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EXPERIMENTAL ANALYSIS OF HYBRID THERMAL COOLING SYSTEMS FOR ENHANCED HEAT DISSIPATION IN HIGH-POWER ELECTRONICS

Area: Department of Mechanical Engineering
Abstract: High-power electronics with unprecedented heat flux density have developed rapidly, thus driving the need for efficient and reliable thermal management solutions. Traditional single-mode cooling solutions–fan driven air convection, and single-phase liquid cooling–were found to be insufficient in addressing the thermal dissipation needs of modern power electronics (e.g. advanced epitaxial silicon or silicon carbide-based power modules/inverters paired high-frequency processors). Introduction: Hybrid cooling systems that combined two or more synergistic heat transfer mechanisms, such as liquid cooling + phase change materials; thermoelectric cooling+microchannel heat sink; and heat pipe networks+impinging jet arrays, have become a promising and feasible paradigm for advanced thermal management in future generations. This review paper provides a complete meta-study on the recent trial studies in last 20 years for high-power electronics applications hybrid cooling systems. The thermal resistance, junction temperature reduction, coefficient of performance (COP), pressure drop and volumetric power density are key performance metrics examined. The survey summarizes experimental results across more than thirty foundational studies to highlight high-level design parameters, frequent failure modes and common performance trade-offs. The methodology section outlines the systematic literature selection criteria, definitions outlined and codified in data extraction protocols, and a comparative evaluation framework employed. Conclusion Hybrid approaches are always better than single mode systems thermally, however analysis has shown that there are lingering difficulties with regards to miniaturization of devices, system complexities, cost and long-term robustness. The paper ends with some suggestions for future research directions such as intelligent adaptive cooling, heat exchangers additively manufactured, and thermal optimization aided by machine learning.
Author: Rahul Rathore1, Khemraj Beragi 2
DUI: 180724/IJORAR-1790
Page: 12
Paper Id: 1790
Publication Date: 12-Jun-2026
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