Large scale applicability of a Fully Adaptive Non-Intrusive Spectral Projection technique: Sensitivity and uncertainty analysis of a transient

• Grid and basis adaptive Polynomial Chaos techniques are presented for S&U analysis.
• Dimensionality reduction and incremental polynomial order reduce computational costs.
• An unprotected loss of flow transient is investigated in a Gas Cooled Fast Reactor.
• S&U analysis is performed with MC and adaptive PC methods, for 42 input parameters.
• PC accurately estimates means, variances, PDFs, sensitivities and uncertainties.

Since the early years of reactor physics the most prominent sensitivity and uncertainty (S&U) analysis methods in the nuclear community have been adjoint based techniques. While these are very effective for pure neutronics problems due to the linearity of the transport equation, they become complicated when coupled non-linear systems are involved. With the continuous increase in computational power such complicated multi-physics problems are becoming progressively tractable, hence affordable and easily applicable S&U analysis tools also have to be developed in parallel.For reactor physics problems for which adjoint methods are prohibitive Polynomial Chaos (PC) techniques offer an attractive alternative to traditional random sampling based approaches. At TU Delft such PC methods have been studied for a number of years and this paper presents a large scale application of our Fully Adaptive Non-Intrusive Spectral Projection (FANISP) algorithm for performing the sensitivity and uncertainty analysis of a Gas Cooled Fast Reactor (GFR) Unprotected Loss Of Flow (ULOF) transient. The transient was simulated using the Cathare 2 code system and a fully detailed model of the GFR2400 reactor design that was investigated in the European FP7 GoFastR project. Several sources of uncertainty were taken into account amounting to an unusually high number of stochastic input parameters (42) and numerous output quantities were investigated.The results show consistently good performance of the applied adaptive PC methods, being superior to standard Monte Carlo sampling both in terms of accuracy and computational cost. This demonstrates that such PC techniques can provide a viable alternative to random sampling even for larger scale systems, which is especially appealing for the S&U analysis of problems using legacy codes common in the nuclear field.