Integrated lifing analysis for gas turbinecomponents

T. Tinga, W.P.J. Visser and W.B. de Wolf

NLR-TP-2000-632

Nationaal Lucht- en Ruimtevaartlaboratorium

National Aerospace Laborator y NLR

NationaalNationaalNationaalNationaal Lucht- en Ruimtevaartlaboratorium Lucht- en Ruimtevaartlaboratorium Lucht- en Ruimtevaartlaboratorium Lucht- en RuimtevaartlaboratoriumNational Aerospace Laboratory NLR

NLR-TP-2000-632

Integrated lifing analysis for gas turbineIntegrated lifing analysis for gas turbineIntegrated lifing analysis for gas turbineIntegrated lifing analysis for gas turbinecomponentscomponentscomponentscomponents

T. Tinga, W.P.J. Visser and W.B. de Wolf

This investigation has been carried out under a contract awarded by the Ministery ofDefence, Scientific Support Devision, contract number NTP98/29.Ministery of Defence, Scientific Support Division has granted NLR permission to publishthis report.

This report is based on a presentation held at the USAF Aircraft Structural IntegrityProgram Congress, 5-7 December 2000, San Antonio, Texas, USA.

The contents of this report may be cited on condition that full credit is given to NLR andthe authors.

Division: Structures and MaterialsIssued: 30 November 2000Classification of title: Unclassified

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Contents

1 Introduction 3

2 Description of the integrated analysis tool 4

3 Case study I: effect of HPT deterioration on 3rd blade creep life 6

consumption rate

4 Case study II: crack growth analysis on 2nd stage fandisc hub 6

5 Discussion 7

6 Conclusions and potential 7

7 Acknowledgement 8

8 References 8

9 Figures

(12 pages in total)

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INTEGRATED LIFING ANALYSIS FOR GAS TURBINE COMPONENTS

Mr. Tiedo Tinga, Mr. Wilfried, P.J. Visser and Dr. Wim B. de WolfNational Aerospace Laboratory

P.O.Box 153, 8300 AD Emmeloord, The NetherlandsTel. +31 527 248727, Fax. +31 527 248210, email: tinga@nlr.nl

ABSTRACTA method to predict gas turbine component life based on engine performance analysis has beendeveloped [1]. Engine performance history is obtained from in-flight monitored engine parameters andflight conditions and downloaded for processing by a tool integrating a number of software tools andmodels. Data acquisition is performed by the FACE system, installed in a large number of RNLAF F-16fighter aircraft. Data then is processed by a thermodynamical engine system model, calculating gasproperties like pressure and temperature at the required station in the engine. With a combination of aheat transfer and thermal finite element model the temperature distribution in the component iscalculated. The stress distribution is obtained with a structural finite element analysis and finally a lifeconsumption model is used to determine the damage accumulation in the component. The applicabilityof the tool is demonstrated with a number of analyses on real engine components. Examples are a creeplife analysis of the F100-PW-220 engine 3rd stage turbine rotor blade during a recorded RNLAF F-16mission and a crack growth analysis for the 2nd stage fandisc hub for a representative mission mix.Furthermore, using the engine system model with a detailed control system, the effect of enginedeterioration on blade life consumption rate was determined. The tool has significant potential toenhance on-condition maintenance and optimize aircraft operational use.

1 INTRODUCTIONMaintenance costs form a major part of total aircraftengine operating costs. A significant reduction in thesecosts would be obtained if inspection intervals could beextended and component service life increased.Inspection intervals and service life are commonly basedon statistical analysis, requiring a limited probability offailure (a certain level of safety) during operation.However in many cases this approach leads toconservative inspection intervals and life limits for themajority of parts or components. The analysis toolpresented offers a way to attempt to reduce maintenancecosts and improve safety by applying usage monitoring topredict operational component condition and therebyfacilitating “on-condition maintenance”.The algorithms and system models incorporated in thistool represent the relation between operational usage ofthe engine and component condition. Optimally, thesystem is able to accurately determine componentcondition and predict life consumption based onoperational data obtained from a number of sensors.

