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Heat dissipation analysis and multi-objective optimization of Raspberry Pi CPU by natural convection.

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Title: Heat dissipation analysis and multi-objective optimization of Raspberry Pi CPU by natural convection.
Authors: Wei, Yin1 (AUTHOR), Yao, Junkai1 (AUTHOR), Feng, Haichao1 (AUTHOR), Wu, Qiaoyun1 (AUTHOR), Li, Yuefeng1 (AUTHOR) yf_li@sit.edu.cn, Zou, Jun1 (AUTHOR), Zheng, Hao2 (AUTHOR)
Source: Numerical Heat Transfer: Part A -- Applications. 2025, Vol. 86 Issue 22, p8173-8196. 24p.
Subjects: Raspberry Pi, Multi-objective optimization, Natural heat convection, Mathematical optimization, Computational fluid dynamics, Energy dissipation, Response surfaces (Statistics), Temperature control
Abstract: With the high-speed and high-frequency operation of CPU chips, as well as miniaturization and dense assembly of integrated circuits, the heat generated by the Raspberry Pi CPU is constantly increasing. To address the urgent need for efficient Raspberry Pi CPU heat dissipation and ensure normal operation within a safe temperature range, this study first established a model to simulate the CPU's thermal physics parameters during operation using CFD simulation software. An experimental platform was then set up to validate the CFD simulation results. Based on the validated CFD model, initial heat dissipation design was carried out. On this basis, the heat dissipation system was optimized by altering the heat sink's length, width, fin height, thickness, spacing, and the thermal grease's thermal conductivity between the heat sink and CPU chip. A surrogate model was established using the response surface methodology, and the NSGA-II genetic algorithm was employed for multi-objective optimization of the heat dissipation system's cooling performance. Compared to the initial solution, the chip temperature was reduced by 6.19 °C, but the heat sink mass was increased by 0.65 g. By conducting multi-objective optimization design and making reasonable choices for the parameters, a suitable and an optimum design, in terms of both economics and efficiency, is obtained. These results demonstrate that this research can provide theoretical guidance and technical support for practical production. Furthermore, the methods and findings from this study can be applied to the design of heat dissipation systems for similar applications. [ABSTRACT FROM AUTHOR]
Copyright of Numerical Heat Transfer: Part A -- Applications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Heat dissipation analysis and multi-objective optimization of Raspberry Pi CPU by natural convection.
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  Data: <searchLink fieldCode="AR" term="%22Wei%2C+Yin%22">Wei, Yin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yao%2C+Junkai%22">Yao, Junkai</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Feng%2C+Haichao%22">Feng, Haichao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Qiaoyun%22">Wu, Qiaoyun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Yuefeng%22">Li, Yuefeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> yf_li@sit.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zou%2C+Jun%22">Zou, Jun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zheng%2C+Hao%22">Zheng, Hao</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Numerical+Heat+Transfer%3A+Part+A+--+Applications%22">Numerical Heat Transfer: Part A -- Applications</searchLink>. 2025, Vol. 86 Issue 22, p8173-8196. 24p.
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  Data: <searchLink fieldCode="DE" term="%22Raspberry+Pi%22">Raspberry Pi</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+heat+convection%22">Natural heat convection</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+fluid+dynamics%22">Computational fluid dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+dissipation%22">Energy dissipation</searchLink><br /><searchLink fieldCode="DE" term="%22Response+surfaces+%28Statistics%29%22">Response surfaces (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Temperature+control%22">Temperature control</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: With the high-speed and high-frequency operation of CPU chips, as well as miniaturization and dense assembly of integrated circuits, the heat generated by the Raspberry Pi CPU is constantly increasing. To address the urgent need for efficient Raspberry Pi CPU heat dissipation and ensure normal operation within a safe temperature range, this study first established a model to simulate the CPU's thermal physics parameters during operation using CFD simulation software. An experimental platform was then set up to validate the CFD simulation results. Based on the validated CFD model, initial heat dissipation design was carried out. On this basis, the heat dissipation system was optimized by altering the heat sink's length, width, fin height, thickness, spacing, and the thermal grease's thermal conductivity between the heat sink and CPU chip. A surrogate model was established using the response surface methodology, and the NSGA-II genetic algorithm was employed for multi-objective optimization of the heat dissipation system's cooling performance. Compared to the initial solution, the chip temperature was reduced by 6.19 °C, but the heat sink mass was increased by 0.65 g. By conducting multi-objective optimization design and making reasonable choices for the parameters, a suitable and an optimum design, in terms of both economics and efficiency, is obtained. These results demonstrate that this research can provide theoretical guidance and technical support for practical production. Furthermore, the methods and findings from this study can be applied to the design of heat dissipation systems for similar applications. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Numerical Heat Transfer: Part A -- Applications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1080/10407782.2024.2357596
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 24
        StartPage: 8173
    Subjects:
      – SubjectFull: Raspberry Pi
        Type: general
      – SubjectFull: Multi-objective optimization
        Type: general
      – SubjectFull: Natural heat convection
        Type: general
      – SubjectFull: Mathematical optimization
        Type: general
      – SubjectFull: Computational fluid dynamics
        Type: general
      – SubjectFull: Energy dissipation
        Type: general
      – SubjectFull: Response surfaces (Statistics)
        Type: general
      – SubjectFull: Temperature control
        Type: general
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      – TitleFull: Heat dissipation analysis and multi-objective optimization of Raspberry Pi CPU by natural convection.
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            NameFull: Wei, Yin
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              Text: 2025
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