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Research Papers

Loading and Design Parameter Uncertainty in the Dynamics and Performance of High-Speed-Parallel-Helical-Stage of a Wind Turbine Gearbox1

[+] Author and Article Information
Fisseha M. Alemayehu

Department of Mechanical Engineering,
Texas Tech University,
Lubbock, TX 79409
e-mail: fisseha.alemayehu@ttu.edu

Stephen Ekwaro-Osire

Department of Mechanical Engineering,
Texas Tech University,
Lubbock, TX 79409
e-mail: stephen.ekwaro-osire@ttu.edu

Part of this paper was presented at the ASME IDETC/CIE 2013, PTG Conference, Aug. 4–7, 2013, Portland, OR.

2Corresponding author.

Contributed by the Power Transmission and Gearing Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received October 4, 2013; final manuscript received April 5, 2014; published online June 11, 2014. Assoc. Editor: Qi Fan.

J. Mech. Des 136(9), 091002 (Jun 11, 2014) (13 pages) Paper No: MD-13-1443; doi: 10.1115/1.4027496 History: Received October 04, 2013; Revised April 05, 2014

In operation, wind turbine gearboxes (WTGs) are subjected to variable torsional and nontorsional loads. In addition, the manufacturing and assembly process of these devices results in uncertainty in the design parameters of the system. WTGs are reported to fail in their early life of operation within 3–7 years as opposed to the expected 20 years of operation. Their downtime and maintenance process is the most costly of the failures of any subassembly of wind turbines (WTs). The objective of this work is to perform a probabilistic multibody dynamic analysis (PMBDA) of the high-speed-parallel-helical-stage (HSPHS) of a WTG that considers the uncertainties of generator-side torque-loading and input-shaft speed as well as assembly and design parameter uncertainties. Component reliability (Rc) or probability of failure (Pf) and probabilistic sensitivities of all the input variables toward five performance functions have been measured and conclusions have been drawn. As opposed to the traditional deterministic approach, PMBDA has demonstrated a new aspect of design and installation of WTGs. In addition to revealing Rc or system reliability or underperformance through Pf, the method will also help designers to critically consider certain variables through the probabilistic sensitivity results.

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References

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Figures

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Fig. 1

Schematic of the 1.5 MW compound WTGs

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Fig. 2

Determination of extreme value (Gumbel) distribution parameters using maximum likelihood evaluation

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Fig. 3

Distribution fitting to 40 s loading data: (a) nG and (b) TP

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Fig. 4

ADAMS 3D-contact based high-speed-parallel-helical-gear-pair model

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Fig. 5

Component reliability for DF

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Fig. 6

Probabilistic sensitivity factors to DF

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Fig. 7

Percentage probabilistic importance factors considering DF

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Fig. 8

Component reliability for root bending stress

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Fig. 9

Probabilistic sensitivity factors of the root bending stress with respect to the (a) mean, μ and (b) the standard deviation, σ

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Fig. 10

Component reliability for surface contact stress

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Fig. 11

Probabilistic sensitivity factors of surface compressive stress of (a) pinion and (b) gear

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Fig. 12

Sensitivity levels for surface compressive stress of (a) level 8 and (b) level 16 for both pinion (red and yellow) and gear (blue and gray)

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Fig. 13

CDF of DF of a rigid and flexible MBD models using MV and AMV methods

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Fig. 14

CDF of DF of the rigid-body (MV and AMV) and flexible (MV and AMV) MBD models

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Fig. 15

CDF of σbP of flexible (using AMV) and rigid-body (using MV and AMV) MBD model

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Fig. 16

Component reliability for root bending stress of rigid-body model (MV: σbG in curve 3 and σbP in curve 4) and flexible model (AMV: σbG in curve 1 and σbP in curve 2)

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Fig. 17

Probabilistic sensitivity factors of the root bending stress of the pinion with respect to the mean, μ (method used: AMV on a rigid-body gear pair model)

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Fig. 18

CDF of σCG of rigid (using MV: curve 2) and flexible (using MV: curve 5 and using LHS of 3k, curve 1; 2k, curve 3; and 1k, curve 4) MBD models

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Fig. 19

Reliability using Hertzian stress of rigid-body model (MV: σCG in curve 2 and σCP in curve 4) and flexible model (AMV: σCG in curve 1 and σCP in curve 3)

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Fig. 20

Effect of truncation on component reliability for compressive surface contact stress of the gear

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Fig. 21

Probabilistic sensitivity levels of component reliability considering compressive surface contact stress of the gear (levels 1 and 15)

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