The developed analysis tool predicts engine component(or part) life based on analysis of engine performance.Engine performance history is obtained from in-flightmonitored engine control parameters and flightconditions and downloaded for processing by a numberof software tools and models.Most of the models and tools used to determine engineperformance, component usage and condition (health)and to predict life consumption were already commonlyapplied at the National Aerospace Laboratory (NLR) asstand-alone. The benefit of the integrated tool is thedirect relation between engine performance andcomponent life.The following section will describe the severalconstituents of the analysis tool and show some sampleresults, where the tool is demonstrated and evaluated on ahot section part (third stage turbine rotor blade) of theF100-PW-220 engine of a Royal Netherlands Airforce(RNLAF) F-16 fighter aircraft. Furthermore a case studyis reported, analyzing the effect of componentdeterioration on life consumption, finally followed by theconclusions and recommendations for further research.

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2 DESCRIPTION OF THE INTEGRATEDANALYSIS TOOL

The integrated analysis tool presented consists of asequence of software tools and models. An overview ofthis sequence is given in Fig. 1. The sequence rangesfrom the measurement of operational engine data toultimately predicting the life consumption during theanalyzed mission. The following tools must subsequentlybe applied to process the data:FACE Fatigue and Air Combat Evaluation

(FACE) system for monitoring flight /engine data

GSP Gas turbine Simulation Program (GSP)for calculating engine systemperformance data

CFD model Computational Fluid Dynamics (CFD)model for calculating the heat transfer tohot section components

MARC Finite Element (FE) model forcalculating thermal and mechanicalstress in hot section components

Lifing model for deriving life consumption datafrom the stress history data

FACEThe FACE system used to measure flight data is based onthe Autonomous Combat Evaluation (ACE) system ofRADA Electronic Industries, which is used for pilotdebriefing purposes. The NLR has developed a fatigueanalysis system that has been combined with ACE toform the FACE system [2]. The FACE system consists ofboth on-board and ground-based hardware. In the aircrafttwo electronic boxes are installed: the Flight MonitoringUnit (FMU) and the Data Recording Unit (DRU). Theground-based hardware relevant for maintenancepurposes is the Logistic Debriefing Station (LDS).The FMU is a programmable unit that determines whichsignals are stored and how they are stored. By generatinga Set-up Configuration File (SCF) and uploading it intothe FMU, the data collection process can be adapted toall requirements. In this way several data reductionalgorithms (e.g. peak and through, time at level) can beselected and the sampling frequency can be adapted. Therelevant signals stored by the DRU are engine parametersfrom the engine’s Digital Electronic Engine Control(DEEC) and avionics data. The DEEC signals can besampled at a maximum frequency of 4 Hz. The followingsignals, which together fully describe engine usage, arestored:

- Fuel flow to the gas generator- Fuel flow to the afterburner- Exhaust nozzle position- Flight conditions: Mach, altitude and air

temperatureThese parameters, as functions of time, are used as inputfor the GSP model, which is the next tool in thesequence. The first three parameters could also besubstituted by the Power Lever Angle (PLA) signal,provided that the GSP model contains a control unit,which translates the PLA to the appropriate fuel flowsand nozzle area. A data reduction algorithm is applied toreduce the amount of operational data before it is used asinput for GSP.

