Search NASA⌕ Search

DOE OSTI · 1418834

Containment vessel drain system

Abstract

A system for draining a containment vessel may include a drain inlet located in a lower portion of the containment vessel. The containment vessel may be at least partially filled with a liquid, and the drain inlet may be located below a surface of the liquid. The system may further comprise an inlet located in an upper portion of the containment vessel. The inlet may be configured to insert pressurized gas into the containment vessel to form a pressurized region above the surface of the liquid, and the pressurized region may operate to apply a surface pressure that forces the liquid into the drain inlet. Additionally, a fluid separation device may be operatively connected to the drain inlet. The fluid separation device may be configured to separate the liquid from the pressurized gas that enters the drain inlet after the surface of the liquid falls below the drain inlet.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Harris, Scott G.. 2018-01-30. Containment vessel drain system. https://www.osti.gov/biblio/1418834

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↗