In this project, we present the optimization process for improving the thermal performance of a microchannel heat sink using the Design of Experiment (DOE) in ANSYS software.

We intend to optimize the design of a microchannel heat sink. Therefore, we defined 3 input parameters: Two geometric factors, including the length and height sizes of the rectangular cross-section of the cooling fluid channel, and one operating factor, i.e., porosity of the porous medium of the channel. Then, we defined the maximum temperature of the microchannel surface as the target output parameter.

We used the Design Exploration tool to perform the optimization process.

First, we start with the Design of Experiment (DOE). We generated the design points using the Latin Hypercube Sampling Design (LHSD). According to the maximum and minimum ranges for all three input parameters, 10 design points are generated.

Then, we continue with the Response Surface Methodology (RSM). We estimated the output parameter values ​​based on the Genetic Aggregation type.

Microchannel Heat Sink Thermal Performance Optimization using Design of Experiments (DOE) in ANSYS

Project Overview

This project presents the optimization process for enhancing the thermal performance of a microchannel heat sink using Design of Experiments (DOE) methodology in ANSYS software.

Microchannel heat sinks are effective devices for dissipating substantial heat generated by high-power electronic components. Their widespread application stems from high heat transfer coefficients and large specific surface areas.

The modeled microchannel heat sink features a solid body containing a cooling fluid channel filled with porous media. While typical microchannel heat sinks comprise multiple channel rows, this model represents a single microchannel section for computational simplicity.

Methodology

Geometry and Meshing: The 3D microchannel heat sink was modeled in Design Modeler software, with subsequent mesh generation performed in ANSYS Meshing software.

Optimization Parameters: The optimization process incorporates three input parameters:

• Geometric Factors (2): Length and height dimensions of the rectangular cooling fluid channel cross-section
• Operating Factor (1): Porosity of the porous medium within the channel

The target output parameter is the maximum temperature on the microchannel surface.

Optimization Process: The Design Exploration tool facilitated optimization through two sequential stages:

1. Design of Experiments (DOE): Design points were generated using Latin Hypercube Sampling Design (LHSD). Based on defined maximum and minimum ranges for all three input parameters, 10 design points were created.

2. Response Surface Methodology (RSM): Output parameter values were estimated using Genetic Aggregation algorithms.

Results and Analysis

Parameter Effects: RSM-generated 2D and 3D plots of maximum temperature illustrate the combined effects of the three input parameters. Results demonstrate that increasing length, height, and porosity all decrease maximum temperature.

Channel Dimension Impact: Increasing the cooling channel’s length and height reduces the heat sink surface’s maximum temperature. Larger length and height dimensions expand the cooling channel cross-section, increasing incoming fluid flow rate. This enhancement improves heat transfer and cooling efficiency.

Porosity Impact: Increasing porous medium porosity within the cooling channel reduces the heat sink’s maximum temperature by enhancing heat transfer processes. However, porosity effects on surface temperature are less pronounced than geometric parameters (length and height).

Validation and Sensitivity: Additional analysis included:

• Local Sensitivity Plots: Quantifying each input parameter’s influence on the output parameter, revealing the relative importance of geometric versus operating parameters
• Goodness of Fit Plots: Validating the accuracy of RSM-estimated results against actual design point results, confirming the optimization methodology’s reliability

Key Finding: The optimal thermal performance is achieved by maximizing channel dimensions (length and height) and porosity, with geometric parameters demonstrating stronger influence on cooling performance than porosity variations.