an integrated approach to wind turbine fatigue analysis

D. J. Laino

A. C. Hansen

Mechanical Engineering Department, tJniversity of Utati,

Salt Lake City, UT 84112

An Integrated Approach to Wind Turbine Fatigue Analysis The wind turbine dynamics codes YawDyn and ADAMS have been interfaced with the LIFE2 code for fatigue life estimation via a new interface program, DynlLIFE. This work is sponsored by the National Renewable Energy Laboratory (NREL) with the intent of making it straightforward and practical for wind turbine designers to determine those aspects of their design and wind environment that will cause the most fatigue damage. Several parameters suspected of affecting turbine fatigue life are investigated through a model of the NREL Phase III Combined Experiment Rotor. This study proved the Dyn2LIFE code useful in creating LIFE2 input from YawDyn and ADAMS output, and also revealed .some areas of possible expansion and improvement. Results from this study of a steel blade root suggest changes affecting the normal operation of the turbine alter fatigue life more than rare, high load events. Understanding how the material fatigue characteristics affect lifetime estimates is discussed in terms of the S-N curve utilized in this study. This paper presents the first results from an ongoing project. In the future, we plan to analyze a variety of turbine configurations to help identify those variables which may have the greatest influence on fatigue life.

Introduction Computer software is currently available to wind turbine en

gineers both for modeling turbine dynamics and for estimating component fatigue life. While these codes perform distinctly different tasks, their abilities complement one another. In essence, a suite of programs exists that allows fully computer-based fatigue analysis of a wind turbine design. The benefit of being able to perform a fatigue analysis on a computer model is in the capability to investigate the effect of different scenarios on the fatigue life of a design. Various wind conditions, control algorithms, operating states, materials, and other such factors which would be difficult and expensive, if not impossible to investigate on a prototype machine, can be examined with relative ease via a computer model.

At the University of Utah, under the sponsorship of the National Renewable Energy Laboratory (NREL), we develop horizontal axis wind turbine computer modeling capabilities. Our efforts have contributed to two significant dynamic analysis tools. YawDyn (Hansen, 1992) is a dynamic simulation program that analyzes wind turbine models with blade flap and nacelle yaw degrees-of-freedom. AeroDyn, the aero-dynamic subroutines of YawDyn, have been isolated for use with the commercial software program ADAMS' (Malcolm and Wright, 1994), which permits far more degrees-of-freedom in a given model.

The ever-increasing power of personal computers has made numerous and lengthy ADAMS and YawDyn simulations more easily attainable. If these extended simulations are performed using appropriately specified input, the resulting output may be considered representative of turbine behavior in the actual environment during its lifetime. Based on this output, a fatigue life can be estimated for the turbine using the LIFE2 program (Sutherland and Schluter, 1989), which is designed specifically for wind turbine fatigue analysis. This paper uses a model of NREL's Phase III Combined Experiment Rotor (CER) (Simms et al., 1995) to demonstrate how these complementary codes

Contributed by the Solar Energy Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the ASME JOURNAL OF SOLAR ENERGY

ENGINEERING. Manuscript received by the ASME Solar Energy Division, Mar. 1996; final revision, Nov. 1996. Associate Technical Editor: P. S. Veers.

' ADAMS (Automatic Dynamic Analysis of Mechanical Systems) is the commercial software package of Mechanical Dynamics, Inc.

can be used to investigate the effect of typical design decisions upon the fatigue life of the system. We introduce an interface program, Dyn2LIFE, which we created to streamline the task of processing YawDyn or ADAMS output into a form acceptable as input to the LIFE2 program.

Dyn2LIFE

The overall goal of the Dyn2LIFE interface is to simplify the task of preparing LIFE2 input from time series of load data provided by dynamic analysis programs. To accomplish this goal it is necessary to know the information LIFE2 requires, then determine how it can be achieved from load data. While a complete fatigue analysis using LIFE2 requires information on material fatigue properties, wind speed distribution, and machine operating parameters (e.g., cut-in/cut-out wind speeds), Dyn2LIFE is only concerned with inputs involving stress or strain data derivable from dynamic analysis load data.

