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At least 1,207 records · Page 67

Anti-symmetric barron functions and their approximation with sums of determinants

A fundamental problem in quantum physics is to encode functions that are completely anti-symmetric under permutations of identical particles. The architecture of neural network models for the electron wave function typically comprises an equivariant component followed by a summation of determinants. The recently introduced Generic Antisymmetric (GA) block is designed to enhance the expressivity of such neural wave functions, and it was found that the 2-layer GA block achieved more accurate energies than the corresponding single-determinant FermiNet architecure, suggesting its promise as a way to improve the expressivity of neural wave functions. In this paper we show how the function expressed by the 2-layer GA block can be decomposed into a sum of determinants. We formalize this result by defining the antisymmetric Barron space as a generalized version of the 2-layer GA block and providing an appromation theorem for this function class. This result can be viewed as a negative result showing that the 2-layer GA block is not more expressive than using multiple determinants.

Abrahamsen, Nilin↗

Exceptionally Fast Ion Diffusion in Block Copolymer-Based Porous Carbon Fibers

Confined ionic liquids in hydrophilic porous media have disrupted lattices and can be divided into two layers: An immobile ion layer adheres to the pore surfaces, and an inner layer exhibits faster mobility than the bulk. In this work, we report the first study of ionic liquids confined in block copolymer-based porous carbon fibers (PCFs) synthesized from polyacrylonitrile-block-polymethyl methacrylate (PAN-b-PMMA). The PCFs contain a network of unimodal mesopores of 13.6 nm in diameter and contain more hydrophilic surface functional groups than previously studied porous carbon. Elastic neutron scattering shows no freezing point for 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM]BF 4 ) confined in PCFs down to 20 K. Quasi-elastic neutron scattering (QENS) is used to measure the diffusion of [BMIM]BF 4 confined in PCFs, which, surprisingly, is 7-fold faster than in the bulk. The unprecedentedly high ion diffusion remarks that PCFs hold exceptional potential for use in electrochemical catalysis, energy conversion, and storage.

36 MATERIALS SCIENCE↗

Copper–Carbon Nanotube Composites Enabled by Electrospinning for Advanced Conductors

The power losses associated with the electrical resistance of copper (Cu) have generated considerable interest in the development of advanced conductors that incorporate carbon nanotubes (CNTs) into the Cu matrix—ultraconductive Cu (UCC) composites—to increase energy efficiency in various industrial and residential applications, ranging from electric power transmission and rotating machinery to electronic devices. To meet this demand, we describe an electrospinning-based polymer nanofiber templating strategy for the fabrication of UCC composites with electrical and mechanical performance exceeding that of Cu. Our approach involves electrospinning of polyvinylpyrrolidone (PVP)-based solutions containing CNTs into aligned PVP/CNT nanofibers onto Cu foil substrates, followed by vacuum-assisted thermal removal of organic solvent/polymer from the CNT matrix to achieve a uniformly distributed CNT layer on the Cu surface. Following additional Cu deposition, the Cu–CNT–Cu composites demonstrated similar electrical conductivity, higher current carrying capacity, and improved mechanical properties compared with those obtained from reference Cu. Importantly, after the heat treatment, Raman analysis of the CNT network displayed an increased metallic character that supports the enhanced electrical properties of the UCC composites. Thus, we believe that these performance characteristics together with the commercial viability of the present approach could open new possibilities in designing advanced conductors for a broad range of electrical systems and industrial applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Insights on seasonal solifluction processes in warm permafrost Arctic landscape using a dense monitoring approach across adjacent hillslopes

