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Research Papers: Heat and Mass Transfer

Multiobjective Optimization of a Heat-Sink Design Using the Sandwiching Algorithm and an Immersed Boundary Conjugate Heat Transfer Solver

[+] Author and Article Information
Tommy Andersson

CFD Engineer Computational
Engineering and Design,
Fraunhofer-Chalmers Centre,
Chalmers Science Park,
Gothenburg SE-412 88, Sweden

Dimitri Nowak

Researcher Optimization,
Fraunhofer Institute for Industrial
Mathematics ITWM,
Fraunhofer-Platz 1,
Kaiserslautern 67663, Germany
e-mail: dimitri.nowak@itwm.fraunhofer.de

Tomas Johnson

Applied Researcher,
Computational Engineering and Design,
Fraunhofer-Chalmers Centre,
Chalmers Science Park,
Gothenburg SE-412 88, Sweden

Andreas Mark

Vice Head of Department Computational
Engineering and Design,
Fraunhofer-Chalmers Centre,
Chalmers Science Park,
Gothenburg SE-412 88, Sweden
e-mail: andreas.mark@fcc.chalmers.se

Fredrik Edelvik

Head of Department Computational
Engineering and Design,
Fraunhofer-Chalmers Centre,
Chalmers Science Park,
Gothenburg SE-412 88, Sweden

Karl-Heinz Küfer

Professor
Head of Department Optimization,
Fraunhofer Institute for Industrial
Mathematics ITWM,
Fraunhofer-Platz 1,
Kaiserslautern 67663, Germany

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF HEAT TRANSFER. Manuscript received January 23, 2018; final manuscript received April 20, 2018; published online May 25, 2018. Assoc. Editor: Danesh K. Tafti.

J. Heat Transfer 140(10), 102002 (May 25, 2018) (10 pages) Paper No: HT-18-1045; doi: 10.1115/1.4040086 History: Received January 23, 2018; Revised April 20, 2018

The thermal management is an ever increasing challenge in advanced electronic devices. In this paper, simulation-based optimization is applied to improve the design of a plate-fin heat-sink in terms of operational cost and thermal performance. The proposed framework combines a conjugate heat transfer solver, a CAD engine and an adapted Sandwiching algorithm. A key feature is the use of novel immersed boundary (IB) techniques that allows for automated meshing which is perfectly suited for parametric design optimization.

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References

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Figures

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Fig. 1

Coupling loop for simulation based optimization using sandwiching

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Fig. 2

Definitions of the different cell types and point positions. The curved thick line represents the surface of the IB.

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Fig. 3

Wall function for the fluid temperature at a solid-fluid interface. y+ is the dimensionless wall distance and T is the temperature in Kelvin.

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Fig. 4

Sandwiching finding the third Pareto optimal solution on the unit circle

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Fig. 5

The commercial heat-sink

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Fig. 6

Illustration of the simulation domain with a heat-sink: (a) A cut through the simulation domain (perpendicular to the flow direction) and (b) The fluid domain is represented by the outline, and the heat-sink is located in the middle of the fluid domain with respect to the flow direction

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Fig. 7

Example of velocity field and solid temperature distribution from a Pareto optimal simulation with 18 fins

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Fig. 8

Results from the grid study of the D0 heat-sink. From right-to-left the number of cells is increasing.

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Fig. 9

Results from the initial study varying only the number of fins but keeping the fin-thickness and fin-height fixed (d = d0, h = h0)

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Fig. 10

Inner and outer approximations with respect to the design spaces X18, X20, X22, and X24. The filled circles show Pareto optima for different weights: (a) X18, (b) X20, (c) X22, and (d) X24.

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Fig. 11

Inner approximations with respect to the design spaces X18, X20, X22, and X24. The filled circles show Pareto optima for different weights. N = 18 is Pareto optimal over almost the entire range, only for low solid max temperatures are N = 20 and N = 22 Pareto optimal.

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Fig. 12

Geometrical comparison of Pareto optimal solutions. Top: Lowest maximal temperature. Middle: Compromize. Bottom: Lowest pressure drop.

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