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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Health Insurance Portability and Accountability Act-Compliant Ocular Telehealth Network for the Remote Diagnosis and Management of Diabetic Retinopathy

In this article, we present the design and implementation of a regional ocular telehealth network for remote assessment and management of diabetic retinopathy (DR), including the design requirements, network topology, protocol design, system work flow, graphics user interfaces, and performance evaluation. The Telemedical Retinal Image Analysis and Diagnosis Network is a computer-aided, image analysis telehealth paradigm for the diagnosis of DR and other retinal diseases using fundus images acquired from primary care end users delivering care to underserved patient populations in the mid-South and southeastern United States.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Physics-informed machine learning

Despite great progress in simulating multiphysics problems using the numerical discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate noisy data into existing algorithms, mesh generation remains complex, and high-dimensional problems governed by parameterized PDEs cannot be tackled. Moreover, solving inverse problems with hidden physics is often prohibitively expensive and requires different formulations and elaborate computer codes. Machine learning has emerged as a promising alternative, but training deep neural networks requires big data, not always available for scientific problems. Instead, such networks can be trained from additional information obtained by enforcing the physical laws (for example, at random points in the continuous space-time domain). Such physics-informed learning integrates (noisy) data and mathematical models, and implements them through neural networks or other kernel-based regression networks. Moreover, it may be possible to design specialized network architectures that automatically satisfy some of the physical invariants for better accuracy, faster training and improved generalization. Furthermore, we review some of the prevailing trends in embedding physics into machine learning, present some of the current capabilities and limitations and discuss diverse applications of physics-informed learning both for forward and inverse problems, including discovering hidden physics and tackling high-dimensional problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Input specific neural networks

Neural networks have emerged as powerful tools for mapping between inputs and outputs. However, their black-box nature limits the ability to encode or impose specific structural relationships between inputs and outputs. Many scientific and engineering problems, such as constitutive modeling in solid mechanics, require networks that can enforce convexity, monotonicity, or other structural constraints to ensure physical consistency. Here, we introduce the Input Specific Neural Network (ISNN), a new architecture that enables multiple, distinct constraints to be imposed on different input subsets for scalar-valued outputs. This framework unifies convex, monotone–convex, monotone, and arbitrary mappings within a single network for the first time. Two ISNN architectures with analytical first- and second-order derivatives are developed. We demonstrate the performance on synthetic toy problems, inverse problems in isotropic hyperelasticity, and finite element simulations. ISNNs achieve improved extrapolation behavior, require fewer invariant inputs than standard input convex networks for polyconvex potentials, and enable significant computational savings via manual differentiation. We also show how ISNNs can be used to learn structural relationships between inputs and outputs via a binary gating mechanism. Particularly, ISNNs are employed to model a homogenized anisotropic free energy potential in a decoupled multiscale setting. The network learns whether or not the potential should be modeled as polyconvex and retains only the relevant layers while using the minimum number of inputs. ISNNs provide a flexible foundation for embedding structural priors into neural networks, enhancing both interpretability and stability. They are broadly applicable across computational mechanics and other scientific domains requiring constrained functional relationships.

Jadoon, Asghar A. [Univ. of Texas, Austin, TX (Uni↗
DOE OSTI2022

Scalable algorithms for physics-informed neural and graph networks

Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space–time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.

42 ENGINEERING↗
NASA NTRS2008

Mars Interoperability 2008-2015: Options for Relay Orbiter Support to Mars Bound Assets

The current relay orbiter infrastructure at Mars, in support of the user assets presently at Mars and those scheduled for the 2008 to 2015 time frame, only have a need to store-and-forward the returned data collected by an asset to Earth as a single non-prioritized data file. In the forward direction, the relay orbiters are only required to relay the forward link data, i.e., command sequences, software and configuration information assembled on the ground, to the Asset. There are currently no requirements for status/control messages or files to be sent in the return direction from an Asset to an application located on the Relay, nor are there requirements in the forward direction to send status/control messages or files originating on a Relay to an Asset. In addition there are currently no networking requirements at Mars where data originally sent by an Asset is to be delivered to another user asset. However, we foresee the day when standard services for 1) on-board file prioritization of user asset data by a Relay, 2) control/status message transfer between assets and a Relay, and 3) networking between assets i.e., Asset to Asset message/file transfer via a Relay will be required. Each of these services will require the Relay to be capable of understanding the data type and routing needs of the user asset data and be capable of processing it for these purposes. This paper will focus on the innovative enhancements required to the existing communications infrastructure at Mars to enable these future services.

