Search NASA⌕ Search

DOE OSTI · 2585607

Lightning Induced Interior Fields And Voltage Bounds For Coaxial Topologies

Abstract

We assemble bounding formulas for the interior fields and pin voltages inside a cylindrical coaxial Faraday cage which has been struck by lightning. Approximate formulas for penetrations through a circumferential door slot with subsequent coupling to the interior center conductor structure. Fields at the opposite open end are estimated and used to drive a capped connector and estimate interior pin voltages. Finally, penetrations through small circular holes and direct diffusion through the barrier are also addressed.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Warne, Larry Kevin [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)], San Martin, Luis [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)], Glover, Jeffrey W. [Pantex Plant (PTX), Amarillo, TX (United States)]. 2025-06-01. Lightning Induced Interior Fields And Voltage Bounds For Coaxial Topologies. https://doi.org/10.2172/2585607

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↗