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Research Papers

Heat Transfer Improvement in Automotive Brake Disks Via Shape Optimization of Cooling Vanes Using Improved TPSO Algorithm Coupled With Artificial Neural Network

[+] Author and Article Information
Javid Karbalaei Mehdi

School of Mechanical Engineering,
College of Engineering,
University of Tehran,
Tehran 111554563, Iran
e-mail: j.karbalaei@ut.ac.ir

Amir Nejat

School of Mechanical Engineering,
College of Engineering,
University of Tehran,
Tehran 111554563, Iran
e-mail: nejat@ut.ac.ir

Masoud Shariat Panahi

School of Mechanical Engineering,
College of Engineering,
University of Tehran,
Tehran 111554563, Iran
e-mail: mshariatp@ut.ac.ir

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS. Manuscript received November 2, 2016; final manuscript received May 22, 2017; published online August 28, 2017. Assoc. Editor: Giulio Lorenzini.

J. Thermal Sci. Eng. Appl 10(1), 011013 (Aug 28, 2017) (14 pages) Paper No: TSEA-16-1316; doi: 10.1115/1.4036966 History: Received November 02, 2016; Revised May 22, 2017

One important safety issue in automotive industry is the efficient cooling of brake system. This research work aims to introduce an optimized cooling vane geometry to enhance heat removal performance of ventilated brake disks. The novel idea of using airfoil vanes is followed as the basis of this investigation (Nejat et al., 2011, “Heat Transfer Enhancement in Ventilated Brake Disk Using Double Airfoil Vanes,” ASME J. Therm. Sci. Eng. Appl., 3(4), p. 045001). In order to perform the optimization technique efficiently, an integrated shape optimization process is designed. According to the aerodynamic and heat transfer considerations, first an appropriate airfoil is selected as the base profile to be optimized. For the shape modification purpose, a curve parameterization method named class shape transformation (CST) is utilized. The control parameters defined in CST method are then established as the geometrical design variables of an improved territorial particle swarm optimization (TPSO) algorithm. In order to overcome the potential bottleneck of high computational cost associated with the required computational fluid dynamics (CFD)-based function evaluations, TPSO algorithm is coupled with a predictive artificial neural networks (ANN), well trained with an input dataset designed based on the Taguchi method. The obtained profile shows an evident convective heat dissipation improvement accomplished mainly via airflow acceleration over the vanes, avoiding early flow detachment and adjusting the flow separation region at the rear part of the suction sides. The results also reveal the approaches by which such a superior performance is achieved by means of the modified surface curvatures.

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Figures

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Fig. 1

Generated hybrid mesh around NACA-0009 airfoil vanes

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Fig. 2

Shape function profile of NASA SC(2)-0714 airfoil

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Fig. 3

CST parametrization error for different orders of Bernstein polynomials (BP)

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Fig. 4

Generated NASA SC(2)-0714 airfoil using Bernstein polynomial of order 7

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Fig. 5

Flowchart of the employed optimization process

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Fig. 6

Linear regression analysis for training, validation and testing of the trained neural network

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Fig. 7

Vane geometry profiles and their CST method shape functions; optimized vane versus the base airfoil

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Fig. 8

Contours of relative velocity magnitude (m/s); suggested profile by Nejat et al. [14] (left) and the optimized profile (right)

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Fig. 9

Relative velocity vectors and streamlines; suggested profile by Nejat et al. [14] (top) and the optimized profile (bottom)

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Fig. 10

Temperature field (K); suggested profile by Nejat et al. [14] (left) and the optimized profile (right)

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Fig. 11

Local HTC values corresponding to upper surface of different vane geometries

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Fig. 12

Local HTC values corresponding to lower surface of different vane geometries

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