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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 793 records · Page 44

All sources NASA NTRS DOE OSTI

DOE OSTI2019

DynamiteNSM

DynamiteNSM is a free Network Security Monitor (NSM), built on top of several leading, enterprise-grade technologies. The tool provides network and cybersecurity operators with holistic insights into their networks while giving them the ability to deep-dive into lower-level activities. The solution presents powerful dashboards, giving comprehensive view into performance and threat-based metrics. Dynamite-NSM can be easily deployed in different environments including high-speed data centers, small-to-large enterprises, IoT & industrial networks, and even at home. Dynamite-NSM handles massive volumes of network traffic through scalable ingestion and optimized network sensors. The solution includes two key components: the agent and the monitor. The agent analyzes and forwards network events, while the monitor processes incoming events and displays analytic information. The monitor component builds upon the OpenSearch stack and is coupled with network packet inspection by Zeek and Suricata. Dynamite-NSM is designed to be deployed very quickly with minimal configuration. Unlike many other tools, it can be installed and managed with a standalone command-line utility. The system is inherently passive without disruption to the network. There is no need to install agents on every computer, perform network scans, or directly interact with network assets.

Becker, Jamin↗

DOE OSTI2024

Continuous automatic polarization channel stabilization from heterodyne detection of coexisting dim reference signals

Quantum networking continues to encode information in polarization states due to ease and precision. The variable environmental polarization transformations induced by deployed fiber need correction for deployed quantum networking. Here, we present a method for automatic polarization compensation (APC) and demonstrate its performance on a metropolitan quantum network. Designing an APC involves many design decisions as indicated by the diversity of previous solutions in the literature. Our design leverages heterodyne detection of wavelength-multiplexed dim classical references for continuous high-bandwidth polarization measurements used by newly developed multi-axis (non-)linear control algorithm(s) for complete polarization channel stabilization with no downtime. This enables continuous relatively high-bandwidth correction without significant added noise from classical reference signals. We demonstrate the performance of our APC using a variety of classical and quantum characterizations. Finally, we use C-band and L-band APC versions to demonstrate continuous high-fidelity entanglement distribution on a metropolitan quantum network with an average relative fidelity of 0.94 ± 0.03 for over 30 hrs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS2006

A Space Operations Network Alternative: Using Globally Connected Research and Education Networks for Space-Based Science Operations

Earth based networking in support of various space agency projects has been based on leased service/circuits which has a high associated cost. This cost is almost always taken from the science side resulting in less science. This is a proposal to use Research and Education Networks (RENs) worldwide to support space flight operations in general and space-based science operations in particular. The RENs were developed to support scientific and educational endeavors. They do not provide support for general Internet traffic. The connectivity and performance of the research and education networks is superb. The connectivity at Layer 3 (IP) virtually encompasses the globe. Most third world countries and all developed countries have their own research and education networks, which are connected globally. Performance of the RENs especially in the developed countries is exceptional. Bandwidth capacity currently exists and future expansion promises that this capacity will continue. REN performance statistics has always exceeded minimum requirements for spaceflight support. Research and Education networks are more loosely managed than a corporate network but are highly managed when compared to the commodity Internet. Management of RENs on an international level is accomplished by the International Network Operations Center at Indiana University at Indianapolis. With few exceptions, each regional and national REN has its own network ops center. The acceptable use policies (AUP), although differing by country, allows any scientific program or project the use of their networks. Once in compliance with the first RENs AUP, all others will accept that specific traffic including regional and transoceanic networks. RENs can support spaceflight related scientific programs and projects. Getting the science to the researcher is obviously key to any scientific project. RENs provide a pathway to virtually any college or university in the world, as well as many governmental institutes and science centers. RENs are not to be used for mission critical types of network traffic, even though RENs performance characteristics would support it.

Bradford, Robert N.↗

NASA NTRS1998

Probabilistic resource allocation system with self-adaptive capability

A probabilistic resource allocation system is disclosed containing a low capacity computational module (Short Term Memory or STM) and a self-organizing associative network (Long Term Memory or LTM) where nodes represent elementary resources, terminal end nodes represent goals, and weighted links represent the order of resource association in different allocation episodes. Goals and their priorities are indicated by the user, and allocation decisions are made in the STM, while candidate associations of resources are supplied by the LTM based on the association strength (reliability). Weights are automatically assigned to the network links based on the frequency and relative success of exercising those links in the previous allocation decisions. Accumulation of allocation history in the form of an associative network in the LTM reduces computational demands on subsequent allocations. For this purpose, the network automatically partitions itself into strongly associated high reliability packets, allowing fast approximate computation and display of allocation solutions satisfying the overall reliability and other user-imposed constraints. System performance improves in time due to modification of network parameters and partitioning criteria based on the performance feedback.

