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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Electronic excitations of α - Fe 2 O 3 heteroepitaxial films measured by resonant inelastic x-ray scattering at the Fe L edge

Resonant inelastic x-ray scattering (RIXS) spectra of hematite (α-Fe 2 O 3 ) were measured at the Fe L 3 edge for heteroepitaxial thin films which were undoped and doped with 1% Ti, Sn, or Zn, in the energy-loss range in excess of 1 eV to study electronic transitions. The spectra were measured for several momentum transfers q, conducted at both low temperature (T=14 K) and room temperature. While we cannot rule out dispersive features possibly owing to propagating excitations, the coarse envelopes of the general spectra did not appreciably change shape with q, implying that the bulk of the observed L-edge RIXS intensity originates from (mostly) nondispersive ligand field excitations. Summing the RIXS spectra over q and comparing the results at T=14 K to those at T=300 K revealed pronounced temperature effects, including an intensity change and energy shift of the ≈1.4 eV peak, a broadband intensity increase of the 3-4 eV range, and higher energy features. The q-summed spectra and their temperature dependencies are virtually identical for nearly all of the samples with different dopants, save for the temperature dependence of the Ti-doped sample's spectrum, which we attribute to being affected by a large number of free charge carriers. Comparing with magnetization measurements for different temperatures and dopings likewise did not show a clear correlation between the RIXS spectra and the magnetic ordering states. To clarify the excited states, we performed spin multiplet calculations which were in excellent agreement with the RIXS spectra over a wide energy range and provide detailed electronic descriptions of the excited states. Furthermore, the implications of these findings to the photoconversion efficiency of hematite photoanodes is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Universal tripartite entanglement signature of ungappable edge states

Gapped two-dimensional topological phases can feature ungappable edge states which are robust even in the absence of protecting symmetries. In this Letter we argue that a multipartite entanglement measure recently proposed in the context of holography, the Markov gap, provides a universal diagnostic of ungappable edge states. Defined as a difference of the reflected entropy and mutual information h(A:B)=S R (A:B)-I(A:B) between two parties, we argue that for A,B being adjacent subregions in the bulk h=$\frac{c+}{3}$ ln 2, where c + is the minimal total central charge of the boundary theory. As evidence, we prove that h=0 for string-net models and numerically verify that h=$\frac{|C|}{3}$ ln 2 for a Chern-C insulator. Our Letter establishes a unique bulk entanglement criteria for the presence of a conformal field theory on the boundary.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Topological edge conduction induced by strong anisotropic exchange interactions

Here, we predict that an interplay between isotropic and anisotropic exchange interactions in a honeycomb lattice structure can lead to topological edge conduction when the anisotropic interaction is at least twice the strength of the isotropic interaction. For materials like Na 2 ⁢IrO 3 , such a strong anisotropic exchange interaction simultaneously induces a zigzag type of antiferromagnetic order that breaks the time-reversal symmetry of the topological edge conductor. We show that the electronic transport in such topological conductors will exhibit a quantized Hall conductance without any external magnetic field when the Fermi energy lies within a particular energy range.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Chiral edge states on spheres for lattice domain wall fermions

Recently Weyl edge states on manifolds in dimension 𝑑 +1 with a connected 𝑑-dimensional boundary were proposed as candidates for lattice regularization of chiral gauge theories, for even 𝑑. The examples considered to date include solid cylinders in any odd dimension, and the 3 ball with boundary 𝑆 2 . Here we consider the general case of a (𝑑+1)-dimensional ball for any even 𝑑 and show that the theory for the edge states on 𝑆 𝑑 describe a conventional Weyl fermion on a sphere with half-integer momenta. A possible advantage of such theories is that they can be discretized by a square lattice without breaking the underlying discrete hypercubic symmetry.

chiral symmetry↗
DOE OSTI2020

Reversal of Simple Hydrogenic Isotope Scaling Laws in Tokamak Edge Turbulence

The role of nonadiabatic electrons in regulating the hydrogenic isotope-mass scaling of gyrokinetic turbulence in tokamak fusion plasmas is assessed in the transition from ion-dominated core transport regimes to electron-dominated edge transport regimes. We propose a new isotope-mass scaling law that describes the electron-to-ion mass-ratio dependence of turbulent ion and electron energy fluxes. Here, the massratio dependence arises from the nonadiabatic response associated with fast electron parallel motion and plays a key role in altering—and in the case of the DIII-D edge, favorably reversing—the naive gyro-Bohm scaling behavior. In the reversed regime hydrogen energy fluxes are larger than deuterium fluxes, which is the opposite of the naive prediction.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Helical Edge States and Quantum Phase Transitions in Tetralayer Graphene

