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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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To Quantize or Not to Quantize: Effects on Generative Models for 2D Heat Sink Design
Abstract In Topology Optimization (TO) and related engineering applications, physics-constrained simulations are often used to optimize candidate designs given some set of boundary conditions. However, such models are computationally expensive and do not guarantee convergence to a desired result, given the frequent non-convexity of the performance objective. Creating data-based approaches to warm-start these models — or even replace them entirely — has thus been a top priority for researchers in this area of engineering design. In this paper, we present a new dataset of two-dimensional heat sink designs optimized via Multiphysics Topology Optimization (MTO). Further, we propose an augmented Vector-Quantized GAN (VQGAN) that allows for effective MTO data compression within a discrete latent space, known as a codebook, while preserving high reconstruction quality. To concretely assess the benefits of the VQGAN quantization process, we conduct a latent analysis of its codebook as compared to the continuous latent space of a deep AutoEncoder (AE). We find that VQGAN can more effectively learn topological connections despite a high rate of data compression. Finally, we leverage the VQGAN codebook to train a small GPT-2 model, generating thermally performant heat sink designs within a fraction of the time taken by conventional optimization approaches. We show the transformer-based approach is more effective than using a Deep Convolutional GAN (DCGAN) due to its elimination of mode collapse issues, as well as better preservation of topological connections in MTO and similar applications.
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- Award ID(s):
- 1943699
- PAR ID:
- 10651202
- Publisher / Repository:
- American Society of Mechanical Engineers
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/DETC2024-142052
