A new method to optimize natural convection heat sinks

K. Lampio, R. Karvinen

    Research output: Contribution to journalArticleScientificpeer-review

    7 Citations (Scopus)

    Abstract

    The performance of a heat sink cooled by natural convection is strongly affected by its geometry, because buoyancy creates flow. Our model utilizes analytical results of forced flow and convection, and only conduction in a solid, i.e., the base plate and fins, is solved numerically. Sufficient accuracy for calculating maximum temperatures in practical applications is proved by comparing the results of our model with some simple analytical and computational fluid dynamics (CFD) solutions. An essential advantage of our model is that it cuts down on calculation CPU time by many orders of magnitude compared with CFD. The shorter calculation time makes our model well suited for multi-objective optimization, which is the best choice for improving heat sink geometry, because many geometrical parameters with opposite effects influence the thermal behavior. In multi-objective optimization, optimal locations of components and optimal dimensions of the fin array can be found by simultaneously minimizing the heat sink maximum temperature, size, and mass. This paper presents the principles of the particle swarm optimization (PSO) algorithm and applies it as a basis for optimizing existing heat sinks.

    Original languageEnglish
    Pages (from-to)2571-2580
    JournalHeat and Mass Transfer/Waerme- und Stoffuebertragung
    Volume54
    Issue number8
    DOIs
    Publication statusPublished - Aug 2018
    Publication typeA1 Journal article-refereed

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Condensed Matter Physics
    • Fluid Flow and Transfer Processes

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