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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

End-to-end microgrid protection using distributed data-driven methods

This paper introduces an end-to-end microgrid protection framework that offers real-time system monitoring, fault-related decision making, and circuit breaker control. This is achieved through the design of distributed data-driven techniques based on the support vector machine method, where each relay is responsible for distributed data collection, fault detection, fault localization, and fault isolation. Local communication is established among neighboring relays, fostering cooperative fault localization and isolation. This decentralized design not only reduces the computational and communication requirements but also enables the adaptability of each relay under varying operational dynamics. The proposed end-to-end protection framework was validated using MATLAB/Simulink simulations on a 100% renewable microgrid, achieving an accuracy of 93.1% with response time of 0.0523 s, in protecting against a range of fault scenarios that are characterized by various types, locations, impedances, load conditions, photovoltaic power levels, and microgrid operating modes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Datum: A Scientific Metadata Catalog

The data catalog market is currently flooded with a myriad of different products, but none serve the scientific community well. There are cloud-native tools like Databricks, Snowflake,to on-premise solutions like Collibra and Datahub. The common failing of all these tools however, is their inability to serve the scientific data community directly. Most catalogs are targeted towards financial, health, or user data - not sensor or scientific domain data. They also prioritize integrations that often don’t exist or are just starting to be used in the scientific realm - all while ignoring common scientific tools and file types. Datum is a catalog which targets the scientific data directly, including the tools and networks in which those tools are used. We work with the producers and consumers of the data where they are, targeting cloud and on-premise with a focus on classified networks. Datum is an Erlang/Elixir application. Technical Features Note: The features listed below are still under development and may change, slightly, upon final delivery of the product. File Formats - Datum has the ability to read additional metadata and provides processing pipelines for the following file formats: Plain Text, PDF, LaTeX, HTML, Open Document Format (.odt), XML, CSV/TSV (and other standard delimiters), OpenDocument Database and Spreadsheets, Geo-Referenced TIFF, Common Data Format, HDF/HDF5, LabView TDMS, Excel, DeltaTables, Parquet, Apache Iceberg, Apache Hudi and many others. Metadata Collection - Scanners for the local and networked file systems and cloud storage providers. Network integration with common databases such as MSSQL and MySQL. User Plugin System - Users are able to provide either file processing, metadata extraction, or sampling plugins in the programming language of their choice. Authentication/Authorization -: OIDC integration, SCIM provisioning and EntraID integration out of the box. Full user and group management system with a “least privilege” operating mode. Governance - Customizable data governance platform; dictate and enforce required metadata, enforce data embargos, and enforce user agreements and NDAs before data access. Ability to create health checks on data, rejecting abandoned or poorly curated data and automatically removing it from the search index. Ability for users to submit corrections. Search - Semantic search is a first class citizen. No licenses to expensive, external software required. Integrated use of vectors and vector-based search allows for AI agent integration at all levels of operation. Metadata Model - Display and control data’s lineage and connections to other data and data directories. Data is modeled after a filesystem - an organization instantly recognizable and navigable by most any user. CLI and SDK - Ships with a Command Line Interface (CLI) tool and with a fully-featured Python SDK. This allows for rapid and programmatic use of Datum by every level of user. Minimal Infrastructure - Datum ships as a single executable file and can be run on any operating system and most CPU architectures. Datum has no reliance on external databases, search indexing tools, or other outside services - and it runs equally well on edge computing devices, cloud services, or in a clustered HPC environment.

darrington, john↗
DOE OSTI2022

Automated Process Planning for Embossing and Functionally Grading Materials via Site-Specific Control in Large-Format Metal-Based Additive Manufacturing

The potential for site-specific, process-parameter control is an attribute of additive manufacturing (AM) that makes it highly attractive as a manufacturing process. The research interest in the functionally grading material properties of numerous AM processes has been high for years. However, one of the issues that slows developmental progress in this area is process planning. It is not uncommon for manual programming methods and bespoke solutions to be utilized for site-specific control efforts. This article presents the development of slicing software that contains a fully automated process planning approach for enabling through-thickness, process-parameter control for a range of AM processes. The technique includes the use of parent and child geometries for controlling the locations of site-specific parameters, which are overlayed onto unmodified toolpaths, i.e., a vector-based planning approach is used in which additional information, such as melt pool size for large-scale metal AM processes, is assigned to the vectors. This technique has the potential for macro- and micro-structural modifications to printed objects. A proof-of-principle experiment is highlighted in which this technique was used to generate dynamic bead geometries that were deposited to induce a novel surface embossing effect, and additional software examples are presented that highlight software support for more complex objects.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Anomaly Detection and Mitigation for Wide-Area Damping Control using Machine Learning