File Transfer↗
DOE OSTI2024

GASNet-EX Specification Collection (Rev. 2024.5.0)

GASNet-EX is a portable, open-source, high-performance communication library designed to efficiently support the networking requirements of PGAS runtime systems and other alternative models in emerging exascale systems. It provides network-independent, high-performance communication primitives including Remote Memory Access (RMA) and Active Messages (AM). GASNet-EX is an evolution of the popular GASNet communication system, building upon over 20 years of lessons learned, and the primary goals are high performance, interface portability, and expressiveness. The library has been used to implement parallel programming models and libraries such as UPC, UPC++, Fortran coarrays, Legion, Chapel, and many others. This anthology collects together the four separate volumes that currently comprise the GASNet-EX specification, as of the 2024.5.0 release of GASNet-EX.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Quantifying automated vehicle benefits in reducing driving stress: a simulation experiment approach

Driving is a stressful activity because of the mental workload required to maneuver a vehicle in certain travel contexts, such as congested traffic, multi-modal networks requiring complex interaction with surrounding vehicles, and aggressive driving. Autonomous vehicles (AVs), on the other hand, can reduce the mental workload by performing most of the driving tasks and providing users with a comfortable ride. This study develops a pathway model to relate different health determinants, including travel reliability, safety, driving comfort, and value of time, to Autonomous vehicles driving and studies their impact on the value of driving stress. A case study example of Autonomous vehicles simulation is used to determine the impact of these health determinants. The value of driving stress in Autonomous vehicles is estimated as a function of the value of these individual health determinants. The results show that the perception of safe or unsafe driving in Autonomous vehicles is the most important factor in changing the perception of driving stress in Autonomous vehicles. Similarly, perceptions of comfortable driving in Autonomous vehicles and reduced workload with a higher value of time also reduce driving stress in Autonomous vehicles. These results allow Autonomous vehicles adoption models to explicitly consider driving stress reduction as a benefit and can improve understanding of Autonomous vehicles adoption, which may require quantitative analysis of underlying motivating benefits, including driving stress reduction.

Khattak, Zulqarnain H.↗
NASA NTRSDate not supplied

Developing and Testing a Physics Guided Machine Learning NeuralNetwork to Predict Tonal Noise Emitted by a Propeller

Artificial neural networks offer a highly nonlinear and adaptive model for predicting complex interactions between input-output parameters. However, these networks require large datasets which often exceed practical considerations in modeling experimental results. To alleviate the dataset size requirement, a method known as physics guided machine learning has been applied to construct several neural networks for predicting propeller tonal noise in the time domain over a broad range of flight conditions. Three space-filling designs, namely, Latin-Hypercube, Sphere-Packing, and Grid-Space, were used to distribute points throughout the input parameter space encompassing nondimensional flight conditions and observer geometry. Each neural network’s performance was validated by conditions outside of the training set and compared to the Propeller Analysis System tool from the NASA Aircraft Noise Prediction Program. Compared to the Grid-Space input design, the Latin-Hypercube and the Sphere-Packing designs provided a better representation of the domain for training. Regarding the network archetype, a fully connected perceptron was found to outperform the partially connected perceptron in their ability to predict tonal noise for small datasets. The black-box nature of these neural networks was also explored to understand how the networks constructed the waveform and understand why some network designs produce better models.

Propeller noise↗
DOE OSTI2013

Management of Large-Scale Wireless Sensor Networks Utilizing Multi-Parent Recursive Area Hierarchies

Autonomously configuring and self-healing a largescale wireless sensor network requires a light-weight maintenance protocol that is scalable. Further, in a battery powered wireless sensor network duty-cycling a node’s radio can reduce the power consumption of a device and extend the lifetime of a network. With duty-cycled nodes the power consumption of a node’s radio depends on the amount of communication is must perform and by reducing the communication the power consumption can also be reduced. Multi-parent hierarchies can be used to reduce the communication cost when constructing a recursive area clustering hierarchy when compared to singleparent solutions that utilize inefficient communication methods such as flooding and information propagation via single-hop broadcasts. The multi-parent hierarchies remain scalable and provides a level of redundancy for the hierarchy.