The main task in the Dyn2LIFE code is to generate a file of rainflow counted stress or strain cycles from a time series of load data as provided by YawDyn and ADAMS. For most cases converting loads to stress or strain is straightforward, being as simple as a linear function based on fundamental engineering calculations. The current Dyn2LIFE code contains just such a function to convert the load, P, to stress, a:

a = a(P + b) (1)

where the factor a and offset b are supplied by the operator. While this suffices for many engineering materials, the use of more complex conversions in Dyn2LIFE can be implemented via user written functions.

At this point the Dyn2LIFE code is capable of reading in a tab delimited file of multiple columns, with a single row of headers on the first line, and time in the first column. This format is typical of YawDyn and ADAMS output files. The capability to read in a wider range of file formats is also under consideration. The user is prompted to indicate the column of load data to be converted using Eq. (1). With more complex conversions it may be necessary to process more than one column; for example, using both flap and edge bending moments to resolve stress at points around a blade circumference.

Once the load data is read and converted, the next task is preparing it for use in LIFE2. This requires providing the length

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of the time series and the wind speed interval it represents, rainflow counting the data, and formatting an output file. As the Stress States module of LIFE2 is already written to perform these tasks (Schluter and Sutherland, 1991; Schluter, 1991), those subroutines have been incorporated directly into Dyn2-LIFE. Since time is assumed to be present in the first column of data, the time length of the data set is read directly from the input file. The wind speed interval must be entered by the operator, but if a wind speed column is present in the input file, Dyn2LIFE can provide statistics to help specify the interval range.

Dyn2LIFE borrows heavily from LIFE2 and, to a lesser extent, from NREL's General-Purpose Post Processor (GPP) program (Buhl et al., 1995). It serves only to assist in generating LIFE2 input from dynamic analysis output. The desire is to streamline this process and eliminate possible sources of operator error. Any function the Dyn2LIFE program performs can be done using other software tools currently available. Dyn2LIFE merely combines the useful capabilities of these tools into a single processor designed for the task.

The CER Model

A YawDyn model of the Phase III Combined Experiment Rotor was used for generating turbine operating load response at various wind speeds. The CER was chosen for this study because of the availability of experimental data, and YawDyn and ADAMS models. This investigation examines the fatigue life of a variation of the cylindrical steel blade root of the CER under flapwise loading. Ten-minute simulations, using three-component, simulated turbulence generated by the SNLWind-3D (Kelley, 1993) program, were performed for average wind speeds of 8, 11, 13, 16, and 20 m/s. Start/stop and buffeting (nonrotating) response was calculated using an ADAMS model of the CER.

For the LIFE2 code to perform its calculations, several additional inputs are necessary. A wind site with a Rayleigh distribution at 7 m/s average wind speed was assumed and calculated using LIFE2. Cut-in and cut-out wind speeds for the CER were specified at 5 and 25 m/s, respectively. These wind speeds and operating parameters remained unchanged throughout this study. Finally, fatigue data for the blade root material was tabulated. An S-N curve for the 8630 cast steel from which this data was taken is shown in Fig. 1. While we discuss these data in detail further on, it is important to provide some comments here.

The fatigue data shown in Fig. 1 is limited; e.g., mean cyclic stresses (R values) are not considered, nor are changes in properties from tension to compression. This is not due to any restriction in analysis capability as LIFE2 can easily handle more complex S-N data. It does facilitate our discussion on the effects of the shape of the S-N curve on fatigue life. Also, designers searching for fatigue data are likely to come across just such a curve, and hence, are likely to use these type of data. Note that Fig. 1 displays the presence of an endurance limit. This

,400

i 300

'250

Tensile Strength 952 MPa

Yield Strength 869 MPa

1 — ^ ^ ^

1 e+4 1 e+5 1 e+6 1 e+7 Cycles to Failure

1 e+8

characteristic is now widely considered unrealistic for variable amplitude cyclic loading (Veers, 1989), and we will address its consequences upon fatigue sensitivity in detail later.

The CER blade roots are solid steel cylinders 80 ram in diameter. Simple cantilever beam theory is used to calculate the maximum stress, a^^^, at the cylinder outer walls, r, based on the root flap bending moment, M:

o-,™x = M- rll. (2)

Fig. 1 Fatigue ctiaracteristics for cast 8630 steel. Notched specimens subjected to R. R. Moore rotating beam tests, K, = 2.2. (Steel Castings Handbook, 5th ed., Steel Founders Society of America, 1980).