In an interconnected multi-area power system, wide-area measurement based damping controllers are used to damp out inter-area oscillations, which jeopardize grid stability and constrain the power flows below to their transmission capacity. The effect of wide-area damping control (WADC) significantly depends on both power and cyber systems. At the cyber system layer, an adversary can inflict the WADC process by compromising either measurement signals, control signals or both. Stealthy and coordinated cyber-attacks may bypass the conventional cybersecurity measures to disrupt the seamless operation of WADC. This paper proposes an anomaly detection (AD) algorithm using supervised Machine Learning and a model-based logic for mitigation. The proposed AD algorithm considers measurement signals (input of WADC) and control signals (output of WADC) as input to evaluate the type of activity such as normal, perturbation (small or large signal faults), attack and perturbation-and-attack. Upon anomaly detection, the mitigation module tunes the WADC signal and sets the control status mode as either wide-area mode or local mode. The proposed anomaly detection and mitigation (ADM) module works inline with the WADC at the control center for attack detection on both measurement and control signals and eliminates the need for ADMs at the geographically distributed actuators. Here, we consider coordinated and primitive data-integrity attack vectors such as pulse, ramp, relay-trip and replay attacks. The performance of the proposed ADM algorithms was evaluated under these attack vector scenarios on a testbed environment for 2-area 4-machine power system. The ADM module shows effective performance with 96:5% accuracy to detect anomalies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Fully Distributed Multi-Material Magnetic Sensing Structures for Multiparameter DAS Applications

This dissertation demonstrates the first of its kind distributed magnetic field sensor based on a fiber optic distributed acoustic sensing (DAS) scheme. Ferromagnetic nickel and Metglas® were dispersed internally within a fiber optic preform and then drawn on an in-house fiber optic draw tower to lengths in the kilometers. Due to the close proximity of the ferromagnetic metals and fiber optic core, the magnetostrictive strain response of the ferromagnetic materials when exposed to a magnetic field would perturbate within the fiber cladding and transfer that strain, internally, to the fiber optic core. Strain resulting from the magnetostrictive effect allows the DAS based sensor to accurately translate strain into readable magnetic field data. Due to the high sensitivity seen in this sensor design, multiparameter sources, acoustic and magnetic fields, were tested and validated and a three dimensional magnetic-field vector sensor was proposed. Numerical analysis of the novel sensor design was first implemented using COMSOL Multiphysics, where inputs such as magnetostrictive element shape, size, distance, and number were first investigated. Upon optimizing system constraints, the sensor design was further modified such that single mode operation was consistent across multiple fiber draws while retaining high strain transfer from the ferromagnetic elements to the fiber optic core. Ferromagnetic material selection was evaluated as a function of the saturation magnetostriction constants and a total of 4 modules were used to fully characterize the complex physics involved in this sensor design. All fabrication and testing were performed in-house using a full scale 3-story fiber draw tower and custom environmental testing stations to imitate naturally occurring events such as magnetic or acoustic point sources. A unique stacking method was used to embed ferromagnetic nickel and Metglas® into a fiber optic preform which when combined with a custom fiber draw process resulted in consistent multi-material fibers drawn to lengths of 1-km. In-house testing facilities included different types of electromagnetic generators, in addition to a soil test bed, and an outdoor test bed which allowed 100 meters of fiber to be tested simultaneously. All tested sensors demonstrated high strain transfer capabilities on the order of 0.01-10 μϵ depending on the materials used, ferromagnetic rod number, and core to metal spacing. Due to the sensitivity of the system the difference between AC and DC was distinct, and directional magnetostriction was studied. Transverse and longitudinal magnetic wave propagation was controlled through a solenoid and rectangular Helmholtz coil, both built in-house. A three-dimensional magnetic field vector sensor was proposed due to the success of the magnetic field sensor, and a design was proposed and initially tested to validate direction as a function of field strength and distance. To summarize, this dissertation explores the first fully distributed magnetic field sensor using DAS based techniques and one of the first multi-material fiber draw processes which can produce consistent single mode fiber up to 1-km. Due to extensive FEA modeling, multiple iterations of the magnetic sensor were fully characterized and an equation describing the relationship between sensor design and strain transfer has been created and validated experimentally. Multi-parameter tests including acoustic and magnetic fields were implemented and an algorithm was developed to separate the mixed signals. Finally, a test was performed to demonstrate the feasibility of sensing magnetic fields directionally. Cumulative results demonstrate a high-quality sensor alternative to current designs which may surpass other magnetic sensors due to innate multi-parameter capabilities, in addition to the inexpensive production cost and extremely long operating lengths.

