Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1511
Title: Comparative gasketed plate heat exchanger performance prediction with computations, experiments, correlations and artificial neural network estimations
Authors: Aradağ, Selin
Genc, Yasin
Türk, Caner
143685
Keywords: Gasketed plate heat exchanger
experiment
correlation
CFD
ANN
Issue Date: 27-Apr-2017
Publisher: Hong Kong Polytechnic Univ, Dept Civil & Structural Eng
Source: Aradag, S., Genc, Y., & Turk, C. (2017). Comparative gasketed plate heat exchanger performance prediction with computations, experiments, correlations and artificial neural network estimations. Engineering Applications of Computational Fluid Mechanics, 11(1), 467-482.
Abstract: Gasketed plate heat exchangers (GPHEX) are popular due to their small volume, ease of cleaning and high thermal performance. Hydraulic and thermal performance of GPHEX are not as easily determined since they solely depend on the corrugation pattern of the heat exchanger (HEX) plates. Every plate needs its own correlation for Nusselt number and friction factor. Correlation development based on plate-specific experiments is one method of performance prediction. Computational fluid dynamics (CFD) is also applicable to understand the Nusselt number and friction characteristics. However, since it is difficult to observe the effects of the corrugation pattern computationally, the pattern of the plates is usually ignored and CFD is performed on flat, nonpatterned plates. In addition, correlations developed using experimental data can not exactly predict the performance. In this article, GPHEX computations are performed with corrugated plates and the results are validated via comparison with experiments performed for the same HEX plates. The use of corrugation patterns in computations is justified with the help of experimental results, and corrugated and flat-plate HEX computations. Artificial neural networks (ANNs) based on experimental findings are used as an alternative to correlations to examine the performance. The results show that ANNs can depict the experimental trends better than the correlations. The ANN results, which are composed of 12 inputs, and two hidden layers consisting of 10 and six neurons, respectively, are within 16% of the experimental results, as opposed to the correlations, which are within 40%.
URI: https://doi.org/10.1080/19942060.2017.1314870
https://hdl.handle.net/20.500.11851/1511
ISSN: 1994-2060
Appears in Collections:Makine Mühendisliği Bölümü / Department of Mechanical Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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