Yazar "Wongwises, Somchai" seçeneğine göre listele

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    Estimation of heat transfer parameters of shell and helically coiled tube heat exchangers by machine learning
    (Elsevier Ltd, 2023) Çolak, Andaç Batur; Akgül, Doğan; Mercan, Hatice; Dalkılıç, Ahmet Selim; Wongwises, Somchai
    Shell and helically coiled tube heat exchangers (SHCTHEXs) are heat exchangers that only take up a small space and enable greater heat transfer area compared to traditional models. Information on 21 different SHCTHEXs obtained from catalog was considered for the modeling. Two other artificial neural network structures have been created to forecast the heat transfer coefficient, pressure drop, Nusselt number, and performance evaluation criteria values as outputs. In contrast, tubing and coil diameters, Reynolds and Dean numbers, curvature ratio, and mass flow rate are designed as inputs. In the network structures with 105 data points, 70% of the data was used for training, 15% for validation, and 15% for the testing stages. The Levenberg-Marquardt procedure was evaluated as the training algorithm in multi-layer perceptron network models. The coefficient of determination was as higher than 0.99. The mean deviation was less than 0.01%. The results show that the created artificial neural network structures can acqurately estimate the outputs.
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    Prediction of nanofluid flows' optimum velocity in finned tube-in-Tube heat exchangers using artificial neural network
    (Walter de Gruyter GmbH, 2022) Çolak, Andaç Batur; Mercan, Hatice; Açlkgöz, Özgen; Dalklllç, Ahmet Selim; Wongwises, Somchai
    The average flow velocity in heat exchangers is considered less often and thus needs further and detailed investigation because of its crucial influence on the overall thermal performance of the application. The use of nanofluids has similar influences to finned tube designs. Considering the rise in heat transfer and pressure drop, uncertainties in cost analyses with the uses of fins and nanoparticles, evaluation of optimum operating velocity of the fluids is necessary. On the contrary, there aren’t enough experimental, parametric, or numerical investigations present on this subject. The use of machine learning techniques to heat transfer applications to make optimization becomes popular recently. In this work, important factors of the process as tube number, cleanliness factor, and overall cost as output factors have been estimated by an artificial intelligence method using 339 data points. The influence of input factors of Reynolds number, thermal conductivity, specific heat, viscosity, and total fin surface efficiency on the outputs have been studied. Total tube number, cleanliness factor, and total cost analysis have been determined with deviations of ?0.66%, 0.001%, and 0.12% as a result of the solution with 6 inputs, correspondingly.