Engineering Papers⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2202599

Water-Resistant Container Testing: A426-1000 Container Design

Davis, John Taylor
View original source ↗Read source document ↗Export metadata ↓

Abstract

The purpose of this report is to detail the results of water ingress testing conducted on the A426-1000 container design and discuss these results against the water-resistant container requirements of TA55-AP- 522 and PA-RD-01009. The A426-1000 is a proposed commercially off the shelf inner container option for material storage inside of a glovebox and/or as an approved non-hermetically sealed inner of an approved packaging configuration inside of an approved outer container as listed in PA-RD-01022. The A426-1000 is shaped like a slip lid container, made of 316L stainless steel, and utilizes a smooth, threaded lid with a silicone O-ring for closure and containment. This container both lid and body is machined out of one billet piece of 316L stainless steel with no welds. See Figure 1 and Table 1 for a summary of the container specifications. For more details on the container design, see Section 1.4.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Davis, John Taylor. 2023-09-07. Water-Resistant Container Testing: A426-1000 Container Design. https://doi.org/10.2172/2202599

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↗