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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Low Power, Radiation Resilient Synchronous Edge Processing for Remote Monitoring

Next-generation space remote sensing systems may be equipped with imaging arrays that sense data at a rate that outstrips the processing capability of any computing hardware that can operate within a satellite’s power budget. This project developed novel convolutional and recurrent neural networks to detect and estimate point-like events amid clutter, and investigated their efficient and accurate implementation on analog in-memory computing systems that are 10-1000× more energy-efficient than digital processors. This project leveraged two memory devices at different levels of technological maturity: a large-scale analog computing prototype using commercial SONOS charge-trap memory, and electrochemical memory (ECRAM) with intrinsic radiation hardness. We experimentally demonstrated end-to-end analog processing of our neural networks on SONOS and characterized the radiation response of both SONOS and ECRAM. We advanced the state-of-the-art in ECRAM precision and reliability, and developed co-design methods to enable accurate long-term operation of SONOS analog accelerators in space radiation environments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2002

3-D Flow Field Diagnostics and Validation Studies using Stereoscopic Tracking Velocimetry

The measurement of 3-D three-component velocity fields is of great importance in both ground and space experiments for understanding materials processing and fluid physics. Here, we present the investigation results of stereoscopic tracking velocimetry (STV) for measuring 3-D velocity fields. The effort includes diagnostic technology development, experimental velocity measurement, and comparison with analytical and numerical computation. The advantages of STV stems from the system simplicity for building compact hardware and in software efficiency for continual near-real-time process monitoring. It also has illumination flexibility for observing volumetric flow fields from arbitrary directions. STV is based on stereoscopic CCD observations of particles seeded in a flow. Neural networks are used for data analysis. The developed diagnostic tool is tested with a simple directional solidification apparatus using Succinonitrile. The 3-D velocity field in the liquid phase is measured and compared with results from detailed numerical computations. Our theoretical, numerical, and experimental effort has shown STV to be a viable candidate for reliably quantifying the 3-D flow field in materials processing and fluids experiments.

Cha, Soyoung Stephen↗
NASA NTRS1998

Ensembles of radial basis function networks for spectroscopic detection of cervical precancer

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.

Cervix Neoplasms/pathology↗
NASA NTRS1989

Implementation of GAMMON - An efficient load balancing strategy for a local computer system

GAMMON (Global Allocation from Maximum to Minimum in cONstant time), an efficient load-balancing algorithm, is described. GAMMON uses the available broadcast capability of multiaccess networks to implement an efficient search technique for finding hosts with maximal and minimal loads. The search technique has an average overhead which is independent of the number of participating stations. The transition from the theoretical concept to a practical, reliable, and efficient implementation is described.

Baumgartner, Katherine M.↗
NASA NTRS1991

NSI customer service representatives and user support office: NASA Science Internet

The NASA Science Internet, (NSI) was established in 1987 to provide NASA's Offices of Space Science and Applications (OSSA) missions with transparent wide-area data connectivity to NASA's researchers, computational resources, and databases. The NSI Office at NASA/Ames Research Center has the lead responsibility for implementing a total, open networking program to serve the OSSA community. NSI is a full-service communications provider whose services include science network planning, network engineering, applications development, network operations, and network information center/user support services. NSI's mission is to provide reliable high-speed communications to the NASA science community. To this end, the NSI Office manages and operates the NASA Science Internet, a multiprotocol network currently supporting both DECnet and TCP/IP protocols. NSI utilizes state-of-the-art network technology to meet its customers' requirements. THe NASA Science Internet interconnects with other national networks including the National Science Foundation's NSFNET, the Department of Energy's ESnet, and the Department of Defense's MILNET. NSI also has international connections to Japan, Australia, New Zealand, Chile, and several European countries. NSI cooperates with other government agencies as well as academic and commercial organizations to implement networking technologies which foster interoperability, improve reliability and performance, increase security and control, and expedite migration to the OSI protocols.