Environmental Molecular Sciences Laboratory↗
NASA NTRS1991

High-speed, cross-continental networks - A necessity

The networking requirements that the Earth Observing System (EOS) program has levied on NASA and the national and international operational networking communities is addressed. The objectives of the EOS program are listed and include the development and operation of an observing system to acquire essential, global earth science data on a long-term, sustained basis and in a manner which maximizes the scientific utility of the data and simplifies data analysis. Network design issues are also discussed, including protocol selection, the Transport Control Protocol/Internet Protocol suite, the Open Systems Interconnect network suite, and the internet evolution. Open and closed network issues are also discussed. A number of networks now in place are assessed including NASNet, and ESNet. These offer possible options and/or solutions to be considered by the NASA Headquarters EOS Data and Information Systems Program.

Wong, C. M.↗
NASA NTRS1992

Technology requirements for mesh VSAT applications

This paper first examines the trends and the roles of VSAT services in the year 2010 time frame based on an overall network and service model for that period. An estimate of the VSAT traffic is then made and the service and general network requirements are identified. In order to accommodate these traffic needs, four satellite VSAT architectures based on the use of fixed or scanning multibeam antennas in conjunction with IF switching or onboard regeneration and baseband processing are suggested. The performance of each of these architectures is assessed and the key enabling technologies are identified.

Kwan, R. K.↗
DOE OSTI2022

Medial packing and elastic asymmetry stabilize the double-gyroid in block copolymers

Triply-periodic networks are among the most complex and functionally valuable self-assembled morphologies, yet they form in nearly every class of biological and synthetic soft matter building blocks. In contrast to simpler assembly motifs – spheres, cylinders, layers – networks require molecules to occupy variable local environments, confounding attempts to understand their formation. Here, we examine the double-gyroid network phase by using a geometric formulation of the strong stretching theory of block copolymer melts, a prototypical soft self-assembly system. The theory establishes the direct link between molecular packing, assembly thermodynamics and the medial map, a generic measure of the geometric center of complex shapes. We show that “medial packing” is essential for stability of double-gyroid in strongly-segregated melts, reconciling a long-standing contradiction between infinite- and finite-segregation theories. Additionally, we find a previously unrecognized non-monotonic dependence of network stability on the relative entropic elastic stiffness of matrix-forming to tubular-network forming blocks. The composition window of stable double-gyroid widens for both large and small elastic asymmetry, contradicting intuitive notions that packing frustration is localized to the tubular domains. This study demonstrates the utility of optimized medial tessellations for understanding soft-molecular assembly and packing frustration via an approach that is readily generalizable far beyond gyroids in neat block copolymers.

36 MATERIALS SCIENCE↗
DOE OSTI2014

TH-A-12A-01: Medical Physicist's Role in Digital Information Security: Threats, Vulnerabilities and Best Practices

I. Information Security Background (Speaker = Kevin McDonald) Evolution of Medical Devices Living and Working in a Hostile Environment Attack Motivations Attack Vectors Simple Safety Strategies Medical Device Security in the News Medical Devices and Vendors Summary II. Keeping Radiation Oncology IT Systems Secure (Speaker = Bruce Curran) Hardware Security Double-lock Requirements “Foreign” computer systems Portable Device Encryption Patient Data Storage System Requirements Network Configuration Isolating Critical Devices Isolating Clinical Networks Remote Access Considerations Software Applications / Configuration Passwords / Screen Savers Restricted Services / access Software Configuration Restriction Use of DNS to restrict accesse. Patches / Upgrades Awareness Intrusion Prevention Intrusion Detection Threat Risk Analysis Conclusion Learning Objectives: Understanding how Hospital IT Requirements affect Radiation Oncology IT Systems. Illustrating sample practices for hardware, network, and software security. Discussing implementation of good IT security practices in radiation oncology. Understand overall risk and threats scenario in a networked environment.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
NASA NTRS2008

Technology Infusion of CodeSonar into the Space Network Ground Segment (RII07): Software Assurance Symposium Technical Summary

Presents a source code analysis tool (CodeSonar) for use in the Space Network Ground Segment. The Space Network requires 99.9% proficiency and 97.0% availability of systems. Software has historically accounted for an annual average of 28% of the Space Network loss of availability and proficiency. CSCI A and CSCI B account for 42% of the previous eight months of software data loss. The technology infusion of CodeSonar into the Space Network Ground segment is meant to aid in determining the impact of the technology on the project both in the expenditure of effort and the technical results of the technology. Running a CodeSonar analysis and performing a preliminary review of the results averaged 3.5 minutes per finding (approximately 20 hours total). An additional 40 hours is estimated to analyze the 37 findings deemed too complex for the initial review. Using CodeSonar's tools to suppress known non-problems, delta tool runs will not repeat findings that have been marked as non-problems, further reducing the time needed for review. The 'non-interesting' finding rate of 70% is a large number, but filtering, search, and detailed contextual features of CodeSonar reduce the time per finding. Integration of the tool into the build process may also provide further savings by preventing developers from having to configure and operate the tool separately. These preliminary results show the tool to be easy to use and incorporate into the engineering process. These findings also provide significant potential improvements in proficiency and availability on the part of the software. As time-to-fix data become available a better cost trade can be made on person hours saved versus tool cost. Selective factors may be necessary to determine where best to apply CodeSonar to balance cost and benefits.

