Graduation Semester and Year
Summer 2024
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Mechanical Engineering
Department
Mechanical and Aerospace Engineering
First Advisor
Dr. Dereje Agonafer
Abstract
Traditional air-cooling along with corresponding heat sinks are beginning to reach performance limits, requiring lower air-supply temperatures and higher air-supply flowrates, in order to meet the rising thermal management requirements of high power-density electronics. A switch from air-cooling to single-phase immersion cooling provides significant thermal performance improvement and reliability benefits. When hardware which is designed for air cooling is implemented within a single-phase immersion cooling regime, optimization of the heat sinks provides additional thermal performance improvements. This work investigates performance of a machine learning (ML) approach to building a predictive model of the multi objective and multi-design variable optimization of an air-cooled heat sink for single-phase immersion-cooled servers. Parametric simulations via high fidelity CFD numerical simulations are conducted by considering the following design variables composed of both geometric and material properties for both forced and natural convection: fin height, fin thickness, number of fins, and thermal conductivity of the heat sink. Generating a databank of 864 points through CFD numerical optimization simulations, the data set is used to train and evaluate the machine PhD Dissertation Defense Announcement Mechanical and Aerospace Engineering Department University of Texas at Arlington learning algorithms' ability to predict heat sink thermal resistance and pressure drop across the heat sink. Three machine learning regression models are studied to evaluate and compare the performance of polynomial regression, random forest, and neural network to accurately predict heat sink thermal resistance and pressure drop as a function of various design inputs. This approach to utilizing numerical simulations for building a databank for machine learning predictive models can be extrapolated to thermal performance prediction and parameter optimization in other electronic thermal management applications and thus reducing the design lead time significantly. Heat sinks designed for electrochemical additive manufacturing (ECAM) with Body Centered Cubic (BCC) lattice structures are evaluated using computational fluid dynamics (CFD) conjugate heat transfer (CHT) analyses in ANSYS Fluent for single-phase immersion cooling applications. More complex heat sink cooling surface geometries enabled by ECAM fabrication technologies have a greater surface area to volume ratio than traditional parallel plate fins. To benchmark performance, we establish a baseline immersion cooling heat sink metric for various dielectric fluid flowrates using a conventional finned heat sink. We then compare the thermal resistance and pressure drop characteristics of this baseline with those of the ECAM BCC lattice heat sink design. Additional design factors of wall thickness and porosity are also considered. This study evaluates the thermal performance of ECAM-fabricated BCC lattice heat sinks as an innovative solution for enhancing cooling efficiency in high power-density electronics immersion cooling applications. The findings are expected to offer valuable insights into the viability and performance advantages of such heat sinks. By leveraging the capabilities of AM designed structures, this research contributes to the development of more effective and sustainable immersion cooling solutions for next generation electronic systems.
Disciplines
Heat Transfer, Combustion
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Herring, Joseph F. II, "Machine Learning-Based Methodology for Multi-Objective and Multi-Design Variable Optimization of Finned Heat Sinks and Evaluation of Electrochemical Additive Manufactured Heat Sink Designs for Single-Phase Immersion Cooling" (2024). Mechanical and Aerospace Engineering Dissertations. 258.
https://mavmatrix.uta.edu/mechaerospace_dissertations/258