Engineering Papers⌕ Search

DOE OSTI · 1892745

Selectively adjustable interface assembly

Abstract

A system includes a first component, a second component, a locking pin, and an interface assembly that is configured to selectively adjust a stiffness of a coupling interface between the first component and the second component. The interface assembly includes at least one support coupler secured to the first component. The support coupler(s) includes a main body, a first arm extending from a first side of the main body, a second arm extending from a second side of the main body, and a flange extending from the main body. The flange includes a locking pin hole that is configured to selectively receive the locking pin. The locking pin is selectively moveable between a retracted position in which the locking pin is out of the locking pin hole, and a deployed position in which the locking pin extends into the locking pin hole and a portion of the first component and locks the interface assembly to the first component.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Weathers, Jeffrey W., Padgett, Dustin Gregory. 2022-03-29. Selectively adjustable interface assembly. https://www.osti.gov/biblio/1892745

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↗