02 PETROLEUM↗
DOE OSTI2022

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗
DOE OSTI2022

Light-Driven Ultrafast Polarization Manipulation in a Relaxor Ferroelectric

Relaxor ferroelectrics have been intensely studied for decades based on their unique electromechanical responses which arise from local structural heterogeneity involving polar nanoregions or domains. Here, we report first studies of the ultrafast dynamics and reconfigurability of the polarization in freestanding films of the prototypical relaxor 0.68PbMg 1/3 Nb 2/3 O 3 -0.32PbTiO 3 (PMN-0.32PT) by probing its atomic-scale response via femtosecond-resolution, electron-scattering approaches. By combining these structural measurements with dynamic phase-field simulations, we show that femtosecond light pulses drive a change in both the magnitude and direction of the polarization vector within polar nanodomains on few-picosecond time scales. This study defines new opportunities for dynamic reconfigurable control of the polarization in nanoscale relaxor ferroelectrics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Cybersecurity Certification Recommendations for Interconnected Grid Edge Devices and Inverter Based Resources

Escalating deployment of PV and grid-edge devices on the distribution grid has increased the sustainability and efficiency of the electric grid. However, the increasing number of distributed energy resources (DERs) deployed creates a heightened cyber-physical interdependency on the distribution grid and thus creates more vectors for cyber-attacks to exploit through information and communication technology (ICT) systems and networks. For example, control signal packets can be modified, intercepted, or corrupted due to vulnerabilities in communication protocols used by microgrid controllers and grid edge devices for power control. Therefore, to mitigate and prevent cyber-attacks on grid edge devices and the inverter-based resources connected to the distribution grid, the U.S. Department of Solar Energy Technologies Office (SETO) awarded funding to the National Renewable Energy Laboratory and Sandia National Laboratory (SNL) to research, develop, and harmonize cybersecurity standards for Photovoltaic (PV) systems and for other kinds of DERs. To help develop a standard for DER cybersecurity, NREL established certification recommendations and test cases, in consensus with the solar industry and UL, for ensuring intrinsic design security for DERs. These recommendations were developed to bolster the cybersecure functionalities such as TLS, MAC, CRL, session resumption/renegotiation, and password, system, and service security management within the DER devices. The proposed test cases verify authentication, authorization, confidentiality, and data integrity for data and communications of DERs that use Transmission Control Protocol/Internet Protocol (TCP/IP). They were also developed to protect DER communications from eavesdropping, replay, man-in-the-middle, denial of service (DoS), spoofing through security certificates, least-privilege violation, and brute-force credentials. This report, which has been validated and reviewed by UL, expands upon those test cases to provide DER cybersecurity certification recommendations which increase DER resiliency and help to mitigate cyber-attacks. UL's collaboration with NREL and approval of this document will accelerate the adoption of a UL standard for DER cybersecurity.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Search for vector-like leptons coupling to first- and second-generation Standard Model leptons in pp collisions at s = 13 TeV with the ATLAS detector

A search for pair production of vector-like leptons coupling to first- and second-generation Standard Model leptons is presented. The search is based on a dataset of proton-proton collisions at s$$ \sqrt{s} $$ = 13 TeV recorded with the ATLAS detector during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 140 fb−1. Events are categorised depending on the flavour and multiplicity of leptons (electrons or muons), as well as on the scores of a deep neural network targeting particular signal topologies according to the decay modes of the vector-like leptons. In each of the signal regions, the scalar sum of the transverse momentum of the leptons and the missing transverse momentum is analysed. The main background processes are estimated using dedicated control regions in a simultaneous fit with the signal regions to data. No significant excess above the Standard Model background expectation is observed and limits are set at 95% confidence level on the production cross-sections of vector-like electrons and muons as a function of the vector-like lepton mass, separately for SU(2) doublet and singlet scenarios. The resulting mass lower limits are 1220 GeV (1270 GeV) and 320 GeV (400 GeV) for vector-like electrons (muons) in the doublet and singlet scenarios, respectively.

