Search NASA⌕ Search

DOE OSTI · 1078323

Electrostatic generator/motor configurations

Abstract

Electrostatic generators/motors designs are provided that include a stator fixedly connected to a first central support centered about a central axis. The stator elements are attached to the first central support. Similarly, a second stator is connected to a central support centered about the central axis, and the second stator has stator elements attached to the second central support. A rotor is located between the first stator and the second stator and includes an outer support, where the rotor is rotatably centered about the central axis, the rotor having elements in contact with the outer support, each rotor element having an extending rotor portion that extends radially from the outer support toward the axis of rotation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Post, Richard Freeman. 2012-09-11. Electrostatic generator/motor configurations. https://www.osti.gov/biblio/1078323

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