Research Papers

Forced Response Reduction of a Blisk by Means of Intentional Mistuning

[+] Author and Article Information
Bernd Beirow

Chair of Structural Mechanics
and Vehicle Vibrational Technology,
Brandenburg University of Technology,
Siemens-Halske-Ring 14,
Cottbus D-03046, Germany
e-mail: beirow@b-tu.de

Arnold Kühhorn

Chair of Structural Mechanics
and Vehicle Vibrational Technology,
Brandenburg University of Technology,
Siemens-Halske-Ring 14,
Cottbus D-03046, Germany
e-mail: kuehhorn@b-tu.de

Felix Figaschewsky

Chair of Structural Mechanics
and Vehicle Vibrational Technology,
Brandenburg University of Technology,
Siemens-Halske-Ring 14,
Cottbus D-03046, Germany
e-mail: Felix.figascheswky@b-tu.de

Alfons Bornhorn

MAN Diesel & Turbo SE,
Stadtbachstr. 1,
Augsburg D-86153, Germany
e-mail: Alfons.Bornhorn@man.eu

Oleg V. Repetckii

Engineering Faculty,
Irkutsk State Agrarian University,
Irkutsk 664038, Russia
e-mail: repetckii@igsha.ru

1Corresponding author.

Manuscript received June 22, 2018; final manuscript received June 26, 2018; published online September 14, 2018. Editor: Jerzy T. Sawicki.

J. Eng. Gas Turbines Power 141(1), 011008 (Sep 14, 2018) (8 pages) Paper No: GTP-18-1289; doi: 10.1115/1.4040715 History: Received June 22, 2018; Revised June 26, 2018

The effect of intentional mistuning has been analyzed for an axial turbocharger blisk with the objective of limiting the forced response due to low engine order excitation (LEO). The idea behind the approach was to increase the aerodynamic damping for the most critical fundamental mode in a way that a safe operation is ensured without severely losing aerodynamic performance. Apart from alternate mistuning, a more effective mistuning pattern is investigated, which has been derived by means of optimization employing genetic algorithms. In order to keep the manufacturing effort as small as possible, only two blade different geometries have been allowed, which means that an integer optimization problem has been formulated. Two blisk prototypes have been manufactured for purpose of demonstrating the benefit of the intentional mistuning pattern identified in this way: A first one with and a second one without employing intentional mistuning. The real mistuning of the prototypes has been experimentally identified. It is shown that the benefit regarding the forced response reduction is retained in spite of the negative impact of unavoidable additional mistuning due to the manufacturing process. Independently, further analyzes have been focused on the robustness of the solution by considering increasing random structural mistuning and aerodynamic mistuning as well. The latter one has been modeled by means of varying aerodynamic influence coefficients (AIC) as part of Monte Carlo simulations. Reduced order models have been employed for these purposes.

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Fig. 2

Magnitude plots of CSM 2 and 4 (BF 1)

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Fig. 3

Blade modes 1, 2, 3, 5, and 7

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Fig. 4

Campbell plot (rotational and temperature effects considered)

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Fig. 5

Aerodynamic damping (normalized, BF 1, 90% speed)

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Fig. 6

Effect of pure random mistuning (Δf = ±0.5/1.0/2.0%) on maximum forced response (BF 1, EO 6)

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Fig. 7

(a) Alternate mistuning pattern and (b) ODS at max. forced response (BF 1, EO 6)

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Fig. 8

TWM decomposition of ODS given in Fig. 7: (a) tuned and (b) alternate mistuning

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Fig. 9

Effect of superimposed alternate mistuning and random mistuning (Δf = ±0.5%/±1.0%/±2.0%) on maximum forced response (5000 samples)

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Fig. 10

Effect of superimposed alternate mistuning and aerodynamic mistuning (ΔAIC = ±2.5%/±5.0%/±10.0%) on maximum forced response (5000 samples)

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Fig. 11

Frequency mistuning of mode 1: (a) intentionally mistuned and (b) tuned [18]

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Fig. 12

Deviation from intended mistuning (ΔΔf = Δfdesign − Δfreal)

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Fig. 13

Mistuning testing of a blisk prototype

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Fig. 14

Frequency mistuning patterns derived from experiment

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Fig. 15

ODS at maximum forced response (BF 1, EO 6), (a) “tuned” as measured, (b) optimum intentional mistuning and (c) intentional mistuning as measured

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Fig. 16

TWM decomposition of ODS given in Fig. 14

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Fig. 17

Maximum displacement magnification (EO 6, BF 1, designed intentional mistuning superimposed by Δf = ±0.5% random mistuning, 5000 Samples)

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Fig. 18

95% percentiles of maximum displacement magnification (EO 6, BF 1, designed intentional mistuning superimposed by random mistuning, 5000 samples)

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Fig. 19

Maximum displacement magnification (EO 6, BF 1), optimized intentional mistuning superimposed by 10% aerodynamic mistuning (5000 samples)

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Fig. 20

(a) Worst superimposed mistuning pattern and (b) ODS at max. forced response (BF 1, EO 6)



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