GSPThe Gas Turbine Simulation Program (GSP) is a tool forgas turbine engine performance analysis, which has beendeveloped at the NLR [3],[4]. This program enables bothsteady state and transient simulations for any kind of gasturbine configuration. A specific gas turbineconfiguration is created by arranging different predefinedcomponents (like fans, compressors, and combustors) in aconfiguration similar to the gas turbine type to besimulated. An example of a model for a twin spoolturbofan engine like the Pratt & Whitney F100-PW-220is given in Fig. 2. The simulation is based on one-dimensional modeling of the processes in the differentgas turbine components with thermodynamic relationsand steady-state characteristics ("component maps"). Acomprehensive gas model based on a NASA code isincluded [5], fully describing effects of gas compositionand dissociation on hot section engine performance.For the current purpose, GSP can be used to calculate gastemperatures, pressures, velocities and composition atrelevant engine stations from measured engine data. Thisparticularly applies to stations for which no measureddata is available such as the critical high-pressure turbineentry temperature. Also, GSP is able to accuratelycalculate dynamic responses of these parameters (criticalto engine life) where measured data is not available orhas unacceptable high time lags or low updatefrequencies.The GSP model input obtained from FACE includes allmeasured flight conditions and engine power setting data.With GSP, the entire engine transient (usually an entiremission) is calculated with an integration step size of0.05 seconds. With a smallest input step size of 0.2seconds, this is sufficient to accurately calculate the

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critical effects such as typical severe acceleration /deceleration temperature transients in the hot section. AGSP report is used to output data for further processingby the CFD and MARC finite element (FE) models.

CFD modelThe Computational Fluid Dynamics (CFD) model is usedto accurately calculate the heat transfer from the hot gasstream to the component. For this calculation it isimportant to have detailed information on the geometryof both the flow channel and the different componentsthat disturb the flow (blades, vanes). From CFD analysisof the gas flow through the gas turbine, values for theheat transfer coefficient h are obtained at specificlocations in the component. The h value variessignificantly along the flow path, due to variations in theflow conditions (gas velocity, type of flow (laminar,turbulent), etc). The CFD model also allowsincorporating the effects of film cooling of the blade onheat transfer. For some components, the results for theheat transfer coefficient, obtained by the CFD model, arenot suitable for use in the thermal model. For thesecomponents an engineering approach was followed [6],which resulted in a number of functions that describe theapproximate distribution of the heat transfer coefficientacross the blade surface. These functions are specific forthe component under consideration.

Finite Element modelThe Finite Element (FE) model consists of twointerrelated models. The thermal model calculates thetemperature distribution in the component, based on theheat input from the hot gas stream. The mechanicalmodel calculates the stresses and strains in thecomponent, caused by the varying temperaturedistribution and the externally applied loads. The finiteelement code used is MARC, which is a commerciallyavailable, multipurpose finite element package.Definition of the geometry and mesh generation isperformed with the pre-processor MSC/PATRAN.MSC/PATRAN is also used as postprocessor to view andanalyze the results.The thermal model calculates the temperaturedistribution in the component. For each finite element onthe surface of the component, the heat transfer coefficientfollows from the CFD model. With the thermalconductivity α of the material, the temperaturedistribution in the component can be calculated. Atransient thermal analysis is performed for the complete

flight under consideration with the time-varying ambientgas temperature obtained from GSP as input. An exampleof the temperature distribution in an internally cooledturbine blade at some point during a flight is shown inFig. 3. A limited number of CFD packages, having theability to incorporate fluid-structure interaction, canperform both the heat transfer coefficient andtemperature distribution calculation. This would makethe MARC thermal model calculation redundant.The mechanical model calculates the stress and straindistribution in a component. There are two sources forthe stress in a rotating component: centrifugal forces dueto rotation of the component and temperature gradients inthe material. Again a transient analysis is performed forthe complete mission. In this case the rotationalfrequency and the temperature distribution, both asfunction of time, are the input for the model and thestress and strain distributions in time appear as output.The temperature distribution is obtained from the resultsof the thermal analysis and the values of the rotationalfrequency are read from the GSP report file. An exampleof the stress variation at 3 different locations on a turbineblade is shown in Fig. 4.

