Experimental Development and Computational Optimization of Flat Heat Pipes for CubeSat Applications

[+] Author and Article Information
Steven A. Isaacs

Department of Mechanical Engineering,
University of Colorado,
Boulder, CO 80309
e-mail: steven.isaacs@colorado.edu

Diego A. Arias

Roccor, LLC,
Longmont, CO 80503
e-mail: diego.arias@roccor.com

Derek Hengeveld

Albuquerque, NM 87108
e-mail: derek.hengeveld@loadpath.com

Peter E. Hamlington

Department of Mechanical Engineering,
University of Colorado,
Boulder, CO 80309
e-mail: peter.hamlington@colorado.edu

Contributed by the Electronic and Photonic Packaging Division of ASME for publication in the JOURNAL OF ELECTRONIC PACKAGING. Manuscript received December 16, 2016; final manuscript received March 28, 2017; published online June 12, 2017. Assoc. Editor: Justin A. Weibel.

J. Electron. Packag 139(2), 020910 (Jun 12, 2017) (10 pages) Paper No: EP-16-1142; doi: 10.1115/1.4036406 History: Received December 16, 2016; Revised March 28, 2017

Due to the compact and modular nature of CubeSats, thermal management has become a major bottleneck in system design and performance. In this study, we outline the development, initial testing, and modeling of a flat, conformable, lightweight, and efficient two-phase heat strap called FlexCool, currently being developed at Roccor. Using acetone as the working fluid, the heat strap has an average effective thermal conductivity of 2149 W/m K, which is approximately five times greater than the thermal conductivity of pure copper. Moreover, the heat strap has a total thickness of only 0.86 mm and is able to withstand internal vapor pressures as high as 930 kPa, demonstrating the suitability of the heat strap for orbital environments where pressure differences can be large. A reduced-order, closed-form theoretical model has been developed in order to predict the maximum heat load achieved by the heat strap for different design and operating parameters. The model is validated using experimental measurements and is used here in combination with a genetic algorithm to optimize the design of the heat strap with respect to maximizing heat transport capability.

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Fig. 1

Model of a 3U CubeSat created in Thermal Desktop, showing the modular rail structure of the CubeSat, a central PCB with a heat-generating component, body mounted radiators, and a deployed solar array (see color figure online)

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Fig. 2

Thermal Desktop representations of hot and cold LEOcases, corresponding to two different orbital angles. Parameters used to define each case are given in Table 1 (see color figure online).

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Fig. 3

Orbit-averaged component temperatures as functions of power dissipation for hot and cold LEO cases, both with and without heat straps. Red and blue lines represent the hot and cold LEO cases, respectively. Solid lines correspond to the component temperature without any heat strap, while dashed lines represent component temperatures if a heat strap with a conductance of 1.6 W/K is used to connect the electronic component to the radiators (see color figure online).

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Fig. 11

Effective thermal conductivity measurements for the prototype FlexCool heat strap at 0 deg inclination compared with the thermal conductivity of an equivalent copper heat spreader

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Fig. 10

Temperature distribution along the prototype FlexCool heat strap for inclination angles from φ=0–70 deg with a constant 4.8 W heat input. The heat strap reaches the capillary limit at an angle of 60 deg (see color figure online).

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Fig. 9

One-dimensional thermal test setup for the prototype FlexCool heat strap. Condenser, adiabatic, and evaporator sections are labeled, and thermocouples provide temperature measurements T0T4 along the length of the strap. The strap can be inclined at varying angles φ for testing (see color figure online).

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Fig. 8

Cross-sectional structure of the prototype FlexCool heat strap used for testing, showing both (a) a schematic and (b) a photograph of the case and mesh layers comprising the FlexCool strap (see color figure online)

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Fig. 7

General dimensions of the prototype FlexCool flat heat strap used for testing, showing (a) horizontal dimensions and (b) measured total thickness (see color figure online)

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Fig. 6

Minimum heat strap conductance and width for a component temperature of 320 K (solid black line). Dashed lines show the strap mass versus strap conduction for copper (blue) and FlexCool (red) heat straps (see color figure online).

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Fig. 5

Orbital-averaged electronic component temperature for the hot LEO case as a function of power dissipation and strap conductance. Results are obtained using the simplified thermal model created in EES and validated against the Thermal Desktop model (see color figure online).

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Fig. 4

Instantaneous component temperatures for hot and cold LEO cases from the transient analysis in Thermal Desktop. Results are shown for a power dissipation of 10 W and a heat strap with conductance of 0.68 W/K (see color figure online).

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Fig. 12

Schematic of the hydrodynamic model for the FlexCool heat strap showing pressure losses across the fine wicks, denoted ΔPw1, and across the coarse mesh vapor core, ΔPw2

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Fig. 15

Fine wick parameters used in the optimization model. The wire diameter is denoted d and the wire spacing is denoted w.

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Fig. 13

Maximum heat transport obtained from experiments on the prototype FlexCool heat strap and from model predictions. Different data points are obtained by considering varying power inputs from roughly 5–15 W.

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Fig. 14

The thermal resistance network model of the heat pipe

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Fig. 21

Wire diameter and spacing trends

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Fig. 16

Contour plots of maximum adiabatic length, Lad, as a function of fine mesh wire diameter and wire spacing for heat loads of (a) 5.0 W, (b) 10.0 W, and (c) 15.0 W

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Fig. 17

Contour plots showing convergence of genetic optimization algorithm after (a) zero generations, (b) three generations, and (c) eight generations for a heat load of 15 W

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Fig. 18

Convergence of the three variable optimization model at a heat load of 15 W

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Fig. 19

Acetone vapor density as a function of saturation temperature

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Fig. 20

Maximum adiabatic length using two variable and three variable optimization models



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