Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1827403

Flow Sensor Test Article (F-STAr) - Status Report for FY2021

Rein, J.·Weathered, M.·Kent, E.·Kultgen, D.·Grandy, C.
View original source ↗Read source document ↗Export metadata ↓

Abstract

This report will review updates on the status of F-STAr. First, the report will describe a general overview of the test article and how each component interacts. Then, each main component will be discussed in a deep dive section which will review the design and other relevant details. F-STAr components which are discussed in more detail include the 120 GPM-pump, the support structure and test sections, 5 kW-heater, the 2 kW-cooler and cooling system, and the submersible flowmeter. Finally, this report will conclude with a brief recap and a bulleted outline of the path moving forward.

Explore related subjects

42 ENGINEERING46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rein, J., Weathered, M., Kent, E., Kultgen, D., Grandy, C.. 2021-10-01. Flow Sensor Test Article (F-STAr) - Status Report for FY2021. https://doi.org/10.2172/1827403

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↗