Search NASA⌕ Search

DOE OSTI · 1580754

URANIUM ACCUMULATIONS IN CASTING OPERATIONS

Abstract

In December 2017, unexpected accumulations of uranium were discovered in several locations throughout the casting process area at the Y-12 National Security Complex that subsequently placed casting operations on hold. The Y-12 uranium casting process takes place in enclosures and hoods, many of which are open to a large floor surface area that has limited visibility and access. At the time of discovery, there were no Nuclear Criticality Safety (NCS) analyses or limits for material accumulations in the casting processing areas. Due to changes in the operation of the casting process and invalid assumptions in the criticality safety evaluation, significant material accumulations were occurring inside the enclosures. The material accumulations were identified as a result of an extent of condition review from another process, and an occurrence report was filed. The NCS organization worked with Production and Operations personnel to identify additional areas of uranium accumulation in the casting area, which resulted in several iterations of cleaning and material collection. Following material recovery, a causal analysis was performed that identified several contributing factors that allowed the accumulations to go unnoticed. In order to resume operations, the criticality safety evaluation was revised to analyze and control uranium accumulations in the casting area. Additionally, improvements in nuclear material control and accountability (NMC&A) controls were required. Casting operations have since resumed with frequently required material collections, which have limited the amount of uranium material accumulation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Wilson, Travis L., Jordan, Spencer P.. 2019-09-15. URANIUM ACCUMULATIONS IN CASTING OPERATIONS. https://www.osti.gov/biblio/1580754

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↗