumps, fans, centrifuges, compressor, crew exercise, structural modes, etc., and decide whether or not to run their experiments, whenever that is an option, based on the acceleration magnitude and frequency sensitivity associated with that experiment. This monitoring system detects primarily the vibratory disturbance sources. The system has built-in capability to detect both known and unknown vibratory disturbance sources. Several soft computing techniques such as Kohonen's Self-Organizing Feature Map, Learning Vector Quantization, Back-Propagation Neural Networks, and Fuzzy Logic were used to design the system.

Jules, Kenol↗
NASA NTRS2004

Development of In-Situ Erosion Measurement Techniques for Application to Real-Time Determination of Plasma Thruster Component Lifetimes

This research has resulted in advancing the laser-based diagnostic capability and the ion optics development in the ion propulsion program at NASA GRC. Laser-based plasma diagnostics have been demonstrated in tabletop experiments and, in the case of LDI, on laboratory hollow cathodes. Assessment by GRC of its resources and priorities two years into the grant lead to a refocusing of the research effort away from the development of a real-time erosion rate measurement technique. The extension of the diagnostic techniques to diagnostic tools has been transferred to graduate students under the technical direction of the PI. These diagnostics may facilitate the development of ion thruster with significantly improved throughput capability for lower-power (10 kW) missions High-Isp, Long-lived ion optics development has proceeded from simple extensions of state-of-the-art geometries to radically different geometries and materials. Full-scale testing of these ion optics has demonstrated a significant advance in the throughput capability of ion thrusters enabling significantly more demanding missions. The capability to predict the throughput was developed and will continue to be upgraded. The performance models have been validated via full-scale testing. Partial validation of the throughput prediction will be completed via an upcoming wear test of the ion optics.

Source record↗
DOE OSTI2025

PyARC Status Report: New Integrations and Upgrades to the Fast Reactor Analysis Workflow Management Tool

PyARC was initially developed as an open source tool to support fast reactor analyses using the Argonne Reactor Computation (ARC) code suite as a part of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench initiative in FY17. The goal of this initiative is to provide a common user interface for model generation, real-time validation, execution, output processing, and visualization for all integrated codes. This is accomplished through the reliance on tools available in the Workbench framework and runtime environment. While initially developed to support the ARC codes, PyARC was extended in FY22 to wrap other NEAMS and non-ARC codes, including Griffin and OpenMC, in the supported other neutronics workflows, and support users in the adoption of NEAMS-supported high fidelity analysis codes. Most recently, NUBOW-3D, a recently adopted ARC code, was integrated to support reactor bowing calculations as well. Integration of these codes into the NEAMS Workbench directly benefits the advanced reactor modeling community by: • Providing a set of controlled, maintained, documented and validated scripts to generate inputs, which promotes best practices, reduces the learning curve, and facilitates project collaboration. • Improving the user experience: the Workbench interface provides assistance for building an input through auto-completion, real-time validation, document navigation, and geometry and results visualization. • Automating complex calculations and workflows for reactor analysis. • Helping users transition to using high-fidelity NEAMS codes along-side the ARC codes. In FY22, a progress report was published that described the state of each of the tools integrated into PyARC. Since then, there have been many enhancements and upgrades to the existing integrations as well as entirely new code integrations as well. This report details all new integrations and major developments in PyARC since the version 2.0.0 release highlighted in the FY22 report.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1996

On-Line Tracking Controller for Brushless DC Motor Drives Using Artificial Neural Networks

A real-time control architecture is developed for time-varying nonlinear brushless dc motors operating in a high performance drives environment. The developed control architecture possesses the capabilities of simultaneous on-line identification and control. The dynamics of the motor are modeled on-line and controlled using an artificial neural network, as the system runs. The control architecture combines the experience and dependability of adaptive tracking systems with potential and promise of the neural computing technology. The sensitivity of real-time controller to parametric changes that occur during training is investigated. Such changes are usually manifested by rapid changes in the load of the brushless motor drives. This sudden change in the external load is simulated for the sigmoidal and sinusoidal reference tracks. The ability of the neuro-controller to maintain reasonable tracking accuracy in the presence of external noise is also verified for a number of desired reference trajectories.

Rubaai, Ahmed↗
DOE OSTI2022

ThunderSecure: deploying real-time intrusion detection for 100G research networks by leveraging stream-based features and one-class classification network

Nowadays, data generated by large-scale scientific experiments are on the scale of petabytes per month. These data are transferred through dedicated high-bandwidth networks (40/100G) across distributed sites for processing, storage, and analysis. Like general purpose networks, research networks experience intrusions. However, monitoring anomalies in such high-speed network traffics is challenging given current cyber-infrastructure. Moreover, traditional network intrusion detection systems (NIDS) are signature based. However, anomaly patterns are difficult to define and that rulesets are often not updated frequently enough to reflect the changes of attack behaviors. We present ThunderSecure, a high-throughput, unsupervised learning-based intrusions detection system for 100G research networks. ThunderSecure implements an efficient packet processing and detection pipeline using multi-cores and GPUs. It extracts statistical and temporal features from real-time network data streams and feeds them to a one-class anomaly detection network. A baseline of normal distribution will be created based on the training observation. Testing traffic deviated from the learned profile will be marked as anomalies. We trained ThunderSecure on hundreds of billions of science data packets mirrored from two 100G network connections at Fermi National Accelerator Laboratory. The detection performance was evaluated on traffic captured from the same research network days and weeks after the training with different types of attack flows injected. Results show that ThunderSecure can recognize science data traffic captured long after the training and made nearly certain detection on the segment of the streams where anomalous flows were injected.

