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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

App2Net: Moving App Functions to Network & a Case Study on Low-latency Feedback

Recent advances in programmable networks enable custom processing of data at hundreds of gigabits per second. These advances can boost the performance of many distributed applications. Yet the high-level languages used by application developers are different from the data plane programming languages (such as P4 and NPL) used by network equipment. This language barrier slows innovation. Our hourglass-shaped architectural solution aims to lower this language barrier. This enables the application developer community to leverage programmable networks for achieving better performance. In this paper we propose a JSON-based intermediate representation to bridge the gap between applications and in-network computing. We demonstrate an instance of the solution in the context of a low-latency feedback application that enables SQL-based data filtering in a P4-based programmable environment. We also present a prototype compiler to convert an intermediate representation in JSON to P4 source.

Sankaran, Ganesh↗
DOE OSTI2023

Snowmass 2021 Computational Frontier CompF4 Topical Group Report Storage and Processing Resource Access

Computing plays a significant role in all areas of high energy physics. The Snowmass 2021 CompF4 topical group's scope is facilities R&D, where we consider "facilities" as the computing hardware and software infrastructure inside the data centers plus the networking between data centers, irrespective of who owns them, and what policies are applied for using them. In other words, it includes commercial clouds, federally funded High Performance Computing (HPC) systems for all of science, and systems funded explicitly for a given experimental or theoretical program. This topical group report summarizes the findings and recommendations for the storage, processing, networking and associated software service infrastructures for future high energy physics research, based on the discussions organized through the Snowmass 2021 community study.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Impact of Concurrent Solubilization and Fines Migration on Fracture Aperture Growth in Shales during Acidized Brine Injection

Acidic hydraulic fracturing fluid chemically and physically alters shale rock fabric during injection and shut-in, creating a “reaction-altered zone” along the fracture faces. To better characterize the variable thickness and composition of this reaction-altered zone under advective flow, we take a coupled experimental and modeling approach. A fluidic cell, with six fiducial markers, is first fabricated to keep the rock sample in place during the core floods and to allow image alignment of acquired images. Then, we conduct a series of reactive core floods in a clay-rich siliceous Wolfcamp shale sample with 10 wt % carbonate, using a synthetic fracturing fluid under no confining stress and at room temperature. High-resolution computed tomography (CT) scans are periodically conducted to observe the spatial alteration of the fracture network. We then perform scanning electron microscopy (SEM-EDS) on the two orthogonal surfaces (fracture surface and freshly cut profile face) to generate high-resolution elemental maps that show the change in mineralogy, both with distance along a given flow path along a fracture surface and with depth from the fracture surface into the shale matrix. These results are contrasted against a two-dimensional (2D) advection-diffusion reaction model developed previously for batch reactions between shale and synthetic fracturing fluids. The model simulates the geochemical interaction occurring at the fracture/matrix interface and penetrating into the shale matrix during the reactive core flood. Both model and experimental results show that the acidic brine is neutralized during the core flood, corresponding to an increase in fracture aperture as a function of fluid volume injected with the greatest change near the inlet. SEM-EDS scans reveal significant dissolution of carbonates on the fracture surface without pyrite oxidation. The reactive transport model indicates that carbonate depletion into the shale interior should be observable, yet SEM-EDS shows no discernible loss of carbonate in the orthogonal profile face. The combination of these observations suggests an additional fracture evolution mechanism in the reactive system, i.e., fines migration. We show that fines migration enhances the access of fracturing fluid to the matrix resulting in a more pronounced fracture widening. Finally, we conclude that coupled mineral dissolution and fines migration govern fracture aperture growth during acidized brine injection. In this work, we effectively show the underlying risk of relying solely on models that do not include an important (transport) process that can alter the system significantly and propose a combined chemomechanical mechanism for fracture evolution appropriate for this shale mineralogy.

58 GEOSCIENCES↗
DOE OSTI2016

Ceria nanoparticles uniformly decorated on graphene nanosheets with coral-like morphology for high-performance supercapacitors