Lifing modelA lifing model generally calculates either total time tofailure or number of cycles to failure for a certaincomponent subjected to a specific load sequence. A largenumber of specific life prediction models have beendeveloped over the last twenty years, where each modelis appropriate for a specific application. The majordivision in lifing models is between total life models andcrack growth models. Total life models, like thePalmgren-Miner model [6],[8], only calculate the time tofailure, not considering the way failure is reached. Thesemodels are representative for the Safe Life philosophy,aiming to retire a component before a crack originates.On the other hand, crack growth models represent theDamage Tolerance philosophy, which accepts thepresence of material defects and aims to monitor crackgrowth and remove the component before the crackbecomes unstable. Note that crack growth modelsperform a local analysis, using stress histories at veryspecific locations on the component, being the location ofcrack initiation. The results are only valid for thatlocation. In addition, several different mechanisms cancause the failure of a component, for example fatigue,creep or oxidation. Every failure mechanism requires aspecific lifing model. In the end, the actual choice of the

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lifing model(s) depends on the expected failuremechanism of the component under consideration.

3 CASE STUDY I: EFFECT OF HPTDETERIORATION ON 3RD BLADE CREEPLIFE CONSUMPTION RATE

The analysis tool that has been presented in the previoussection is demonstrated by applying it to the F100-PW-220 engine of the RNLAF F-16 fighter aircraft. Thecomponent selected for analysis is the 3rd stage turbineblade, which is the first stage rotor of the low-pressureturbine (LPT) module. As input an arbitrary mission hasbeen selected. Figure 5 shows the variation in altitudeand fuel flow as measured by FACE during the mission.The purpose of this case study is to show the effect ofhigh-pressure turbine (HPT) deterioration on the lifeconsumption of the 3rd stage blade.Common deterioration types include compressor fouling,increased tip clearance, and corrosion and erosion ofespecially the HP turbine blades. All these types result inlower component efficiencies and usually require higherfuel flows and turbine inlet temperature to maintainminimum thrust. The consequence is an increasing rate ofdeterioration. Also, transient performance changes andboth lower acceleration rates and higher temperaturerates in the HP turbine may be expected with adeteriorated engine in many cases.HPT deterioration is incorporated in GSP by applying a+1% change in HPT flow capacity and –2% change inHPT isentropic efficiency. This is a typical combinationof the two deterioration modes. Simulating componentdeterioration is a standard option in GSP.The engine control system usually tries to compensateloss of performance due to deterioration by maintainingcompressor or fan rotor speed or another thrust relatedparameter. With the F100-PW-220 engine, fan rotorspeed is maintained, which means thrust is virtuallyunaffected by HPC (High Pressure Compressor), HPT orLPT deterioration. However, to maintain fan rotor speedwith a deteriorated HPT, TIT (Turbine Inlet Temperature)and FTIT (Fan Turbine Inlet Temperature) levelsincrease, and compressor speed may drop. This impliesthat in order to analyze deterioration effects on thermalloads during operation, integration of the control systemin the gas turbine performance model is required.The effect of HPT deterioration is calculated with GSP.The FTIT appears to increase by 25 to 35 degrees in thedeteriorated engine. The calculated temperatures (FTIT)can also be compared to actually measured values

obtained from FACE, which is done in Fig. 6. Thefluctuation in measured signal appears to be much lessthan in the calculated value, which is due to the finiteresponse time of the thermocouple used to measure thetemperature in the engine. The differences in steady statevalues are due to the fact that the actual engine alreadyhas sustained some deterioration, while the GSPcalculation is based on a new engine. Although it ispossible to include deterioration effects in the GSPanalysis, the non-deteriorated engine model results areshown here because the actual amount of deterioration inthe real engine is hard to quantify.From the GSP-outputted FTIT the FE models firstlydetermine the temperature distribution and its variation intime and secondly the stress and strain distribution and itsvariation in time. In the mechanical model the creepphenomenon is incorporated, because creep is assumed tobe the life-limiting factor for the 3rd stage turbine blade.For a new component the amount of creep strain is niland after sustaining a certain amount of creep failureoccurs. Therefore the creep strain is a damage parameterand the evolution of creep strain is representing damageaccumulation. Note that in this way the lifing analysis isintegrated into the mechanical FE model. The results ofthis combined analysis are given in Fig. 7, which showsthe accumulation of creep strain in the blade during themission for both a new engine and a deteriorated engine.The creep strain accumulation appears to be faster in thedeteriorated engine, which implies that the rate of lifeconsumption is higher, in this case, a factor of 1.9. Thisshows the very high sensitivity of creep strainaccumulation for temperature changes: a 3% temperatureincrease causes a 90% increase in creep strain.