Source record↗
DOE OSTI2026

Machine learning surrogates for ion energy–angle distributions in thermal and RF plasma sheaths

Ion energy–angle distributions (IEADs) at material surfaces are a critical input for plasma–material interaction (PMI) studies in fusion devices, yet they are computationally expensive to obtain using particle-in-cell (PIC) simulations. In this work, we develop a machine learning surrogate based on a deep deconvolutional neural network (DDeCNN) trained on large databases generated with the hPIC2 code. The surrogate is capable of reconstructing IEADs from sheath parameters for both thermal and radio-frequency (RF) plasmas, including cases with multiple ion species. Across thousands of test cases, the model achieves high accuracy, with over 97 % of predictions classified as good or average based on standard error metrics (MAE, MSE, L2). Even in the more challenging RF and multi-species regimes, the surrogate reliably captures the multi-peak structure of PIC results. Once trained, the surrogate produces IEADs in milliseconds on a common workstation, yielding speedups of six to seven orders of magnitude compared with running a full PIC simulation. This computational gain enables dense parameter scans and direct coupling of IEAD predictions with PMI and erosion models on whole-device scales in fusion-relevant conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Non-destructive structural characterization of graphite components using mechanical resonance and deep learning

As compared to conventional nuclear reactors, microreactors have the potential to significantly reduce construction timelines and capital costs, decreasing the barriers for advanced nuclear reactor technologies. However, the lower power output of these microreactors (typically < 20 MWe) creates challenging economics if operation and maintenance costs cannot be sufficiently reduced. The compact size of these designs presents an opportunity for comprehensive in-situ structural health monitoring to provide real-time feedback in order to reduce operational costs associated with maintenance and downtime. Many microreactor concepts use graphite for both in-core neutron moderation and as a structural material, which has typically required some form of periodic and laborious inspection. This report provides a description and assessment of recent work with graphite to couple acoustic-based experimental measurements and characterization with machine learning models to mature structural health monitoring capabilities and generate benefits for the nuclear microreactor industry. With resilient embedded sensors in development in other programs funded by the US Department of Energy’s Office of Nuclear Energy and elsewhere, the work described herein builds upon previously funded efforts to mature non-destructive testing technology that relates measured vibrational signatures to structural changes, using a combination of new experimental measurements and machine learning processing. Building on past successful demonstrations of predictive workflows to identify structural changes in a hexagonal stainless steel test article with excellent acoustic propagation, we first performed baseline characterization on graphite samples with canonical geometries to ensure compatibility and confidence in the applied techniques for a material with distinctly different mechanical properties. In contrast to efforts in previous years, we worked exclusively with unidirectional vibration data that is more comparable to those expected from the existing embedded sensor technologies which are suitable for deployment in a reactor setting. Established acoustic and modern machine-learning-based characterization approaches were applied to the resulting datasets from these simple geometries. Both approaches were found to be highly capable of detecting even small geometric irregularities amongst nominally identical samples. As such, we then moved to testing these approaches for detection of artificial local stress perturbations introduced into a more complex geometry: a hexagonal block with drilled holes. A main outcome of this work is that a generalizable ML workflow can be used to detect and predict the characteristics of small artificial anomalies in a graphite component with a relevant geometry. While this work was performed using surficial vibration data, we expect the approach to be flexible and viable for other monitoring scenarios, such as those with different arrangements or types of sensor arrays. As compared to previously funded efforts, an existing ML workflow based on neural networks was enhanced through the addition of recently developed Fourier neural operators. As applied to previously collected and new vibration datasets, prediction accuracies of anomaly characterizations were greatly improved with minimal added computational cost. As trained on small durations of vibration data (tens of seconds) collected over a realistic number of locations, the model was able to reliably determine the presence of a subtle stress anomaly and begin to provide location estimates. Such an approach is likely to be viable for more relevant reactor damage scenarios for graphite components, such as progressive crack growth or creep.

36 MATERIALS SCIENCE↗
NASA NTRS1978

Payload operations control center network (POCCNET) systems definition phase study report

The results of the studies performed during the systems definition phase of POCCNET are presented. The concept of POCCNET as a system of standard POCCs is described and an analysis of system requirements is also included. Alternative systems concepts were evaluated as well as various methods for development of reliable reusable software. A number of POCC application areas, such as command management, on board computer support, and simulation were also studied. Other areas of investigation included the operation of POCCNET systems, the facility requirements and usage.

