Graphical Abstract Figure
Graphical Abstract Figure
Close modal

Abstract

The uncertainty quantification in the turbine components' aerodynamic and heat transfer performances is widely considered to be the most challenging topic due to its intricate and nonlinear characteristics. This paper first proposes an efficient uncertainty quantification method based on an original parallel framework combining Polynomial Chaos Expansions (PCE) with two forms (stochastic response surface-based and Galerkin projection-based) and the Universal Kriging method. The rigorous mathematical tests are performed to verify the reliability and computational efficiency of the proposed method, and the results support that this method can dramatically reduce computational samples compared to the conventional PCE method while maintaining computational accuracy. Then, the genetic algorithm was introduced to establish an efficient uncertainty quantification framework, and it is applied to the aerothermal performance robustness investigation of the GE-E3 rotor blade tip with and without film cooling. Based on the findings of uncertainty quantification, the injection of cooling air drastically enhances the unstable tendency of the flow and thermal fields, resulting in the actual aerothermal performance of the squealer tip being much lower than that predicted by deterministic calculations. The setting of the film cooling, although effective in reducing the heat flux around the cooling holes, also induces more chaotic flow and thermal fields, leading to sharp heat flux fluctuations around the cooling holes. Finally, our novel reliability analysis algorithm, rooted in the quantification of uncertainty, corroborates the assertion that the introduction of coolant gas, while extending the operational longevity of turbine blades, confers only marginal improvements in the mitigation of lifespan variability. The comprehensive lifespan assessment elucidates that the mean operational longevity of the conventional squealer tip design stands at an estimated 16,169.44 h, accompanied by a standard deviation of 2,750.31 h. In stark contrast, the mean operational longevity of the squealer tip integrated with film cooling measures a significantly enhanced 17,035.17 h, exhibiting a standard deviation of 2,492.73 h. Consequently, the operational lifespan of the conventional squealer tip experiences a decrement of 10.17% in comparison to the anticipated mean lifespan, whereas the reduction for the film-cooled squealer tip registers at 5.36%.

References

1.
Bunker
,
R. S.
,
Bailey
,
J. C.
, and
Ameri
,
A. A.
,
2000
, “
Heat Transfer and Flow on the First-Stage Blade Tip of a Power Generation Gas Turbine: Part 1—Experimental Results
,”
ASME J. Turbomach.
,
122
(
2
), pp.
263
271
.
2.
Xie
,
Y. J.
,
Wang
,
M. C.
,
Zhang
,
G.
, and
Chang
,
M.
,
2006
, “
Analysis of Superalloy Turbine Blade Tip Cracking During Service
,”
Eng. Fail. Anal.
,
13
(
8
), pp.
1429
1436
.
3.
Schabowski
,
Z.
, and
Hodson
,
H.
,
2014
, “
The Reduction of Over Tip Leakage Loss in Unshrouded Axial Turbines Using Winglets and Squealers
,”
ASME J. Turbomach.
,
136
(
4
), p.
041001
.
4.
Wang
,
H.
,
Tao
,
Z.
,
Zhou
,
Z.
,
Zhao
,
G.
,
Han
,
F.
, and
Li
,
H.
,
2019
, “
An Investigation for the Turbine Blade Film Cooling Performance on the Suction Side Tip Region Under Rotating Condition
,”
Appl. Therm. Eng.
,
150
, pp.
864
874
.
5.
Moore
,
J. O. H. N.
, and
Tilton
,
J. S.
,
1988
, “
Tip Leakage Flow in a Linear Turbine Cascade
,”
ASME J. Turbomach.
,
110
(
1
), pp.
18
26
.
6.
Metzger
,
D. E.
,
Bunker
,
R. S.
, and
Chyu
,
M. K.
,
1989
, “
Cavity Heat Transfer on a Transverse Grooved Wall in a Narrow Flow Channel
,”
ASME J. Heat Tranf.
,
111
(
1
), pp.
73
79
.
7.
Kwak
,
J. S.
,
Ahn
,
J.
, and
Han
,
J. C.
,
2004
, “
Effects of Rim Location, Rim Height, and Tip Clearance on the Tip and Near Tip Region Heat Transfer of a Gas Turbine Blade
,”
Int. J. Heat Mass Transf.
,
47
(
26
), pp.
5651
5663
.
8.
Park
,
J. S.
,
Lee
,
D. H.
,
Rhee
,
D. H.
,
Kang
,
S. H.
, and
Cho
,
H. H.
,
2014
, “
Heat Transfer and Film Cooling Effectiveness on the Squealer Tip of a Turbine Blade
,”
Energy
,
72
, pp.
331
343
.
9.
Zhang
,
G.
,
Sundén
,
B.
