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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Impact of Network Activity Levels on the Performance of Passive Network Service Dependency Discovery

Network services often do not operate alone, but instead, depend on other services distributed throughout a network to correctly function. If a service fails, is disrupted, or degraded, it is likely to impair other services. The web of dependencies can be surprisingly complex---especially within a large enterprise network---and evolve with time. Acquiring, maintaining, and understanding dependency knowledge is critical for many network management and cyber defense activities. While automation can improve situation awareness for network operators and cyber practitioners, poor detection accuracy reduces their confidence and can complicate their roles. In this paper we rigorously study the effects of network activity levels on the detection accuracy of passive network-based service dependency discovery methods. The accuracy of all except for one method was inversely proportional to network activity levels. Our proposed cross correlation method was particularly robust to the influence of network activity. The proposed experimental treatment will further advance a more scientific evaluation of methods and provide the ability to determine their operational boundaries.

correlation↗
DOE OSTI2018

Differentiation in stem/progenitor cells along fetal or adult hepatic stages requires transcriptional regulators independently of oscillations in microRNA expression

Highlights: • The role of microRNAs in differentiation of stem cells is not well understood. • This study found microRNA levels oscillated during differentiation of hepatocytes. • Many functions but not differentiation were regulated by microRNAs in hepatocytes. • Regulators of gene networks independently maintained hepatic differentiation. • These insights will advance how stem cells may contribute in disease mechanisms and therapies. Understanding mechanisms in lineage differentiation is critical for organ development, pathophysiology and oncogenesis. To determine whether microRNAs (miRNA) may serve as drivers or adjuncts in hepatic differentiation, we studied human embryonic stem cell-derived hepatocytes and primary hepatocytes representing fetal or adult stages. Model systems were used for hepatic lineage advancement or regression under culture conditions with molecular assays. Profiles of miRNA in primary fetal and adult hepatocytes shared similarities and distinctions from pluripotent stem cells or stem cell-derived early fetal-like hepatocytes. During phenotypic regression in fetal or adult hepatocytes, miRNA profiles oscillated to regain stemness-associated features that had not been extinguished in stem cell-derived fetal-like hepatocytes. These oscillations in stemness-associated features were not altered in fetal-like hepatocytes by inhibitory mimics for dominantly-expressed miRNA, such as hsa-miR-99b, -100, -214 and -221/222. The stem cell-derived fetal-like hepatocytes were permissive for miRNA characterizing mature hepatocytes, including mimics for hsa-miR-122, -126, -192, -194 and -26b, although transfections of the latter did not advance hepatic differentiation. Examination of genome-wide mRNA expression profiles in stem cell-derived or primary fetal hepatocytes indicated targets of highly abundant miRNA regulated general processes, e.g., cell survival, growth and proliferation, functional maintenance, etc., without directing cell differentiation. Among upstream regulators of gene networks in stem cell-derived hepatocytes included HNF4A, SNAI1, and others, which affect transcriptional circuits directing lineage development or maintenance. Therefore, miRNA expression oscillated in response to microenvironmental conditions, whereas lineage-specific transcriptional regulators, such as HNF4A, were necessary for directing hepatic differentiation. This knowledge will be helpful for understanding the contribution of stem cells in pathophysiological states and oncogenesis, as well as for applications of stem cell-derived hepatocytes.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2019

The Space Communications and Navigation Testbed aboard International Space Station: Seven Years of Space-based Reconfigurable Software Defined Communications, Navigation, and Networking