Yufik, Yan M.↗

NASA NTRS1996

Probabilistic resource allocation system with self-adaptive capability

A probabilistic resource allocation system is disclosed containing a low capacity computational module (Short Term Memory or STM) and a self-organizing associative network (Long Term Memory or LTM) where nodes represent elementary resources, terminal end nodes represent goals, and directed links represent the order of resource association in different allocation episodes. Goals and their priorities are indicated by the user, and allocation decisions are made in the STM, while candidate associations of resources are supplied by the LTM based on the association strength (reliability). Reliability values are automatically assigned to the network links based on the frequency and relative success of exercising those links in the previous allocation decisions. Accumulation of allocation history in the form of an associative network in the LTM reduces computational demands on subsequent allocations. For this purpose, the network automatically partitions itself into strongly associated high reliability packets, allowing fast approximate computation and display of allocation solutions satisfying the overall reliability and other user-imposed constraints. System performance improves in time due to modification of network parameters and partitioning criteria based on the performance feedback.

Yufik, Yan M.↗

DOE OSTI2024

Carbon Fiber-Based Vitrimer Composites: A Path toward Current Research That Is High-Performing, Useful, and Sustainable

In the polymeric material industry, thermosets and related composites have played a substantial role in the production of rubber and plastics. One important subset of these is thermoset composites with carbon reinforcement. The incorporation of carbon fillers and fibers gives polymeric materials improved electrical and mechanical properties, among other benefits. However, the covalently crosslinked network of thermosets presents significant challenges for recycling and reprocessing because of its intractable nature. The introduction of vitrimer materials opens a new avenue to produce biodegradable and recyclable thermosets. Carbon-reinforced vitrimer composites are pursued for high-performance, long-lasting materials with attractive physical properties, the ability to be recycled and processed, and other features that respond uniquely to stimuli. The development of carbon-reinforced vitrimer composites over the last few years is summarized in this article. First, an overview of vitrimers and the methods used to prepare carbon fiber-reinforced vitrimer composites is provided. Because of the vitrimer nature of such composites, reprocessing, healing, and recycling are viable ways to greatly extend their service life; these approaches are thoroughly explained and summarized. The conclusion is our prediction for developing carbon-based vitrimer composites.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Wastepaper-derived porous carbon supported cobalt nanocomposites for all solid-state flexible supercapacitor

The conversion of wastepaper into high-value carbon materials has gained significant attention as a sustainable strategy for energy storage applications. Owing to its low cost, abundance, and intrinsic fibrous structure, wastepaper serves as an attractive precursor for carbon-based electrode materials. In this work, a novel electrode comprising cobalt nanoparticles and Co 3 O 4 embedded in wastepaper-derived porous carbon has been developed for use in a flexible all-solid-state asymmetric supercapacitor. The integration of cobalt nanoparticles and Co 3 O 4 introduces redox-active centres, which enhance the electrochemical activity of the carbon framework. Mean while, the interconnected porous and conductive carbon network enables rapid ion diffusion and efficient electron transport, synergistically improving the overall electrochemical performance. It delivers a high specific capacitance of 61.5 Fg -1 , a power density of 5143 W kg -1 , and an energy density of 9 Wh kg -1 at a current density of 2.5 A/ g. Furthermore, the device maintains outstanding cycling stability, retaining 82.86 % of its capacitance after 10,000 charge-discharge cycles. Practical applicability is confirmed through its ability to power

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network

Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance. Current image-based denoising methods tend to produce a blur effect on the final reconstructed results especially in high noise levels. In this paper, a deep learning-based approach was proposed to mitigate this problem. An adversarially trained network and a sharpness detection network were trained to guide the training process. Experiments on both simulated and real dataset show that the results of the proposed method have very small resolution loss and achieves better performance relative to state-of-the-art methods both quantitatively and visually.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

NASA NTRS2000

Experimental Verification of Electric Drive Technologies Based on Artificial Intelligence Tools

In this report, a fully integrated prototype of a flight servo control system is successfully developed and implemented using brushless dc motors. The control system is developed by the fuzzy logic theory, and implemented with a multilayer neural network. First, a neural network-based architecture is introduced for fuzzy logic control. The characteristic rules and their membership functions of fuzzy systems are represented as the processing nodes in the neural network structure. The network structure and the parameter learning are performed simultaneously and online in the fuzzy-neural network system. The structure learning is based on the partition of input space. The parameter learning is based on the supervised gradient decent method, using a delta adaptation law. Using experimental setup, the performance of the proposed control system is evaluated under various operating conditions. Test results are presented and discussed in the report. The proposed learning control system has several advantages, namely, simple structure and learning capability, robustness and high tracking performance and few nodes at hidden layers. In comparison with the PI controller, the proposed fuzzy-neural network system can yield a better dynamic performance with shorter settling time, and without overshoot. Experimental results have shown that the proposed control system is adaptive and robust in responding to a wide range of operating conditions. In summary, the goal of this study is to design and implement-advanced servosystems to actuate control surfaces for flight vehicles, namely, aircraft and helicopters, missiles and interceptors, and mini- and micro-air vehicles.

