Search NASA⌕ Search

DOE OSTI · 1827564

High Fidelity Tomographic Density Reconstructions [Slides]

Abstract

Pressed high explosives have small density variations associated with the pressing process, from a few to sub percent depending on the material. Variations at these levels are known to have an impact on HE performance. Currently, a destructive testing method based on physical sectioning followed by immersion density measurements (slice and dice) is utilized. However, this method has poor spatial resolution limited by the the sectioning size, 1-2 cm. If possible, radiographic methods including Computed Tomography (CT) would be preferred, as they are non-destructive and have higher (less than .1 mm) spatial resolution. However, beam hardening, scattered radiation, and detector effects make density reconstructions at this level extremely challenging.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Klasky, Marc Louis, Espy, Michelle A., Disterhaupt, Jennifer Lynn Schei, McCann, Michael Thompson. 2021-10-01. High Fidelity Tomographic Density Reconstructions [Slides]. https://doi.org/10.2172/1827564

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↗