Solifluction processes in the Arctic are highly complex, introducing uncertainties in estimating current and future soil carbon storage and fluxes, and assessment of hillslope and infrastructure stability. This study aims to enhance our understanding of triggers and drivers of soil movement of permafrost-affected hillslopes in the Arctic. To achieve this, we established an extensive soil deformation and temperature sensor network, covering 48 locations across multiple hillslopes within a 1 km² watershed on the Seward Peninsula, AK. We report depth-resolved measurements down to 1.8 m depth for May to September 2022, a period conducive to soil movement due to deepening thaw layers and frequent rain events. Over this period, surface movements of up to 334 mm were recorded. In general, these movements occur close to the thawing front, and are initiated as thawing reaches depths of 0.4 to 0.75 m. The largest movements were observed at the top of the south-east facing slope, where soil temperatures are cold (mean annual soil temperatures averaging -1.13°C) and slopes are steeper than 15°. Our analysis highlights three primary factors influencing movements: slope angle, soil thermal conditions, and thaw depth. The latter two significantly impact the generation of pore water pressures at the thaw–freeze interface. Specifically, soil thermal conditions govern the liquid water content, while thaw depth influences both the height of the water column and, consequently, the pressure at the thawing front. These factors affect soil properties, such as cohesion and internal friction angle, which are crucial determinants of slope stability. This underscores the significance of a precise understanding of subsurface thermal conditions, including spatial and temporal variability in soil temperature and thaw depth, when assessing and predicting slope instabilities. Based on our observations, we developed a Factor of Safety proxy that consistently falls below the triggering threshold for all probes exhibiting displacements exceeding 50 mm. This study offers novel insights into patterns and triggers of hillslope movements in the Arctic and provides a venue to evaluate their impact on soil redistribution.

54 ENVIRONMENTAL SCIENCES↗

Assessment of the Distributed Ledger Technology for Energy Sector Industrial and Operational Applications Using the MITRE ATT&CK® ICS Matrix

In recent times, Distributed Ledger Technology (DLT) has gained significant attention for its potential application in the energy sector. Utilizing blockchain and DLT has demonstrated the ability to enhance the resilience of the electric infrastructure, which will support a more flexible infrastructure and advance grid modernization. However, the deployment of these technologies increases the overall attack surface. The MITRE ATT&CK® matrices have been developed to document an adversary’s tactics and techniques based on real-world observations. The MITRE ATT&CK® matrices provide a common taxonomy for offense and defense and have become a valuable conceptual tool across multiple cybersecurity disciplines for conveying threat intelligence, performing testing through red teaming or adversary emulation, and enhancing network and system defenses against intrusions. The MITRE ATT&CK® for Industrial Control Systems (ICS) matrix was created to provide knowledge about adversary behavior in the ICS technology domain. This study analyzes the relevance of various tactics and techniques across a seven-layer DLT engineering and cybersecurity stack, known as the DLT stack, designed by the Cybersecurity Taskforce under IEEE P2418.5 - Standard for Blockchain in Energy working group sponsored by Power and Energy Systems - Smart Buildings, Loads and Customer Systems (PES/SBLC) Technical Committee. Additionally, this paper identifies specific mitigation strategies tailored to the energy ICS environment

42 ENGINEERING↗

Knowledge-based network operations

An expert system for enhancing the operability of the ground communication element of the Jet Propulsion Laboratory's Deep Space Network is described. The system performs network fault management, configuration management, and performance management in real time. Extracted management information serves as input to the expert system and is used to update a management information data base. The monitor and control activities involve dividing software for each processor into layers which are each modeled as a finite state machine.

Wu, Chuan-Lin↗

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

lidar↗

Solid-state reprogrammable analog resistive devices for electronic neural networks

The fabrication and performance of WO3-based, solid-state, three-terminal device configurations as programmable analog memory elements are reported. These transistorlike device structures exhibit good resistance progammability with a remarkable resolution of a few percent of the resistive strength over a four orders of magnitude dynamic range. The most critical component of these devices is an insulating layer between the active WO3 and the cation donor layer. The progamming characteristics and operation mechanisms of the device are described, and probable reaction mechanisms critical to the device stability are discussed.

