Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2279194

LCLS-II Helium Refrigeration System Commissioning Results

Apte, A.·Ravindranath, V.·Fauve, E.·Pflueckhahn, D.·Vyawahare, S.·Rama, B.·Shrishrimal, S.·Pucci, J.·Robinson, D.·Keenan, M.·Creel, J.·Norton, R.
View original source ↗Read source document ↗Export metadata ↓

Abstract

SLAC National Accelerator Laboratory has upgraded to LCLS-II, featuring a 4 GeV superconducting linear accelerator composed of 37 cryomodules and two large helium refrigeration systems with a cooling capacity of 4 kW at 2.0 K. The LCLS-II Helium Refrigeration System (HRS) consists of two compressor stations, each with a power of approximately 4.5 MW, two 4.5 K cold boxes, each with a power of 18 kW equivalent at 4.5 K, and two sets of cold compressors that can each produce a flow of 230 g/s at 31 mbar (2.0K). Performance tests of the HRS were meticulously planned and successfully carried out, with results demonstrating that it exceeded the process requirements for LCLS-II operations. This paper provides a detailed presentation of the LCLS-II HRS performance and the challenges encountered during the commissioning phase.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Apte, A., Ravindranath, V., Fauve, E., Pflueckhahn, D., Vyawahare, S., Rama, B., Shrishrimal, S., Pucci, J., Robinson, D., Keenan, M., Creel, J., Norton, R.. 2024-05-01. LCLS-II Helium Refrigeration System Commissioning Results. https://doi.org/10.1088/1757-899x%2F1301%2F1%2F012112

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↗