4 CASE STUDY II: CRACK GROWTHANALYSIS ON 2ND STAGE FANDISC HUB

The lifing analysis tool has also been applied to the hubof the 2nd stage fandisc [9]. During inspections, crackswere found in this component at one of the 21 thrustbalance holes (Fig. 8). To investigate the failure, a crackgrowth analysis was performed for this location. Adatabase with FACE measured RNLAF F-16 missionswas used to construct a representative loading history of1000 flights. In this sequence the distribution of thedifferent mission types represents the actual mission mix.Because the fan disc is a cold section component, thermalloads can be omitted in this analysis and the CFD andthermal FE model can be skipped. The dominant loadingparameter in the hub is the varying torque in the shaft

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coming from the fan drive turbine (LPT). The engineperformance program GSP is used to calculate thevariation in torque for all different mission types. Thetorque values are then easily transformed to shearstresses in the hub, which can be used as input for thecrack growth analysis. Therefore a FE analysis is notneeded to calculate the stresses in this case.However, a crack growth analysis requires a stressintensity factor (SIF) solution for the geometry underconsideration, which relates the local stress at the cracktip to the remotely applied stress for every crack length.A FE analysis is used to obtain this SIF solution. Themodel is shown in Fig. 9. For symmetry reasons, onlyone-seventh part of the hub has been modeled. The rigidbore of the disc is modeled by a fixed boundary conditionand the torsional load is applied at the opposite edge ofthe model. Finally, the material crack growth properties(crack growth rate da/dN vs. stress intensity K∆ ) areobtained from material handbooks.

When all these preparations have been completed, theactual crack growth analysis is performed with theCRAGRO program. The initial crack size is taken to be0.254 mm (1/100 inch). The loading sequence is applieduntil either the critical crack length is reached or netsection yielding occurs. The resulting crack propagationlife is very well comparable to the value provided by theOEM.

5 DISCUSSIONAn important point of discussion for this tool is theaccuracy of the calculated results. The accuracy of theintegrated tool is obviously dependent on the accuracy ofthe separate tools and models. The measurements of theFACE system combined with the data reductionalgorithm introduce a maximum error of about 1%. TheGSP model inaccuracy is considered to be less than 2%,provided that a suitable integration time step has beenchosen. The accuracy of the temperatures calculated withthe thermal FE model is mainly determined by theaccuracy of the heat transfer coefficient value h. Usingthe approximation functions for the heat transfer, theuncertainty in heat transfer coefficient is about a factor 2,which is caused by uncertainty about the degree ofturbulence. This could be less when a CFD model is usedto calculate the heat transfer. The uncertainty in heattransfer coefficient will cause uncertainty in thetemperatures during transients. However, the steady statetemperatures are unaffected by the heat transfer rates.

This means that for creep life calculations the value ofthe heat transfer coefficient is not very important, but forfatigue life calculations it is of crucial importance. Themechanical FE model has an inaccuracy of less than 2%,due to variances in material data. Obviously theinaccuracy of the FE calculations will be larger when aninaccurate geometry or a course mesh is used, but thiscan be improved rather easily and is therefore notconsidered to be a limitation of the tool. Note howeverthat refining the FE mesh rapidly increases thecomputation time and the required memory.All together this means that the loading of a componentcan be calculated with an inaccuracy of about 10%,provided that sufficient (aerodynamic) information aboutthe specific component is available.However, performing the actual life prediction willintroduce an additional inaccuracy of 20 to even 50%.This large inaccuracy is due to the large scatter inexperimentally determined material data used for the lifeprediction. The actual inaccuracy depends on the type ofmaterial data used by the model. For example, S,N-curves, representing the relation between number ofcycles to failure and applied stress level, show a higherscatter than crack growth curves. It is therefore afundamental material property phenomenon, which hasits effect on the lifing model inaccuracy. Development oflifing models must be focused on model types, which arebased on material data with little scatter (like crackgrowth data). Another problem is the unavailability ofmaterial data for the component under consideration.Using material data for a slightly different material(regarding to composition or heat treatment) leads to lessaccurate results.

