The present investigation aims at performing a probabilistic analysis of the secondary air system (SAS) of a three-stage low-pressure turbine rotor in a jet engine. Geometrical engine to engine variations due to the tolerance of the different parts as well as the variation of engine performance parameters are taken into account to analyze the impact on the aerodynamic behavior of the SAS. Three main functions of the SAS have been investigated at one engine condition—takeoff. At first the variation of the turbine rotor cooling flow consumption was studied. Second, the axial bearing loads were considered and finally the system was analyzed with regard to its robustness toward disk space hot gas ingestion. To determine the uncertainty in the accomplishment of these tasks and to identify the major variation drivers, a Latin hypercube sampling (LHS) method coupled with the correlation coefficient analysis was applied to the 1D flow model. The incapability of the correlation coefficient analysis to deal with functional relationships of not monotonic behavior or strong interaction effects was compensated by additionally applying in such cases an elementary effect analysis to determine the influential variables. As the 1D flow model cannot consider thermal and centrifugal growth effects, a simple mathematical model was deduced from the physical dependencies enhancing the 1D flow model to approximately capture the impact of these effects on the labyrinth seals. Results showed that the cooling mass flow and axial bearing load are both normally distributed while their uncertainties are mainly induced by the uncertainties of the state variable of the primary air system. The investigated chamber temperature ratio to analyze the hot gas ingestion showed a not normally distributed histogram and a strong influence of interaction terms. Therefore, the results of the correlation coefficient analysis were complemented with the results of an elementary effect analysis.

References

1.
Rolls-Royce,
1996
,
The Jet Engine
, 5th ed.,
Rolls-Royce plc
,
Derby, UK
.
2.
Grieb
,
H.
,
2004
,
Projektierung von Turboflugtriebwerken
,
Birkhäuser-Verlag
,
Basel, Switzerland
.
3.
Kütting
,
H.
, and
Sauer
,
M. J.
,
2011
,
Elementare Stochastik, Vol. 1 of Mathematik Primarstufe und Sekundarstufe I + II
,
Spektrum Akademischer Verlag
,
Heidelberg, Germany
.
4.
Cloud
,
D.
, and
Stearns
,
E.
,
2004
, “
Probabilistic Analysis of a Turbofan Secondary Flow System
,”
ASME
Paper No. GT2004-53197.10.1115/GT2004-53197
5.
Stearns
,
E.
,
Cloud
,
D.
, and
Filburn
,
T.
,
2006
, “
Probabilistic Thermal Analysis of Gas Turbine Internal Hardware
,”
ASME
Paper No. GT2006-90881. 10.1115/GT2006-90881
6.
Bischoff
,
T.
,
Voigt
,
M.
,
Chehab
,
E.
, and
Vogeler
,
K.
,
2006
, “
Probabilistic Analysis of Stationary Gas Turbine Secondary Air Systems
,”
ASME
Paper No. GT2006-90261. 10.1115/GT2006-90261
7.
Muller
,
Y.
,
2009
, “
Coupled Thermomechanical Fluid–Structure Interaction in the Secondary Air System of Aircraft Engines: Contribution to an Integrated Design Method
,” Ph.D. thesis, Laboratoire de Mécanique et Energétique de l'Université de Valenciennes, Valenciennes, France.
8.
Spurk
,
J. H.
,
1992
,
Dimensionsanalyse in der Strömungslehre
,
Springer-Verlag
,
Berlin, Germany
.
9.
Sidwell
,
V.
, and
Darmofal
,
D.
,
2003
, “
Probabilistic Analysis of a Turbine Cooling Air Supply System: The Effect on Airfoil Oxidation Life
,”
ASME
Paper No. GT2003-38119.10.1115/GT2003-38119
10.
Antinori
,
G.
,
Muller
,
Y.
,
Duddeck
,
F.
, and
Fischersworring-Bunk
,
A.
,
2013
, “
Statistical Methods for a Stochastic Analysis of the Air System of a Jet Engine Low Pressure Turbine
,”
ASME
Paper No. GT2013-94881. 10.1115/GT2013-94881
11.
McKay
,
M. D.
,
Beckman
,
R. J.
, and
Conover
,
W. J.
,
1979
, “
A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code
,”
Technometrics
,
21
(
2
), pp.
239
245
.10.2307/1268522
12.
Stein
,
M.
,
1987
, “
Large Sample Properties of Simulations Using Latin Hypercube Sampling
,”
Technometrics
,
29
(
2
), pp.
143
151
.10.1080/00401706.1987.10488205
13.
Storm
,
R.
,
2007
,
Wahrscheinlichkeitsrechnung, mathematische Statistik und statistische Qualitätskontrolle
, 12th ed.,
Carl Hanser Verlag
,
München, Germany
.
14.
Gray
,
P. A.
,
2009
, “
A Probabilistic Analysis of a High Pressure Turbine Pre-Swirl Cavity and Capture System to Identify Input Variability of Design Parameters
,” Master's thesis, Faculty of Rensselaer Polytechnic Institute Hartford, Hartford, CT.
15.
Traupel
,
W.
,
2001
,
Thermische Turbomaschinen—2.Geänderte Betriebsbedingungen, Regelung, Mechanische Probleme, Temperaturprobleme
, 4th ed., Vol.
2
,
Springer-Verlag
,
Berlin, Germany
.
16.
Balke
,
H.
,
2010
,
Einführung in die Technische Mechanik: Festigkeitslehre
, 2nd ed.,
Springer-Verlag
,
Berlin, Germany
.
17.
Siebertz
,
K.
,
Bebber
,
D.
, and
Hochkirchen
,
T.
,
2010
,
Statistische Versuchsplanung
,
Springer-Verlag
,
Berlin, Germany
.
18.
Sobol
,
I.
,
2001
, “
Global Sensitivity Indices for Nonlinear Mathematical Models and Their Monte Carlo Estimates
,”
Math. Comput. Simul.
,
55
(
1–3
), pp.
271
280
.10.1016/S0378-4754(00)00270-6
19.
Spearman
,
C.
,
1904
, “
The Proof and Measurement of Association Between Two Things
,”
Am. J. Psychol.
,
15
(
1
), pp.
72
101
.10.2307/1412159
20.
Morris
,
M. D.
,
1991
, “
Factorial Sampling Plans for Preliminary Computational Experiments
,”
Technometrics
,
33
(
2
), pp.
161
174
.10.1080/00401706.1991.10484804
21.
Saltelli
,
A.
,
Ratto
,
M.
,
Andres
,
T.
,
Campolongo
,
F.
,
Cariboni
,
J.
,
Gatelli
,
D.
,
Saisana
,
M.
, and
Tarantola
,
S.
,
2008
,
Global Sensitivity Analysis: The Primer
,
Wiley
,
Chichester, UK
.
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