Ramesham, R.↗

Compact Interconnection Networks Based on Quantum Dots

Architectures that would exploit the distinct characteristics of quantum-dot cellular automata (QCA) have been proposed for digital communication networks that connect advanced digital computing circuits. In comparison with networks of wires in conventional very-large-scale integrated (VLSI) circuitry, the networks according to the proposed architectures would be more compact. The proposed architectures would make it possible to implement complex interconnection schemes that are required for some advanced parallel-computing algorithms and that are difficult (and in many cases impractical) to implement in VLSI circuitry. The difficulty of implementation in VLSI and the major potential advantage afforded by QCA were described previously in Implementing Permutation Matrices by Use of Quantum Dots (NPO-20801), NASA Tech Briefs, Vol. 25, No. 10 (October 2001), page 42. To recapitulate: Wherever two wires in a conventional VLSI circuit cross each other and are required not to be in electrical contact with each other, there must be a layer of electrical insulation between them. This, in turn, makes it necessary to resort to a noncoplanar and possibly a multilayer design, which can be complex, expensive, and even impractical. As a result, much of the cost of designing VLSI circuits is associated with minimization of data routing and assignment of layers to minimize crossing of wires. Heretofore, these considerations have impeded the development of VLSI circuitry to implement complex, advanced interconnection schemes. On the other hand, with suitable design and under suitable operating conditions, QCA-based signal paths can be allowed to cross each other in the same plane without adverse effect. In principle, this characteristic could be exploited to design compact, coplanar, simple (relative to VLSI) QCA-based networks to implement complex, advanced interconnection schemes. The proposed architectures require two advances in QCA-based circuitry beyond basic QCA-based binary-signal wires described in the cited prior article. One of these advances would be the development of QCA-based wires capable of bidirectional transmission of signals. The other advance would be the development of QCA circuits capable of high-impedance state outputs. The high-impedance states would be utilized along with the 0- and 1-state outputs of QCA.

Fijany, Amir↗

Structural and Interphasial Stabilities of Sulfurized Polyacrylonitrile (SPAN) Cathode

Sulfurized polyacrylonitrile (SPAN) has attracted a lot of attention because of its low cost, high capacity, and great reversibility. Due to its structural complexity and amorphous nature, reaction mechanism of SPAN is little understood. Here, for this paper, we study the structural and interphasial changes of SPAN using synchrotron-based pair distribution function (PDF) analysis and soft X-ray absorption spectroscopy (sXAS). PDF identifies key structural features, including C–S bond, sulfur dimer, and sulfur chain in SPAN. The sulfur dimer bridging the pyridine network partially converts to sulfur chain during the first charging. In the following cycles, sulfur chain goes through lithiation and delithiation with reversibility dependent on the electrolytes. SXAS reveals surface changes of SPAN. After the first cycle, a negatively charged carbon or fused benzene layer is formed, on top of which is another layer formed by the electrolyte decomposition. The layer formed by localized high concentration electrolyte is stable during cycling.

25 ENERGY STORAGE↗

Cyber-Physical Power Systems Protection: The Byzantine Cybersecurity Framework

Cybersecurity of smart grids have been topic of much interest in recent years. As this critical infrastructure operation increases dependency on automated processes and controls, exposure to cyber-physical threats become inevitable. Considering cyber-physical security of the grid, much focus of attention has been made towards smart grids real-time monitoring solutions, including the state estimation process. Analyzing the relevant literature, one can note though that seldom research has been done on cyber-physical security of smart grids protection systems. Protection systems have intangible value towards grid reliability. This paper presents a cybersecurity framework for smart grids protection systems. A physics-based inspired machine learning solution is at the core process of the framework. Processed relay inputs and outputs are used by a deep predictive coding network. Formal models, a quasi-static state estimator, provides an oracle when low confidence decision is reached. Evolving knowledge is derived through reinforcement learning. Implementation aspects considering the Pacific Northwest National Laboratory Electricity Infrastructure Operations Center are presented. Built as an extra control layer to protection systems, without hard-to-derive parameters, highlights potential aspects towards real-life applications.

Bretas, Arturo Suman↗

An Integrated Electric Vehicle Drive Motor and Wireless Charger

This work proposes an in-wheel electric vehicle-motor in which the motor windings also act as receiver coils in a wireless charging system. Vehicle wireless charging systems normally include a receiver coil, compensation network, rectifier and battery charger. The proposed concept would eliminate the need for a separate receiver coil. Studies based on two-dimensional finite element analysis are carried out to verify the feasibility of the concept. An external rotor Halbach array rotor with single layer fractional slot concentrated wingdings is found to be a suitable motor topology which would enable the use of one or more motor coils as a receiver coil. The paper also discusses the design modifications and considerations required to enable this.