100G research network↗
NASA NTRS2003

Design and Preparation of a Particle Dynamics Space Flight Experiment, SHIVA

ABSTRACT This paper describes the flight experiment, supporting ground science, and the design rationale for project SHIVA (Spaceflight Holography Investigation in a Virtual Apparatus). SHIVA is a fundamental study of particle dynamics in fluids in microgravity. Gravity often dominates the equations of motion of a particle in a fluid, so microgravity provides an ideal environment to study the other forces, such as the pressure and viscous drag and especially the Basset history force. We have developed diagnostic recording methods using holography to save all of the particle field optical characteristics, essentially allowing the experiment to be transferred from space back to earth in what we call the "virtual apparatus" for on-earth microgravity experimentation. We can quantify precisely the three-dimensional motion of sets of particles, allowing us to test and apply new analytical solutions developed by members of the team as reported in the 2001 Conference (Banff, Canada). In addition to employing microgravity to augment the fundamental study of these forces, the resulting data will allow us to quantify and understand the ISS environment with great accuracy. This paper shows how we used both experiment and theory to identify and resolve critical issues and produce an optimal the study. We examined the response of particles of specific gravity from 0.1 to 20, with radii from 0.2 to 2mm. to fluid oscillation at frequencies up to 80 Hz with amplitudes up to 200 microns. To observe some of the interesting effects predicted by the new solutions requires the precise location of the position of a particle in three dimensions. To this end we have developed digital holography algorithms that enable particle position location to a small fraction of a pixel in a CCD array. The spaceflight system will record holograms both on film and electronically. The electronic holograms can be downlinked providing real time data, essentially acting like a remote window into the ISS experimental chamber. Ground experiments have provided input to a flight system design that can meet the requirements for a successful experiment on ISS. Moreover the ground experiments have provided a definitive, quantitative observation of the Basset History force over a wide range of conditions. Results of the ground experiments, the flight experiment design, preliminary flight hardware design, and data analysis procedures are reported.

Trolinger, James↗
NASA NTRS1993

Reactive control and reasoning assistance for scientific laboratory instruments

Scientific laboratory instruments that are involved in chemical or physical sample identification frequently require substantial human preparation, attention, and interactive control during their operation. Successful real-time analysis of incoming data that supports such interactive control requires: (1) a clear recognition of variance of the data from expected results; and (2) rapid diagnosis of possible alternative hypotheses which might explain the variance. Such analysis then aids in decisions about modifying the experiment protocol, as well as being a goal itself. This paper reports on a collaborative project at the NASA Ames Research Center between artificial intelligence researchers and planetary microbial ecologists. Our team is currently engaged in developing software that autonomously controls science laboratory instruments and that provides data analysis of the real-time data in support of dynamic refinement of the experiment control. the first two instruments to which this technology has been applied are a differential thermal analyzer (DTA) and a gas chromatograph (GC). coupled together, they form a new geochemicstry and microbial analysis tool that is capable of rapid identification of the organiz and mineralogical constituents in soils. The thermal decomposition of the minerals and organics, and the attendance release of evolved gases, provides data about the structural and molecular chemistry of the soil samples.

Thompson, David E.↗
DOE OSTI2022

Quantifying Dynamic Signal Spread in Real-Time High-Energy X-ray Diffraction

Abstract Measured intensity in high-energy monochromatic X-ray diffraction (HEXD) experiments provides information regarding the microstructure of the crystalline material under study. The location of intensity on an areal detector is determined by the lattice spacing and orientation of crystals so that changes in the heterogeneity of these quantities are reflected in the spreading of diffraction peaks over time. High temporal resolution of such dynamics can now be experimentally observed using technologies such as the mixed-mode pixel array detector (MM-PAD) which facilitates in situ dynamic HEXD experiments to study plasticity and its underlying mechanisms. In this paper, we define and demonstrate a feature computed directly from such diffraction time series data quantifying signal spread in a manner that is correlated with plastic deformation of the sample. A distinguishing characteristic of the analysis is the capability to describe the evolution from the distinct diffraction peaks of an undeformed alloy sample through to the non-uniform Debye–Scherrer rings developed upon significant plastic deformation. We build on our previous work modeling data using an overcomplete dictionary by treating temporal measurements jointly to improve signal spread recovery. We demonstrate our approach in simulations and on experimental HEXD measurements captured using the MM-PAD. Our method for characterizing the temporal evolution of signal spread is shown to provide an informative means of data analysis that adds to the capabilities of existing methods. Our work draws on ideas from convolutional sparse coding and requires solving a coupled convex optimization problem based on the alternating direction method of multipliers.

Engineering↗
NASA NTRS1976

A real-time adaptive Hadamard transform video compressor

An adaptive video compressor was designed as part of the CTS Digital Video Curriculum Sharing Experiment. The compressor was constructed using field-to-field differencing on fields processed using a Hadamard transform method. The spatial resolution was improved using a fixed-rate, three-mode adaptive system to compress each field.

Jones, H. W., Jr.↗
DOE OSTI2024

Human-in-the-Loop: The Future of Machine Learning in Automated Electron Microscopy

Machine learning (ML) methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition. The introduction of the application programming interfaces (APIs) by major instrument manufacturers now allows the deployment of ML workflows in microscopes, not only for data analytics but also for real-time decision-making and feedback for microscope operation. However, the number of use cases for real-time ML remains remarkably small. Furthermore, we discuss some considerations in designing ML-based active experiments and pose that the likely strategy for the next several years will be human-in-the-loop automated experiments (hAE). In this paradigm, the ML learning agent directly controls beam position and image and spectroscopy acquisition functions, and a human operator monitors experiment progression in real and feature space of the system and tunes the policies of the ML agent to steer the experiment toward specific objectives.

47 OTHER INSTRUMENTATION↗