Abstract:
In this paper, uncertainty quantification in natural frequencies for laminated
soft core sandwich plates is presented by employing finite element (FE)
coupled polynomial neural network (PNN) approach. The computational efficiency
and accuracy is achieved by using PNN as surrogate model. Latin hypercube
sampling method is employed for training of data in PNN model. The stochastic
first three natural frequencies of sandwich plates are studied for individual
variation in input parameters. The stochasticity in individual input parameters
are considered in order to assess their influence on global response of the structure.
The algorithm discussed in this article is observed to be converging with the
previously published literature (for deterministic case) and validated with full
scale Monte Carlo simulation (MCS) i.e. original finite element approach (for
stochastic case). The computational time and cost reduced significantly by employing
the present surrogate based FE approach compared to that of conventional
Monte Carlo simulation approach.