Desjardins, R.↗
NASA NTRS2015

TTEthernet for Integrated Spacecraft Networks

Aerospace projects have traditionally employed federated avionics architectures, in which each computer system is designed to perform one specific function (e.g. navigation). There are obvious downsides to this approach, including excessive weight (from so much computing hardware), and inefficient processor utilization (since modern processors are capable of performing multiple tasks). There has therefore been a push for integrated modular avionics (IMA), in which common computing platforms can be leveraged for different purposes. This consolidation of multiple vehicle functions to shared computing platforms can significantly reduce spacecraft cost, weight, and design complexity. However, the application of IMA principles introduces significant challenges, as the data network must accommodate traffic of mixed criticality and performance levels - potentially all related to the same shared computer hardware. Because individual network technologies are rarely so competent, the development of truly integrated network architectures often proves unreasonable. Several different types of networks are utilized - each suited to support a specific vehicle function. Critical functions are typically driven by precise timing loops, requiring networks with strict guarantees regarding message latency (i.e. determinism) and fault-tolerance. Alternatively, non-critical systems generally employ data networks prioritizing flexibility and high performance over reliable operation. Switched Ethernet has seen widespread success filling this role in terrestrial applications. Its high speed, flexibility, and the availability of inexpensive commercial off-the-shelf (COTS) components make it desirable for inclusion in spacecraft platforms. Basic Ethernet configurations have been incorporated into several preexisting aerospace projects, including both the Space Shuttle and International Space Station (ISS). However, classical switched Ethernet cannot provide the high level of network determinism required by real-time spacecraft applications. Even with modern advancements, the uncoordinated (i.e. event-driven) nature of Ethernet communication unavoidably leads to message contention within network switches. The arbitration process used to resolve such conflicts introduces variation in the time it takes for messages to be forwarded. TTEthernet1 introduces decentralized clock synchronization to switched Ethernet, enabling message transmission according to a time-triggered (TT) paradigm. A network planning tool is used to allocate each device a finite amount of time in which it may transmit a frame. Each time slot is repeated sequentially to form a periodic communication schedule that is then loaded onto each TTEthernet device (e.g. switches and end systems). Each network participant references the synchronized time in order to dispatch messages at predetermined instances. This schedule guarantees that no contention exists between time-triggered Ethernet frames in the network switches, therefore eliminating the need for arbitration (and the timing variation it causes). Besides time-triggered messaging, TTEthernet networks may provide two additional traffic classes to support communication of different criticality levels. In the rate-constrained (RC) traffic class, the frame payload size and rate of transmission along each communication channel are limited to predetermined maximums. The network switches can therefore be configured to accommodate the known worst-case traffic pattern, and buffer overflows can be eliminated. The best-effort (BE) traffic class behaves akin to classical Ethernet. No guarantees are provided regarding transmission latency or successful message delivery. TTEthernet coordinates transmission of all three traffic classes over the same physical connections, therefore accommodating the full spectrum of traffic criticality levels required in IMA architectures. Common computing platforms (e.g. LRUs) can share networking resources in such a way that failures in non-critical systems (using BE or RC communication modes) cannot impact flight-critical functions (using TT communication). Furthermore, TTEthernet hardware (e.g. switches, cabling) can be shared by both TTEthernet and classical Ethernet traffic.

Loveless, Andrew↗
DOE OSTI2026

Multi-Agent Control Planes for Quantum Networks: A Scalable Architecture for Autonomous Quantum Internet Management

Quantum networks are expected to enable distributed quantum computing, secure communication, and global entanglement distribution. However, operating such networks presents significant challenges, including stochastic quantum processes, fragile entanglement resources, dynamic topology, and cross-layer control requirements. Current quantum network control architectures largely rely on centralized or hierarchical controllers inspired by classical software-defined networking (SDN). While effective for small testbeds, these approaches face scalability, latency, and reliability limitations as quantum networks grow. This paper proposes a multi-agent control plane architecture for quantum networks. In this design, intelligent software agents operate at quantum nodes, repeaters, and orchestration layers, collectively managing entanglement generation, routing, purification, and scheduling. The distributed intelligence of the agent system allows the network to adapt dynamically to quantum hardware variability and environmental noise. We argue that multi-agent systems provide significant advantages over centralized control approaches, including scalability, resilience, local autonomy, and real-time adaptation. The paper discusses architectural design principles, agent coordination mechanisms, and research challenges in deploying multi-agent control planes for the emerging quantum Internet.

