Research Papers

Aerodynamic Optimization Design of Airfoil Shape Using Entropy Generation as an Objective

[+] Author and Article Information
Wei Wang, Xiao-Pei Yang, Yan-Yan Ding

School of Energy and Power Engineering,
Huazhong University of Science and Technology,
Wuhan, Hubei Province 430074, China

Jun Wang

China-EU Institute for Clean
and Renewable Energy,
Huazhong University of Science and Technology,
Wuhan, Hubei Province 430074, China;
School of Energy and Power Engineering,
Huazhong University of Science and Technology,
Wuhan, Hubei Province 430074, China
e-mail: wangjhust@hust.edu.cn

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS. Manuscript received August 4, 2018; final manuscript received October 15, 2018; published online December 6, 2018. Assoc. Editor: Gerard F. Jones.

J. Thermal Sci. Eng. Appl 11(2), 021015 (Dec 06, 2018) (9 pages) Paper No: TSEA-18-1387; doi: 10.1115/1.4041793 History: Received August 04, 2018; Revised October 15, 2018

An entropy analysis and design optimization methodology is combined with airfoil shape optimization to demonstrate the impact of entropy generation on aerodynamics designs. In the work herein, the entropy generation rate is presented as an extra design objective along with lift-drag ratio, while the lift coefficient is the constraint. Model equation, which calculates the local entropy generation rate in turbulent flows, is derived by extending the Reynolds-averaging of entropy balance equation. The class-shape function transform (CST) parametric method is used to model the airfoil configuration and combine the radial basis functions (RBFs) based mesh deformation technique with flow solver to compute the quantities such as lift-drag ratio and entropy generation at the design condition. From the multi-objective solutions which represent the best trade-offs between the design objectives, one can select a set of airfoil shapes with a low relative energy cost and with improved aerodynamic performance. It can be concluded that the methodology of entropy generation analysis is an effective tool in the aerodynamic optimization design of airfoil shape with the capability of determining the amount of energy cost.

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Fig. 1

Class-shape function transform method's geometric parameters for airfoil parameterization

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Fig. 2

RAE2822 and NACA0012 parameterized by seven design variables

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Fig. 3

Computational domain around the airfoil: (a) complete grid and (b) fine grid around airfoil

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Fig. 4

Deformed meshes for RAE2822 and NACA0012 airfoils: (a) RAE2822 and (b) NACA0012 at 15 angle-of-attack

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Fig. 5

Comparisons of pressure distributions on NACA0012 at different angle-of-attack

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Fig. 6

Streamlines and contours of log(s•gen) at different angle-of-attack

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Fig. 7

Flowchart of the optimization iteration

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Fig. 8

Population distribution in optimization procedure based on entropy generation and lift-drag ratio

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Fig. 9

Pareto curve of the airfoil shapes optimization based on entropy generation and lift-drag ratio: (a) pressure contours and (b) log(s•gen)contours

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Fig. 10

Configuration and pressure distribution of three representative members of the Pareto front based on entropy generation and lift-drag ratio: (a) configuration and (b) pressure distribution

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Fig. 11

Local mesh of RAE2822

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Fig. 12

Comparison of pressure distributions on RAE2822

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Fig. 13

Pareto curve of the airfoil shapes optimization based on entropy generation and lift-drag ratio: (a) pressure contours and (b) log(s•gen)contours

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Fig. 14

Configuration and pressure distribution of four representative members of the Pareto front: (a) configuration and (b) pressure distribution



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