You are hereStochastic load models from limited data: A general approach with applications to wind and waves
Stochastic load models from limited data: A general approach with applications to wind and waves
Over the last few decades the design of structures has become increasingly based on a probabilistic description of the variables involved. Among these variables, the loads applied to the structure are perhaps the most critical and difficult to model, due to their inherent randomness and the difficulty of obtaining loads data in extreme conditions. Here we present a review of stochastic load modeling, followed by our work on three different topics in this field. We focus on developing models that take into account the limited nature of available data.
We first consider the problem of finding the short-term load distributions within a particular environment condition. We use a moment-based approach to fit parametric probability distributions to the loads. We show that one-sided distributions that match four statistical moments estimated from limited loads data may show erratic behavior in the distribution tail. In particular, for modeling fatigue loads on wind-turbine blades, the quadratic Weibull distribution is found to be more robust than the cubic Weibull distribution suggested previously.
We then use a regression model to estimate the moments of the short-term distributions from environment parameters and suggest a method to evaluate the importance of different environment parameters in predicting the load moments. We apply this approach to the problem of modeling fatigue loads on wind-turbine blades and show that for two of the three turbines studied, a parameter found by high-pass filtering the wind speed can explain a large fraction of the variations in the load moments. For a third turbine, the mean wind speed is found to explain an even larger fraction of the moment variations, suggesting that the important wind parameters may be structure-dependent.
Finally, we discuss the calibration of response prediction models. Calibration methods based on marginal error statistics or bias-adjustment factors, inherent in many structural design codes, are shown to be insufficient for predicting the distribution of measured loads. A linear regression model is shown to solve this problem and is used to calibrate fluid drag load models for offshore structures and to examine the effect of various model parameters on the accuracy of the response predictions.