Alnajjar, Anees [ORNL] (ORCID:0000000237101601)↗
NASA NTRS2006

Time Triggered Protocol (TTP) for Integrated Modular Avionics

Traditional avionics computing systems are federated, with each system provided on a number of dedicated hardware units. Federated applications are physically separated from one another and analysis of the systems is undertaken individually. Integrated Modular Avionics (IMA) takes these federated functions and integrates them on a common computing platform in a tightly deterministic distributed real-time network of computing modules in which the different applications can run. IMA supports different levels of criticality in the same computing resource and provides a platform for implementation of fault tolerance through hardware and application redundancy. Modular implementation has distinct benefits in design, testing and system maintainability. This paper covers the requirements for fault tolerant bus systems used to provide reliable communication between IMA computing modules. An overview of the Time Triggered Protocol (TTP) specification and implementation as a reliable solution for IMA systems is presented. Application examples in aircraft avionics and a development system for future space application are covered. The commercially available TTP controller can be also be implemented in an FPGA and the results from implementation studies are covered. Finally future direction for the application of TTP and related development activities are presented.

Motzet, Guenter↗
NASA NTRSDate not supplied

NASA's Unsteady Pressure-Sensitive Paint Research and Operational Capability Developments

In the last three years, several advancements have been made to produce a new state-of-the-art capability in the field of Aerosciences. NASA’s Aerosciences Evaluations and Test Capabilities (AETC) Portfolio Office has funded a multi-year project to produce the unsteady Pressure-Sensitive Paint (uPSP) technology as an operational capability in key ground test facilities at NASA. The research and development has primarily been conducted at NASA Ames Research Center’s (ARC) Unitary Plan Wind Tunnel (UPWT) 11-by 11-ft Transonic Wind Tunnel (TWT). The NASA ARC UPWT is one of the ground test facilities under NASA AETC’s Portfolio Office. AETC’s goals are to provide the tools to deliver the technology innovations and breakthroughs necessary to address increasingly complex research and development challenges. AETC’s integrated approach will consider the complimentary high-end compute capabilities necessary to advance analysis in conjunction with ground experimental capabilities. The uPSP Capability Challenge Project is a demonstration of several different technologies: 1) the unsteady Pressure-Sensitive Paint (uPSP) technology, and 2) Project: Red Rover, establishing a secure, reliable, fast connection between experimental and computation facilities, leveraging NASA’s computational resources within the High-End Compute Capability (HECC) Project for processing, storing, and sharing data efficiently. This project demonstrates the technical diversity and technical inclusion need to advance the field of Aerosciences. The approach to combine subject matter experts in experimental methods, optical methods, production wind tunnel testing, network engineering, high-end computing, signal processing, grid generation, and visualization while establishing the required infrastructure for subject matter experts to have access to the data while the wind tunnel test is being conducted. The most recent advancements for the uPSP technology have focused on three key areas: development of data products, robust processing pipeline, operational efficiencies and uncertainty quantification.

buffet↗
NASA NTRSDate not supplied

Towards a Cloud-Based Flight Management System

This paper summarizes the results of an initial concept exploration into the Cloud Flight Management System, or Cloud FMS, idea. Cloud FMS is a concept in which part of the aircraft’s FMS is on the aircraft itself, while another part resides in a ground-based cloud environment. This paper is limited to understanding whether this idea can be practically implemented within the current constraints of air-ground network throughput, availability, reliability, and latency. Simulation and live flight tests were conducted to study this concept, and specific findings that further fine-tune the Cloud FMS concept are provided.

