Dynamic characterization of a single phase square natural circulation loop

Saha, Ritabrata; Ghosh, Koushik; Mukhopadhyay, Achintya; Sen, Swarnendu

doi:10.1016/j.applthermaleng.2017.09.092

Cited by 15 publications

(5 citation statements)

References 46 publications

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“…The governing equations (e.g., conservation of mass, momentum, and energy) for the loop fluid flow, and coolant and wall temperatures are solved numerically. These equations, which are similar to those in earlier publications [18][19][20], are only briefly explained here. For example, the continuity, momentum, and energy equations are represented by Equations (1)-(3), respectively.…”

mentioning

confidence: 68%

“…It is observed in the numerical simulation that the flow dynamics change with an increase in heater power. For relatively low heater power, the flow dynamics approximately exhibit a steady-state nature; as the heater power is gradually increased, the dynamic behavior first changes to an oscillatory nature and then, with further increase in the heater power, it exhibits flow-reversal [19], as seen in Figure 2. The fluid flow in an NCL system is established by the temperature difference between its two vertical limbs: the riser (with a higher temperature) and the downcomer (with a lower temperature).…”

Section: Numerical Resultsmentioning

confidence: 98%

“…The numerical model used in this paper was compared and validated with the experimental and numerical results in the previous publications [18][19][20]; therefore, a validation study is not repeated here for compactness of the paper.…”

Section: Conservation Of Energy (Heat Exchanger Tube Wall)mentioning

confidence: 99%

“…A numerical model of a single-phase NCL loop, which was already validated with experimental data [18][19][20], has been used to generate the time-series response in the work reported in the current paper. For different inputs of heater power under different (physically relevant) initial conditions, the simulated NCL system is observed to stabilize to a steady-state regime of operation within the 7200 s (2 h) mark.…”

Section: Introductionmentioning

confidence: 99%

“…An annular-type cooler, with a length of 600 mm, is placed with a 100 mm offset on the upper horizontal arm of the loop to cool the loop fluid; this offset is necessary to start the simulation. Although detailed descriptions of the loop geometry, model construction and validation, and grid and time independence studies are reported in previous publications [18][19][20], a concise description of the model equations is provided in this section for completeness of the paper and ease of readability. The nomenclature of the terms used in the development of equations is provided at the end of the paper.…”

mentioning

confidence: 99%

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

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Saha

Mukhopadhyay

et al. 2021

Sci

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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, an algorithm is developed using tools of symbolic time-series analysis (e.g., probabilistic finite state automata (PFSA)) for the purpose of identifying selected regimes of operation in NCL systems using only data from the early transient operation, 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 datasets, 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.

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mentioning

confidence: 68%

Section: Numerical Resultsmentioning

confidence: 98%