Abstract
In the International Thermonuclear Experimental Reactor, plasma is magnetically confined with Superconductive Magnets (SMs) that must be maintained at the cryogenic temperature of 4.5 K by one or more Superconducting Magnet Cryogenic Cooling Circuits (SMCCC). To guarantee cooling, Loss-of-Flow Accidents (LOFAs) in the SMCCC are to be avoided. In this work, we develop a three-step methodology for the prompt detection of LOFA precursors (i.e., those combinations of component failures causing a LOFA). First, we randomly generate accident scenarios by Monte Carlo sampling of the failures of typical SMCCC components and simulate the corresponding transient system response by a deterministic thermal-hydraulic code. In this phase, we also employ quick-running Proper Orthogonal Decomposition (POD)-based Kriging metamodels, adaptively trained to reproduce the output of the long-running code, to decrease the computational time. Second, we group the generated scenarios by a Spectral Clustering (SC) employing the Fuzzy C-Means (FCM), in order to identify the main patterns of system evolution towards abnormal states (e.g., a LOFA). Third, we develop an On-line Supervised Spectral Clustering (OSSC) technique to associate time-varying parameters measured during plant functioning to one of the prototypical groups obtained, which may highlight the related LOFA precursors (in terms of SMCCC components failures). We apply the proposed technique to the simplified model of a cryogenic cooling circuit of a single module of the ITER Central Solenoid Magnet (CSM). The framework developed promptly detects 95% of LOFA events and around 80% of the related precursors.
Highlights
The International Thermonuclear Experimental Reactor (ITER) will employ a plasma of Deuterium and Tritium to produce a net energy output by means of thermonuclear fusion reactions for the first time [1]
A computational framework for the identification of abnormal conditions and of the corresponding precursors was proposed in three steps: (i) a “database” of simulated accident scenarios is created by Monte Carlo Sampling (MCS) and Proper Orthogonal Decomposition (POD)-based Kriging metamodels; (ii) the transients of the database are grouped in clusters according to their similarity through Spectral Clustering (SC) embedding the Fuzzy C-Means (FCM), in order to identify the principal patterns of system evolution towards failure and the “prototypical” precursors; (iii) the Online Supervised Spectral Clustering (OSSC) is employed to assign a new developing transient to one of the clusters previously discovered, enabling the identification of the precursors of the abnormal conditions
The devised method has been applied for the identification of the Loss-of-Flow Accident (LOFA) Precursors in a simplified Superconducting Magnet Cryogenic Cooling Circuit (SMCCC) that keeps one ITER Central Solenoid Module (CSM) cooled
Summary
The International Thermonuclear Experimental Reactor (ITER) will employ a plasma of Deuterium and Tritium to produce a net energy output by means of thermonuclear fusion reactions for the first time [1]. (KHNP) (Central Research Institute, KHNP, 70, 1312-gil, Yuseong-daero, Yuseong-gu, Daejeon 34101, Republic of Korea) [64]; Bayesian Networks for the modelbased diagnosis in a single-phase heat exchanger [65]; Support Vector Machines combined with Gaussian Process Regression for the transient analysis of seven different (normal and accidental) conditions (LOCAs, load rejection, steam generator rupture, etc.) in a simulated nuclear plant [66]; incremental learning and reconciliation of different clustering approaches by unsupervised schemes applied to a fleet of nuclear power plant turbines during shut-down transients [67] While acknowledging this wide and diversified framework of algorithms and applications, it is important to notice that to the best of the authors’ knowledge: (i) the structured, integrated combination of advanced methods proposed in this work is new and original; (ii) no intelligent techniques for prompt anomaly detection, fault diagnosis and precursor identification have yet been developed for, and applied to, nuclear fusion systems. It does not have any impact on the algorithmic structure, generalization properties and applicability of the methodological framework for LOFA detection and precursor identification here proposed
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