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Research Papers: Gas Turbines: Structures and Dynamics

Torsional Robustness of the Combined Cycle Power Train Arrangement: Application of Statistical Methods to Accelerate Shaft-Line Design Cycles

[+] Author and Article Information
Mateusz Golebiowski

General Electric (Switzerland) GmbH,
Baden 5401, Switzerland
e-mail: mateusz.golebiowski@ge.com

John Ling

General Electric (Switzerland) GmbH,
Baden 5401, Switzerland
e-mail: john.ling@ge.com

Eric Knopf

General Electric (Switzerland) GmbH,
Baden 5401, Switzerland
e-mail: eric.knopf@ge.com

Andreas Niedermeyer

General Electric (Switzerland) GmbH,
Baden 5401, Switzerland
e-mail: andreas.niedermeyer@ge.com

Contributed by the Structures and Dynamics Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received June 20, 2016; final manuscript received December 6, 2016; published online March 28, 2017. Editor: David Wisler.

J. Eng. Gas Turbines Power 139(8), 082502 (Mar 28, 2017) (13 pages) Paper No: GTP-16-1242; doi: 10.1115/1.4035893 History: Received June 20, 2016; Revised December 06, 2016

This article presents the application of statistical methods to guide the rotordynamic design of a turbogenerator shaft-line. One of the basic requirements is all shaft components must survive the event of a short circuit at the terminals of the generator. This is typically assessed via a transient response simulation of the complete machine train (including generator's electrical model) to check the calculated response torque against the allowable value. With an increasing demand of a shorter design cycle and competition in performance, cost, footprint, and safety, the probabilistic approach is starting to play an important role in the power train design process. The main challenge arises with the size of the design space and complexity of its mapping onto multiple objective functions and criteria which are defined for different machines. In this paper, the authors give an example demonstrating the use of statistical methods to explore (design of experiment (DoE)) and understand (surface response methods) the design space of the combined cycle power train with respect to the typically most severe constraint (fault torque torsional response), which leads to a quicker definition of a turbogenerator's arrangement. Further statistical analyses are carried out to understand the robustness of the chosen design against future modifications as well as parameters' uncertainties.

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Figures

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Fig. 1

Example of a transient response torque calculation result

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Fig. 2

The rotordynamic model of the shaft-line divided into the “subelements”

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Fig. 3

P-Diagram for electrical fault analysis

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Fig. 4

Workflow of the statistical screening process

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Fig. 5

DoE screening—47 Hz 2PH SC

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Fig. 6

DoE screening 50 Hz 2PH SC

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Fig. 7

DoE screening 52.5 Hz 2PH SC

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Fig. 8

DOE screening. Main effects plot 47 Hz 2PH SC.

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Fig. 9

DoE screening. Main effects plot 50 Hz 2PH SC.

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Fig. 10

DoE screening. Main effects plot 52.5 HZ 2PH SC.

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Fig. 11

Residuals summary for the 47 Hz SC model

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Fig. 12

Residuals summary for the 52.5 Hz SC model

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Fig. 13

Optimal shaft-line arrangement for both 47 Hz and 52.5 Hz SC scenarios

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Fig. 14

Contour plot UF% (f41, f32)—physics check f41: IP shaft-end stiffness (left); f32: HP shaft-end stiffness (right)

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Fig. 15

Surface plot UF% (f31, f32) f31: HP shaft-end stiffness (left); f32: HP shaft-end stiffness (right)

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Fig. 16

Contour plot UF% (f31, f32) f31: HP shaft-end stiffness (left); f32: HP shaft-end stiffness (right)

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Fig. 17

Surface plot UF% (f12, f1)—GEN NDE shaft-end sizing f12: GEN shaft-end stiffness (right); f1: GEN inertia

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Fig. 18

Surface plot UF% (f11, f1)—GEN DE shaft-end sizing f11: GEN shaft-end stiffness (left); f1: GEN inertia

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Fig. 19

Surface plot UF% (f42, f5)—IP shaft-end sizing f42: IP shaft-end stiffness (right); f5: LP inertia

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Fig. 20

UFmax variation from Monte Carlo

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Fig. 21

Surface plot UF% (f42, f4) f42: IP shaft-end stiffness (right); f4: IP inertia

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