Print Email Facebook Twitter Adaptive data-driven reduced-order modelling techniques for nuclear reactor analysis Title Adaptive data-driven reduced-order modelling techniques for nuclear reactor analysis Author Alsayyari, F.S. (TU Delft RST/Reactor Physics and Nuclear Materials) Contributor Kloosterman, J.L. (promotor) Lathouwers, D. (promotor) Perko, Z. (copromotor) Degree granting institution Delft University of Technology Corporate name Delft University of Technology Date 2020-10-06 Abstract Large-scale complex systems require high-fidelity models to capture the dynamics of the system accurately. For example, models of nuclear reactors capture multiphysics interactions (e.g., radiation transport, thermodynamics, heat transfer, and fluid mechanics) occurring at various scales of time (prompt neutrons to burn-up calculations) and space (cell and core calculations). The complexity of thesemodels, however, renders their use intractable for applications relying on repeated evaluations, such as control, optimization, uncertainty quantification, and sensitivity studies. Subject Proper Orthogonal DecompositionLocally adaptive sparse gridsGreedyNonintrusiveMachine learningUncertainty quantificationSensitivity analysisMolten Salt ReactorLarge-scale systems To reference this document use: https://doi.org/10.4233/uuid:feb1b467-f601-489d-87cf-a99e4cbbb055 ISBN 978-94-6421-022-4 Part of collection Institutional Repository Document type doctoral thesis Rights © 2020 F.S. Alsayyari Files PDF dissertation_1_.pdf 24.41 MB PDF propositions_1_.pdf 150.57 KB Close viewer /islandora/object/uuid:feb1b467-f601-489d-87cf-a99e4cbbb055/datastream/OBJ1/view