Search NASA⌕ Search

DOE OSTI · 2573469

Enhanced pool boiling heat transfer using novel inverted trapezoid microgroove structures

Abstract

Pool boiling heat transfer improvements with enhanced surfaces have been extensively studied for various hydrofluorocarbon (HFC) refrigerants. However, there is a limited number of studies in the open literature that focus on the pool boiling heat transfer characteristics of hydrofluoroolefin (HFO) refrigerants on enhanced surfaces. This study aims to address this gap by analyzing the pool boiling behavior of HFO refrigerants, specifically R1234yf and R1234ze(E). Experiments were conducted on both plain surfaces and novel inverted trapezoid microgroove structures. Further, the experimental results indicate that the microgroove structures can improve heat transfer coefficients by up to 80% compared to plain surfaces. Additionally, the performance of R1234yf and R1234ze(E) was compared to that of R134. It was observed that R1234yf exhibited comparable performance to R134a, while R1234ze(E) showed slightly lower performance. To assess the influence of saturation temperature on pool boiling heat transfer, tests were conducted at different saturation temperatures of 15℃, 25℃, and 35℃. The findings revealed that the pool boiling heat transfer coefficient increases with rising saturation temperature.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Muneeshwaran, M. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)], Brechtl, Jamieson [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000217394283), Nawaz, Kashif [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000251612491), Yang, Cheng-Min [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)]. 2025-07-20. Enhanced pool boiling heat transfer using novel inverted trapezoid microgroove structures. https://doi.org/10.1016/j.tsep.2025.103895

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↗