Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1877995

EM and beam dynamics modeling of CCL with CST Studio

Kurennoy, Sergey S.
View original source ↗Read source document ↗Export metadata ↓

Abstract

The 800-MeV proton linac at LANSCE consists of a drift-tube linac, which brings the beam to 100 MeV, followed by a coupled-cavity linac (CCL). Each of 44 CCL modules contain multiple tanks, and it is fed by a single 805-MHz klystron. CCL tanks are multi-cell blocks of identical re-entrant sidecoupled cavities, which are followed by drifts with magnetic quadrupole doublets. Bridge couplers – special cavities displaced from the beam axis – electromagnetically couple CCL tanks over such drifts. We have developed 3D CST models of CCL tanks of the LANSCE linac. Their electromagnetic analysis is performed using MicroWave Studio. Beam dynamics is modeled with Particle Studio for bunch trains with realistic beam distributions using the CST calculated RF fields and quadrupole magnetic fields to determine the output beam parameters.

Explore related subjects

42 ENGINEERING43 PARTICLE ACCELERATORSAccelerator Design, Technology, and Operations

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kurennoy, Sergey S.. 2022-07-08. EM and beam dynamics modeling of CCL with CST Studio. https://doi.org/10.2172/1877995

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

KEEP EXPLORING

Related reports

DOE OSTI2026

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↗