, and
Xie
,
G.
,
2020
, “
Corrigendum to Combined Experimental and Numerical Investigations on Heat Transfer Augmentation in Truncated Ribbed Channels Designed by Adopting Fractal Theory
,”
Int. Commun. Heat. Mass.
,
121
, p.
105080
.
10.
Carnevale
,
M.
,
D’Ammaro
,
A.
,
Montomoli
,
F.
, and
Salvadori
,
S.
,
2014
, “
Film Cooling and Shock Interaction: An Uncertainty Quantification Analysis With Transonic Flows
,”
ASME Turbo Expo 2014: Turbine Technical Conference and Exposition
,
Düsseldorf, Germany
,
June 16–20
, ASME, V05BT13A001.
11.
Huang
,
M.
,
Li
,
Z.
,
Li
,
J.
, and
Song
,
L.
,
2022
, “
Efficient Uncertainty Quantification and Sensitivity Analysis on the Aerothermal Performance of Turbine Blade Squealer Tip
,”
ASME J. Turbomach.
,
144
(
5
), p.
051014
.
12.
Huang
,
M.
,
Li
,
Z.
,
Li
,
J.
, and
Song
,
L.
,
2022
, “
Uncertainty Quantification and Sensitivity Analysis of Aerothermal Performance for the Turbine Blade Squealer tip
,”
Int. J. Therm. Sci.
,
175
, p.
107460
.
13.
Wang
,
X.
, and
Zou
,
Z.
,
2019
, “
Uncertainty Analysis of Impact of Geometric Variations on Turbine Blade Performance
,”
Energy
,
176
, pp.
67
80
.
14.
D’Ammaro
,
A.
, and
Montomoli
,
F.
,
2013
, “
Uncertainty Quantification and Film Cooling
,”
Comput. Fluids
,
71
, pp.
320
326
.
15.
Luo
,
J.
,
Xia
,
Z.
, and
Liu
,
F.
,
2021
, “
Robust Design Optimization Considering Inlet Flow Angle Variations of a Turbine Cascade
,”
Aerosp. Sci. Technol.
,
116
, p.
106893
.
16.
Huang
,
M.
,
Zhou
,
Z.
,
Zhang
,
K.
,
Li
,
Z.
, and
Li
,
J.
,
2023
, “
Investigation on High-Dimensional Uncertainty Quantification and Reliability Analysis of Aero-Engine
,”
Aerosp. Sci. Technol.
,
142
, p.
108685
.
17.
Montomoli
,
F.
,
Carnevale
,
M.
,
D’Ammaro
,
A.
,
Massini
,
M.
, and
Salvadori
,
S
,
2015
,
Uncertainty Quantification in Computational Fluid Dynamics and Aircraft Engines[M]
,
Springer International Publishing
,
New York
.
18.
Zhang
,
X.
,
Wang
,
L.
, and
Sørensen
,
J. D.
,
2019
, “
REIF: A Novel Active-Learning Function Toward Adaptive Kriging Surrogate Models for Structural Reliability Analysis
,”
Reliab. Eng. Syst. Saf.
,
185
, pp.
440
454
.
19.
Shi
,
Y.
,
Lu
,
Z.
,
Chen
,
S.
, and
Xu
,
L.
,
2018
, “
A Reliability Analysis Method Based on Analytical Expressions of the First Four Moments of the Surrogate Model of the Performance Function
,”
Mech. Syst. Signal Proc.
,
111
, pp.
47
67
.
20.
Xiao
,
N. C.
,
Zhan
,
H.
, and
Yuan
,
K.
,
2020
, “
A New Reliability Method for Small Failure Probability Problems by Combining the Adaptive Importance Sampling and Surrogate Models
,”
Comput. Meth. Appl. Mech. Eng.
,
372
, p.
113336
.
21.
Kwak
,
J. S.
, and
Han
,
J.-C.
,
2001
, “
Heat Transfer Coefficients and Film Cooling Effectiveness on the Squealer Tip of a Gas Turbine Blade
,”
ASME J. Turbomach.
,
125
(
4
), pp.
648
657
.
22.
You
,
Y.
, and
Ding
,
L.
,
2023
, “
Numerical Investigation of Unsteady Film Cooling on Turbine Blade Squealer Tip With Pressure Side Coolant
,”
Int. Commun. Heat. Mass
,
143
, p.
106720
.
23.
Ye
,
M.
,
He
,
K.
, and
Yan
,
X.
,
2023
, “
Influence of Wear Damages on Aerodynamic and Heat Transfer Performance in Squealer Tip Gap
,”
Appl. Therm. Eng.
,
159
, p.
113976
.
24.
Li
,
F.
,
Jia
,
Z.