Helical conductors with spin-momentum locking are promising platforms for Majorana fermions. Here we report observation of two topologically distinct phases supporting helical edge states in charge neutral Bernal-stacked tetralayer graphene in Hall bar and Corbino geometries. As the magnetic field B ⊥ and out-of-plane displacement field D are varied, we observe a phase diagram consisting of an insulating phase and two metallic phases, with 0, 1, and 2 helical edge states, respectively. These phases are accounted for by a theoretical model that relates their conductance to spin-polarization plateaus. Transitions between them arise from a competition among interlayer hopping, electrostatic and exchange interaction energies. Finally, our work highlights the complex competing symmetries and the rich quantum phases in few-layer graphene.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Cognitive IoT and Edge Computing for Intrusion Detection with Federated TinyML

Internet of Things (IoT) and Edge Computing (EC) are rapidly becoming an integral part of the modern society. By 2030, there is estimated to be over 40 billion active and connected IoT devices [1]. This rapid progress also comes with a significant implication on cybersecurity. Back-end infrastructure and systems have a much broader attack than they did previously due to vulnerable IoT/EC devices being connected to wireless networks. This expanding attack surface is a growing concern because IoT/EC are increasingly being used in critical systems such as power grids, health care, and smart homes. To effectively address a problem of this scale, cognitive cyber methods—which can autonomously detect and react to cyber attacks as they develop—are needed. To address this, we bring Artificial Intelligence (AI) and Machine Learning (ML) to IoT/EC devices, using tinyML to monitor voluminous IoT data against cyber threats, and using Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. We propose a novel three-layer architecture: (1) an IoT layer for tinyML-based inference, (2) an edge layer for ML model training, and (3) a cloud layer for FL operations. Using the publicly available 11-class N-BaIoT dataset [2], we demonstrate that this architecture mitigates resource constraints at the IoT layer while improving detection accuracy over standard two-layer designs. An outlier-resistant scaler, feature reduction, and quantization enable the tinyML model to maintain detection accuracy with a reduced model size. Additionally, federated learning that only utilizes the intersection (across heterogenous devices) of the reduced feature set achieves superior detection accuracy compared to locally trained models.

Li, Mingyan [ORNL] (ORCID:0009000569532640)↗
DOE OSTI2023

ProvLight: Efficient Workflow Provenance Capture on the Edge-to-Cloud Continuum

Modern scientific workflows require hybrid infrastructures combining numerous decentralized resources on the IoT/Edge interconnected to Cloud/HPC systems (aka the Computing Continuum) to enable their optimized execution. Understanding and optimizing the performance of such complex Edge-to-Cloud workflows is challenging. Capturing the provenance of key performance indicators, with their related data and processes, may assist in understanding and optimizing workflow executions. However, the capture overhead can be prohibitive, particularly in resource-constrained devices, such as the ones on the IoT/Edge.To address this challenge, based on a performance analysis of existing systems, we propose ProvLight, a tool to enable efficient provenance capture on the IoT/Edge. We leverage simplified data models, data compression and grouping, and lightweight transmission protocols to reduce overheads. We further integrate ProvLight into the E2Clab framework to enable workflow provenance capture across the Edge-to-Cloud Continuum. This integration makes E2Clab a promising platform for the performance optimization of applications through reproducible experiments.We validate ProvLight at a large scale with synthetic workloads on 64 real-life IoT/Edge devices in the FIT IoT LAB testbed. Evaluations show that ProvLight outperforms state-of-the-art systems like ProvLake and DfAnalyzer in resource-constrained devices. ProvLight is 26—37x faster to capture and transmit provenance data; uses 5—7x less CPU; 2x less memory; transmits 2x less data; and consumes 2—2.5x less energy. ProvLight [1] and E2Clab [2] are available as open-source tools.

Rosendo, Daniel↗
DOE OSTI2024

Grid Edge Waveform Analytics Framework for Event Detection and Classification

This paper provides a grid edge waveform analytics framework for power system event detection and classification in the local as well as in the wide area. This framework overviews data excellence for event detection and classification. The data excellence describes the data acquisition process and requirements, data processing, data quality, and data integrity. Power system event detection in the local area based on different features such as energy-based, cyclostationary approach, template matching, and wavelet transform are also discussed. Furthermore, local area event detection and classification using approaches such as statistical, signal processing, artificial intelligence, and hybrid are also discussed. Moreover, an overview of wide-area event detection and classification along with several other aspects such as wide-area events, wide-area event detection approaches, event location and system performance, event pattern recognition, inter-area oscillation, and wide-area frequency response under variable deployment of inverter-based resources are also provided. The proposed framework is the first step toward the goal of developing appropriate tools and methodologies to detect and classify local as well as wide-area events using waveform analytics. The appropriate event detection and classification framework development is especially important now as more and more grid edge devices with communication capabilities are being deployed in the modern power grid than ever before.