Thus, the factor a in Eq. (1) is r/I in Eq. (2), where / is the area moment of inertia for the cylindrical blade root. This equation was used to determine the nominal stress, while a stress concentration factor, discussed later, was applied in the LIFE2 analyses.

A preliminary investigation using LIFE2 returned an exceedingly long-life estimate for the CER solid blade root. This result indicates stress levels are well below the endurance limit—the near-horizontal, lower limit of the S-N curve of Fig. 1. This result is consoling to the designer, but is not useful for the purposes of this study. Therefore, we have artificially inflated the stress levels on the blade root by assuming a hollow design with an inside diameter of 60 mm. For this design, the value a = 29.1 MPa/kN-m is applied to Eq. (1) in Dyn2LIFE for calculating stresses from moment provided by YawDyn and ADAMS. This conversion factor was used to calculate stress for all results shown herein.

LIFE2

LIFE2 is a wind turbine specific program that uses Miner's rule to estimate component fatigue lifetime. (LIFE2 is also capable of performing linear crack propagation fracture analyses, though this capacity is not utilized for this study.) The LIFE2 code is explained in great detail by the authors of the program in various references (Schluter and Sutherland, 1989; Sutherland, 1989). We would like to relate our experiences during the course of this study in hopes that it will help future LIFE2 users. LIFE2 is not very complex, and we think most users can master its use after several trials.

One of the most difficult problems faced in using the LIFE2 code is understanding some of the results. Depending on factors such as the shape of the S-N curve, seemingly small differences can in fact translate into large differences. Sometimes lifetime estimates can be as small as seconds, while other times on the order of 10^" years! In either case, the results are initially hard to swallow. However, they are not altogether meaningless. Such results should, at the very least, raise concern in the case of the former, or bring some consolation (as mentioned previously) in the case of the latter. These results can be explained in terms of the S-N curve of Fig. 1 using the CER as an example.

As Fig. 1 shows, the S-N curve for cast 8630 steel shows the onset of an endurance limit beyond the "knee" at approximately 275 MPa maximum stress. It is this endurance limit that leads to the extreme sensitivity mentioned above. The endurance limit indicates a theoretical cyclic stress level below which the material sustains no fatigue damage. As the endurance limit is approached, a small decrease in stress level returns a large increase in number of cycles to failure, hence, much less fatigue damage per cycle. Conversely, a small increase in stress level yields a large increase in fatigue damage per cycle. Thus, if all stress cycles fall below the knee, the majority of damage may well be the result of just a single type of extreme loading event. All other smaller cycles intercept the S-N curve much farther out along the abscissa. Also, a small change in the stress level of this one cycle, will yield a significant change in the estimated fatigue life. Above the knee of the S-N curve, a steep slope causes such small changes to be less influential. This demonstrates the importance of knowing where on this S-N curve the stress levels fall so the magnitude of variations in lifetime estimates can be gauged.

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As mentioned previously, a hypothetical hollow blade root design was assumed for this study to reduce life estimates. However, even for this hypothetical design, the largest stress levels were still well below the endurance limit. To further decrease the life estimates for this study, a stress concentration factor (SCF) was applied in LIFE2 analyses. A SCF of 3 brought a few of the largest stress cycles above the endurance limit, yielding lifetime estimates on the order of hundreds of years. Still, the final LIFE2 estimates were very dependent on the largest stress cycle; while the majority of cycles were playing little role as they remained below the endurance limit. Finally, a stress concentration factor of 4 was chosen to bring the life estimates to the order of ten years. The largest stress levels were well above the endurance limit, reducing the effect of any single stress cycle.

From a fatigue design standpoint, keeping stress levels below the endurance limit is desired. All of this manipulation to bring stress levels up above the endurance limit only serves the purpose of attaining results desired for this comparative study.

It is important to remember that any result from LIFE2 can only be an estimate based on the information provided. As to the accuracy of this estimate, there is much room for debate. However, when performing a sensitivity study, as is the case here, the absolute lifetime estimates are not so important as the relative results. Knowing what will increase the fatigue life is very useful information.