Aad, G↗
DOE OSTI2023

Changing temperature profiles and the risk of dengue outbreaks

As temperatures change worldwide, the pattern and competency of disease vectors will change, altering the global distribution of both the burden of infectious disease and the risk of the emergence of those diseases into new regions. To evaluate the risk of potential summer dengue outbreaks triggered by infected travelers under various climate scenarios, we develop an SEIR-type model, run numerical simulations, and conduct sensitivity analyses under a range of temperature profiles. Our model extends existing theoretical frameworks for studying dengue dynamics by introducing temperature dependence of two key parameters: the mosquito extrinsic incubation period and the lifespan of mosquitoes, which empirical data suggests are both highly temperature dependent. We find that changing temperature significantly alters dengue risk in an inverted U-shape, with temperatures in the range 27-31°C producing the highest risk. As temperatures increase beyond 31°C, the determinants of dengue risk begin to shift from mosquito biting rate and carrying capacity to the duration of the human infectious period, suggesting that changing temperatures not only alter dengue risk but also the potential efficacy of control measures. To illustrate the role of spatial and temporal temperature heterogeneity, we select five US cities where the primary dengue vector, the mosquito Aedes aegypti , has been observed, and which have had dengue cases in the past: Los Angeles, Houston, Miami, Brownsville, and Phoenix. Our analysis suggests that an increase of 3°C leads to an approximate doubling of the risk of dengue in Los Angeles and Houston, but a reduction of risk in Miami, Brownsville, and Phoenix due to extreme heat.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Powering the Blue Economy: A Survey of Station-Keeping Methods for Mooringless Platforms

The term “ocean platform” is used to reference everything from stationary, typically moored, buoys to mobile water vehicles, whether they operate on the ocean’s surface or underwater. For certain applications for which relatively stationary station-keeping conditions are desired, the use of mooring systems is not always a viable alternative either for economic, environmental, regulatory, or otherwise practical reasons, or a combination thereof, (e.g., short deployments, sensitive ecosystems, very deep project sites). Maintaining a platform at a single waypoint or reference location without being moored would require additional control systems and a power source to counteract the drift forces that would naturally displace it. Mobile platforms, which are usually untethered except for remotely operated vehicles, typically require energy input to power their station-keeping capabilities so that they hold or control their location in the ocean. Currently, most of these platforms use combustion engines or batteries for this purpose, which, depending on the specific systems, may be costly, pollute the environment, or create limitations on the length of the deployment. However, powering this kind of platforms with surrounding renewable resources (waves, currents, winds, or sun) has been identified as a promising solution to expand their application. The intent of this report is to investigate station-keeping methods for various ocean platforms that are not moored or otherwise anchored to the ocean floor, or another platform or vessel, paying particular interest to technologies that use marine renewable resources to power their operation, because that is of particular interest to the U.S. Department of Energy’s Powering the Blue Economy (PBE) initiative. As a first step, 72 articles and technical reports related to mooringless station-keeping methods were collected for review. The preliminary literature review provided a broad overview of common themes across the literature from which a descriptive methodology for analyzing various platforms was developed. That is, station-keeping methods were categorized based on their predominant energy source and consumption (renewable, nonrenewable, or hybrid if the platform uses renewable and nonrenewable resources equally), and their localization strategy (drift reduction, “path-planning or “waypoint-holding”). In addition, platform types were segregated into the following groups: buoys, surface drifters, and unoccupied surface vehicles (USVs); offshore renewable energy systems; and unoccupied underwater vehicles (UUVs). The main types of station-keeping methods encountered in this report achieve their intended localization strategy by means of drift mitigation, steering, and/or propulsion. Drift mitigation is commonly accomplished via drogues and sea anchors. Stand-along steering subsystems use control surfaces (e.g., ship rudder, wing sail, etc.) that react to ocean currents, waves, or winds to provide varying-degrees of course adjustments. Combined steering and propulsion subsystems include differential thrusters, directional thrusters separate from a primary thruster that cause the platform to pitch up/down or yaw clockwise/counterclockwise, or vectored thrusters that direct the propulsion in a range of directions relative to the platform’s local coordinate system. Propulsion is often achieved by running a motor and applying active control strategies but can also involve buoyancy shifts and using sails to generate lifting forces that propel a platform in a desired direction. Future research is primarily expected to take place in the form of a technoeconomic analysis that would aim to determine the technological viability, cost, and added value of mooringless station-keeping use cases identified through this research, including docking for UUV recharging or for georeferencing drifter buoys, deep-sea floating wind farms, U.S. Navy sonar arrays, and a Pacific Ocean wave buoy network.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Surface plasmon polariton pulse shaping via two-dimensional Bragg grating pairs

