Open Access

REVIEW

A Review of Pressure Drop Characteristics and Optimization Measures of Two-Phase Flow with Low Boiling Point Working Fluids in Microchannels

Zongyu Jie^1,2, Chao Dang^1,2,*, Qingliang Meng ^3,4

1 Beijing Key Laboratory of Flow and Heat Transfer of Phase Changing in Micro and Small Scale, Beijing, 100044, China
2 Institute of Thermal Engineering, School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China
3 Beijing Institute of Space Mechanics and Electricity, Beijing, 100094, China
4 Beijing Key Laboratory of Advanced Optical Remote Sensing Technology, Beijing, 100094, China

* Corresponding Author: Chao Dang. Email:

(This article belongs to the Special Issue: Fluid Flow, Heat and Mass Transfer within Novel Cooling Structures)

Frontiers in Heat and Mass Transfer 2025, 23(4), 1053-1089. https://doi.org/10.32604/fhmt.2025.066792

Received 17 April 2025; Accepted 12 June 2025; Issue published 29 August 2025

Abstract

With the increasing miniaturization of systems and surging demand for power density, accurate prediction and control of two-phase flow pressure drop have become a core challenge restricting the performance of microchannel heat exchangers. Pressure drop, a critical hydraulic characteristic, serves as both a natural constraint for cooling systems and determines the power required to pump the working fluid through microchannels. This paper reviews the characteristics, prediction models, and optimization measures of two-phase flow pressure drop for low-boiling-point working fluids in microchannels. It systematically analyzes key influencing factors such as fluid physical properties, operating conditions, channel geometry, and flow patterns, and discusses the complex mechanisms of pressure drop under the coupling effect of multi-physical fields. Mainstream prediction models are reviewed: the homogeneous flow model simplifies calculations but shows large deviations at low quality; the separated flow model considers interphase interactions and can be applied to micro-scales after modification; the flow-pattern-based model performs zoned modeling but relies on subjective classification; machine learning improves prediction accuracy but faces the “black-box” problem. In terms of optimization, channel designs are improved through porous structures and micro-rib arrays, and flow rate distribution is optimized using splitters to balance pressure drop and heat transfer performance. This study provides theoretical support for microchannel thermal management in high-power-density devices.

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