Procedure In this study we look at five potential influences on fatigue

life:

(1) turbulence in the ambient wind (Smooth terrain vs lEC specified Kaimal spectra) (2) free-yaw versus locked-yaw operation (3) blade first-flap natural frequency (4.2p versus 3.5p frequency) (4) startup and shutdown cycles including emergency stops (5) extreme events specified by the lEC as Class I ultimate load conditions.

For a baseline case we use 50 minutes of simulated operation in smooth turbulence—one ten-minute simulation at each of the five wind speeds mentioned previously. Additional cases used for comparison are listed in Table 1. For each case listed in Table 1 only the parameter associated with that case is changed from the Baseline value. This serves to isolate each parameter. Hence, for most cases only one parameter is varied from the baseline, with the exception being the Start/Stop case. For the Start/Stop case, both emergency shutdown and normal start/stops, as well as nonoperating buffeting is considered. All three of these represent load scenarios under transient operating conditions, which require the capabilities of ADAMS to model properly. ADAMS is also used for the Extreme Events case, where each of six distinct lEC cases for a Class I turbine (Inter-

Table 1 CER simulation cases listed by varied parameters

Case Name

Varied Parameters

Baseline Values

Case Values

lEC

Locked Yaw

3.SP Blade

Start/Stop

Extreme Events

Turbulence Model (Intensity)

YawlUodal .

Blade 1st Flap Rotating Natural Frequency

Start/Stops per y6ar Emergency Stops

per year Buffeting Considered 6 lEC Extreme Load

Events per year

SMOOTH (0.14)

FREE

4.2P

0

0

NO

NO

lEC (0.18)

LOCKED

3.5P

i'm'

p. JYES

YES

national Electrotechnical Commission, 1994) are considered to occur once per year over the turbine life. These conditions are specified by the lEC to produce one-time, ultimate loading to be withstood by a certified turbine. We are using them to see their effect on fatigue life. For details about the smooth turbulence model see Hojstrup (1982) and Olesen et al. (1984), and for the lEC Kaimal model see Kaimal et al. (1972) and Kaimal (1978).

The parameters listed in Table 1 were the only variables altered in the study. As mentioned previously, material characteristics, wind speed distribution, stress concentration factor, and load-to-stress conversion factor were held constant for all cases. (In a latter section of this paper we examine the effect of changing the shape of the S-N curve.) For all cases, except as noted for the lEC case, the simulated turbulence used was identical as well. This also serves to isolate the parameter of interest in each case.

A valid question can be raised as to the length of time series required to accurately represent the turbine operating lifetime. Obviously, the less data required, the more quickly and economically it can be obtained. To help answer this question, an additional study was performed on the ability to reproduce the results obtained from just five ten-minute simulations, or only 50 minutes of data. To do this, a total of four LIFE2 analyses were conducted using the configuration for the Baseline case listed in Table 1. For each of these four cases, the only difference is in the random seed for generating turbulence using SNLWind-3D. The results for these cases are charted in Fig. 2. Comparing the results gives an idea of the error associated with using only 50 minutes of data. In their validation study, Wright et al. (1996) use 31 simulations to reach good agreement with measured data.

Figure 2 shows the lowest lifetime estimate (Case IV) for raw data to be 38 percent lower than the highest estimate (Case I) . From just these four cases we can draw a minimum range over which results will vary just due to differences in the random nature of turbulence. This variation may be reduced by increasing sample size from the 50 minutes (five ten-minute data sets) used here, but it probably can not be eliminated.

An additional LIFE2 analysis was performed for each of these four Baseline cases after fitting a WeibuU curve to the stress cycle distribution for each wind speed interval. The idea here is to reduce the dominating effect of single, large stress cycles in a sparse data set. To smooth out the rainflow counted data in the low-cycle region, a much larger sample of data could be generated. As an alternative, we fit a curve to the stress cycle data used in LIFE2. This was accomplished via the Fitting routine, developed at Stanford University (Winterstein et al., 1995), which was implemented in Dyn2LIFE. An example of a Weibull curve fit is shown in Fig. 3. The ability to fit a curve to rainflow counted data exists in LIFE2, also based on the Fitting routine. Unlike LIFE2, Dyn2LIFE can apply Fitting directly to the actual stress cycle data, not the rainflow counted data. Theoretically, it will be more accurate to use the actual cycles rather than a ' 'binned'' summary of the cycles. However, we do not yet know if this difference has practical merit sufficient to outweigh the inconvenience of storing all load histories to facilitate curve fitting.

