Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1825052

Test Results of a High-Gradient 2.856-GHz Negative Harmonic Accelerating Waveguide

Kutsaev, S. V.·Agustsson, R.·Boucher, S.·Chimalpopoca, O.·Meyer, D.·Murokh, A.·Mustapha, B.·Nassiri, A.·Yu Smirnov, A.·Smith, T.·Yang, Y.
View original source ↗Read source document ↗Export metadata ↓

Abstract

We report the high-gradient tests results of a novel traveling wave accelerating structure for β=0.3 based on a novel approach of operating at the first negative spatial harmonic. Accelerating gradients of 50 MV/m and peak electric fields of 160 MV/m were achieved in a single structure consisting of 15 coupled cells during tests at the advanced photon source. This work was performed by RadiaBeam, in collaboration with the Argonne National Laboratory, as a part of a Research and Development Program for the development of an ultrahigh-gradient linear accelerator, the Advanced Compact Carbon Ion Linac, for hadron therapy.

Explore related subjects

42 ENGINEERINGAccelerating structurecarbon therapydisk-loaded waveguide (DLW)high gradientsnegative spatial harmonic

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kutsaev, S. V., Agustsson, R., Boucher, S., Chimalpopoca, O., Meyer, D., Murokh, A., Mustapha, B., Nassiri, A., Yu Smirnov, A., Smith, T., Yang, Y.. 2021-05-17. Test Results of a High-Gradient 2.856-GHz Negative Harmonic Accelerating Waveguide. https://doi.org/10.1109/lmwc.2021.3081358

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↗