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Abstract This paper proposes a computational fluid dynamics (CFD) simulation methodology for the multi-design variable optimization of heat sinks for natural convection single-phase immersion cooling of high power-density Data Center server electronics. Immersion cooling provides the capability to cool higher power-densities than air cooling. Due to this, retrofitting Data Center servers initially designed for air-cooling for immersion cooling is of interest. A common area of improvement is in optimizing the air-cooled component heat sinks for the fluid and thermal properties of liquid cooling dielectric fluids. Current heat sink optimization methodologies for immersion cooling demonstrated within the literature rely on a server-level optimization approach. This paper proposes a server-agnostic approach to immersion cooling heat sink optimization by developing a heat sink-level CFD to generate a dataset of optimized heat sinks for a range of variable input parameters: inlet fluid temperature, power dissipation, fin thickness, and number of fins. The objective function of optimization is minimizing heat sink thermal resistance. This research demonstrates an effective modeling and optimization approach for heat sinks. The optimized heat sink designs exhibit improved cooling performance and reduced pressure drop compared to traditional heat sink designs. This study also shows the importance of considering multiple design variables in the heat sink optimization process and extends immersion heat sink optimization beyond server-dependent solutions. The proposed approach can also be extended to other cooling techniques and applications, where optimizing the design variables of heat sinks can improve cooling performance and reduce energy consumption.
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Heat Sink Design Optimization via GAN-CNN Combined Deep-Learning
Abstract This work proposes a combined deep learning based approach to improve thermal component heat sinks involving turbulent fluid flow. A Generative Adversarial Network (GAN) is trained to learn and recreate the new ellipse based heat sinks. Simulation data for new designs is efficiently generated using OpenFOAM 7 (Open Source Computational Fluid Dynamics software) along with high throughput computing. To improve the speed of design evaluation, a Convolutional Neural Network (CNN) is trained to predict the entire temperature field for a given design. The trained CNN is able to predict the entire temperature field for the design with a mean average error of 1.140 degrees kelvin in 0.04 seconds (22,500 times faster than the simulation). A combined model is formed using the trained CNN and GAN networks to create and simulate new designs. The combined model optimizes the latent representation of 64 random designs on a Graphical Processing Unit (GPU) in ten minutes. The optimized designs perform fourteen degrees kelvin better on average than the non-optimized designs. The highest preforming design outperforms any design in the training data by 1.83 degrees kelvin.
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- PAR ID:
- 10623757
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- ISBN:
- 978-0-7918-8730-1
- Format(s):
- Medium: X
- Location:
- Boston, Massachusetts, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/DETC2023-116429
