Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 993180

ASME Code Efforts Supporting HTGRs

Morton, D K
View original source ↗Read source document ↗Export metadata ↓

Abstract

In 1999, an international collaborative initiative for the development of advanced (Generation IV) reactors was started. The idea behind this effort was to bring nuclear energy closer to the needs of sustainability, to increase proliferation resistance, and to support concepts able to produce energy (both electricity and process heat) at competitive costs. The U.S. Department of Energy has supported this effort by pursuing the development of the Next Generation Nuclear Plant, a high temperature gas-cooled reactor. This support has included research and development of pertinent data, initial regulatory discussions, and engineering support of various codes and standards development. This report discusses the various applicable American Society of Mechanical Engineers (ASME) codes and standards that are being developed to support these high temperature gascooled reactors during construction and operation. ASME is aggressively pursuing these codes and standards to support an international effort to build the next generation of advanced reactors so that all can benefit.

Explore related subjects

42 ENGINEERINGCONSTRUCTIONELECTRICITYENGINEERSGAS COOLED REACTORSNext Generation Nuclear Plant ProjectNGNPNUCLEAR ENERGYPROCESS HEATPROLIFERATION

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Morton, D K. 2010-09-01. ASME Code Efforts Supporting HTGRs. https://doi.org/10.2172/993180

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↗