All the curve fit cases in Fig. 2 yield less conservative estimates than their raw data counterparts, but the lowest of these is only 24 percent less than the highest. This suggests the use of curve fitting does help reduce variation in results when using small data samples. The trend of increased life estimates over the raw data counterparts may be in part due to the curve fitting limits of the Fitting routine in Dyn2LIFE. Figure 3 shows that the extent of the curve fit to the raw data extends only as far as the largest cycle included in the data set. The recurrence of this largest cycle is always reduced for the curve fit data, although many otherwise empty, smaller cycle bins now contain curve fit data. The infrequent large-amplitude cycles tend to



12

2.10

0

ED Raw Data • Weibull Curve Fit

Case I Case I. Case I Case iV

Fig. 2 Lifetime estimates from LIFE2 for 4 Baseiine cases with turbulence generated on different random seeds. Aiso sliown are accompanying results using Weibull curve fit to stress cycle data.

Baseline Start/ Extreme Locked stop Events Yaw

Fig. 4 Lifetime estimates from LiFE2 for all six cases listed In Table 1. The result for ttie Locked Yaw case registers well off the scale.

play a significant role in fatigue life. Assuming the stress cycle distribution does follow this trendline, then extending the fitted data further out into the low-cycle region may decrease the life estimates, though this has not yet been investigated.

Results and Discussion

Lifetime estimates from LIFE2 for the six cases listed in Table 1 are summarized in Fig. 4. The result for the locked yaw case, over 200,000 years, is most notable in comparison to the others. We will begin by trying to understand the reasons behind this result.

For the YawDyn model of the Phase III CER used in this study, a strong dependence was discovered between the yaw and flap motions. Evidence of this can be seen in Fig. 5. Fig. 5(a) shows the frequency response of the blade for the Baseline case. Though the blade rotating natural frequency is 4.2P, a peak is displayed near 4.6P. A modal analysis using the ADAMS/Linear modal analysis tool affirms that this peak represents an asymmetric rotor mode coupled to yaw motion. Indeed, for the Locked Yaw case. Fig. 5(&) shows this mode is absent from the blade response. Of more significance, however, is the order of magnitude reduction of IP response for the Locked Yaw case. For Free yaw the IP response dominates the spectrum, but not in the Locked Yaw case. This indicates substantial blade flap loading due to rotor gyroscopic effects in the free yaw configuration. Figure 6 shows what this difference in response means in terms of rainflow counted cycles. The largest cycles experienced in the Locked Yaw configuration are clearly less than those experienced in free yaw (Baseline case). So much so that, compared to the Baseline case, only a very few cycles (after applying the SCF of 4.0) even exceed the knee of the S-N curve of Fig. 1. This difference accounts for the tremendous difference seen in Fig. 4.

The other results are not so dramatic as the Locked Yaw case, but we found them interesting nonetheless. The largest difference out of the remaining cases is for the lEC result.

The nearly threefold reduction in life for the lEC case can be attributed to the increased turbulence intensity for the Kaimal

10000

1000 i : 100 )

I ^° ' 1

0.1

0 Raw Data Weibull distribution curve fit

^ ^ * S t t 0

0 o S s ^ <>

1 1 1

0 50 100 150 200 Peal< to Peak Stress, H/lPa

Fig. 3 Example of rainflow counted stress cycles from one ten-minute data set and subsequent curve fit data using Fitting routine In Dyn2LIFE

turbulence model over that of the smooth model. This result was not surprising as the smooth model was purposely chosen to represent relatively benign wind conditions. The more active turbulence causes an increase in the size of the stress cycles experienced at each wind speed. Figure 7 shows an example of this in terms of rainflow counted cycles for 16 m/s average wind speed. The reason this increase does not cause as drastic a difference as for the Locked Yaw case is twofold. First, it can be argued that the increase in cycle size for the lEC case is not as great as the decrease seen for the Locked Yaw case. Regardless of this argument, the fatigue life could not decrease as drastically as it could increase, as explained in our second reason: The S-N curve is so much steeper above the knee