Graphical abstract: The study reported the synthesis of coral-like CeO{sub 2}/GNs for supercapacitors. The unique architecture with overall connected framework and good conducting network of the graphene greatly boosts the Faradaic redox reaction. Therefore, the CeO{sub 2}/GNs delivers an excellent electrochemical performance. - Highlights: • The study reported the synthesis of CeO{sub 2}/GNs. • The graphene was woven into CeO{sub 2}, acting as a good conducting network. • The CeO{sub 2}/GNs shows a coral-like structure. • The architecture creates an overall connected framework. • The CeO{sub 2}/GNs delivers good capacitive performances. - Abstract: CeO{sub 2}/graphene was synthesized by a simple microwave method along with subsequent calcination. CeO{sub 2} nanoparticles with an average size of 68.8 nm are uniformly decorated on graphene nanosheets (CeO{sub 2}/GNs). The CeO{sub 2}/GNs displays a like-coral morphology. The architecture including overall connected framework, abundant intercrossed and interconnected nanochannels and perfect conducting network of the graphene, endows the CeO{sub 2}/GNs material with a superior electron and mass transport. As a result, the CeO{sub 2}/GNs gives a high specific capacitance of 503.4 F/g at 2 A/g and good cycle performance with 91.8% capacitance retention after 3000 cycles. Further, an asymmetric supercapacitors was assembled by using CeO{sub 2}/GNs as the positive electrode and activated carbon as the negative electrode, the asymmetric device demonstrate a favorable energy density of 30.2 Wh/kg at the power density of 750.0 W/kg and superior cycle life with 86.4% the capacitance retenion at 5 A/g after 3000 cycles.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Flexible and high-performance electrochromic devices enabled by self-assembled 2D TiO 2 /MXene heterostructures

Transition metal oxides (TMOs) are promising electrochromic (EC) materials for applications such as smart windows and displays, yet the challenge still exists to achieve good flexibility, high coloration efficiency and fast response simultaneously. MXenes (e.g. Ti 3 C 2 T x ) and their derived TMOs (e.g. 2D TiO 2 ) are good candidates for high-performance and flexible EC devices because of their 2D nature and the possibility of assembling them into loosely networked structures. Here we demonstrate flexible, fast, and high-coloration-efficiency EC devices based on self-assembled 2D TiO 2 /Ti 3 C 2 T x heterostructures, with the Ti 3 C 2 T x layer as the transparent electrode, and the 2D TiO 2 layer as the EC layer. Benefiting from the well-balanced porosity and connectivity of these assembled nanometer-thick heterostructures, they present fast and efficient ion and electron transport, as well as superior mechanical and electrochemical stability. We further demonstrate large-area flexible devices which could potentially be integrated onto curved and flexible surfaces for future ubiquitous electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Hierarchical 3D Nanolayered Duplex-Phase Zr with High Strength, Strain Hardening, and Ductility

Nanolayered, bimetallic composites are receiving increased attention due to an exceptional combination of strength and thermal stability not possible from their coarse-layered counterparts or constituents alone. Yet, due to their 2D planar, unidirectional arrangement, they are highly anisotropic, which results in limited strain hardening and ductility. Therefore, like many high-performance, ultrastrong materials of our time, they succumb to the usual strength-ductility trade-offs. Here we present the formation of a novel hierarchical microstructure, comprised of crystals consisting of 3D nanolayered α/β–Zr networks. By direct comparison with coarse-layered material of the same chemistry, we show that the unusual hierarchical 3D structure gives rise to high strain hardening, high strength, and high ductility. Using TEM analysis and hysteresis testing, we discovered that the 3D randomly oriented biphase boundaries result in progressively dispersive rather than localized slip with increasing strain. Dislocation activity in the α–Zr lamellae transitions from single slip to multislip and eventually to multimodal slip as strain increases. Furthermore, the diffusive slip-promoting properties of 3D layered networks can potentially invoke simultaneous high strength, strain hardening, and ductility, and reveal a new target in the microstructural design of high performance structural materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Study of interconnect errors, network congestion, and applications characteristics for throttle prediction on a large scale HPC system

Today’s High Performance Computing (HPC) systems contain thousand of nodes which work together to provide performance in the order of petaflops. The performance of these systems depends on various components like processors, memory, and interconnect. Among all, interconnect plays a major role as it glues together all the hardware components in an HPC system. A slow interconnect can impact a scientific application running on multiple processes severely as they rely on fast network messages to communicate and synchronize frequently. Unfortunately, the HPC community lacks a study that explores different interconnect errors, congestion events and applications characteristics on a large-scale HPC system. In our previous work, we process and analyze interconnect data of the Titan supercomputer to develop a thorough understanding of interconnects faults, errors, and congestion events. In this work, we first show how congestion events can impact application performance. We then investigate application characteristics interaction with interconnect errors and network congestion to predict applications encountering congestion with more than 90% accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Entanglement Traffic Engineering in Quantum Optical Fiber Networks based on High-Dimensional Kerr Optical Frequency Combs

The purpose of the project was to explore the performance of entanglement protocols based on microresonator Kerr optical frequency combs. We have addressed these challenges by developing a rigorous frequency-bin approach that allowed us to determine an explicit steady-state density operator for quantum microcombs below threshold. Our novel approach has allowed us to derived an explicit formula for the density operator on the frequency-bin basis, and to propose a complete description of quantum Kerr combs regardless of the number of sidemodes or loss-induced coupling to the environment. This formalism also allows for the explicit determination of their fidelity, purity, and entropy. These results are expected to permit the exploitation of the full potential of entangled microcombs for quantum technology.