Flight Management System, Cloud Computing, Urban A↗
NASA NTRSDate not supplied

Development of Physics-Based Transition Models for Unstructured-Mesh CFD Codes Using Deep Learning Models

Predicting transition locations over a vehicle surface is of fundamental importance for many engineering applications. With the transition information, the Reynolds-averaged Navier-Stokes (RANS) computations can turn on the turbulence model at the right locations so that drag, lift and other aerodynamic quantities can be accurately predicted. In contrast to the popularity of RANS-based transition modeling in which transition onset is governed by the turbulence equations, physics-based transition models that account for instability waves within the boundary layer, thus more compliant to flow physics, only gained more attention in recent years. This paper describes the development of a new physics-based transition model based on either the linear stability theory (LST) or parabolized stability equations (PSE). The model is designed to communicate with a structured or unstructured-mesh RANS solver back and forth in order to more accurately compute transition fronts over a three-dimensional body. In the developed model, the Python suite of interface codes in conjunction with the LASTRAC software can be executed autonomously to produce transition onset locations for a given laminar or RANS-computed transitional state. In addition, as a proof of concept, the tool set consists of a deep learning neural network model that has been designed and trained to predict instability wave evolutions inside the boundary layer for various instability wave mechanisms across a selected speed range. A machine-learned intelligent profile interpolation model has also been devised to enable reliable instability-wave spectra predictions with just a few points in the mean flow profiles.

Transition Modeling↗
DOE OSTI2025

Motion Dynamics of Motile Microbes in Pore-Networks and its Implications for Reactive Transport Processes

This report outlines new methods to improve simulations of microbial transport and microbially mediated reactions in porous media. A range of experimental, modeling, and machine learning tools are introduced to make these simulations faster, more reliable, and useful for real-world applications. At the microscopic level, the study investigates how different types of bacteria move through confined spaces. A new artificial intelligence tool called DeepTrackStat, is introduced to track motions dynamics as observed in videos of particles migrating through pore networks. This tool is especially helpful for studying fast-moving microbes and requires less computing power than traditional tracking methods. At larger scales, the research looks at how microbes and chemicals interact in zones where surface water and groundwater meet. To connect the small- and large-scale findings, the study presents a neural network model called STAMNet. This tool helps scale up detailed small-scale microbial motion behaviors to predict large-scale environmental changes more efficiently. By combining lab experiments, computer models, and artificial intelligence, the research presented supports smarter environmental decision-making, especially in bioremediation of contaminated groundwater and protection of water quality.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1977

Instrumentation and data acquisition systems

A comprehensive and integrated measurement system was identified and a design and development effort initiated to meet the criteria imposed by the National Transonic Facility operating environment. Specific measurement areas receiving concentrated attention include: data acquisition, force measurement, pressure instrumentation, flow visualization techniques, model attitude and model deformation measurement, and temperature measurement. The NTF instrument complex will be centered around four 32-bit, 1-microsecond-cycle-time central processing units connected in a multipoint-distributed network configuration. The principal activities to be supported by these computers are: (1) data base management and processing; (2) research measurement data acquisition and display; (3) tunnel and model control; and (4) process monitoring and communication control. The distributed network approach was chosen to modularize the functional software into definable and implementable parts by the various groups involved in the design and to permit use of similar hardware configurations to improve reliability and maintainability.

Guarino, J. F.↗
NASA NTRS1989

DNET: A communications facility for distributed heterogeneous computing

This document describes DNET, a heterogeneous data communications networking facility. DNET allows programs operating on hosts on dissimilar networks to communicate with one another without concern for computer hardware, network protocol, or operating system differences. The overall DNET network is defined as the collection of host machines/networks on which the DNET software is operating. Each underlying network is considered a DNET 'domain'. Data communications service is provided between any two processes on any two hosts on any of the networks (domains) that may be reached via DNET. DNET provides protocol transparent, reliable, streaming data transmission between hosts (restricted, initially to DECnet and TCP/IP networks). DNET also provides variable length datagram service with optional return receipts.

Tole, John↗
NASA NTRS2011

Bayesian Software Health Management for Aircraft Guidance, Navigation, and Control

Modern aircraft, both piloted fly-by-wire commercial aircraft as well as UAVs, more and more depend on highly complex safety critical software systems with many sensors and computer-controlled actuators. Despite careful design and V&V of the software, severe incidents have happened due to malfunctioning software. In this paper, we discuss the use of Bayesian networks (BNs) to monitor the health of the on-board software and sensor system, and to perform advanced on-board diagnostic reasoning. We will focus on the approach to develop reliable and robust health models for the combined software and sensor systems.

Schumann, Johann↗