Rubaai, Ahmed↗

NASA NTRS2018

Mars Planetary Network for Human Exploration Era – Potential Challenges and Solutions

During 2016-2017, a study was conducted under the sponsorship of the NASA’s Space Communications and Navigation (SCaN) Program to investigate the deep space communications capacity taking into account the needs of all the present and envisioned future missions toward 2030s. It was soon recognized that planning for human exploration to Mars would impose certain unprecedent challenges, both fiscal and technical, to the current space communications paradigm. Targeting the assumed missions concepts, i.e., Crewed Mission to Phobos (CMTP) and Mars Short Stay Mission (MSSM), a Mars Planetary Network for the human exploration era has been formulated. The activity modeling and network traffic simulation/modeling we performed gave some insight into the technical challenges in space communications for the envisioned human Mars exploration era. Chief among the potential challenges are: (1) the high demand on the deep space network (DSN) assets for achieving the high-rate links, both return and forward, from Mars farthest/farther distance (up to 2.67 AU); (2) the need for resilient, persistent communication coverage for crewed vehicles, on surface and in orbits; (3) the significant period of outage for the Mars-Earth link due to superior solar conjunction; (4) the need for on-demand, simultaneous access to the proximity link by multiple vehicles and astronauts in the exploration zone; (5) the capability of determining precise, real-time, positions of surface vehicles and astronauts by the deep space habitat and/or other tele-operations entities. Solution space to each of the above challenges has been explored and analyzed in the context of the individual problem domain and, more importantly, in conjunction with that for the other challenges. This has led to an end-to-end definition of a Mars Planetary Network that would feature: (1) the fusion of deep space Ka-band and optical communications for achieving Mars-Earth high-rate links taking advantage of the optical/RF hybrid 8m/34m antennas in DSN; (2) the integrated application of the Multiple Spacecraft Per Antenna (MSPA) technique, for return link data acquisition, and the Multiple Uplink Per Antenna (MUPA) technique, for forward link data trasnmission, to reduce the number of 34m beam-wave guide (BWG) antennas needed for the era; (3) the integration of three, arrayed, 34m beam-wave guide (BWG) antennas, to provide a high G/T aperture, with the MSPA/MUPA techniques, and a dual “trunk link”approach to cut down the needed G/T -- hence, reducing the number of 34m antennas, relative to that in the single trunk approach by 50%; (4) the deployment of two areostationary/areosynchronous Mars relay orbiters; one of them could also function as (or be served by) a notional Deep Space Habitat (DSH); (5) the opportunistic deployment of a science orbiter in a Pioneer-6 type orbit, equidistant Mars/Earth, that could also serve as an intermediary relay during the Mars superior solar conjunction perod; (6) the existence of the multi-function Mars proximity link that provides the demand-assigned, multiple access (DAMA) capability; and (7) the provision of tracking observables, by leveraging on the planned Mars orbiting and surface infrastructure, to enable the in-situ navigation for surface and orbiting vehicles. This paper provides the description of the proposed Mars Planetary Network in the human exploration era, the trade-off analysis for the various alternative architectures, and the optimal solutions to the key challenges in defining this end-to-end network.

Cheung, Kar-Ming↗

DOE OSTI2021

Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

Abstract Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li 7 P 3 S 11 , a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.

Chemistry↗

DOE OSTI2025

A Hands-On Curriculum for Training in HPC Cluster Deployment and Management

This paper presents the design, methodology, and outcomes of the High-Performance Computing Technologies (HPCT) course, a hands-on training program focused on the system-side of HPC cluster deployment and administration. Delivered as part of the Master in High Performance Computing (MHPC) program, the course introduces students to key concepts in cluster configuration, including networking, software stack provisioning, job scheduling, and monitoring. Initially taught in person, the course was transitioned to an online format during the COVID-19 pandemic. This shift led to the development of openly available instructional material and a flipped-classroom approach that continues to support both in-person and hybrid delivery. All course materials are publicly available at www.hpc.temple.edu/mhpc/hpc-technology/index.html. By documenting the structure, infrastructure, and evolution of HPCT, this paper offers a model for accessible HPC system training that supports workforce development in computational science.

