Search NASA⌕ Search

DOE OSTI · 1803455

Thin hybrid capillary two-phase cooling system

Abstract

A novel hybrid two-phase cooling system was developed that integrated a mechanically pumped two-phase loop with a capillary-driven two-phase cooling device. The latter cooling mechanism was based on evaporation/boiling from wick structures made by sintering copper particles on the interior surfaces of a copper cold plate. The cold plate provided cooling to two surfaces and each of them included four heaters in series. The novelty of the developed technology was preventing flooding of the evaporator wicks by isolating the evaporation surface from the pumped liquid flow that fed them. This arrangement allowed for a high liquid feed flow rate much greater than would be allowed by a capillary pumped system while maintaining a low thermal resistance at the evaporation surface. Using this approach, the cooling system removed over 850 W with a low pumping power below 1.0 W while using R245fa as the working fluid. The equivalent heat fluxes exceeded 970 W/cm 2 over areas less than 0.12 cm 2 . The measured thermal resistance was as low as 0.09 K-cm 2 /W. The presented thermal management solution enables an increase in the power of high heat flux electronic devices beyond the state-of-the-art.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shaeri, Mohammad Reza, Bonner, Richard W., Ellis, Michael C.. 2020-01-28. Thin hybrid capillary two-phase cooling system. https://doi.org/10.1016/j.icheatmasstransfer.2020.104490

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↗