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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).
Högskolan Väst, Institutionen för ingenjörsvetenskap, Avdelningen för svetsteknologi (SV). (PTW)ORCID-id: 0000-0002-6102-9021
2021 (Engelska)Ingår i: International Communications in Heat and Mass Transfer, ISSN 0735-1933, E-ISSN 1879-0178, Vol. 126, artikel-id 105391Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
Elsevier, 2021. Vol. 126, artikel-id 105391
Nyckelord [en]
Condensation heat transfer coefficient; Flow regime; Adaptive neuro-fuzzy inference system; Fuzzy C-means clustering
Nationell ämneskategori
Energiteknik
Forskningsämne
Produktionsteknik
Identifikatorer
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
Tillgänglig från: 2021-10-18 Skapad: 2021-10-18 Senast uppdaterad: 2022-04-04Bibliografiskt granskad

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Noori Rahim Abadi, Seyyed Mohammad Ali

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Noori Rahim Abadi, Seyyed Mohammad Ali
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Avdelningen för svetsteknologi (SV)
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International Communications in Heat and Mass Transfer
Energiteknik

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