Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Identification of Long-term Behavior of Natural Circulation Loops: A Thresholdless Approach from an Initial Response

Version 1 : Received: 15 January 2021 / Approved: 18 January 2021 / Online: 18 January 2021 (09:52:01 CET)

A peer-reviewed article of this Preprint also exists.

Bhattacharya, C.; Saha, R.; Mukhopadhyay, A.; Ray, A. Identification of Long-Term Behavior of Natural Circulation Loops: A Thresholdless Approach from an Initial Response. Sci 2021, 3, 14. Bhattacharya, C.; Saha, R.; Mukhopadhyay, A.; Ray, A. Identification of Long-Term Behavior of Natural Circulation Loops: A Thresholdless Approach from an Initial Response. Sci 2021, 3, 14.

Journal reference: Sci 2021, 3, 14
DOI: 10.3390/sci3010014

Abstract

Natural circulation loop (NCL) systems are buoyancy-driven heat exchangers that are used in various industrial applications. The concept of passive heat exchange in NCL systems is attractive, because there is no need for an externally driven equipment (e.g., a pump) to maintain the fluid circulation. However, relying on buoyancy as the sole driving force may lead to several potential difficulties, one of which is generation of (possibly) time-varying nonlinearities in the dynamical system, where a difference in the time scales of heat transfer and fluid flow causes the flow to change from a steady-state regime to either an oscillatory regime or a flow-reversal regime, both of which are undesirable. In this paper, tools of symbolic time series analysis (e.g., probabilistic finite state automata (PFSA)) are proposed to identify selected regimes of operation in NCL systems, where the underlying principle is built upon the concept of pattern classification from measurements of fluid-flow dynamics. The proposed method is shown to be capable of identifying the current regime of operation from the initial time response under a given set of operational parameters. The efficacy of regime classification is demonstrated by testing on two data sets, generated from numerical simulation of a MATLAB SimuLink model that has previously been validated with experimental data. The results of the proposed PFSA-based classification are compared with those of a hidden Markov model (HMM) that serves as the baseline.

Keywords

Natural circulation loops; Symbolic time series analysis; Early detection of anomalous events

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