Multiscale hybrid wick: Two types of dry-out mechanisms and optimization strategy

  • Duhyeon Lee
  • , Sanghun Lee
  • , Soosik Bang
  • , Hyunghoon Song
  • , Joongmyeon Bae
  • , Youngsuk Nam

Research output: Contribution to journalArticlepeer-review

Abstract

Capillary-driven transport in porous wicks are core elements in phase-change-based thermal management solutions such as vapor chambers, cold plates, heat pipes, and heat exchangers. The biggest challenge in developing high performance wicks is the conflict between the heat transfer coefficient and critical heat flux. To address these challenges, hybrid wicks composed of liquid supply wicks connected to thin evaporator wicks have been suggested. Nevertheless, the understanding of the dry-out mechanism of multiscale hybrid wicks was insufficient, which limits the development of thermal–hydraulic performance prediction models. In this work, the dry-out types of multiscale hybrid wick (MSHW) were classified into two categories, and a numerical model reflecting the dry-out mechanism was developed. The two types of dry-out mechanisms and the developed model were validated by capillary-rise and evaporative heat transfer experiments. The design criterion determining the type of dry-out was also suggested. By considering the dry-out mechanisms, the developed model provided 10 times smaller error in CHF prediction compared to that of conventional model. Then the artificial neural networks (ANN) model was trained using the data from the numerical model to efficiently identify the optimal design of the multiscale wicks. The optimized multiscale wick achieved a CHF approximately 24 times higher than that of single scale wicks at a comparable thermal resistance.

Original languageEnglish
Article number120703
JournalEnergy Conversion and Management
Volume348
DOIs
StatePublished - 15 Jan 2026

Bibliographical note

Publisher Copyright:
© 2025 Elsevier Ltd.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Artificial neural network
  • Capillary-driven evaporation
  • Dry-out mechanism
  • Multiscale hybrid wick
  • Optimization

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