The adoption of software defined radios offers space missions a new way to develop and operate space transceivers for communications and navigation.The SCaN Testbed on-board the ISS led groundbreaking efforts to champion use of software defined radios for space communications. The SCaN Testbed has allowed NASA, industry, academia, and international partners to pursue their respective interests in joint collaboration with NASA, and move this technology and it's applications to the space domain. Launched in 2012, The SCaN Testbed has logged over 4000 hours of operation exploring the development, reconfiguration, and operation of software defined radios and their software applications. Over the past seven years, experimenters and organizations from across the United States and around the world, have advanced the applications of software defined radios and networks using the SCaN Tested. Some of SCaN Testbed's successful experiments include the demonstration of the first Ka-band full duplex space transceiver, which became an R&D 100 award winning technology, and was inducted into the Space Technology Hall of Fame, following the launch and space deployment of a successful commercial product line based on the Testbed radios.Experiments have focused on new software development and operations concepts for understanding how to manage and apply this relatively new technology to space to improve communications and navigation for space science and exploration missions. The advanced capabilities of the software radios allow for multiple applications or functions (e.g., communication and navigation) to operate from the same radio platform. Multiple software waveform applications enable software component reuse and improve efficiency for multiple applications operating over different mission phases. The new capabilities of software defined radios such as on-orbit reconfiguration, also present new challenges such as increased operational complexity. Experiments of the SCaN testbed include more intelligent or cognitive applications to improve communications efficiency and manage the complexity of the radios, the communication channels, and the network itself. The software defined radios on the SCaN Testbed are each compliant to NASA's Space Telecommunications Radio System (STRS) Architecture. The STRS Architecture provides commonality among radio developments from different providers and different mission applications, and is designed to reduce the cost, risk, and complexity of unique and custom radio developments. This radio architecture standard defines common waveform software interfaces, methods of instantiation, operation, and documentation. As the SCaN Testbed concludes its operations on ISS, this presentation explores the advancements and accomplishments made to advance software defined radio technology and its applications for exploration. The accomplishments cover a number of experiment areas in Ka-band and S-band communications with TDRS, high rate communications, adaptive waveform operation, navigation using both GPA and Galileo constellations, complex networking and disruptive tolerant link protocols, user initiative service, and initial experiments with intelligent and cognitive applications which demonstrate the significant potential of software defined and cognitive radios.

Mortensen, Dale↗
DOE OSTI2015

Strategic supply system design – a holistic evaluation of operational and production cost for a biorefinery supply chain

Abstract Pioneer cellulosic biorefineries across the United States rely on a conventional feedstock supply system based on one‐year contracts with local growers, who harvest, locally store, and deliver feedstock in low‐density format to the conversion facility. While the conventional system is designed for high biomass yield areas, pilot scale operations have experienced feedstock supply shortages and price volatilities due to reduced harvests and competition from other industries. Regional supply dependency and the inability to actively manage feedstock stability and quality, provide operational risks to the biorefinery, which translate into higher investment risk. The advanced feedstock supply system based on a network of depots can mitigate many of these risks and enable wider supply system benefits. This paper compares the two concepts from a system‐level perspective beyond mere logistic costs. It shows that while processing operations at the depot increase feedstock supply costs initially, they enable wider system benefits including supply risk reduction (leading to lower interest rates on loans), industry scale‐up, conversion yield improvements, and reduced handling equipment and storage costs at the biorefinery. When translating these benefits into cost reductions per liter of gasoline equivalent ( LGE ), we find that total cost reductions between –$0.46 to –$0.21 per LGE for biochemical and –$0.32 to –$0.12 per LGE for thermochemical conversion pathways are possible. Naturally, these system level benefits will differ between individual actors along the feedstock supply chain. Further research is required with respect to depot sizing, location, and ownership structures. Published 2015. This article is a U.S. Government work and is in the public domain in the USA. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd.

09 BIOMASS FUELS↗
NASA NTRS2005

Distributed network scheduling

We investigate missions where communications resources are limited, requiring autonomous planning and execution. Unlike typical networks, spacecraft networks are also suited to automated planning and scheduling because many communications can be planned in advance.

scheduling↗
DOE OSTI2025

Exocortex Network for AI-Augmented Human-Led Scientific Expedition

AI advances in science can be viewed along two main directions with a fluid boundary: enhancing efficiency through automation and smart tools to accelerate tasks that humans can already perform; and enabling exploration into uncharted territories and potentially toward AGI. These advances manifest in the AI cognitive core through the development and explainability of foundation models; in the physical embodiment of instruments and facilities; and in the integrated agency of AI workflows exemplified by the science exocortex. To address the role of humans in this evolving landscape, in this Perspective, we suggest a third direction: the development of personalized agents that form human-centered networks, supporting both efficiency and exploration while ensuring that AI remains aligned with human vision.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Tracer-Guided Characterization of Dominant Pore Networks and Implications for Permeability and Wettability in Shale

