Abstract
The reliability of blades is vital to the system reliability of a hydrokinetic turbine. A time-dependent reliability analysis methodology is developed for river-based composite hydrokinetic turbine blades. Coupled with the blade element momentum theory, finite element analysis is used to establish the responses (limit-state functions) for the failure indicator of the Tsai–Hill failure criterion and blade deflections. The stochastic polynomial chaos expansion method is adopted to approximate the limit-state functions. The uncertainties considered include those in river flow velocity and composite material properties. The probabilities of failure for the two failure modes are calculated by means of time-dependent reliability analysis with joint upcrossing rates. A design example for the Missouri river is studied, and the probabilities of failure are obtained for a given period of operation time.
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Acknowledgments
The authors gratefully acknowledge the support from the Office of Naval Research through contract ONR N000141010923 (Program Manager - Dr. Michele Anderson) and the Intelligent Systems Center at the Missouri University of Science and Technology.
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Appendices
Appendix A: MCS for time-dependent reliability analysis
The MCS for time-dependent reliability analysis involves both a stochastic process (river flow discharge) and random variables. To generate samples for the stochastic process, we discretize the time interval [t 0, t s ] into N points. Then the samples of the normalized and standardized river flow discharge process D m is generated by
where \(\boldsymbol\varsigma =\left( {\varsigma_1 ,\varsigma_2 ,\cdots ,\varsigma_N } \right)^T\) is the vector of N independent standard normal random variables; \({\bf m}_{D_m } =( \mu_{D_m } ( {t_1 } )\), \({\mu}_{D_m } ( {t_2 } ),\cdots ,{\mu}_{D_m } ( {t_N } ) )^T\) is the vector of mean values of \({{\bf D}}_{{\bf m}} =\left( {D_m \left( {t_1 } \right),D_m \left( {t_2 } \right),\cdots ,D_m \left( {t_N } \right)} \right)^T\); and M is a lower triangular matrix obtained from the covariance matrix of D m .
Let the covariance matrix of D m at the N points be C N×N , we have
Then M can be obtained by
in which D is a diagonal eigenvalue matrix of the covariance matrix C N×N , and P is the N×N square matrix whose i-th column is the i-th eigenvector of C N×N .
After samples of the stochastic process of river flow discharge are generated, they are plugged into the limit-state functions, and then the samples (trajectories) of the limit-state functions are obtained. A trajectory is traced from the initial time to the end of the time period. Once the trajectory upcrosses the limit state, then a failure occurs; and the remaining curve will not be checked anymore. The process is illustrated in Fig. 17.
Appendix B: Computation of v ++ (t 1, t 2)
Madsen has derived the expression for \(\emph{v}^{++}(t_{1}\), t 2) as follows (Madsen and Krenk 1984)
in which
\(\boldsymbol\upbeta=[\beta_{1}, \beta_{2}]\) represents the time-invariant reliability index at time t 1 and t 2. μ 1 and μ 2, and λ 1 and λ 2, κ are the mean values, standard deviations, and correlation coefficient of \(\left. {\dot{\bf L} \left( {t_1 } \right)} \right| \boldsymbol\upbeta \) and \(\left. {\dot{\bf L}\left( {t_2 } \right)} \right| \boldsymbol\upbeta \), respectively. They are calculated by the following equations (Hu and Du 2011):
where
and
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Hu, Z., Li, H., Du, X. et al. Simulation-based time-dependent reliability analysis for composite hydrokinetic turbine blades. Struct Multidisc Optim 47, 765–781 (2013). https://doi.org/10.1007/s00158-012-0839-8
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DOI: https://doi.org/10.1007/s00158-012-0839-8