Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1838287

ALDWP Operational Highlights 2021

Lunn, Maureen Elizabeth·Vigil, Kenneth C.
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Weapons Pit production manufacturing team completed pit build 21-1 assembly operations on December 23, 2020. This build utilized all essential flow sheet steps and included the second -01 long tube cut off extension and welding activity. This is the best build produced in the MC4597-01 pit program to date. Final out-of-line operations, non-destructive evaluation, and radiography was completed during the week of January 4. Depending on the results from high energy radiography, this pit will be considered to be a candidate for CERT-02 and shipment to Lawrence Livermore National Laboratory in support of pit certification. It is notable that this build was completed while concurrently making Plutonium (Pu) metal, producing additional castings, machining Pu parts for Build 22, completing disassembly of two pits, supporting Nightshade B Pu target characterization, and precision machining of Jasper certification base plates.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lunn, Maureen Elizabeth, Vigil, Kenneth C.. 2022-01-04. ALDWP Operational Highlights 2021. https://doi.org/10.2172/1838287

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↗