Direct method and Bayesian inference for uncertainty quantification on stochastic NDT problem

In this paper, we present a framework to deal with uncertainty quantification in case where the ranges of variability of the random parameters are ill-known. Namely the physical properties of the corrosion product (magnetite) which frequently clogs the tube support plate of steam generator, which is inaccessible in nuclear power plants. The methodology is based on Polynomial Chaos (PC) direct approaches and on Bayesian inference for inverse problems. The direct Non Intrusive Spectral Projection (NISP) method is first employed by considering prior probability densities and therefore construct a PC surrogate model of the large-scale NDT finite element model. To face the prohibitive computational cost underlying the high dimensional random space, the adaptive sparse grid technique is applied on NISP resulting in a significant time reduction. The PC surrogate model, with reduced dimensionality, is used as a forward model in the Bayesian procedure. The posterior probability densities are then identified by inferring from few noisy experimental data. We demonstrate the effectiveness of the approach on the identification the permeability and conductivity of the magnetite using Sax ratio measurements.

ISBN:9782960114317

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