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Research Papers: Gas Turbines: Structures and Dynamics

A Robust Optimization Technique for Calculating Scaling Coefficients in an Energy-Based Fatigue Life Prediction Method

[+] Author and Article Information
Todd Letcher

e-mail: letcher.7@osu.edu

John Wertz

e-mail: wertz.46@osu.edu

M.-H. Herman Shen

e-mail: shen.1@osu.edu
Mechanical and Aerospace Engineering
Department,
The Ohio State University,
Building 148,
201 West 19th Avenue,
Columbus, OH 43210

Contributed by the Structures and Dynamics Committee of ASME for publication in the Journal of Engineering for Gas Turbines and Power. Manuscript received May 29, 2013; final manuscript received August 28, 2013; published online September 20, 2013. Editor: David Wisler.

J. Eng. Gas Turbines Power 135(12), 122502 (Sep 20, 2013) (7 pages) Paper No: GTP-13-1151; doi: 10.1115/1.4025315 History: Received May 29, 2013; Revised August 28, 2013

The energy-based lifing method is based on the theory that the cumulative energy in all hysteresis loops of a specimens' lifetime is equal to the energy in a monotonic tension test. Based on this theory, fatigue life can be calculated by dividing monotonic tensile energy by a hysteresis energy model, which is a function of stress amplitude. Due to variations in the empirically measured hysteresis loops and monotonic fracture area, fatigue life prediction with the energy-based method shows some variation as well. In order to account for these variations, a robust design optimization technique is employed. The robust optimization procedure uses an interval uncertainty technique, eliminating the need to know an exact probability density function for the uncertain parameters. The robust optimization framework ensures that the difference between the predicted lifetime at a given stress amplitude and the corresponding experimental fatigue data point is minimized and within a specified tolerance range while accounting for variations in hysteresis loop energy and fracture diameter measurements. Accounting for these experimental variations will boost confidence in the energy-based fatigue life prediction method despite a limited number of test specimens.

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References

Figures

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Fig. 2

Scaling coefficients that produce 0.014 MJ/m3 at 227.5 MPa fatigue loading

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Fig. 3

Fatigue life predictions using two sets of scaling coefficients

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Fig. 6

Specimen dimensions

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Fig. 5

Single objective robust optimization framework

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Fig. 4

Sensitivity region in Δp space

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Fig. 11

Robust life prediction framework

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Fig. 1

Hysteresis loop in generalized coordinates

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Fig. 7

Typical monotonic specimen after fracture

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Fig. 8

Difference in monotonic curves when using maximum and minimum fracture diameters

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Fig. 9

Cyclic energy variation throughout fatigue life

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Fig. 10

Minimum and maximum SN predictions based on variations in measured parameters

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Fig. 12

Fatigue life predictions using both MSE and robust techniques

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