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Research Papers: Design of Energy, Fluid, and Power Handling Systems

Optimal Flow Control and Single Split Architecture Exploration for Fluid-Based Thermal Management

[+] Author and Article Information
Satya R. T. Peddada

Department of Industrial and Enterprise Systems Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: speddad2@illinois.edu

Daniel R. Herber

Department of Industrial and Enterprise Systems Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: herber1@illinois.edu

Herschel C. Pangborn

Department of Mechanical Science and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: pangbor2@illinois.edu

Andrew G. Alleyne

Department of Mechanical Science and Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: alleyne@illinois.edu

James T. Allison

Department of Industrial and Enterprise Systems Engineering,
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: jtalliso@illinois.edu

This work was presented in part at the 44th ASME Design Automation Conference, Quebec City, Canada, August 26–29, 2018 [1].

Contributed by the Design Automation Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received November 16, 2018; final manuscript received March 1, 2019; published online April 18, 2019. Assoc. Editor: Ashvin Hosangadi.

J. Mech. Des 141(8), 083401 (Apr 18, 2019) (12 pages) Paper No: MD-18-1843; doi: 10.1115/1.4043203 History: Received November 16, 2018; Accepted March 06, 2019

High-performance cooling is often necessary for thermal management of high power density systems. However, human intuition and experience may not be adequate to identify optimal thermal management designs as systems increase in size and complexity. This article presents an architecture exploration framework for a class of single-phase cooling systems. This class is specified as architectures with multiple cold plates in series or parallel and a single fluid split and junction. Candidate architectures are represented using labeled rooted tree graphs. Dynamic models are automatically generated from these trees using a graph-based thermal modeling framework. Optimal performance is determined by solving an appropriate fluid flow distribution problem, handling temperature constraints in the presence of exogenous heat loads. Rigorous case studies are performed in simulation, with components subject to heterogeneous heat loads and temperature constraints. Results include optimization of thermal endurance for an enumerated set of 4051 architectures. The framework is also applied to identify cooling system architectures capable of steady-state operation under a given loading.

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References

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Figures

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Fig. 1

Illustration of the class of cooling system architectures considered, consisting of cold plate heat exchangers (CPHXs) in series and parallel, and constrained to have a single split/junction in the coolant flow

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Fig. 2

Notional graph example to demonstrate key features of the modeling approach

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Fig. 3

Graph for the class of thermal management architectures considered in this article. Vertices representing fluid temperatures are colored white, while vertices representing wall temperatures are colored gray.

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Fig. 4

Two labeled rooted tree graphs in the class of graphs of interest: (a) 1–23 and (b) 3–5–62–714

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Fig. 5

The 13 unique architectures when Nc = 3: (a) 1–2–3, (b) 1–23, (c) 1–32, (d) 2–13, (e) 2–31, (f) 3–12, (g) 3–21, (h) 132, (i) 123, (j) 213, (k) 231, (l) 312, and (m) 321

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Fig. 6

Optimal temperature and flow trajectories for the architectures with Ps = [5, 5, 5]′ kW and Tmax = 45 °C: (a) Nf = 3, (b) Nf = 2, and (c) Nf = 1

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Fig. 7

Optimal temperature and flow trajectories for the best architectures with Ps = [2.5, 5, 7.5]′ kW and Tmax = 45 °C: (a) best architecture with Nf = 3 (1–2–3), (b) best architecture with Nf = 2 (1–32), and (c) best architecture with Nf = 1 (321)

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Fig. 8

Optimal temperature and flow trajectories for the best architectures with Ps = [3.75, 3.75, 7.5]′ kW and Tmax = 45 °C: (a) best architecture with Nf = 3 (1–2–3), (b) best architecture with Nf = 2 (1–32), and (c) best architecture with Nf = 1 (321)

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Fig. 9

Results from case study 2 for enumeration with four CPHXs: (a) sorted results for all 73 architectures, (b) best architecture (3–421), and (c) best pure series architecture (3–4–2–1)

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Fig. 10

Results from case study 2 for enumeration with six CPHXs: (a) sorted results for all 4051 architectures, (b) best architecture (2–3–46–51), and (c) pure parallel architecture (1–2–3–4–5–6)

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Fig. 11

Select results for Ps = [2.25, 4.5, 6.75] kW and Tmax = 45 °C with steady-state solutions: (a) feasible architecture with Nf = 3 (1–2–3), (b) feasible architecture with Nf = 1 (321), and (c) infeasible architecture with least tend (123)

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Fig. 12

Thermal endurance results for Ps = [2.25, 4.5, 6.75] kW and Tmax = 45 °C with varying values of the pump mass flow rate (select architectures are labeled)

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Fig. 13

Results from case study 4 with time-varying power flows: (a) time-varying power flows, (b) best architecture (1–2–3), and (c) worst architecture (312)

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