Search NASA⌕ Search

DOE OSTI · 1884908

Collisional Effects on Electron Trajectories in Crossed-Field Devices

Abstract

Crossed-field diodes (CFDs) are used in multiple high-power applications and are characterized by an applied magnetic field orthogonal to the electric field, induced by the applied voltage across the anode-cathode gap. In vacuum, the Hull cutoff magnetic field (HCMF) represents the maximum applied magnetic field for which an electron from the cathode can reach the anode. This study investigates the effects of non-vacuum conditions on electron trajectories by introducing electron mobility, which represents particle collisions. We used numerical solutions of the electron force law and particle-in-cell simulations (XPDP1) to assess electron motion for various electron mobilities. For magnetic fields above the HCMF in vacuum, reducing the electron mobility increases the time for an electron emitted from the cathode to reach the anode. Reducing mobility below 22 C s/kg eliminates the HCMF for any magnetic field, meaning that an emitted electron will always cross the gap. We derived the magnetic field, mobility, and electron transit time corresponding to this condition by solving for the condition when the electron velocity in the direction across the anode-cathode gap going to zero at the anode. A parametric study of these conditions using theory and XPDP1 is performed under different gap distances, voltages, and magnetic fields.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Komrska, Allison M., Breen, Lorin I., Loveless, Amanda M., Cartwright, Keith L., Garner, Allen L.. 2022-08-01. Collisional Effects on Electron Trajectories in Crossed-Field Devices. https://doi.org/10.2172/1884908

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↗