2 3 4 5 6

Frequency, per rev

Fig. 5(a) ffl 1 e+06

g. 1 e+05 CO

« N 1 e+04

§<5E, no Q. -p 1 e+03

^ 1 e+02

1 e+01

1 e+00 2 3 4 5 6 Frequency, per rev

Fig. S(b)

2 3 4 6 6 Frequency, per rev

Fig. 5(c)

Fig. 5 PSD of flap moment for ttiree 20 m/s wind speed cases; (a) Baseline; (b) Locked yaw; and (c) 3.SP Blade

Journal of Solar Energy Engineering AUGUST 1997, Vol. 1 1 9 / 2 4 5


10000

1000

100 ^

10

« Free Yaw

• Locked Yaw

c*v St % •

• • • • • « * • •

50 100 150

Peak to Peak Stress, MPa

200

Fig. 6 Rainflow count comparison for 16 m/s average wind speed data sets of Baseline and Locked Yaw cases

where the larger lEC cycles fall that, as mentioned previously, the effect on fatigue life is not as great as below the knee where the endurance limit is approached. This again demonstrates the importance of knowing where on the S-N curve the damage is occurring.

We next turn to the model having blades with 3.5P rotating natural frequency. The reason behind tuning the model's blades to 3.5P from 4.2P was to place the first flap frequency farther from 4P resonance. It was thought that this might be beneficial to the fatigue life, but the results shown in Fig. 4 appear to indicate the opposite is true. As in the Locked Yaw case, the power spectrum of the blade flap moments, shown in Fig. 5, can help explain this. As noted previously, there is an asymmetric rotor mode near 4.6P for the 4.2P blade, as shown in Fig. 5(a) . Figure 5(c) shows that reducing the blade rotating first flap frequency to 3.5P also lowers this rotor mode frequency to where it is sitting right on a 4P resonance. In addition, the 3.5P response appears greater than the 4.2P response in Fig. 5(a) . This explains the slight decrease in life estimated for the 3.5P blades. This decrease in fatigue life is within the range of estimates for the various baseline cases, shown in Fig. 2. This suggests that altering the blade natural frequency is of limited consequence; no greater than that of the randomness of turbulence. We believe the result is still valid, however, as the turbulence in the Baseline and 3.5P cases is identical. Changing the frequency of the blades from 4.2P to 3.5P will likely lower the lifetime regardless of the turbulence input. This result also highlights the importance of considering the complete system response, not just the isolated blade characteristics.

The Start/Stop and Extreme Events cases both represent life estimates of the Baseline configuration plus a number of distinct events representing various rare or noncontinuous operating conditions. In both cases the results are virtually unaffected by the addition of these events.

The negligible effect of emergency stop and buffeting events, and the lEC extreme events is likely because there is a small

400

I 350

^300

55 E 250 £ SZOO

150 4

1 e+5 1 e+6 1 e+7 Cycles to Failure

1 e+8

Fig. 8 S-N curve for cast 8630 steel data and associated curve for a power-law material showing no endurance limit

number of events (listed in Table 1). Taking as an example one of the six lEC Extreme Events, the largest stress cycle (near 300 MPa) is greater than any experienced during normal operation for any other case considered here. However, with only one occurrence per year its addition is inconsequential. In fact, the six lEC Extreme Events must occur 100 times per year for the life estimation to reduce from 11 to 10 years.

The routine start/stop sequences, numbering 1000 per year for this analysis, cause little damage because they occur at the slow, less damaging cut-in wind speed. Hence, the stress cycles resulting from these start/stop events are for the most part smaller than those experienced during normal operation at higher wind speed. These results suggest the effects of limited events are negligible compared to the repetitive cycles experienced during continuous operation.

Endurance Limit The existence of an endurance limit for this material, evident

in Fig. 1, is responsible for the wide range in lifetime estimates displayed in Fig. 4. The existence of an endurance limit, however, is now believed by many to be an anomaly of the fatigue test process in which stress levels on a test specimen never exceed this limit. If this stress level is occasionally exceeded, as pointed out by Veers (1989), failure can occur below this apparent limit. Hence, this threshold is essentially erased.

