Searched for: subject%3A%22Uncertainty%255C%252BQuantification%22
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Santanoceto, M. (author), Tiberga, M. (author), Perko, Z. (author), Dulla, Sandra (author), Lathouwers, D. (author)
In this work, we present the results of a preliminary uncertainty quantification and sensitivity analysis study of the Molten Salt Fast Reactor (MSFR) behavior at steady-state performed by applying a non-intrusive Polynomial Chaos Expansion (PCE) approach. An in-house high-fidelity multi-physics simulation tool is used as reactor reference...
journal article 2021
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Santanoceto, M. (author), Tiberga, M. (author), Perko, Z. (author), Dulla, Sandra (author), Lathouwers, D. (author)
Uncertainty Quantification (UQ) of numerical simulations is highly relevant in the study and design of complex systems. Among the various approaches available, Polynomial Chaos Expansion (PCE) analysis has recently attracted great interest. It belongs to non-intrusive spectral projection methods and consists of constructing system responses...
conference paper 2020
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Alsayyari, F.S. (author), Tiberga, M. (author), Perko, Z. (author), Lathouwers, D. (author), Kloosterman, J.L. (author)
We use a novel nonintrusive adaptive Reduced Order Modeling method to build a reduced model for a molten salt reactor system. Our approach is based on Proper Orthogonal Decomposition combined with locally adaptive sparse grids. Our reduced model captures the effect of 27 model parameters on k<sub>eff</sub> of the system and the spatial...
journal article 2020