Scholarlink

<p>The rising thermal load in modern microelectronic devices necessitates compact cooling solutions with improved heat removal capability. This work examines the thermo-hydraulic performance of straight and secondary-wavy microchannels (SWMC), additively manufactured in AlSi10Mg using Direct Metal Laser Sintering (DMLS). Experiments were conducted under heat fluxes of 20–40 W/cm<sup>2</sup>, and the flow rates of 100–475&#xa0;ml/min (Re = 25–123) using a water-ethylene glycol base fluid and Al<sub>2</sub>O<sub>3</sub> /CuO nanofluids at 0.02 and 0.05% concentrations. The nanofluids enhanced convective performance, yielding up to 12.35% improvement in straight channels and 16.97% in SWMC at 30 W/cm<sup>2</sup> and Re = 123, with the wavy geometry consistently offering superior heat transfer due to curvature-induced secondary flows. In parallel, five machine-learning models were developed to predict wall temperature and Nusselt number; among them, the Gradient Boosting model provided the closest agreement with experimental data. The findings highlight how additively manufactured microchannel geometries, nanoparticle-enhanced coolants, and data-driven predictive tools can be jointly leveraged to advance thermal management in high-power electronic applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integrating machine learning and experimental analysis for nanofluid-enhanced heat transfer in additively manufactured microchannel heat sinks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月20日

查看全文
  • K. Vijetha,
  • Dumpala Lingaraju,
  • Satish Geeri

摘要

The rising thermal load in modern microelectronic devices necessitates compact cooling solutions with improved heat removal capability. This work examines the thermo-hydraulic performance of straight and secondary-wavy microchannels (SWMC), additively manufactured in AlSi10Mg using Direct Metal Laser Sintering (DMLS). Experiments were conducted under heat fluxes of 20–40 W/cm2, and the flow rates of 100–475 ml/min (Re = 25–123) using a water-ethylene glycol base fluid and Al2O3 /CuO nanofluids at 0.02 and 0.05% concentrations. The nanofluids enhanced convective performance, yielding up to 12.35% improvement in straight channels and 16.97% in SWMC at 30 W/cm2 and Re = 123, with the wavy geometry consistently offering superior heat transfer due to curvature-induced secondary flows. In parallel, five machine-learning models were developed to predict wall temperature and Nusselt number; among them, the Gradient Boosting model provided the closest agreement with experimental data. The findings highlight how additively manufactured microchannel geometries, nanoparticle-enhanced coolants, and data-driven predictive tools can be jointly leveraged to advance thermal management in high-power electronic applications.