Research Papers

Isogeometric Fatigue Damage Prediction in Large-Scale Composite Structures Driven by Dynamic Sensor Data

[+] Author and Article Information
Y. Bazilevs

Department of Structural Engineering,
University of California–San Diego,
La Jolla, CA 92093
e-mail: yuri@ucsd.edu

X. Deng, A. Korobenko, F. Lanza di Scalea, M. D. Todd

Department of Structural Engineering,
University of California–San Diego,
La Jolla, CA 92093

S. G. Taylor

Los Alamos National Laboratory,
Los Alamos, NM 87545

Contributed by the Applied Mechanics Division of ASME for publication in the JOURNAL OF APPLIED MECHANICS. Manuscript received April 16, 2015; final manuscript received June 4, 2015; published online June 25, 2015. Assoc. Editor: Chad M. Landis.

The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States government purposes.

J. Appl. Mech 82(9), 091008 (Sep 01, 2015) (12 pages) Paper No: JAM-15-1199; doi: 10.1115/1.4030795 History: Received April 16, 2015; Revised June 04, 2015; Online June 25, 2015

In this paper, we combine recent developments in modeling of fatigue-damage, isogeometric analysis (IGA) of thin-shell structures, and structural health monitoring (SHM) to develop a computational steering framework for fatigue-damage prediction in full-scale laminated composite structures. The main constituents of the proposed framework are described in detail, and the framework is deployed in the context of an actual fatigue test of a full-scale wind-turbine blade structure. The results indicate that using an advanced computational model informed by in situ SHM data leads to accurate prediction of the damage zone formation, damage progression, and eventual failure of the structure. Although the blade fatigue simulation was driven by test data obtained prior to the computation, the proposed computational steering framework may be deployed concurrently with structures undergoing fatigue loading.

Copyright © 2015 by ASME
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Fig. 1

Composite layup with nonuniform and nonsymmetric distribution of the lamina

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Fig. 4

Stress redistribution at the clamped cross section at cycle: (a) N = 8,000 and (b) N = 650,000. Comparison of the IGA results with the finite-element simulation data.

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Fig. 3

Plot of the vertical force at the right clamp versus cycle number. Comparison of the IGA fatigue-damage simulation with experimental data.

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Fig. 2

Setup of the bending-fatigue test of a cantilever composite plate taken from Ref. [13]

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Fig. 16

Visual comparison of the fatigue-test and simulation results. Location and shape of the damage zone in a DBM layer near the root are in very good agreement with the location and orientation of the crack observed in the fatigue test.

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Fig. 5

Layup of the trailing edge, leading edge, and spar cap

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Fig. 6

Left: five primary sections of the CX-100 blade and right: 32 distinct material zones of the CX-100 blade

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Fig. 7

NURBS mesh of the CX-100 blade. A few top-surface patches are removed to show the shear web attachment and mesh.

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Fig. 8

Blade fatigue test setup and sensor layout. Square symbols gives the location of the accelerometer providing dynamic acceleration data for displacement amplitude and fatigue-model parameter calibration.

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Fig. 9

Fatigue cycle count versus date. Triangular symbols indicate calibration points for fatigue-damage simulations.

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Fig. 10

(a) Flowchart of the outer DDDAS loop responsible for fatigue-damage prediction and model parameter calibration and (b) flowchart of the inner DDDAS loop responsible for applied displacement amplitude calibration

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Fig. 11

Amplitude (A) of applied displacement forcing as a function of cycle number (N)

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Fig. 12

Acceleration data comparison between the fatigue test and simulation at four calibration points. Left: time domain comparison and right: frequency domain comparison.

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Fig. 13

Damage model material parameters c1 (left) and c3 (right) plotted versus cycle number

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Fig. 14

Progression of damage index D1 up to 1.5 M cycles in a DBM layer: (a) cycle N = 10,000, (b) cycle N = 100,000, (c) cycle N = 1,000,000, and (d) cycle N = 1,500,000

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Fig. 15

Progression of damage index D1 from 1.5 M to 8.0 M cycles in a DBM layer: (a) cycle N = 1,500,000, (b) cycle N = 5,000,000, (c) cycle N = 7,000,000, and (d) cycle N = 8,000,000




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