Abstract. The present paper characterizes the performance of non-intrusive
uncertainty quantification methods for aeroservoelastic wind turbine
analysis. Two different methods are considered, namely non-intrusive
polynomial chaos expansion and Kriging. Aleatory uncertainties are
associated with the wind inflow characteristics and the blade surface
state, on account of soiling and/or erosion, and propagated throughout the
aeroservoelastic model of a large conceptual offshore wind turbine. Results are compared with a brute-force extensive Monte Carlo sampling,
which is used as benchmark. Both methods require at least 1 order of
magnitude less simulations than Monte Carlo, with a slight advantage of
Kriging over polynomial chaos expansion. The analysis of the solution space
clearly indicates the effects of uncertainties and their couplings, and
highlights some possible shortcomings of current mostly deterministic
approaches based on safety factors.
Efficient posterior exploration of a high-dimensional groundwater model from two-stage Markov chain Monte Carlo simulation and polynomial chaos expansion