A number of measurement campaigns have sought to quantify fatigue damage on drilling and production risers in the field. The data from these campaigns have recently been used to test the accuracy of software predictions of riser motions and hence fatigue. It is often found that state-of-the-art software shows a conservative bias in predicting fatigue damage — conservative factors of 10 or more. At the same time, short-term damage estimates (e.g., over 10-minute intervals) show significant scatter: COV (coefficient of variation) values of 1.0–2.0 are typical.
We suggest methods here to better incorporate the information from these measurements into fatigue reliability predictions. We note that (1) software error statistics show markedly different behavior in different regimes; e.g., for different levels of predicted damage; and (2) our fatigue reliability concern lies with the variability not in individual 10-minute damage rates, but rather in the long-run damage rate over the entire structural life. These features suggest a non-parametric approach, in which software error statistics are binned and separately analyzed for different predicted damage levels. The sample means in each bin, together with their variabilities, then form the basis for our reliability assessment.
Because our interest lies with the average behavior in each bin, variability reduces notably: COVs of 1.0–2.0 (on individual 10-minute rates) are reduced by an order of magnitude or more. This variability is commonly dominated by the uncertainty in fatigue capacity (S–N curve, Miner’s rule, etc.) Thus, our measurement campaigns are of most interest in predicting only the mean bias in damage predictions; conventional methods are then available to propagate these into a standard fatigue reliability analysis. Results are shown for a number of applications, and implications considered for design (i.e., load factors).