Benson, Markland J.↗
NASA NTRS2009

Software Configurable Multichannel Transceiver

Emerging test instrumentation and test scenarios increasingly require network communication to manage complexity. Adapting wireless communication infrastructure to accommodate challenging testing needs can benefit from reconfigurable radio technology. A fundamental requirement for a software-definable radio system is independence from carrier frequencies, one of the radio components that to date has seen only limited progress toward programmability. This paper overviews an ongoing project to validate the viability of a promising chipset that performs conversion of radio frequency (RF) signals directly into digital data for the wireless receiver and, for the transmitter, converts digital data into RF signals. The Software Configurable Multichannel Transceiver (SCMT) enables four transmitters and four receivers in a single unit the size of a commodity disk drive, programmable for any frequency band between 1 MHz and 6 GHz.

Freudinger, Lawrence C.↗
DOE OSTI2019

Evaluating Quality of Service Traffic Classes on the Megafly Network

An emerging trend in High Performance Computing (HPC) systems that use hierarchical topologies (such as dragonfly) is that the applications are increasingly exhibiting high run-to-run performance variability. This poses a significant challenge for application developers, job schedulers, and system maintainers. One approach to address the performance variability is to use newly proposed network topologies such as megafly (or dragonfly+) that offer increased path diversity compared to a traditional fully connected dragonfly. Yet another approach is to use quality of service (QoS) traffic classes that ensure bandwidth guarantees. In this work, we select HPC application workloads that have exhibited performance variability on current 2-D dragonfly systems. We evaluate the baseline performance expectations of these workloads on megafly and 1-D dragonfly network models with comparably similar network configurations. Our results show that the megafly network, despite using fewer virtual channels (VCs) for deadlock avoidance than a dragonfly, performs as well as a fully connected 1-D dragonfly network. We then exploit the fact that megafly networks require fewer VCs to incorporate QoS traffic classes. We use bandwidth capping and traffic differentiation techniques to introduce multiple traffic classes in megafly networks. In some cases, our results show that QoS can completely mitigate application performance variability while causing minimal slowdown to the background network traffic.

Mubarak, Misbah↗
DOE OSTI2023

Nuclear explosion monitoring network design considerations

Design of an efficient monitoring network requires information on the type and size of releases to be detected, the accuracy and reliability of the measuring equipment, and the desired network performance. This work provides a scientific basis for optimizing or minimizing networks of 133 Xe samplers to achieve a desired performance level for different levels of release. The approach of this work varies the density of sampling locations to find optimal location subsets, and to explore the properties of variations of those subsets – how crucial is a specific subset; are substitutions problematic? The choice of possible station locations is arbitrary but constrained to some extent by the location of islands, land masses, difficult topography (mountains, etc.) and the places where infrastructure exists to run and support a sampler. Performance is evaluated using hypothetical releases and atmospheric transport models that cover an entire year.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Resilient Entanglement Distribution in a Multihop Quantum Network

The evolution of quantum networking requires architectures capable of dynamically reconfigurable entanglement distribution to meet diverse user needs and ensure tolerance against transmission disruptions. We introduce multihop quantum networks to improve network reach and resilience by enabling quantum communications across intermediate nodes, thus broadening network connectivity and increasing scalability. We present multihop two-qubit polarization-entanglement distribution within a quantum network at the Oak Ridge National Laboratory campus. Our system uses wavelength-selective switches for adaptive bandwidth management on a software-defined quantum network that integrates a quantum data plane with classical data and control planes, creating a flexible, reconfigurable mesh. Our network distributes entanglement across six nodes within three subnetworks, each located in a separate building, optimizing quantum state fidelity and transmission rate through adaptive resource management. Additionally, we demonstrate the network's resilience by implementing a link recovery approach that monitors and reroutes quantum resources to maintain service continuity despite link failures—paving the way for scalable and reliable quantum networking infrastructures.

Alshowkan, Muneer [Oak Ridge National Laboratory (↗