Rallabandi, Vandana↗

A Co-Registered In-Situ and Ex-Situ Dataset from an Electron Beam Powder Bed Fusion Additive Manufacturing Process (Peregrine v2023-09)

This release contains a co-registered in-situ and ex-situ Peregrine dataset from a single Arcam Q10 Electron Beam Powder Bed Fusion (EB-PBF) Inconel 738 build. These data were collected at the Manufacturing Demonstration Facility (MDF) located at Oak Ridge National Laboratory (ORNL). The dataset includes layer-wise Near Infrared (NIR) in-situ imaging data, in-situ temporal sensor data, ex-situ X-Ray Computed Tomography (X-CT) scans, and the target part geometries. Additionally, anomaly detections produced by a trained Dynamic Segmentation Convolutional Neural Network (DSCNN) are provided.

36 MATERIALS SCIENCE↗

Structure of the moon

Seismic data fron the four stations of the Apollo passive seismic network have been analyzed to obtain the velocity structure of the moon. Analysis of body wave phases from artificial impacts of known impact time and position yields a crustal section. In the Mare Cognitum region the crust is about 60 km thick and is layered. In the 20-km-thick upper layer, velocity gradients are high and microcracks may play an important role. The 40-km-thick lower layer has a nearly constant 6.8-km/sec velocity. There may be a thin high-velocity layer present beneath the crust. The determination of seismic velocities in the lunar mantle is attempted by using natural impacts and deep moonquakes. The simplest model that can be proposed for the mantle consists of a 'lithosphere' overlying an 'asthenosphere'.

Toksoz, M. N.↗

Three-dimensional compressible convection at low Prandtl numbers

Numerical simulations are used to study fully compressible thermal convection at large Rayleigh numbers. Results are presented from a sequence of three-dimensional simulations that reveal a transition from gradually-evolving laminar convection to nearly turbulent convection as the Prandtl number is reduced from a value of unity to one-tenth. The convective flows form irregular cellular patterns near the upper surface, possesing a network of fast downflow at cell peripheries and gentler upflow at cell centers. At greater depths the curving sheets of downflow collapse into plumes which may twist and possess substantial vertical vorticity. For the lowest Prandtl number, the convection near the bottom of the layer appears to be turbulent, yet the rapidly varying small-scale flow structure there is accompanied by more ordered sites of wavering upflow, with the latter able to penetrate all the way to the upper boundary. Thus a significant component of the flow is able to extend over multiple density scale heights, in contrast to what is argued in formulating mixing-length models for stellar convection. Results are also shown from two-dimensional simulations carried out with very high spatial resolution, which reveal that supersonic convection with fluttering shock systems can be realized.

Toomre, Juri↗

Lidar Remote Sensing for Industry and Environment Monitoring

Contents include the following: 1. Keynote paper: Overview of lidar technology for industrial and environmental monitoring in Japan. 2. lidar technology I: NASA's future active remote sensing mission for earth science. Geometrical detector consideration s in laser sensing application (invited paper). 3. Lidar technology II: High-power femtosecond light strings as novel atmospheric probes (invited paper). Design of a compact high-sensitivity aerosol profiling lidar. 4. Lasers for lidars: High-energy 2 microns laser for multiple lidar applications. New submount requirement of conductively cooled laser diodes for lidar applications. 5. Tropospheric aerosols and clouds I: Lidar monitoring of clouds and aerosols at the facility for atmospheric remote sensing (invited paper). Measurement of asian dust by using multiwavelength lidar. Global monitoring of clouds and aerosols using a network of micropulse lidar systems. 6. Troposphere aerosols and clouds II: Scanning lidar measurements of marine aerosol fields at a coastal site in Hawaii. 7. Tropospheric aerosols and clouds III: Formation of ice cloud from asian dust particles in the upper troposphere. Atmospheric boundary layer observation by ground-based lidar at KMITL, Thailand (13 deg N, 100 deg. E). 8. Boundary layer, urban pollution: Studies of the spatial correlation between urban aerosols and local traffic congestion using a slant angle scanning on the research vessel Mirai. 9. Middle atmosphere: Lidar-observed arctic PSC's over Svalbard (invited paper). Sodium temperature lidar measurements of the mesopause region over Syowa Station. 10. Differential absorption lidar (dIAL) and DOAS: Airborne UV DIAL measurements of ozone and aerosols (invited paper). Measurement of water vapor, surface ozone, and ethylene using differential absorption lidar. 12. Space lidar I: Lightweight lidar telescopes for space applications (invited paper). Coherent lidar development for Doppler wind measurement from the International Space Station. 13. Space lidar II: Using coherent Doppler lidar to estimate river discharge. 14. Poster session: Lidar technology, optics for lidar. Laser for lidar. Middle atmosphere observations. Tropospheric observations (aerosols, clouds). Boundary layer, urban pollution. Differential absorption lidar. Doppler lidar. and Space lidar.

