What question did this study set out to answer?

This research aims to identify and quantify heat-transfer regimes in closed-loop pulsating heat pipes (CLPHPs) and to assess critical thermal thresholds.

February 22, 2026

Identification of heat-transfer regime in closed-loop pulsating heat pipes via critical thermal analysis

Key Points

This research aims to identify and quantify heat-transfer regimes in closed-loop pulsating heat pipes (CLPHPs) and to assess critical thermal thresholds.
Conducted experiments on CLPHPs with deionized water at three heat inputs: 80 W, 100 W, and 120 W.
Utilized an in-house Python library to perform polynomial regressions on thermal resistance data.
Identified operational regimes using the first, second, and third derivatives of thermal resistance with respect to temperature.
Three operational regimes were delineated: ineffective heat transfer, effective slug–plug pulsation, and transition toward dry-out.
Thermal resistance ranges were quantified for each heat input: 0.29–0.59 KW −1 at 80 W, 0.22–0.45 KW −1 at 100 W, and 0.20–0.39 KW −1 at 120 W.
Results indicated that vigorous oscillations and peak heat-removal rates occurred between effective pulsation boundaries.

Abstract

Closed-loop pulsating heat pipes (CLPHPs) have emerged as versatile, passive two-phase thermal management devices for electric-vehicle batteries, electronics cooling, and aerospace systems. Traditional performance assessments rely on average thermal resistance (TR) over the entire operating range, obscuring dynamic transitions among start-up, effective pulsation, and dry-out regimes. The objectives of this work are to identify and quantify ineffective and effective heat-transfer regimes in a vertical CLPHP charged with deionized water at 60% filling ratio, and to determine critical thermal thresholds for evaporator temperature (T e ), condenser temperature (T c ) and internal pressure. To achieve these objectives, experiments were conducted at heat inputs of 80 W, 100 W, and 120 W. An in-house Python library, PyPulseHeatPipe , was used to fit smooth polynomial regressions by optimal degree to high-resolution TR data and compute its first, second and third derivatives with respect to T e . Four characteristic points (A–D) were extracted from derivative extrema to delineate three operational regimes: A → B (ineffective heat transfer), B → C (effective slug–plug pulsation) and C → D (transition toward dry-out). The novelty of this study lies in applying derivative-informed critical thermal analysis to explicitly identify regime boundaries and corresponding TR ranges—0.29–0.59 KW −1 at 80 W, 0.22–0.45 KW −1 at 100 W and 0.20–0.39 KW −1 at 120 W—in place of conventional single-value metrics. Results demonstrate that the most vigorous oscillations and highest heat-removal rates occur between points B and C, while rapid T c rises beyond point C signal imminent dry-out. This methodology furnishes thermal engineers with a powerful tool to optimize CLPHP design parameters and enhance reliability and safeguard against thermal instability in real-world applications.

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