Nanofluids in microchannel heat sinks for efficient flow cooling of power electronic devices
Source abstract
Abstract
Nanofluids hold significant potential in the field of heat transfer intensification. The objective of this paper is to investigate the thermal-hydraulic characteristics of Ag, SiO2, and Al2O3 nanofluids in rectangular (MC-R), trapezoidal (MC-T), and omega-shaped (MC-O) microchannel heat sinks (MCHSs) through conjugate numerical simulations under a constant heat flux of 100 W/cm2. Water was used as the base fluid, and nanoparticle volume fraction ranged from 0 to 6 %. The average heat transfer coefficient (HTC), Nusselt number, thermal resistance, pressure drop, and pump power as a function of Reynolds number are studied for different nanofluids and cross-sectional shapes in microchannels. The overall performance is also characterized by thermal performance factor, coefficient of performance (COP), and entropy generation analysis. The simulation results show that adding Al2O3 nanoparticles with a volume fraction of 6 % in MC-R at a Reynolds number of 500 results in a substantial 12.3 % increase in the average HTC and an approximately 8 % reduction in total thermal resistance compared to pure water. For the same nanofluid, MC-O exhibits the highest convective HTC, the highest Nusselt number, and the lowest maximum heat sink temperature. The total entropy generation in the MCHSs decreases obviously with increasing Reynolds number. MC-O has the smallest total entropy generation, meaning it has the optimal flow and heat transfer irreversibility. These findings can not only provide valuable guidance for the rational design of MCHSs but also hold significant implications for the effective cooling of microelectronic components via nanofluid-based thermal management technology.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Nanofluid Flow and Heat Transfer · Heat Transfer and Optimization · Heat Transfer Mechanisms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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