,
Zhang
,
W.
,
Liu
,
Z.
, and
Feng
,
Z.
,
2022
, “
Investigation Into the Film Cooling Performance of Multi-Cavity Tips With Different Cavity Depths
,”
Int. J. Therm. Sci.
,
181
, p.
107766
.
25.
Zhang
,
J.
,
Zheng
,
Q.
,
Xu
,
J.
,
Yue
,
G.
, and
Jiang
,
Y.
,
2023
, “
Conjugate Heat Transfer and Flow Analysis on Double-Wall Cooling With Impingement Induced Swirling and Film Cooling
,”
Appl. Therm. Eng.
,
223
, p.
120014
.
26.
Giangiacomo
,
P.
,
Michelassi
,
V.
, and
Martelli
,
F.
,
2000
, “
Analysis of the Mixing Plane Interface Between Stator and Rotor of a Transonic Axial Turbine Stage
,”
Proceedings of the Turbo Expo: Power for Land, Sea, and Air
,
Munich, Germany
,
May 8–11
, Vol. 78545, American Society of Mechanical Engineers, p. V001T03A107.
27.
Laveneziana
,
L.
,
Rosafio
,
N.
,
Salvadori
,
S.
,
Misul
,
D. A.
,
Baratta
,
M.
,
Forno
,
L.
,
Valsania
,
M.
, and
Toppino
,
M.
,
2022
, “
Conjugate Heat Transfer Analysis of the Aero-Thermal Impact of Different Feeding Geometries for Internal Cooling in Lifetime Extension Processes for Heavy-Duty Gas Turbines
,”
Energies
,
15
(
9
), p.
3022
.
28.
Zou
,
Z.
,
Shao
,
F.
,
Li
,
Y.
,
Zhang
,
W.
, and
Berglund
,
A.
,
2017
, “
Dominant Flow Structure in the Squealer Tip Gap and Its Impact on Turbine Aerodynamic Performance
,”
Energy
,
138
, pp.
167
184
.
29.
Efron
,
B.
,
Hastie
,
T.
,
Johnstone
,
I.
, and
Tibshirani
,
R.
,
2004
, “
Least Angle Regression
,”
Ann. Stat.
,
32
(
2
), pp.
407
499
.
30.
Meana-Fernández
,
A.
,
Fernández Oro
,
J. M.
,
Argüelles Díaz
,
K. M.
,
Galdo-Vega
,
M.
, and
Velarde-Suárez
,
S.
,
2019
, “
Application of Richardson Extrapolation Method to the CFD Simulation of Vertical-Axis Wind Turbines and Analysis of the Flow Field
,”
Eng. Appl. Comp. Fluid Mech.
,
13
(
1
), pp.
359
376
.
31.
Eça
,
L.
, and
Hoekstra
,
M.
,
2014
, “
A Procedure for the Estimation of the Numerical Uncertainty of CFD Calculations Based on Grid Refinement Studies
,”
J. Comput. Phys.
,
262
, pp.
104
130
.
32.
Ye
,
M.
, and
Yan
,
X.
,
2020
, “
Investigations of Heat Transfer and Film Cooling Effect on a Worn Squealer tip
,”
Proceedings of the Turbo Expo: Power for Land, Sea, and Air
,
Virtual, Online
,
Sept. 21–25
, Vol. 84171, American Society of Mechanical Engineers, p. V07BT12A033.
33.
Daum
,
F.
, and
Huang
,
J.
,
2003
, “Curse of Dimensionality and Particle Filters,” IEEE Paper No. 03TH8652.
34.
Wiener
,
N.
,
1938
, “
The Homogeneous Chaos
,”
Am. J. Math.
,
60
(
4
), pp.
897
936
.
35.
Xiu
,
D.
, and
Karniadakis
,
G. E.
,
2002
, “
The Wiener–Askey Polynomial Chaos for Stochastic Differential Equations
,”
SIAM. J. Sci. Comput.
,
24
(
2
), pp.
619
644
.
36.
Xiao
,
D.
,
Lin
,
Z.
,
Fang
,
F.
,
Pain
,
C. C.
,
Navon
,
I. M.
,
Salinas
,
P.
, and
Muggeridge
,
A.
,
2017
, “
Non-Intrusive Reduced-Order Modeling for Multiphase Porous Media Flows Using Smolyak Sparse Grids
,”
Int. J. Numer. Methods Fluids
,
83
(
2
), pp.
205
219
.
37.
Kaya
,
H.
,
Tiftikçi
,
H.
,
Kutluay
,
Ü
, and
Sakarya
,
E.
,
2019
, “
Generation of Surrogate-Based Aerodynamic Model of an UCAV Configuration Using an Adaptive Co-Kriging Method
,”
Aerosp. Sci. Technol.
