Modeling and Experimental Data Analysis of Oscillating Heat Pipes: A Review

Author:

Mohammadian Shahabeddin K.12,H. Mohammed Ramy3,Nunez Roberto3,Rupam Tahmid3ORCID,Spitzenberger Jeremy3,Hoelle James3,Ibrahim Omar T.3,Feng Frank Z.3ORCID,Miller Alex4,Taft Brenton56,Allison Jonathan5,Abu-Heiba Ahmed7ORCID,Mahderekal Isaac7,Ma Hongbin3ORCID

Affiliation:

1. Multiphysics Energy Research Center (MERC), College of Engineering, University of Missouri-Columbia , Columbia, MO 65211 ; , Boulder City, NV 89005

2. Rocky Research, Honeywell Aerospace Technologies , Columbia, MO 65211 ; , Boulder City, NV 89005

3. Multiphysics Energy Research Center (MERC), College of Engineering, University of Missouri-Columbia , Columbia, MO 65211

4. ThermAvant Technologies, LLC , Columbia, MO 65202

5. U.S. Air Force Research Laboratory , Kirtland Air Force Base, NM 87117

6. Air Force Research Laboratory Directed Energy Directorate

7. Rocky Research, Honeywell Aerospace Technologies , Boulder City, NV 89005

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 nondeterministic 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.

Publisher

ASME International

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