Abstract We demonstrate control over the spatial and temporal properties of surface plasmon polaritons (SPPs) launched from nanohole arrays in silver. The arrays provide wave vector matching to allow the conversion of free-space photons into counter-propagating SPPs. SPPs launched from multiple arrays interfere at well-defined spatial positions, and the interference fringes form an all-SPP periodic nano-optical grating which evolves in space and time as the SPPs propagate. The spatio-temporal characteristics of the optical grating can be tuned through various nanohole array parameters such as tilt angle, separation, and array width. In addition, we examine multiperiodic arrays (MPAs) consisting of arrays with different pitches placed adjacent to one another. This platform allows the temporal interference of SPPs with different central wavelengths to be tailored through the MPA geometric and structural parameters. The temporal interference serves as an encoded signal, whereby the frequency components can be controlled by the array properties.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Machine Learning-Based Model Predictive Control of Two-Time-Scale Systems

In this study, we present a general form of nonlinear two-time-scale systems, where singular perturbation analysis is used to separate the dynamics of the slow and fast subsystems. Machine learning techniques are utilized to approximate the dynamics of both subsystems. Specifically, a recurrent neural network (RNN) and a feedforward neural network (FNN) are used to predict the slow and fast state vectors, respectively. Moreover, we investigate the generalization error bounds for these machine learning models approximating the dynamics of two-time-scale systems. Next, under the assumption that the fast states are asymptotically stable, our focus shifts toward designing a Lyapunov-based model predictive control (LMPC) scheme that exclusively employs the RNN to predict the dynamics of the slow states. Additionally, we derive sufficient conditions to guarantee the closed-loop stability of the system under the sample-and-hold implementation of the controller. A nonlinear chemical process example is used to demonstrate the theory. In particular, two RNN models are constructed: one to model the full two-time-scale system and the other to predict solely the slow state vector. Both models are integrated within the LMPC scheme, and we compare their closed-loop performance while assessing the computational time required to execute the LMPC optimization problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Magnetic field properties in non-axisymmetric divertors

Stellarator power plants require a plan for the removal of the particles and the heat that are exhausted across the plasma edge. Unless a flowing liquid metal can be used to carry the helium exhaust to places where it can be removed from the plasma chamber, the particle exhaust must be magnetically diverted into pumping chambers. Studies are required to determine how magnetic features relate to the required divertor properties, how these magnetic features can be produced, and how they can be controlled. General studies are clarified and simplified by the use of the magnetic field line Hamiltonian $ψ_p(ψ,θ,φ)$ and a vector $\vec{x}(ψ,θ,φ)$ that gives the point in space associated with each point in the $(ψ,θ,φ)$ canonical coordinates, a flux and two angles. The non-resonant Fourier terms in ψp can be removed by a canonical transformation, so only resonant Fourier terms can determine the field line properties in the plasma edge and divertor. This paper discusses the important divertor properties and explains how $ψ_p(ψ,θ,φ)$ and $\vec{x}(ψ,θ,φ)$ can be obtained numerically in a special form for any stellarator magnetic field, $\vec{B}(\vec{x})$. This form holds between an arbitrary magnetic surface and the chamber walls with the non-resonant terms eliminated. Studies based on variations in the terms in such derived field-line Hamiltonians can determine what magnetic features are mathematically possible and how they could be produced and controlled by the external magnetic field coils.

Physics↗
DOE OSTI2024

Efficacy of commercial recombinant HVT vaccines against a North American clade 2.3.4.4b H5N1 highly pathogenic avian influenza virus in chickens

