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Modeling of condensation heat transfer coefficients and flow regimes in flattened channels
Clean Energy Research Group, Department of Mechanical and Aeronautical Engineering, University of Pretoria,Hatfield (ZAF).
University West, Department of Engineering Science, Division of Welding Technology. (PTW)ORCID iD: 0000-0002-6102-9021
2021 (English)In: International Communications in Heat and Mass Transfer, ISSN 0735-1933, E-ISSN 1879-0178, Vol. 126, article id 105391Article in journal (Refereed) Published
Abstract [en]

In this paper, an adaptive neuro-fuzzy inference system (ANFIS) with fuzzy C-means clustering (FCM) structure identification is proposed to model condensation heat transfer and flow regimes in flattened smooth tubes with different aspect ratios. The FCM-ANFIS model was trained by using experimental data points for six effective chosen parameters of saturation temperature, heat flux, mass flux, aspect ratio and hydraulic diameter of the flattened tube, and vapor quality. Three flow regimes of annular flow, stratified, and intermittent flow were linked to the effective parameters based on the experimental data. Two models were proposed to predict the condensation heat transfer coefficient and the flow regime of R134a and R410a in flattened smooth tubes. Three statistical criteria were used to ascertain the accurateness of the models compared to the experimental results. It is found that while among benchmarked cases, the proposed model for the condensation heat transfer coefficient performs well, the best result with the lowest error (MAE = 0.029, RMSE = 0.036 and MRE = 2.83%) is when T-sat = 45 degrees C, q ‘’ = 10 kW/m(2), G = 100 kg/m(2). s, beta = 6 and D-h = 2.3 mm. On the other hand, in the worst-performing case when the errors are MAE = 0.239, RMSE = 0.239 and MRE = 13.36%, the predicted results are still in the uncertainty range of the experimental result when T-sat = 45 degrees C, q ‘’ = 5 kW/m(2), G = 200 kg/m(2). s, beta = 6 and D-h = 2.3 mm.

Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 126, article id 105391
Keywords [en]
Condensation heat transfer coefficient; Flow regime; Adaptive neuro-fuzzy inference system; Fuzzy C-means clustering
National Category
Energy Engineering
Research subject
Production Technology
Identifiers
URN: urn:nbn:se:hv:diva-17453DOI: 10.1016/j.icheatmasstransfer.2021.105391ISI: 000685642600005Scopus ID: 2-s2.0-85108074637OAI: oai:DiVA.org:hv-17453DiVA, id: diva2:1604060
Available from: 2021-10-18 Created: 2021-10-18 Last updated: 2022-04-04Bibliographically approved

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Noori Rahim Abadi, Seyyed Mohammad Ali

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