  Frequency response functions (FRFs) are important for assessing the behavior
of stochastic linear dynamic systems. For large systems, their evaluations are
time-consuming even for a single simulation. In such cases, uncertainty
quantification by crude Monte-Carlo simulation is not feasible. In this paper,
we propose the use of sparse adaptive polynomial chaos expansions (PCE) as a
surrogate of the full model. To overcome known limitations of PCE when applied
to FRF simulation, we propose a frequency transformation strategy that
maximizes the similarity between FRFs prior to the calculation of the PCE
surrogate. This strategy results in lower-order PCEs for each frequency.
Principal component analysis is then employed to reduce the number of random
outputs. The proposed approach is applied to two case studies: a simple 2-DOF
system and a 6-DOF system with 16 random inputs. The accuracy assessment of the
results indicates that the proposed approach can predict single FRFs
accurately. Besides, it is shown that the first two moments of the FRFs
obtained by the PCE converge to the reference results faster than with the
Monte-Carlo (MC) methods.
