%0 Generic
%A Abrate, Nicolò
%A Caruso, Nicolò
%A Dulla, Sandra
%A Pedroni, Nicola
%A Lorenzi, Stefano
%D 2023
%T Data underlying the research of Innovative control model and strategy development and applications to MSFR
%U 
%R 10.4121/0ae20eee-97a6-4634-9f57-eb1887018fc2.v1
%K Molten Salt Fast Reactor
%K Nuclear Power Plant
%K Incident Detection Method
%K kNN classification
%K System level modelling
%X <p>The dataset refers to the research activity performed in the framework of the EU project SAMOSAFER, Task 6.3 - Innovative control model and strategy development</p><p>and applications to MSFR.</p><p><br></p><p>In this activity, an innovative incident detection method has been developed, aiming at improving the safety and reliability of the Molten Salt Fast Reactor</p><p>power plant, focusing on operational scenarios involving some deviations from normal operational conditions.</p><p><br></p><p>The data-driven incident detection and classification methodology (based on the kNN algorithm) aims at identifying abnormal plant conditions thanks to a</p><p>continuous monitoring of some measurable system parameters and variables (e.g., the molten salt temperatures in the secondary circuit).</p><p><br></p><p>In order to train the algorithm, a set of numerical, time-dependent simulation is carried out at the system-level (primary circuit, secondary circuit and</p><p>balance of plant) with the Modelica language.</p>
%I 4TU.ResearchData