The outbreak of clade 2.3.4.4b H5 highly pathogenic avian influenza (HPAI) in North America that started in 2021 has increased interest in applying vaccination as a strategy to help control and prevent the disease in poultry. Two commercially available vaccines based on the recombinant herpes virus of turkeys (rHVT) vector were tested against a recent North American clade 2.3.4.4b H5 HPAI virus isolate: A/turkey/Indiana/22-003707-003/2022 H5N1 in specific pathogen free white leghorn (WL) chickens and commercial broiler chickens. One rHVT-H5 vaccine encodes a hemagglutinin (HA) gene designed by the computationally optimized broadly reactive antigen method (COBRA-HVT vaccine). The other encodes an HA gene of a clade 2.2 virus (2.2-HVT vaccine). There was 100% survival of both chicken types COBRA-HVT vaccinated groups and in the 2.2-HVT vaccinated groups there was 94.8% and 90% survival of the WL and broilers respectively. Compared to the 2.2-HVT vaccinated groups, WL in the COBRA-HVT vaccinated group shed significantly lower mean viral titers by the cloacal route and broilers shed significantly lower titers by the oropharyngeal route than broilers. Virus titers detected in oral and cloacal swabs were otherwise similar among both vaccine groups and chicken types. To assess antibody-based tests to identify birds that have been infected after vaccination (DIVA-VI), sera collected after the challenge were tested with enzyme-linked lectin assay-neuraminidase inhibition (ELLA-NI) for N1 neuraminidase antibody detection and by commercial ELISA for detection of antibodies to the NP protein. As early as 7 days post challenge (DPC) 100% of the chickens were positive by ELLA-NI. ELISA was less sensitive with a maximum of 75% positive at 10DPC in broilers vaccinated with 2.2-HVT. Both vaccines provided protection from challenge to both types of chickens and ELLA-NI was sensitive at identifying antibodies to the challenge virus therefore should be evaluated further for DIVA-VI.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Coherent Spin Pumping Originated from Sub-Terahertz Néel Vector Dynamics in Easy Plane α -Fe 2 O 3 /Pt

We present a thorough study of spin-to-charge current interconversion in bulk and thin films of (0001) 𝛼-Fe2O3/Pt heterostructures by all-optical polarization-controlled microwave excitation at sub-Terahertz frequencies. Our results demonstrate that coherent spin pumping is generated through excitations of both the q-FM and q-AFM modes of antiferromagnetic resonance, provided that the corresponding selection rules are met. Our findings significantly advance current understanding of spin pumping in easy-plane antiferromagnets not only by claiming a substantial contribution from the Néel-vector dynamics of the q-AFM mode but also by experimentally determining the relative strength of the intra- and cross-sublattice components of the spin-mixing conductance, challenging recent reports where the absence of spin pumping from the q-AFM mode in Hematite was interpreted as a complete cancellation between these two components. We also provide an explanation for the previously reported observations and show how the q-AFM spin pumping actually vanishes for thin films, which we speculate being either due to an increased level of inhomogeneities or to insufficient film thickness for the q-AFM mode to fully realize.

Fritjofson, Gregory [University of Central Florida↗
DOE OSTI2021

A Phase-Disposition PWM Enabled Model Predictive Control for a Nine-Level Inner-Interleaved Hybrid Multilevel Converter

This article brings forward a phase-disposition pulse width modulation enabled model predictive control (PDPWM-MPC) for a nine-level inner-interleaved hybrid multilevel converter (9L-IHMC). Firstly, three layers of virtual space vector diagrams (VSVD) are established based on the sign patterns of the original and virtual reference vectors in the abcframe, respectively, to achieve the phase-disposition pulse width modulation (PD-PWM) in a non-independent three-phase way. Then, three adjacent virtual vectors in the third-layer VSVD, together with their optimized duty cycles, are applied to guarantee the optimal current tracking. Finally, through the use of the duty cycle alternation approach, dc-link and floating capacitors voltages are balanced and circulating currents are mitigated as well. The proposed PDPWM-MPC can not only enable the decoupling of the low-and high-frequency stages in the 9L-IHMC, but also reduce both output current ripples and computational burden. In addition, it can achieve a constant equivalent switching frequency and address the disproportion of power losses associated with the PD-PWM. Here, both simulations and experiments on a silicon carbide device-based prototype substantiate the effectiveness of the proposed control strategy.

42 ENGINEERING↗
DOE OSTI2023

The infinite level normal forms for non-resonant double Hopf singularities

Here, in this paper, we explore hypernormal forms of vector fields that have non-resonant double Hopf singularities with a non-zero radial cubic part. Our primary focus is on investigating the infinite-level normal form classification of this type of singularities. We provide a normal form decomposition in terms of planar-rotating and planar-radial vector fields, which greatly facilitate the pattern recognition and analysis of the corresponding generalized homological maps. Notably, our paper represents the first instance of the normal form classification for general non-resonant double Hopf singularities without structural symmetry.

97 MATHEMATICS AND COMPUTING↗