The upper part of the S-N curve of Fig. 1 is a straight line when drawn on a log-log plot. The equation of this line can be written in the form A = (c^*)" ' , where c and b are material constants. This so-called power-law material has no endurance limit. For the S-N curve used here the values c = 6.113 • 10"^^ and b = 6.29 produce a good fit to the upper portion of the data curve as is shown in Fig. 8.

The power-law S-N curve of Fig. 8 lacks the endurance limit evident in fatigue test data curve. To see how this difference affects fatigue life estimates, the LIFE2 analyses were repeated using this power-law curve. The results are shown in Fig. 9.

From the lifetimes shown in Fig. 9 it is clear that the endurance limit plays a significant role in the life estimates. Not only

10000

!= 1000

o

100

250

Peak to Peak Stress, MPa

Fig. 7 Rainflow sets of Baseline

count comparison for 16 m/s average wind speed data and lEC cases

Baseline lEC 3.5P Start/ Extreme Locked stop Events Yaw

Fig. 9 Lifetime estimates from LIFE2 for all 6 cases listed in Table 1 using the power-law S-N curve of Fig. 8. The result for the Locked Yaw case still registers well off the scale, though the increase from the baseline case is less drastic.



are all the life estimates reduced by at least twofold, but even the locked yaw case now shows signs of mortality. Where the previous lifetimes may have been marginally acceptable, these are cause for great concern. The existence of an endurance limit in S-N data, and whether or not it is valid to use in a fatigue analysis, is something the designer must carefully consider. As demonstrated herein, the results can be markedly different depending on the decision.

Conclusions

Besides Dyn2LIFE, this study used three other wind turbine specific computer codes: SNLWind-3D, YawDyn/AeroDyn, and LIFE2. This combination of codes is a powerful package for conducting a fully computer-based wind turbine fatigue analysis. Through this study we have begun to expose the potential of this combination of codes.

The Dyn2LIFE program was very useful in quickly processing the results from YawDyn and ADAMS for use in LIFE2. This interface is still new, so there are bugs to be eliminated and capabilities to be evaluated and expanded in the future. The Dyn2LIFE code does not make the tasks of learning to use, or using LIFE2 any simpler. As it borrows heavily from LIFE2, experience with LIFE2 may actually help users of Dyn2LIFE.

The most surprising result from our study of the CER was the remarkable reduction in fatigue damage realized by simply locking out the yaw motion of the turbine. For our model there is a strong dependency between yaw and flap motion. This dependency makes locking the yaw axis a great boon to fatigue life. While not following the wind direction may reduce energy output, the real tradeoff may be in increased loads elsewhere, such as in the yaw control system (e.g., yaw drive or brakes).

From this study of just a single turbine, and a rather unconventional one at that, it is unfair to make sweeping judgments about all turbines of different size, configuration, environmental siting, and material construction. The results presented here, however, suggest that factors affecting the turbine during normal operation—turbulence and frequency response—can play a role in fatigue damage. This statement needs to be qualified by a remark made throughout this paper: It is important to be familiar with the S-N curve being used as its shape will greatly affect the sensitivity to changes in stress cycles. This is especially true when dealing with data that displays an endurance limit.

Rather than provide broad guidelines for turbine design, the goal of this work is to make available tools by which designers can evaluate the specific characteristics of their turbines and sites. While we will continue to demonstrate the codes on existing turbine models, the purpose of this paper is not so much to determine the fatigue factors affecting all turbines in general as to demonstrate the codes themselves. Ideally, these codes

will make it practical for designers to evaluate their systems on an individual basis.

Acknowledgments In addition to NREL, which provides us with our funding, we

would like to express our appreciation to Dr. Herb Sutherland of Sandia National Laboratories for helping us understand the operation of LIFE2.

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International Electrotechnical Commission, 1994, "International Standard: Wind Turbine Generator Systems Part 1: Safety Requirements," lEC 1400-1, 1st ed., 1994-12,

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Kaimal, J. C , Wyngaard, J. C , Izumi, Y., and Cote, O. R., 1972, "Spectral Characteristics of Surface Layer Turbulence,'' The Quarterly Journal of the Royal Meteorological Society, Vol. 98, pp. 563-589.

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Journal of Solar Energy Engineering AUGUST 1997, Vol. 119 /247


an integrated approach to wind turbine fatigue analysis

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