42 ENGINEERING↗
NASA NTRS1993

Performance of a distributed superscalar storage server

The RS/6000 performed well in our test environment. The potential exists for the RS/6000 to act as a departmental server for a small number of users, rather than as a high speed archival server. Multiple UniTree Disk Server's utilizing one UniTree Disk Server's utilizing one UniTree Name Server could be developed that would allow for a cost effective archival system. Our performance tests were clearly limited by the network bandwidth. The performance gathered by the LibUnix testing shows that UniTree is capable of exceeding ethernet speeds on an RS/6000 Model 550. The performance of FTP might be significantly faster if asked to perform across a higher bandwidth network. The UniTree Name Server also showed signs of being a potential bottleneck. UniTree sites that would require a high ratio of file creations and deletions to reads and writes would run into this bottleneck. It is possible to improve the UniTree Name Server performance by bypassing the UniTree LibUnix Library altogether and communicating directly with the UniTree Name Server and optimizing creations. Although testing was performed in a less than ideal environment, hopefully the performance statistics stated in this paper will give end-users a realistic idea as to what performance they can expect in this type of setup.

Finestead, Arlan↗
DOE OSTI2020

Heterogeneous Machine Learning on High Performance Computing for End to End Driving of Autonomous Vehicles

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving. For machines though, this gap is guarded by at least two challenges: 1) machine learning techniques remain brittle and unable to generalize to a wide range of scenarios, and 2) effective training data that enhances generalization and generates the desired driving behavior. Further, each challenge can be computationally intensive on its own thereby exasperating the gap. Moreover, is has not yet been shown that a single form of learning or training is capable of addressing a large range of scenarios. As a result, solving the first challenge does not inherently solve the second and vice versa. The work described here discusses an approach to address the first challenge that would also provide a foundation for solving the second. Our approach utilizes a combination of conditional imitation learning with a static dataset, reinforcement learning with a simulation environment, and high-performance computing to train a neural network. As a result, this reduces the “time to solution” from to the existing techniques for autonomous driving and provides an extensible framework to address the second key challenge.

autonomous vehicles↗
DOE OSTI2025

Learning broken symmetries with approximate invariance

Recognizing symmetries in data allows for significant boosts in neural network training, which is especially important where training data are limited. In many cases, however, the exact underlying symmetry is present only in an idealized dataset, and is broken in actual data, due to asymmetries in the detector, or varying response resolution as a function of particle momentum. Standard approaches, such as data augmentation or equivariant networks fail to represent the nature of the full, broken symmetry, effectively overconstraining the response of the neural network. We propose a learning model which balances the generality and asymptotic performance of unconstrained networks with the rapid learning of constrained networks. This is achieved through a dual-subnet structure, where one network is constrained by the symmetry and the other is not, along with a learned symmetry factor. In a simplified toy example that demonstrates violation of Lorentz invariance, our model learns as rapidly as symmetry constrained networks but escapes its performance limitations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2005

A high performance frequency standard and distribution system for Cassini Ka-band experiment

This paper provides an overview and update of a specialized frequency reference system for the NASA Deep Space Network (DSN) to support Ka-band radio science experiments with the Cassini spacecraft, currently orbiting Saturn. Three major components, a Hydrogen Maser, Stabilized Fiber-optic Distribution Assembly (SFODA), and 10 Kelvin Cryocooled Sapphire Oscillator (10K CSO) and frequency-lock-loop, are integrated to achieve the very high performance, ground based frequency reference at a remote antenna site located 16 km from the hydrogen maser. Typical measured Allan Deviation is 1.6 -14 1 0a't 1 second and 1.7 x 10 -15 at 1000 seconds averaging intervals. Recently two 10K CSOs have been compared in situ while operating at the remote DSN site DSS-25. The CSO references were used operationally to downconvert the Ka band downlink received from the Cassini spacecraft in a series of occultation measurements performed over a 78 day period from March to June 2005.

sapphire↗
NASA NTRS1996

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

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

Rubaai, Ahmed↗
DOE OSTI2024

E(n)-Equivariant cartesian tensor message passing interatomic potential

Machine learning potential (MLP) has been a popular topic in recent years for its capability to replace expensive first-principles calculations in some large systems. Meanwhile, message passing networks have gained significant attention due to their remarkable accuracy, and a wave of message passing networks based on Cartesian coordinates has emerged. However, the information of the node in these models is usually limited to scalars, and vectors. In this work, we propose High-order Tensor message Passing interatomic Potential (HotPP), an E(n) equivariant message passing neural network that extends the node embedding and message to an arbitrary order tensor. By performing some basic equivariant operations, high order tensors can be coupled very simply and thus the model can make direct predictions of high-order tensors such as dipole moments and polarizabilities without any modifications. The tests in several datasets show that HotPP not only achieves high accuracy in predicting target properties, but also successfully performs tasks such as calculating phonon spectra, infrared spectra, and Raman spectra, demonstrating its potential as a tool for future research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Disentangling direct from indirect relationships in association networks

Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks. Using copula-based transitivity, iDIRECT eliminates/ameliorates several challenging mathematical problems, including ill-conditioning, self-looping, and interaction strength overflow. With simulation data as benchmark examples, iDIRECT showed high prediction accuracies. Application of iDIRECT to reconstruct gene regulatory networks in Escherichia coli also revealed considerably higher prediction power than the best-performing approaches in the DREAM5 (Dialogue on Reverse Engineering Assessment and Methods project, #5) Network Inference Challenge. In addition, applying iDIRECT to highly diverse grassland soil microbial communities in response to climate warming showed that the iDIRECT-processed networks were significantly different from the original networks, with considerably fewer nodes, links, and connectivity, but higher relative modularity. Further analysis revealed that the iDIRECT-processed network was more complex under warming than the control and more robust to both random and target species removal ( P < 0.001). As a general approach, iDIRECT has great advantages for network inference, and it should be widely applicable to infer direct relationships in association networks across diverse disciplines in science and engineering.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1991

The NASA Science Internet: An integrated approach to networking

An integrated approach to building a networking infrastructure is an absolute necessity for meeting the multidisciplinary science networking requirements of the Office of Space Science and Applications (OSSA) science community. These networking requirements include communication connectivity between computational resources, databases, and library systems, as well as to other scientists and researchers around the world. A consolidated networking approach allows strategic use of the existing science networking within the Federal government, and it provides networking capability that takes into consideration national and international trends towards multivendor and multiprotocol service. It also offers a practical vehicle for optimizing costs and maximizing performance. Finally, and perhaps most important to the development of high speed computing is that an integrated network constitutes a focus for phasing to the National Research and Education Network (NREN). The NASA Science Internet (NSI) program, established in mid 1988, is structured to provide just such an integrated network. A description of the NSI is presented.

Rounds, Fred↗
DOE OSTI2020

A Single‐Ion Conducting Borate Network Polymer as a Viable Quasi‐Solid Electrolyte for Lithium Metal Batteries

Abstract Lithium‐ion batteries have remained a state‐of‐the‐art electrochemical energy storage technology for decades now, but their energy densities are limited by electrode materials and conventional liquid electrolytes can pose significant safety concerns. Lithium metal batteries featuring Li metal anodes, solid polymer electrolytes, and high‐voltage cathodes represent promising candidates for next‐generation devices exhibiting improved power and safety, but such solid polymer electrolytes generally do not exhibit the required excellent electrochemical properties and thermal stability in tandem. Here, an interpenetrating network polymer with weakly coordinating anion nodes that functions as a high‐performing single‐ion conducting electrolyte in the presence of minimal plasticizer, with a wide electrochemical stability window, a high room‐temperature conductivity of 1.5 × 10 −4 S cm −1 , and exceptional selectivity for Li‐ion conduction ( t Li+ = 0.95) is reported. Importantly, this material is also flame retardant and highly stable in contact with lithium metal. Significantly, a lithium metal battery prototype containing this quasi‐solid electrolyte is shown to outperform a conventional battery featuring a polymer electrolyte.

Shin, Dong‐Myeong↗
DOE OSTI2017

Optimum aggregation of geographically distributed flexible resources in strategic smart-grid/microgrid locations

This paper determines optimum aggregation areas for a given distribution network considering spatial distribution of loads and costs of aggregation. An elitist genetic algorithm combined with a hierarchical clustering and a Thevenin network reduction is implemented to compute strategic locations and aggregate demand within each area. The aggregation reduces large distribution networks having thousands of nodes to an equivalent network with few aggregated loads, thereby significantly reducing the computational burden. Furthermore, it not only helps distribution system operators in making faster operational decisions by understanding during which time of the day will be in need of flexibility, from which specific area, and in which amount, but also enables the flexibilities stemming from small distributed resources to be traded in various power/energy markets. A combination of central and local aggregation scheme where a central aggregator enables market participation, while local aggregators materialize the accepted bids, is implemented to realize this concept. The effectiveness of the proposed method is evaluated by comparing network performances with and without aggregation. Finally, for a given network configuration, steady-state performance of aggregated network is significantly accurate (≈ ±1.5% error) compared to very high errors associated with forecast of individual consumer demand.

24 POWER TRANSMISSION AND DISTRIBUTION↗