Research Papers: Gas Turbines: Structures and Dynamics

A Probabilistic Secondary Flow System Design Process for Gas Turbine Engines

[+] Author and Article Information
Douglas L. Ramerth

Methods and Tools Development, Honeywell International, Phoenix, AZ 85034

Alexander V. MirzaMoghadam

Advanced Technology Development, Honeywell International, Phoenix, AZ 85034

Ashish Kiratsingh, Gaurav Banda

 Honeywell Technical Services, Bangalore 10, India 560037

J. Eng. Gas Turbines Power 133(9), 092502 (Apr 19, 2011) (8 pages) doi:10.1115/1.4002829 History: Received July 02, 2010; Revised July 16, 2010; Published April 19, 2011; Online April 19, 2011

Gas turbine engine secondary flow systems are sensitive to variation in part dimensions, clearances, flow coefficients, swirl ratios, head loss factors, tolerances, boundary conditions, etc. This paper reveals a process and software application, which embodies the process, wherein both offer a measurable contribution to secondary airflow system reliability. The probabilistic methodology is empirically validated by (1) applying it to an engine component that failed in a validation test and (2) demonstrating that a multiple order sensitivity analysis performed during detailed design was unable to detect a failure mode while a probabilistic analysis revealed a small yet significant risk of failure. Therefore, a secondary flow analyst does not have a justifiable reason to be highly confident of a design qualified by a first, second, or higher order sensitivity analysis. The last example empirically demonstrates the compatibility of optimization techniques with probabilistic methods (as part of the process) to quantify the likelihood of failure and reveal an optimized design space of key characteristics, where risk is eliminated and the effects of variation are controlled. Trade study analysis is more valuable if it includes a quantitative evaluation of the effects of variation on alternate designs and the response to failure modes. A key feature of the software application is a relational database with the capability to configure and effectively manage flow networks in many forms including a status model, failure modes of the status model, multiple alternative designs, as well as failure modes specific to an alternative design.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 1

VNET process diagram

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Figure 2

PT1-PT2 interstage labyrinth seal

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Figure 3

Disk cavity ingestion

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Figure 4

(a) Labyrinth seal temperature runaway and (b) labyrinth seal clearance runaway

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Figure 5

PT1 aft hub-line ingestion (%) variability

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Figure 6

(a) HP-LP shaft cooling and (b) HP-LP shaft cooling schematic

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Figure 7

HPT disk bore cooling sensitivity (%)

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Figure 8

Probability distribution of HPT disk bore cooling flow with labyrinth seal 2

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Figure 9

Probability distribution of HPT disk bore cooling without labyrinth seal 2

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Figure 10

LPT aft seal plate inner orifice diameter (normalized with respect to orifice inlet radius)



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