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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 559 records · Page 31

An Adaptive Virtual Oscillator Control Structure for Grid-Forming Inverters

The electrical grid is facing unprecedented challenges due to the increasing penetration of inverter-based resources. Grid forming inverters (GFMIs) are a promising technology to address these challenges. Recently, the virtual oscillator based GFMI control is attracting more attention due to its superior performance over other control strategies. In this article, an adaptive control strategy is proposed to provide flexible operation and transition between grid-connected and islanded modes. The controller adapts the virtual oscillator's parameter values depending on the operation mode. It also provides a feedback signal using a measured frequency to account for any steady-state errors and to allow a seamless transition from grid-connected to islanded mode. Finally, to show the feasibility of the proposed controller, this article discusses the simulation results from the implementation of the controller on a single inverter system and on a group of inverters on a large practical utility feeder, the IEEE 13 node feeder, using the DIgSILENT simulation environment.

adaptive control↗

Structural and functional ramifications of antigenic drift in recent SARS-CoV-2 variants

Defenses against SARS-CoV-2 variants Our key defense against the COVID-19 pandemic is neutralizing antibodies against the SARS-CoV-2 virus elicited by natural infection or vaccination. Recent emerging viral variants have raised concern because of their potential to escape antibody neutralization. Wang et al . identified four antibodies from early-outbreak convalescent donors that are potent against 23 variants, including variants of concern, and characterized their binding to the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Yuan et al . examined the impact of emerging mutations in the receptor-binding domain of the spike protein on binding to the host receptor ACE2 and to a range of antibodies. These studies may be helpful for developing more broadly effective vaccines and therapeutic antibodies. —VV

59 BASIC BIOLOGICAL SCIENCES↗

Infusible thermoplastic resin based sandwich structures for wind blade applications and the influence of scrim on facesheet to core interface debonding

Infusible thermoplastic Elium® family of resins from Arkema have garnered much attention in recent years as a possible replacement for thermoset resins in laminate and sandwich composite manufacturing for wind blade applications due to its ease of recyclability and the ability to utilize existing manufacturing processes without imposing complicated variations. However, physical and mechanical properties of the proposed Elium® based thermoplastic composites must be comparable to existing epoxy (thermoset) based composites using manufacturing processes relevant for large wind turbine blades. A 13-meter-long demonstration blade was manufactured for that purpose and sandwich samples were obtained from that project for a detailed study. This paper details three-point flexural properties of unidirectional E-glass fiber reinforced acrylic and epoxy based sandwich panels with identical balsa wood core materials. In addition, to evaluate the relative merit considering debond failure mode, the interfacial critical strain energy release rate, predominantly in mode-1, was compared via single cantilever beam testing. In sandwich composites constructed with balsa wood core material, resin uptake by the balsa core is traditionally impeded via the insertion of a scrim material at the facesheet to core interface. Results revealed that inclusion of scrim mesh layer at the facesheet to core interface reduced flexural properties and strain energy release rates in panels infused with acrylic resin but did not significantly reduce these properties in epoxy infused facesheets.

Engineering↗

Dataset for Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures

This repository hosts an AI-assisted image segmentation and analysis pipeline for Pantoea sp. YR343 cryo-electron microscopy (cryoEM) datasets. The workflow automates membrane thickness measurements, flagella detection, and field-of-view (FOV) screening from low-dose, high-resolution cryoEM micrographs eliminating the need for slow manual annotation. By integrating deep-learning based segmentation (YOLOv11) with quantitative post-processing, this toolkit provides a scalable and reproducible way to study bacterial morphology under hydrated, near-native conditions. The GitHub repository for AI-based tools for cryoEM bacteria ultrastructures can be found here: https://github.com/Sireesiru/Cryo-EM-Ultrastructures/tree/main

60 APPLIED LIFE SCIENCES↗