,
95
, p.
105511
.
38.
Lee
,
H.
,
Lee
,
D. J.
, and
Kwon
,
H.
,
2018
, “
Development of an Optimized Trend Kriging Model Using Regression Analysis and Selection Process for Optimal Subset of Basis Functions
,”
Aerosp. Sci. Technol.
,
77
, pp.
273
285
.
39.
Li
,
Z.
, and
Zheng
,
M.
,
2009
, “
Development of a Numerical Model for the Simulation of Vertical U-Tube Ground Heat Exchangers
,”
Appl. Therm. Eng.
,
29
(
5–6
), pp.
920
924
.
40.
Shi
,
Y.
, and
Zhang
,
Y.
,
2022
, “
The Neural Network Methods for Solving Traveling Salesman Problem
,”
Procedia Comput. Sci.
,
199
, pp.
681
686
.
41.
Cherry
,
D. G.
,
Gay
,
C. H.
, and
Lenahan
,
D. T.
,
1982
, “
Low Pressure Turbine Test Hardware Detailed Design Report
,” National Aeronautics and Space Administration, Lewis Research Center.
42.
De Maesschalck
,
C.
,
Lacor
,
C.
,
Paniagua
,
G.
,
Lavagnoli
,
S.
,
Remiot
,
A.
, and
Bricteux
,
L.
,
2017
, “
Performance Robustness of Turbine Squealer Tip Designs Due to Manufacturing and Engine Operation
,”
J. Propul. Power
,
33
(
3
), pp.
740
749
.
43.
Koratikere
,
P.
,
Leifsson
,
L.
,
Koziel
,
S.
, and
Pietrenko-Dabrowska
,
A.
,
2023
, “
Efficient Uncertainty Quantification Using Sequential Sampling-Based Neural Networks
,”
Proceedings of the International Conference on Computational Science
,
Prague, Czech Republic
,
July 3–5
, pp.
536
547
.
44.
Wang
,
J.
, and
Zheng
,
X.
,
2020
, “
Review of Geometric Uncertainty Quantification in Gas Turbines
,”
ASME J. Eng. Gas. Turbines Power
,
142
(
7
), p.
070801
.
45.
Bryant
,
F. B.
, and
Satorra
,
A.
, “
Principles and Practice of Scaled Difference Chi-Square Testing
,”
Struct. Equ. Modeling
,
19
(
3
), pp.
372
398
.
46.
Ishaq
,
A. I.
, and
Abiodun
,
A. A.
,
2020
, “
The Maxwell–Weibull Distribution in Modeling Lifetime Datasets
,”
Ann. Data Sci.
,
7
(
4
), pp.
639
662
.
47.
Djeddi
,
A. Z.
,
Hafaifa
,
A.
,
Guemana
,
M.
, and
Kouzou
,
A.
,
2020
, “
Gas Turbine Reliability Modelling Based on a Bath Shaped Rate Failure Function: Modified Weibull Distribution Validation
,”
Life Cycle Reliab. Saf. Eng.
,
9
(
4
), pp.
437
448
.
48.
Ozonuwe
,
S. N.
,
Onyekachi
,
D.
, and
Oside
,
C. U.
,
2020
, “
Application of the Two-Parameter Weibull Distribution Method to Assess the Reliability of Gas Turbine Compressors
,”
J. Eng. Res. Rep.
,
18
(
4
), pp.
12
20
.
49.
Yu
,
H.
, and
Wilamowski
,
B. M.
,
2018
, “Levenberg–Marquardt Training,”
Intelligent Systems.
,
CRC Press
,
Boca Raton, FL
, p.
12-1
.
50.
Rowe
,
J. P.
,
Freeman
,
J. W.
, and
Voorhees
,
H. R.
,
1957
,
Final Report to the General Electric Company Aircraft Gas Turbine Division on Effect of Overheating on the Creep-Rupture Properties of Udimet 500 Alloy at 16000F and 28,500 PSI
.
51.
Cao
,
D.
, and
Bai
,
G.
,
2020
, “
A Study on Aeroengine Conceptual Design Considering Multi-Mission Performance Reliability
,”
Appl. Sci.
,
10
(
13
), p.
4668
.
52.
Kinnison
,
H. A.
,
2013
,
Aviation Maintenance Management
,
McGraw-Hill Education
,
New York
.
53.
Saxena
,
A.
,
Goebel
,
K.
,
Simon
,
D.
, and
Eklund
,
N.
,
2008
, “
Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation
,”
Proceedings of the 2008 International Conference on Prognostics and Health Management
,
Denver, CO
,
Oct. 6–9
.
You do not currently have access to this content.