Pore network characterization is an important aspect in unconventional reservoir evaluation. While application of the technique of scanning electron microscope (SEM) brings substantial advances in pore characterization in shale, understanding the connected pore network that dominates flow in shale samples is limited by using SEM alone because of small fields of view and lack of views of connectivity in 3D. In this research, a technique integrating tracer imbibition, micro-computed tomography (CT) imaging, and SEM imaging was developed to provide a solution for multiscale imaging in shale. Tracer imbibition indicates pore connectivity; micro-CT imaging after tracer imbibition thus provides an overview of the connected pore network at the millimeter scale. With guidance from micro-CT images after tracer imbibition, a more accurate and detailed characterization of pore systems and related mineralogy can be conducted using higher-resolution SEM. The method was applied to five samples from Wolfcamp and Eagle Ford Formations. Results reveal the effectiveness of the integrated method by showing different patterns of distribution of the dominant pore network and different controlling mineralogy. Dominant porosity, estimated from grayscale analyses, displays a good correlation with permeability. This result indicates that dominant porosity is more relevant to permeability than is total porosity. Results from imbibition tests are also compared with that from contact angle measurement, and important implications on wettability can be obtained. The integrated method thus has the capacity to link the dominant pore network and wettability with microscale to submicroscale mineralogy, which can help better understand the pore systems and fluid flow in shale. Plain Language Summary Pore network characterization and wettability are two important aspects in unconventional reservoir evaluation and are crucial to understanding the nature of shale oil or gas storage and production. Here, a new technique integrating tracer imbibition, micro-computed tomography imaging, and scanning electron microscope imaging was developed for a more representative and effective pore characterization in shale. This integrated technique can provide a direct visualization of the dominant pore network in a millimeter-scale sample and, at the same time, can provide a guided pore network characterization under nanoscale resolution. This method thus can generate results that are more representative and more accurate in a more efficient way compared with the traditional scanning electron microscope method. The new technique can also reveal the local wettability in microscale. High-resolution analysis on the tracer imbibition or fluid displacement and mineralogy can shed light on the local wettability and the controlling mineralogy in shale.

58 GEOSCIENCES↗
DOE OSTI2024

Effect of penetrant–polymer interactions and shape on the motion of molecular penetrants in dense polymer networks

The diffusion of dilute molecular penetrants within polymers plays a crucial role in the advancement of material engineering for applications such as coatings and membrane separations. The potential of highly cross-linked polymer networks in these applications stems from their capacity to adjust the size and shape selectivity through subtle changes in network structures. In this paper, we use molecular dynamics simulation to understand the role of penetrant shape (aspect ratios) and its interaction with polymer networks on its diffusivity. We characterize both local penetrant hopping and the long-time diffusive motion for penetrants and consider different aspect ratios and penetrant–network interaction strengths at a variety of cross-link densities and temperatures. The shape affects the coupling of penetrant motion to the cross-link density- and temperature-dependent structural relaxation of networks and also affects the way a penetrant experiences the confinement from the network meshes. The attractive interaction between the penetrant and network primarily affects the former since only the system of dilute limit is of present interest. These results offer fundamental insights into the intricate interplay between penetrant characteristics and polymer network properties and also suggest future directions for manipulating polymer design to enhance the separation efficiency.

Chemistry↗
NASA NTRS1986

The new Langley Research Center advanced real-time simulation (ARTS) system

Based on a survey of current local area network technology with special attention paid to high bandwidth and very low transport delay requirements, NASA's Langley Research Center designed a new simulation subsystem using the computer automated measurement and control (CAMAC) network. This required significant modifications to the standard CAMAC system and development of a network switch, a clocking system, new conversion equipment, new consoles, supporting software, etc. This system is referred to as the advanced real-time simulation (ARTS) system. It is presently being built at LaRC. This paper provides a functional and physical description of the hardware and a functional description of the software. The requirements which drove the design are presented as well as present performance figures and status.

Crawford, D. J.↗
NASA NTRSDate not supplied

Snow Depth from AMSR-2 Using Multispectral Satellite Data in an Artificial Neural Network

By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu et al. (2022) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, an artificial neural network (ANN) algorithm, employing several channels from Advanced Microwave Scanning Radiometer 2 (AMSR-2) and the humidity vertical profiles from Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System for Instrument Teams (GEOS-IT) product, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained ANN snow-depth was applied to 2018 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. The validation data (different from the training set) of ANN snow depth from AMSR-2 showed a good agreement with time matched and co-located snow-depth values from ICESat-2. The bias was near zero, with mean absolute error (MAE) 0.05 cm and a root-mean-square-error (RMSE) 0.08 cm. Prior applying the trained ANN snow depth to AMSR-2 data, a cloud screening algorithm was developed with a similar approach. A separate ANN cloud mask was trained to determine an AMSR-2 pixel is clear or cloudy with time and geolocation matched 2015 CALIOP Vertical Feature Mask (VFM) over Arctic sea ice. The ANN cloud mask from AMSR-2 under-estimated cloud fraction by 3-6% compared to CALIOP . The additional research is needed to conclusively evaluate the ANN cloud mask accuracy. Finally, this paper will lay the foundation for a sustained long-term snowfall and snow-storm monitoring system. The future Cloud Aerosol LIdar for Global scale Observations of the ocean-Land Atmosphere system (CALIGOLA) mission will provide a means to calculate snow depth from the lidar backscattering pathlength distribution, benefiting from the UV, visible and infrared pulses. With the calculated snow depth as the truth one could develop a machine learning algorithm, as it was done in this paper, using a passive microwave instrument available at that time to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.