Posada Correa, Fernando [ORNL] (ORCID:000000022565↗

DOE OSTI2018

High-performance and scalable on-chip digital Fourier transform spectroscopy

Abstract On-chip spectrometers have the potential to offer dramatic size, weight, and power advantages over conventional benchtop instruments for many applications such as spectroscopic sensing, optical network performance monitoring, hyperspectral imaging, and radio-frequency spectrum analysis. Existing on-chip spectrometer designs, however, are limited in spectral channel count and signal-to-noise ratio. Here we demonstrate a transformative on-chip digital Fourier transform spectrometer that acquires high-resolution spectra via time-domain modulation of a reconfigurable Mach-Zehnder interferometer. The device, fabricated and packaged using industry-standard silicon photonics technology, claims the multiplex advantage to dramatically boost the signal-to-noise ratio and unprecedented scalability capable of addressing exponentially increasing numbers of spectral channels. We further explore and implement machine learning regularization techniques to spectrum reconstruction. Using an ‘elastic-D 1 ’ regularized regression method that we develop, we achieved significant noise suppression for both broad (>600 GHz) and narrow (<25 GHz) spectral features, as well as spectral resolution enhancement beyond the classical Rayleigh criterion.

47 OTHER INSTRUMENTATION↗

DOE OSTI2016

shiftNMFk 1.1: Robust Nonnegative matrix factorization with kmeans clustering and signal shift, for allocation of unknown physical sources, toy version for open sourcing with publications

This code is a toy (short) version of CODE-2016-83. From a general perspective, the code represents an unsupervised adaptive machine learning algorithm that allows efficient and high performance de-mixing and feature extraction of a multitude of non-negative signals mixed and recorded by a network of uncorrelated sensor arrays. The code identifies the number of the mixed original signals and their locations. Further, the code also allows deciphering of signals that have been delayed in regards to the mixing process in each sensor. This code is high customizable and it can be efficiently used for a fast macro-analyses of data. The code is applicable to a plethora of distinct problems: chemical decomposition, pressure transient decomposition, unknown sources/signal allocation, EM signal decomposition. An additional procedure for allocation of the unknown sources is incorporated in the code.

Alexandrov, BoianS.↗

DOE OSTI2018

Hyundai FCEV Testing, Data Collection and Validation Fleet

This report summarizes the work conducted under Award Number DE-EE0005970 by Hyundai America Technical Center, Inc. (HATCI) to demonstrate and validate Hyundai Tucson Fuel Cell Electric Vehicles (FCEV) reliability in real world applications. HATCI operated 10 light-duty FCEVs throughout various geographical regions in United States to collect and submit vehicle performance durability data over 5 years. HATCI introduced FCEVs in two phases. First phase deployed 5 vehicles in Southern California to study customer real world usage and prove its reliability under commute and fleet driving pattern from 2013 to 2015. As the hydrogen station infrastructure expanded its network, second phase deployed and operated 5 additional vehicles under various climate zones (hot, cold, high altitude) and validated fuel cell performance from 2016 to 2017. California Air Resources Board (Sacramento, CA), California Fuel Cell Partnership, US Department of Energy (Washington DC), Hyundai Motor America, HATCI (Ann Arbor, Michigan), National Renewable Energy Laboratory (Golden, CO), and South Coast Air Quality Management District were the fleet operators. Four different stack degradation mechanism issues were addressed through validation project: fuel cell stack dry-out, flooding, cathode oxidation, and platinum dissolution. By developing counter-measures to each fuel cell stack degradation mechanism through performance validation fleet, Hyundai was able to successfully develop the next generation fuel cell vehicle NEXO, with longest range, large cost reduction, and best fuel cell powertrain warranty in its class (100,000 miles, 10 years warranty).

08 HYDROGEN↗

DOE OSTI2016

shiftNMFk 1.1: Robust Nonnegative matrix factorization with kmeans clustering and signal shift, for allocation of unknown physical sources, toy version for open sourcing with publications

Alexandrov, Boian S.↗

DOE OSTI2021

Integrated Models for Electrical Distribution Network Planning and District-Scale Building Energy Use

The increase of greenhouse emissions caused by a rise in global energy consumption is pushing the scientific community to develop tools that address key environmental, techno-economic and social issues. One specific area of focus is to create tools that enable the design of high-performance energy districts, including grid-interactive efficient buildings and electrical distribution infrastructure. This paper outlines the approach of integrating a synthetic distribution network tool (RNM-US) with URBANopt™, a modular and customizable software development kit for thermal and electrical modelling of buildings and energy systems at a district scale. First, we describe the algorithms implemented to integrate RNM-US with URBANopt to automatically generate the distribution network for the district considered. Following, we present a case study for the potential uses of the RNM-US integrations with URBANopt, highlighting the capabilities for economic and technical analysis of the distribution network built in relation to the electricity needs of the modelled buildings.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2023

A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel

Abstract U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.

36 MATERIALS SCIENCE↗