Bhusal, Narayan↗
DOE OSTI2025

Evolution at the Edge: Real-Time Evolution for Neuromorphic Engine Control

Neuromorphic computing systems are attractive for real-time control at the edge because of their low power operation, real-time processing capabilities and their potential ability to do online learning. In this work, we describe an approach for performing real-time evolution of spiking neural networks for neuromorphic systems at the edge called Neuromorphic Optimization using Dynamic Evolutionary Systems or NODES. We apply this approach to real-time combustion engine control and develop an engine-specific hardware platform for NODES called FireBox. We demonstrate how the real-time evolution approach works in simulation and the performance of networks trained in simulation on the physical engine.

Maldonado Puente, Bryan [ORNL] (ORCID:000000033880↗
DOE OSTI2021

Transactive Implementation of Decentralized Electricity Market for Grid-Edge Systems

The electricity landscape is evolving towards more decentralized approaches due to the proliferation of distributed energy resources and the participation of increasingly smart consumers and producers (prosumers). Recent advances in information and communication technologies and smart metering, provides strategic opportunities for “prosumers” to reform their conventional energy practices towards more consumer-centric economies. From an operational perspective, managing power distribution networks is becoming more difficult with such active grid-edge systems providing limited to no visibility or control. Transactive Energy (TE) has been emerging as a key enabler towards effectively and efficiently integrating prosumers into competitive electricity markets. This work presents a transactive implementation of community-centric markets. A co-simulation framework is developed for evaluating the proposed market structure with high-fidelity models. Case studies on the IEEE-123 node test system demonstrate that community-centric transactive markets can enable communities of prosumers to operate collaboratively as grid-edge systems. The potential benefits of implementing community-centric TE systems are also illustrated.

transactive, microgrids, centralized market operat↗
DOE OSTI2020

Cooperative Model Predictive Control Scheme for Dispersed Smart Inverters at the Grid Edge

Growing number of distributed energy resources (DERs) requires cutting-edge control schemes to ensure uniform but not identical involvement of DERs to address rapid fluctuations on voltage of the grid clusters. Designing such control schemes for a grid with high penetration of DERs connected various clusters is complex and depends on physical configuration of the clusters themselves. This paper proposes a two-layered cooperative model predictive control scheme for fleet of dispersed smart inverters connected to a three-phase grid cluster located at the edge of a grid cluster. The upper layer of the controller ensures cooperative operation of fleet of smart inverters to perform voltage regulation and address voltage fluctuations in the grid cluster; while the lower level controller guarantees each individual inverter obeys the dictated references for active/reactive powers by the upper layer controller. In addition, the proposed twolayered controller has low voltage ride through (LVRT) capability to support the grid clusters during voltage sags. By employing the proposed control approach, the smart inverters will inject desired active/reactive powers based on circumstance of the entire cluster while guaranteeing proper power curtailment among the generation units. The control scheme is verified by simulation.

Fard, Amin Y.↗
DOE OSTI2024

Robust Restoration From Cyber-Physical Attacks in Active Distribution Grids With Grid-Edge IBRs

The inverter-based resources (IBRs) have enabled the integration of renewable energy at the grid edge with enhanced control capabilities to support the reliable operation of power grids. Different control frameworks, such as hierarchical or distributed architecture, have been proposed with the expansion of cyber networks for real-time monitoring and control. This evolution of critical infrastructure into cyber-physical systems also brings more vulnerabilities for the broadened attack surfaces, and significantly increases the possibility of physical system failures or outages caused by cyberattacks. Among tremendous efforts in the defense-in-depth approach, it remains challenging to provide prompt detection and accurate location of attack entry points or paths. Therefore, the prevailing restoration framework may struggle to fully consider the cyber-physical interdependence, successfully isolate the compromised cyber and physical components, and safely recover the systems without the potential risks leading to secondary outages. This paper is motivated to develop a cyber-physical restoration framework for distribution grids to recover from cyber attacks by harnessing grid-edge IBRs. The framework is first built on the operational guidelines of IBRs considering the compromised cyber layer. Then, an ambiguity set is established to represent the uncertainty of attack scenarios and their possibility levels. Next, a distributionally robust optimization model is developed to provide the optimal load restoration strategy across all scenarios. The effectiveness of the proposed model is demonstrated through various use cases on the modified IEEE 13-node and 123-node test systems. Finally, simulation results demonstrate the effectiveness and advancement of developed post-attack restoration strategies.