Neural Network↗
DOE OSTI2019

Future SDN-HPON Control Plane Architecture and Protocol for On-Demand Terabit End-to-End Extreme-Scale Science Applications

The UC Davis team conducted architecture, algorithm and experimental studies for next generation ultra-high-bandwidth optical networks in support of extreme-scale science applications. In particular, over the course of this 3-year project, the UC Davis team achieved the following main accomplishments: Designed a network architecture for automated multi-domain service provisioning: Designed information exchange schemes between autonomous systems (domains) Designed application programming interfaces to orchestrate resource reservation in data centers, high performance computation facilities, and communication networks belonging to different autonomous systems Experimentally assessed the multi-domain network architecture in a distributed field trial set-up connecting premises in three continents Designed a broker-based orchestrated multi-domain optical networking framework supporting observe-analyze-act cycle based cognitive inter-domain service provisioning: Designed an inter-domain operation mechanism, e.g., topology abstraction, for the broker plane and domain managers to collaboratively accomplish inter-domain service provisioning while respecting the autonomy of each administrative domain Designed an alien wavelength monitoring scheme and an artificial neural network based optical signal-to-noise ratio estimator for quality-of-transmission aware inter-domain service provisioning Developed a deep neural network based multi-domain traffic predictor and a cognitive routing and spectrum assignment algorithm to realize knowledge-based autonomous traffic engineering in multi-domain networks Implemented an optical signal-to-noise ratio estimator and traffic predictor with Google’s Tensor flow and assessed their performance using experimental and synthetic data Demonstrated the benefits of the proposed cognitive schemes with experiments ona two-domain elastic optical network testbed and evaluations on a six-domain simulation framework Built a two-domain software-defined networking control plane testbed using the ONOS platform and demonstrated the workflow of broker and domain manager cooperation, including hierarchical performance monitoring, topology abstraction and cognitive resource allocation. Designed self-learning cognitive service provisioning schemes using advanced machine learning technologies: Designed a self-taught anomaly detection framework for optical networks, including an unsupervised data clustering module for pattern recognition, a supervised data regression and classification module for online anomaly detection, and the system operation principle Experimentally validated the effectiveness of the self-taught anomaly detection framework with various failure/anomaly scenarios. Designed a self-learning autonomic routing, modulation and spectrum assignment (RMSA) provisioning framework for elastic optical networks with deep reinforcement learning. Designed effective modeling and training mechanisms for the self-learning RMSA framework Conducted extensive numerical simulations with various traffic patterns and topologies to verify the advantage of the self-learning RMSA framework.

42 ENGINEERING↗
NASA NTRS1983

Integrated flight/propulsion control system architectures for a high speed aircraft

The present investigation is concerned with the impact of integrated flight, inlet, and engine control system architectures on system effectiveness and life cycle cost (LCC). The aircraft employed in the investigation is a modified YF-12 with a cruise Mach number of 2.5. The aircraft has two advanced technology aircraft JT-69 afterburning, low-bypass turbofan engines. Advanced technologies suitable for use in future flight/propulsion control systems are related to distributed computer networks, fault tolerant computers and software, analytical redundancy, self-checking microprocessor pairs, advanced direct drive actuators, fiber optic data buses, VLSI microcircuits, and skewed sensors. The integrated architectures were found to have fewer sensors, actuators, and power sources than the nonintegrated architectures.

