Microchannel Heat Sink Optimization: DOE Applying LHSD Method

Microchannel Heat Sink Optimization: DOE Applying LHSD Method

Description

This project presents an optimization process for improving the thermal performance of a microchannel heat sink using Design of Experiment (DOE) techniques in ANSYS. Microchannel heat sinks are well-suited for dissipating the substantial heat generated by high-power electronic devices, valued for their high heat transfer coefficients and large specific surface area.

The heat sink modeled here consists of a solid zone body containing a cooling fluid channel filled with a porous medium. While microchannel heat sinks typically contain several parallel channel rows, this project models only a single representative section for construction simplicity.

Methodology

The 3D microchannel heat sink geometry was designed in Design Modeler and meshed in ANSYS Meshing. Three input parameters were defined for optimization: two geometric factors — the length and height of the cooling channel's rectangular cross-section — and one operating factor, the porosity of the channel's porous medium. Maximum microchannel surface temperature was defined as the target output parameter.

The Design Exploration tool drove the optimization process. Design points were first generated using Latin Hypercube Sampling Design (LHSD), producing 10 design points based on the defined minimum and maximum ranges for all three input parameters. The Response Surface Methodology (RSM), using Genetic Aggregation, was then applied to estimate output parameter values across the full design space.

Conclusion

The resulting 2D and 3D response surface plots reveal the combined effect of all three input parameters on maximum temperature. Increasing channel length and height both reduce maximum surface temperature, since larger cross-sectional dimensions increase the incoming fluid's flow rate, enhancing heat transfer and improving overall cooling performance. Increasing the porous medium's porosity similarly reduces maximum temperature, since a more porous medium enhances the heat transfer process — though this porosity effect proved smaller in magnitude than the dimensional (length and height) parameters.

Local sensitivity plots further quantify how strongly each input parameter influences the output, while goodness-of-fit plots confirm the accuracy of the RSM-estimated results relative to the actual values obtained at the sampled design points — together validating this DOE-based approach as an effective method for optimizing microchannel heat sink thermal performance.