Data underlying the research of Innovative control model and strategy development and applications to MSFR
doi: 10.4121/0ae20eee-97a6-4634-9f57-eb1887018fc2
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
and applications to MSFR.
In this activity, an innovative incident detection method has been developed, aiming at improving the safety and reliability of the Molten Salt Fast Reactor
power plant, focusing on operational scenarios involving some deviations from normal operational conditions.
The data-driven incident detection and classification methodology (based on the kNN algorithm) aims at identifying abnormal plant conditions thanks to a
continuous monitoring of some measurable system parameters and variables (e.g., the molten salt temperatures in the secondary circuit).
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
balance of plant) with the Modelica language.
- 2023-12-06 first online, published, posted
- Severe Accident Modeling and Safety Assessment for Fluid-fuel Energy Reactors (grant code 847527) [more info...] Euratom
DATA
- 2,387 bytesMD5:
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README.txt - 11,889,564,608 bytesMD5:
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DS2_TEST_I.zip - 8,713,025,831 bytesMD5:
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DS2_TEST_II.zip - 9,758,280,182 bytesMD5:
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DS2_TRAINING_I.zip - 9,758,280,182 bytesMD5:
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DS2_TRAINING_I.zip - 10,452,371,993 bytesMD5:
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DS2_TRAINING_II.zip - 11,868,846,600 bytesMD5:
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DS2_TRAINING_III.zip - 19,229,848,652 bytesMD5:
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DS2_TRAINING_IV.zip - 18,409,029,743 bytesMD5:
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DS2_TRAINING_V.zip - 682,050,938 bytesMD5:
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DS2_TRAINING_VI.zip - 9,201,606 bytesMD5:
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matlab_codes.zip - 166,475,098 bytesMD5:
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temperature_dset_TEST.zip - 258,115,334 bytesMD5:
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temperature_dset_TRAINING.zip -
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101,195,093,154 bytes unzipped