Adaptive data-driven reduced-order modelling techniques for nuclear reactor analysis

Adaptive data-driven reduced-order modelling techniques for nuclear reactor analysis

Alsayyari, F.S. (TU Delft RST/Reactor Physics and Nuclear Materials)

Kloosterman, J.L. (promotor)
Lathouwers, D. (promotor)
Perko, Z. (copromotor)

Delft University of Technology

2020-10-06

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.

Proper Orthogonal Decomposition
Locally adaptive sparse grids
Greedy
Nonintrusive
Machine learning
Uncertainty quantification
Sensitivity analysis
Molten Salt Reactor
Large-scale systems

https://doi.org/10.4233/uuid:feb1b467-f601-489d-87cf-a99e4cbbb055

978-94-6421-022-4

Institutional Repository

doctoral thesis

© 2020 F.S. Alsayyari