Singh, Upendra N.↗

Productive Programming of Distributed Systems with the SHAD C++ Library

High-performance computing (HPC) is often perceived as a matter of making large-scale systems (e.g., clusters) run as fast as possible, regardless the required programming effort. However, the idea of "bringing HPC to the masses" has recently emerged. Inspired by this vision, we have designed SHAD, the Scalable High-performance Algorithms and Data-structures library. SHAD is open source software, written in C++, for C++ developers. Unlike other HPC libraries for distributed systems, which rely on SPMD models, SHAD adopts a shared-memory programming abstraction, to make C++ programmers feel at home. Underneath, SHAD manages tasking and data-movements, moving the computation where data resides and taking advantage of asynchrony to tolerate network latency. At the bottom of his stack, SHAD can interface with multiple runtime systems: this not only improves developer’s productivity, by hiding the complexity of such software and of the underlying hardware, but also greatly enhance code portability. Thanks to its abstraction layers, SHAD can indeed target different systems, ranging from laptops to HPC clusters, without any need for modifying the user-level code. We have prototyped and open-sourced the implementation of (a subset of) the C++ standard library (STL) targeting multi-node HPC clusters. Our work allows plain STL-based C++ code to scale on HPC systems, with no need for rewriting the code to exploit the complex hardware. SHAD is available under Apache v2 License at https://github.com/pnnl/SHAD. In this paper we overview the design of the SHAD library, depicting its main components: runtime systems abstractions for tasking; parallel and distributed data-structures; STL-compliant interfaces and algorithms.

Castellana, Vito G.↗

Impurity gas detection for SNF canisters using probabilistic deep learning and acoustic sensing *

Abstract Monitoring impurity gases in spent nuclear fuel (SNF) canisters is a novel structural health monitoring approach for SNF in dry storage. The SNF canisters are sealed containers that do not facilitate visual access to the inside. Acoustic sensing can be deployed by taking advantage of the pathways unobstructed by internal hardware. Although the ultrasonic time-of-flight measurement can provide valuable information, it is limited in its ability to discern the concentration of only one impurity gas. As such, deep learning algorithms, particularly convolutional neural networks (CNNs), offer a promising solution. In this study, CNN-based probabilistic deep learning models were implemented to detect and quantify multiple impurity gases in helium. An experimental platform was established to simulate canister conditions, and ultrasonic test data were collected. The presence of argon and air in helium at concentrations ranging from 0% to 1.2% at increments of 0.05% was considered. The multi-layer perceptron, decision tree, and logistic regression classifiers achieved high accuracies when distinguishing pure helium from helium with impurities. CNN with dropout layers and CNN using maximum likelihood estimation showed a similar performance, indicating their ability to capture uncertainties. The ensemble CNN model exhibited improved predictions and the ability to balance individual gas concentration by integrating 1D- and 2D-CNN models. These findings contribute probabilistic deep learning solutions for impurity gas detection and analysis within SNF canisters, thus ensuring safe storage and management of SNFs.

47 OTHER INSTRUMENTATION↗