Abstract
Abstract An oscillating heat pipe (OHP) is a special kind of heat pipe in which the working fluid experiences an oscillatory motion without the need for wick structures or external electrical power input beyond a driving temperature difference. In contrast to traditional heat pipes and thermosyphons, which rely on capillarity or gravitation, OHPs operate based on pressure difference which causes oscillating motion. This oscillation is very important since it is the main reason behind the higher heat flux acquisition capability that OHPs exhibit with respect to other types of heat pipes. However, this oscillation is non-deterministic and thus difficult to model, which hinders the ability to control and design OHPs. Since the invention of OHPs in the early 1990s, many researchers have tried to analyze and predict the oscillating motions in OHPs under different working conditions to enhance their performance and reliability to make them suitable for industrial applications. This review presents the evolution of OHP modeling, as well as mathematical approaches to the analysis of experimental data obtained from OHPs. Furthermore, the machine learning (ML) models applied on OHPs are reviewed.
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