Engineering Papers⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 3097477

Cataloging Legacy Data from the Tritium Systems Test Assembly Program

Lizardi-Lobb, Vicky [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000272656397)·Bullock, Claire L. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0009000150419032)·Hypes-Mayfield, Victoria [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000173057663)·Dumont, Joseph H. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000196980995)
View original source ↗Read source document ↗Export metadata ↓

Abstract

The Tritium Systems Test Assembly (TSTA) at Los Alamos National Laboratory, operational from 1984 to 2001, was critical in advancing fusion fuel cycle technologies, including tritium storage, gas separation, and pumping. TSTA’s contributions, particularly in safe tritium operations, have influenced subsequent fusion projects. This paper discusses the ongoing effort to digitize and catalog TSTA’s historical data to create a searchable resource for the fusion research community. While the long-term objective is to develop a relational database for structured data management, the project remains in the early phase, with current efforts focused on scanning and indexing physical documents. Initial plans for database implementations are also presented, outlining key considerations for structure, query indexing, and standardization. As digitization progresses, future discussions will refine these implantation details to ensure an efficient and comprehensive system. This initiative aims to preserve critical legacy data, enhance the design of tritium system facilities, and support the next generation of fusion energy research.

Explore related subjects

42 ENGINEERING96 KNOWLEDGE MANAGEMENT AND PRESERVATIONTritium Systems Test Assembly (TSTA)data digitizationfusion fuel cycle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lizardi-Lobb, Vicky [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000272656397), Bullock, Claire L. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0009000150419032), Hypes-Mayfield, Victoria [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000173057663), Dumont, Joseph H. [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000196980995). 2025-06-16. Cataloging Legacy Data from the Tritium Systems Test Assembly Program. https://doi.org/10.1080/15361055.2025.2498231

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↗