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Research Papers

Development, Application, and Validation of a Quick Optimization Method for the Class of Axial Fans

[+] Author and Article Information
Konrad Bamberger

Institute for Fluid Dynamics
and Thermodynamics,
Department of Mechanical Engineering,
University of Siegen,
Paul-Bonatz-Str. 9-11,
Siegen 57076, Germany
e-mail: konrad.bamberger@uni-siegen.de

Thomas Carolus

Institute for Fluid Dynamics
and Thermodynamics,
Department of Mechanical Engineering,
University of Siegen,
Paul-Bonatz-Str. 9-11,
Siegen 57076, Germany
e-mail: thomas.carolus@uni-siegen.de

1Corresponding author.

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received May 3, 2016; final manuscript received April 19, 2017; published online July 6, 2017. Assoc. Editor: Seung Jin Song.

J. Turbomach 139(11), 111001 (Jul 06, 2017) (10 pages) Paper No: TURBO-16-1096; doi: 10.1115/1.4036764 History: Received May 03, 2016; Revised April 19, 2017

This article discusses the development, application, and validation of an optimization method for the impellers of axial fans. The method is supposed to be quick, accurate, and applicable to optimization at an extensive range of design points (DPs). Optimality here means highest possible total-to-static efficiency for a given design point and is obtained by an evolutionary algorithm in which the target function is evaluated by computational fluid dynamics (CFD)-trained artificial neural networks (ANN) of the multilayer perceptron (MLP) type. The MLPs were trained with steady-state CFD (i.e., Reynolds-averaged Navier–Stokes (RANS)) results of approximately 14,000 distinct impellers. After this considerable one-time effort to generate the CFD dataset, each new fan optimization can be performed within a few minutes. It is shown in this article that the MLPs are reliably applicable to all typical design points of axial fans according to Cordier's diagram. Moreover, an extension of the design space toward the classic realm of mixed-flow or even centrifugal fans is observed. It is also shown that the optimization method successfully handles geometrical and operational constraints proving the high degree of universality of the method. Another focus of this article is on the application of the newly developed optimization method to numerous design points. This yields two major findings: the estimation of maximum achievable total-to-static efficiency as a function of the targeted design point (with and without geometrical constraints) as well as a quantification of the improvement over fans designed with classic methods. Both investigations are supported by flow field analyses to aerodynamically explain the findings. Experimental validation of the method was performed with a total of nine prototypes. The positive correlation between MLP, CFD, and experiment successfully validates the methodology.

Copyright © 2017 by ASME
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References

Figures

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Fig. 1

Cordier diagram. The gray band indicates the range of design points at which high efficiency can be expected. Typically, the specific fan speed increases from radial fans over mixed-flow fans to axial fans, whereas the specific fan diameter decreases.

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Fig. 2

Illustration of geometrical parameters. Top left: Impeller in a duct. Top right: Definition of the sweep angle in a 3D and a 2D view (here: constant chord length and sweep from hub to tip). Bottom left: NACA section. Bottom right: Definition of the angle of incidence.

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Fig. 3

Sketch of the computational domain used

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Fig. 4

CFD-simulated and modeled curve of λts(φ)

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Fig. 5

Difference between the MLP-predicted total-to-static efficiency of optimized impellers and the respective CFD results

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Fig. 6

Comparison between CFD and experimental results

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Fig. 7

Maximum achievable total-to-static efficiency

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Fig. 8

Share of the hydraulic losses (Lh) in the overall losses (Lo) of optimized impellers

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Fig. 9

Total-to-static efficiency penalty due to the constraint of avoidance of undercuts. The gray area indicates design points that became infeasible due to the constraint.

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Fig. 10

Total-to-static efficiency penalty due to the constraint of limited axial depth for the example lax ≤ 0.2 D. The gray area indicates design points that became infeasible due to the constraint.

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Fig. 11

Design points used for the case study

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Fig. 12

Three-dimensional view of the nine fans investigated in the case study

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Fig. 13

Comparison of the design methods at DP1

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Fig. 14

Comparison of the design methods at DP2

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Fig. 15

Comparison of the design methods at DP3

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Fig. 16

Load distributions at DP1

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Fig. 17

Distribution of circumferential flow velocity downstream of the impeller at DP1/CLAS2

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Fig. 18

Distribution of local blade efficiency at DP1/CLAS2

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