6 CONCLUSIONS AND POTENTIALAn integrated analysis tool for life prediction of gasturbine components has been demonstrated. The toolconsists of a sequence of software tools and models,which were already commonly applied at the NLR asstand-alone. It has been demonstrated that the mechanicaland thermal loads of gas turbine components can becalculated from operational flight data, and thatsubsequently a life prediction can be performed. As theoverall life prediction inaccuracy of the tool is dominatedby the relatively high inaccuracy of lifing models and thelarge scatter in the associated material behavior, futurework must be focussed on improving those models. Amore accurate FE model geometry, a more detailed GSP

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engine control model and an accurate CFD model canimprove the accuracy further.The potential of the analysis tool presented here istwofold. Firstly the tool can be used to apply on-condition maintenance. The load history of everyindividual component could be tracked and could be usedto determine the inspection interval or actual life limit ofthat specific component. The general and mostly veryconservative life limits supplied by the manufacturer arebased on a certain assumed usage, on top of which asafety factor has been applied to account for heavierusage. This safety factor can now be quantified andprobably decreased, which leads to a huge saving in spareparts and inspection costs.Secondly, the tool can be used to compare differentmissions with respect to life consumption. The resultscan be used to optimize the use of the aircraft.

7 ACKNOWLEDGEMENTSThis work was supported by the Dutch Ministry ofDefense, Scientific Support Division, under contractnumber NTP98/29.

8 REFERENCES[1] Tinga, T., Visser, W.P.J., Wolf, W.B. de, Broomhead,

M.J., “Integrated Lifing Analysis Tool for GasTurbine Components”, ASME-2000-GT-646, 2000.

[2] Spiekhout, D.J., “F-16 loads / usage monitoring”,NLR TP 98172, National Aerospace Laboratory,1998, Amsterdam.

[3] Visser, W.P.J., “Gas turbine Simulation Program -GSP – Description and Status”, NLR-CR-91022 L,National Aerospace Laboratory, 1991, Amsterdam.

[4] Visser, W.P.J., “Gas turbine Simulation at NLR”,NLR TP 95574 L, National Aerospace Laboratory,1995, Amsterdam. Paper presented at the CEASSymposium on Simulation Technology, October 30,31 and November 1, 1995, Delft, the Netherlands.

[5] Kluiters, S.C.A., “A New Combustor and EmissionModel for the Gas Turbine Simulation ProgramGSP”, Memorandum VH-98-010, NationalAerospace Laboratory, 1998, Amsterdam.

[6] Wolf, W.B. de, “Heat load prediction for the F100-PW-220 engine LPT first stage rotor blades”, NLR-CR-99278, National Aerospace Laboratory, 1999,Amsterdam.

[7] Miner, M.A., “Cumulative Damage in Fatigue”,ASME Journal of Applied Mechanics, Vol. 67(1945), pp. 159-164.

[8] Palmgren, A., “Die Lebensdauer von Kugellagern”,Verfahrenstechnik, Vol.68 (1924), pp. 339-341.

[9] Kogenhop, O., “Propagation lifetime calculation ofthe P&W compressor fan disc”, NLR-TP-2000-302,National Aerospace Laboratory, 2000, Amsterdam.

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Figure 1: Overview of the integrated analysis tool.

Figure 2: GSP model of the Pratt & Whitney F100-PW-220 turbofan engine.

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Figure 7: Effect of HPT deterioration on creep strain accumulation.

Figure 8: Schematic cross section of 2nd stage fan disc Figure 9: Finite Element model to determine stress intensity factor solution for 2nd

stage fan disc hub