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DOE OSTI · 1600391

Variable pin fin construction to facilitate compliant cold plates

Schultz, Mark Delorman
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Abstract

A device can comprise a plurality of layers stacked and bonded on one another, wherein at least one layer of the plurality of layers comprises: a first active region comprising first pin portions positioned in a first planar arrangement; and a second active region comprising second pin portions positioned in a second planar arrangement, wherein the second planar arrangement is different from the first planar arrangement. The device can also comprise a conformable layer adjacent to at least one of the plurality of layers.

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BibTeXRIS

Schultz, Mark Delorman. 2019-12-10. Variable pin fin construction to facilitate compliant cold plates. https://www.osti.gov/biblio/1600391

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DOE OSTI2026

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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.

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