Cybersecurity↗
DOE OSTI2020

An Edge-Cloud Integrated Solution for Buildings Demand Response Using Reinforcement Learning

Buildings, as major energy consumers, can provide great untapped demand response (DR) resources for grid services. However, their participation remains low in real-life. One major impediment for popularizing DR in buildings is the lack of cost-effective automation systems that can be widely adopted. Existing optimization-based smart building control algorithms suffer from high costs on both building-specific modeling and on-demand computing resources. To tackle these issues, this paper proposes a cost-effective edge-cloud integrated solution using reinforcement learning (RL). Beside RL’s ability to solve sequential optimal decision-making problems, its adaptability to easy-to-obtain building models and the off-line learning feature are likely to reduce the controller’s implementation cost. Using a surrogate building model learned automatically from building operation data, an RL agent learns an optimal control policy on cloud infrastructure, and the policy is then distributed to edge devices for execution. Simulation results demonstrate the control efficacy and the learning efficiency in buildings of different sizes. A preliminary cost analysis on a 4-zone commercial building shows the annual cost for optimal policy training is only 2.25% of the DR incentive received. Results of this study show a possible approach with higher return on investment for buildings to participate in DR programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Smart Meter Pinging and Reading Through AMI Two-Way Communication Networks to Monitor Grid Edge Devices and DERs

Today’s power distribution system is changing to a power-electronics-enabled distribution system, especially with the increasing penetration of distributed energy resources (DERs). To monitor and manage those electronic devices and DERs at the grid edge, the advanced metering infrastructure (AMI) with two-way communications presents great potential. At present, extensive research explores the upstream communication from smart meters to electric utilities (e.g., meter reading) but few examine the downstream communication from the utilities to smart meters (e.g., meter pinging). This article discusses the AMI two-way communication and its recent industrial practice in the U.S., especially for applying the smart meter pinging functionality to monitor grid-edge devices and DERs. This paper then develops the two-way communication model and the network calculus method to quantify the impact of the two-way communication on the AMI network. In the end, the proposed method is validated with ns-3 simulation using the modified 13-node test feeder and real-world feeder systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Reliable edge machine learning hardware for scientific applications

Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementations of ML algorithms: enabling bit-accurate functional simulations for performance validation in experimental software frameworks, verifying those ML models are robust under extreme quantization and pruning, and enabling ultra-fine-grained model inspection for efficient fault tolerance. We discuss approaches to developing and validating reliable algorithms at the scientific edge under such strict latency, resource, power, and area requirements in extreme experimental environments. We study metrics for developing robust algorithms, present preliminary results and mitigation strategies, and conclude with an outlook of these and future directions of research towards the longer-term goal of developing autonomous scientific experimentation methods for accelerated scientific discovery.

Baldi, Tommaso↗
DOE OSTI2021

Dynamics of filaments during the edge-localized mode crash on NSTX

Edge localized modes (ELMs) are routinely observed in H-mode plasma regimes of the National Spherical Torus Experiment (NSTX). Due to the explosive nature of the instability, only diagnostics with high temporal and spatial resolution could provide a detailed insight into the dynamics associated with the ELMs. Gas-puff imaging (GPI) at NSTX provides 2D measurements of the magnetic field aligned fluctuations (e.g. ELM filaments) in the scrape-off layer and the at the plasma edge with 2.5 us temporal and 10 mm optical resolution.A novel analysis technique was developed to estimate the frame-by-frame velocities and the spatial parameters of the dominant structures associated with the ELMs. The analysis was applied to single ELM events to characterize the ELM crash dynamics, and then extended to a database of 169 ELM events.Statistical analysis was performed in order to find the characterizing dynamics of the ELM crash. The results show that on average an ELM crash consists of a filament with a circular cross-section which is propelled outwards with a characterizing peak radial velocity of ~3.3 km/s. The radial velocity was found to be linearly dependent on the distance of the filament from the separatrix, which has never been seen before. The ELM filament is characterized by propagation in the ion-diamagnetic direction poloidally with a peak velocity of 11.4 km/s. The ELM crash lasts for approximately 100us until the radial propulsion settles back to the pre-ELM level. The experimental findings were compared with analytical theory. Two possible mechanisms were identified for explaining the observations: the curvature interchange model and the current-filament interaction model.

correlation↗
DOE OSTI2021

Source noise suppression in attosecond transient absorption spectroscopy by edge-pixel referencing

Attosecond transient absorption spectroscopy (ATAS) is used to observe photoexcited dynamics with outstanding time resolution. The main experimental challenge of this technique is that high-harmonic generation sources show significant instabilities, resulting in sub-par sensitivity when compared to other techniques. This paper proposes edge-pixel referencing as a means to suppress this noise. Two approaches are introduced: the first is deterministic and uses a correlation analysis, while the second relies on singular value decomposition. Each method is demonstrated and quantified on a noisy measurement taken on WS 2 and results in a fivefold increase in sensitivity. The combination of the two methods ensures the fidelity of the procedure and can be implemented on live data collection but also on existing datasets. The results show that edge-referencing methods bring the sensitivity of ATAS near the detector noise floor. An implementation of the post-processing code is provided to the reader.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