Bangert, L. H.↗
DOE OSTI2014

A New Approach to Estimating the Probability for β-delayed Neutron Emission

The probability for neutron emission following β decay, Pn, is a crucial property for a wide range of physics and applications including nuclear structure, r-process nucleosynthesis, the control of nuclear reactors, and the post-processing of nuclear fuel. Despite much experimental effort, knowledge of Pn values is still lacking in very neutron-rich nuclei, requiring predictions from either systematics or theoretical models. Traditionally, systematic predictions were made by investigating the Pn value as a function of the decay Q value and the neutron separation energy in the daughter nucleus. A new approach to Pn systematics is presented which incorporates the half-life of the decay and the Q value for β-delayed neutron emission. This prescription correlates the known data better, and thus improves the estimation of Pn values for neutron-rich nuclei. Such an approach can be applied to generate input values for r-process network calculations or in the modeling of advanced fuel cycles.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Predicting taxonomic and functional structure of microbial communities in acid mine drainage

Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

1.2 Mfps standalone X-ray detector for Time-Resolved Experiments

We present a standalone and autonomous X-ray detector capable of operation with the speed of up to1.2Mfps. The detector utilizes UFXC32k hybrid pixel detectors for sensing X-rays, Spartan-6 LX45 FPGA placed in commercially available sbRIO 9628 controller for data acquisition and processing including a compression with zero-suppression algorithm. A Linux-RT system working on the 400 MHz Dual-Core CPU is used for FPGA control and data streaming to the higher-level system over 1 Gbps Ethernet connection. 1.2 M frames per second is achieved in so-called burst mode of operation while in zerodead-time mode 70 kfps is possible. Due to efficient data compression in FPGA there’s no need of using high-speed transceivers and Frame-Grabber cards on the data server side and the detector can stream the data infinitely over standard 1 Gbps network connection. Operation modes were tested at Advanced Photon Source Synchrotron at Argonne National Laboratory.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Isotope effects in x-ray absorption spectra of liquid water

The isotope effects in x-ray absorption spectra of liquid water are studied by a many-body approach within electron-hole excitation theory. The molecular structures of both light and heavy water are modeled by path-integral molecular dynamics based on the advanced deep-learning technique. The neural network is trained on ab initio data obtained with SCAN density functional theory. The experimentally observed isotope effect in x-ray absorption spectra is reproduced semiquantitatively in theory. Compared to the spectrum in normal water, the blueshifted and less pronounced pre- and main-edge in heavy water reflect that the heavy water is more structured at short- and intermediate-range of the hydrogen-bond network. In contrast, we find the isotope effect on the spectrum is negligible at post-edge, which is consistent with the identical long-range ordering in both liquids as observed in the diffraction experiment.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Efficient Management of Certificate Revocation Lists in Smart Grid Advanced Metering Infrastructure

Advanced Metering Infrastructure (AMI) forms a communication network for the collection of power data from smart meters in Smart Grid. As the communication within an AMI needs to be secure, key management becomes an issue due to overhead and limited resources. While using public-keys eliminate some of the overhead of key management, there is still challenges regarding certificates that store and certify the publickeys. In particular, distribution and storage of certificate revocation list (CRL) is major a challenge due to cost of distribution and storage in AMI networks which typically consist of wireless multi-hop networks. Motivated by the need of keeping the CRL distribution and storage cost effective and scalable, in this paper, we present a distributed CRL management model utilizing the idea of distributed hash trees (DHTs) from peer-to-peer (P2P) networks. The basic idea is to share the burden of storage of CRLs among all the smart meters by exploiting the meshing capability of the smart meters among each other. Thus, using DHTs not only reduces the space requirements for CRLs but also makes the CRL updates more convenient. We implemented this structure on ns-3 using IEEE 802.11s mesh standard as a model for AMI and demonstrated its superior performance with respect to traditional methods of CRL management through extensive simulations.

25 ENERGY STORAGE↗
DOE OSTI2025

Low Activity Tritium Detection in CCDs Using Deep Learning Techniques

Here, this study explores the use of charge-coupled devices (CCDs) for detecting low-energy beta particles from tritium decay - a critical signal for nuclear safety, nuclear nonproliferation, and environmental monitoring. We employ a dual approach utilizing both measured CCD data and detailed Geant4 simulations. Our analysis compares classical techniques with advanced deep learning methods, including convolutional neural networks (CNNs), autoencoders trained exclusively on tritium data, and preliminary studies on boosted decision trees (BDTs). The CNN, trained on mixed signal/background datasets, demonstrates superior classification performance, while the autoencoder shows the potential of unsupervised, background-agnostic strategies when background characteristics are poorly defined. These results highlight the excellent sensitivity achievable thanks to the background rejection made possible by information-rich CCD data, paving the way for improved portable tritium monitoring.

Autoencoder↗