Hybrid Data Assimilation methods, Part II: Application to the DYNASTY experimental facility

Abstract

Hybrid Data Assimilation (HDA) methods aim at combining the advantages of mathematical models and experimental observations by integrating Model Order Reduction techniques into a Data Assimilation framework, thus reducing the solution time whilst keeping the accuracy of the models to the desired level. HDA methods provide tools able to estimate the state of a system assuming to have measurements available per each field describing the system (e.g., neutron flux, temperature, velocity, …). However, it is not always possible to measure every field of interest for various reasons, thus these techniques should be adapted: in particular, it is legitimate to investigate the possibility of extracting some information from indirect measurement. Following the assessment of the two selected HDA methods (Generalised Empirical Interpolation Method and Parameterised Background Data-Weak) performed on a numerical benchmark test case discussed in the first part of this two-part work, the present paper now deals with their testing on a real-world experimental facility and their validation with experimental data. This work represents the first step for a deep validation phase to assess their efficiency and reliability, applying the Generalised Empirical Interpolation Method (GEIM), the Parameterised-Background Data-Weak (PBDW) formulation and the Indirect Reconstruction (IR) algorithm to DYNASTY, an experimental facility for studying natural circulation built at Politecnico di Milano. The robustness of these methods is high when the models are accurate, however when real system are analysed the model discrepancy can be present and these techniques may suffer.