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Research Papers

Inverse Aeroacoustic Design of Axial Fans Using Genetic Optimization and the Lattice-Boltzmann Method

[+] Author and Article Information
Michael Stadler

Ninsight,
Maiffredygasse 4,
Graz 8010, Austria
e-mail: Michael.Stadler@ninsight.at

Michael B. Schmitz

e-mail: Michael.Schmitz@de.ebmpapst.com

Wolfgang Laufer

e-mail: Wolfgang.Laufer@de.ebmpapst.com

Peter Ragg

e-mail: Peter.Ragg@de.ebmpapst.com
ebm-papst,
Hermann-Papst-Straße 1,
St. Georgen 78112, Germany

Contributed by the International Gas Turbine Institute (IGTI) of ASME for publication in the JOURNAL OF TURBOMACHINERY. Manuscript received June 12, 2013; final manuscript received July 23, 2013; published online September 26, 2013. Editor: Ronald Bunker.

J. Turbomach 136(4), 041011 (Sep 26, 2013) (10 pages) Paper No: TURBO-13-1096; doi: 10.1115/1.4025167 History: Received June 12, 2013; Revised July 23, 2013

The noise emitted by axial fans plays an integral role in product design. When conventional design procedures are applied, the aeroacoustic properties are controlled via an extensive trial-and-error process. This involves building physical prototypes and performing acoustic measurements. In general, this procedure makes it difficult for a designer to gain an understanding of the functional relationship between the noise and geometrical parameters of the fan. Hence, it is difficult for a human designer to control the aeroacoustic properties of the fan. To reduce the complexity of this process, we propose an inverse design methodology driven by a genetic algorithm. It aims to find the fan geometry for a set of given objectives. These include, most notably, the sound pressure frequency spectrum, aerodynamic efficiency, and pressure head. Individual bands of the sound pressure frequency spectrum may be controlled implicitly as a function of certain geometric parameters of the fan. In keeping with inverse design theory, we represent the design of axial fans as a multi-objective multiparameter optimization problem. The individual geometric components of the fan (e.g., rotor blades, winglets, guide vanes, shroud, and diffusor) are represented by free-form surfaces. In particular, each blade of the fan is individually parameterized. Hence, the resulting fan is composed of geometrically different blades. This approach is useful when studying noise reduction. For the analysis of the flow field and associated objectives, we utilize a standard Reynolds averaged Navier–Stokes (RANS) solver. However, for the evaluation of the generated noise, a meshless lattice-Boltzmann solver is employed. The method is demonstrated for a small axial fan, for which tonal noise is reduced.

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Figures

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Fig. 1

Exploded view of the axial fan under investigation. The conical winglet design and the shape-optimized turbulator are indicated.

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Fig. 2

Sections A-A to G-G (which are aligned normal to axis χ), illustrating the conical winglet geometry (only one blade is shown)

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Fig. 3

Flow chart of the inverse design algorithm

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Fig. 4

Geometric model for the Lattice-Boltzmann simulation showing the inlet, outlet, the fan under investigation, and the array of pressure sensors (the sensors are only shown at the outlet). For the sensor marked by a filled black dot, we show the frequency spectrum of the sound pressure level in Fig. 11.

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Fig. 5

Structure of parallel computing resources on the Amazon Elastic Compute Cloud (EC2)

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Fig. 6

Experimental setup

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Fig. 7

Aerodynamic testing rig

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Fig. 8

Acoustic testing rig

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Fig. 9

Population of individuals chosen for evaluation by the NSGA-II algorithm. The Pareto front of optimal designs is discontinuous. Three individuals, P1, P2, and P3, are chosen for subsequent study.

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Fig. 10

Parameters for three selected individuals of the Pareto front. Individual P1 was selected from the center of the Pareto front in Fig. 9. The individuals P2 (aerodynamic efficiency more important than noise reduction) and P3 (noise reduction more important than aerodynamic efficiency) were selected from the peripheral regions of the Pareto front.

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Fig. 11

Typical frequency spectrum of the sound-pressure level obtained from the Lattice–Boltzmann simulation for one of the sensors at the outlet, which is marked by a filled black dot in Fig. 4. Blade-passing frequencies (BPF) up to order three are indicated.

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Fig. 12

Comparison between the physical test and simulation for the fan with varying blade geometry. The frequency spectrum of the fan noise is shown as 1/3-octave bands for the optimized fan operating at the design point. For each frequency record, the left bar refers to the physical test, the right bar to the simulation.

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Fig. 13

Comparison of two physical specimens: the frequency spectrum of the fan noise is shown as 1/3-octave bands. For each frequency record, the left bar refers to the identical blade geometry, the right bar refers to the varying blade geometry. Clearly, the peak at BPF1 is reduced and the acoustic energy is distributed to adjacent frequencies (circle 1). The peak at BPF2 has almost disappeared (circle 2). The overall reduction in the sound power level is 1.3 dB(A).

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