Engineering Papers⌕ Search

DOE OSTI · 1568486

Compact thermoelastic cooling system

Abstract

A compact cooling system based on thermoelastic effect is provided. In one embodiment, the system comprises a pair of rollers serving as a heat sink, stress applicator and belt drive, a cold reservoir and a solid refrigerant belt coupled to the cold reservoir and to the heat sinks to pump heat from the cold reservoir to the heat sink. The refrigerant belt comprises solid thermoelastic materials capable of thermoelastic effect. The refrigerant material is mechanically compressed when entering the gap of the roller and subsequently released after passing through. When compressed the refrigerant material transforms to martensite phase and releases heat to the roller and neighboring materials. After released by the rollers, the refrigerant material transforms back to austenite and absorbs heat from the ambient atmosphere.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Cui, Jun, Takeuchi, Ichiro. 2019-06-18. Compact thermoelastic cooling system. https://www.osti.gov/biblio/1568486

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↗