This paper gives an overview of recent advances in the field of non-probabilistic uncertainty
quantification. Both techniques for the forward propagation and inverse quantification of …

Probabilistic analysis is increasing in popularity and importance within engineering and the
applied sciences. However, the stochastic perturbation technique is a fairly recent …

Generalized polynomial chaos (gPC) has non-uniform convergence and tends to break
down for long-time integration. The reason is that the probability density distribution (PDF) of …

The present paper aims to establish a systematic robust optimization framework for the
hydrodynamic performance of marine current turbines against uncertain conditions. To this …

The Proper Generalized Decomposition (PGD) is a methodology initially proposed for the
solution of partial differential equations (PDE) defined in tensor product spaces. It consists in …

Stochastic collocation (SC) methods for uncertainty quantification (UQ) in computational
problems are usually limited to hypercube probability spaces due to the structured grid of …

Uncertainty propagation and quantification analysis in nonlinear systems are among the
most challenging issues in engineering practice. Probabilistic analysis methods, based on …

Studying multibody dynamic systems, a common way to evaluate the effects of uncertainty
parameters is the response surface method, which works by building a polynomial surrogate …

The aim of the current paper is the robust optimization of an internally cooled gas turbine
vane by increasing the cooling performance and decreasing the sensitivity of the …

The stochastic computations of a NASA gas turbine vane are conducted to investigate the
effects of the operational uncertainties on the flow and heat transfer characteristics of the …