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

Alsayyari, F.S.

doi:10.4233/feb1b467-f601-489d-87cf-a99e4cbbb055

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

Doctoral thesis (2020)

Authors

F.S. Alsayyari RST/Reactor Physics and Nuclear Materials -

Research Group

RST/Reactor Physics and Nuclear Materials () (TU Delft)

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

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

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Published Date

2020

Language

English

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Faculty

Applied Sciences

Department

RST/Radiation, Science and Technology

Research Group

RST/Reactor Physics and Nuclear Materials

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.

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