Browsing by Author "Koca, Aliihsan"

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Improving Pressure Drop Predictions for R134a Evaporation in Corrugated Vertical Tubes Using a Machine Learning Technique Trained With the Levenberg-Marquardt Method
(Springer, 2024) Colak, Andac Batur; Bacak, Aykut; Karakoyun, Yakup; Koca, Aliihsan; Dalkilic, Ahmet Selim
The present investigation utilized a machine learning structure to ascertain the pressure drop in vertically positioned, corrugated copper tubes during the evaporation process of R134a. The evaporator was a counter-flow heat exchanger, in which R134a flowed in the inner corrugated tube and hot water flowed in the smooth annulus. Different evaporation mass fluxes (195-406 kg m-2 s-1) and heat fluxes (10.16-66.61 kW m-2) were used with artificial neural networks at different corrugation depths. A multilayer perceptron artificial neural network model with 13 neurons in the hidden layer was proposed. Tan-Sig and Purelin transfer functions were used in the network model developed with the Levenberg-Marquardt training algorithm. The dataset, which consisted of 252 data points, related to the evaporation process, was divided into training (70%), validation (15%), and testing (15%) groups in an arbitrary manner. The artificial neural network model has been demonstrated to effectively forecast the pressure drop that occurs during evaporation. The mean squared error was computed for the Delta P values observed during the evaporation processes, yielding a value of 1.96E-03. The artificial neural network exhibited a high correlation coefficient value of 0.94479. The estimation fluctuations exhibited a range of +/- 10%, whereas the experimental and anticipated Delta P data demonstrated a divergence of +/- 10.3%.
Machine Learning Application With Bayesian Regularization for Predicting Pressure Drop in R134a's Annular Evaporation and Condensation
(Springer Heidelberg, 2025) Colak, Andac Batur; Bacak, Aykut; Karakoyun, Yakup; Koca, Aliihsan; Dalkilic, Ahmet Selim
The study of condensation and evaporation in plain pipes is a significant area of engineering and scientific inquiry, as it has relevance for enhancing and developing various industrial procedures. This study utilized a Bayesian artificial intelligence method to determine the pressure drop in a vertically oriented plain copper pipe during R134a's annular condensation and vaporization. The heat exchanger uses R134a and water in the tube and annulus sides, in turn. The tube has an inner diameter of 8 and a length of 500 mm. The training sets for artificial neural networks comprise R134a mass fluxes within the interval of 260-515 kg/m2s for in-tube condensation and 200-405 kg/m2s for evaporation. The obtained data on pressure drop during condensation and evaporation experiments were utilized in artificial neural network analysis using a differential pressure transducer in the test section. The study randomly divided 368 and 50 data points into training (85%) and testing (15%) sets for condensation and evaporation. The Bayesian method, primarily applied on this subject, effectively forecasts pressure drop during experimental condensation and evaporation. Regarding margin of deviation analyses, the models' condensation and evaporation conditions display variations of approximately +/- 7.5 and +/- 8.9%, respectively. The artificial neural network training technique was optimized to achieve minimal mean squared error values of 1.4211E-06 after 32 iterations for condensation and 1.3511E-06 after 40 iterations for evaporation. The predicted pressure drop data has a deviation of +/- 10% with the experimental one for both condensation and evaporation. The artificial neural network model produced R-values of 0.99903 and 0.98451 for condensation and evaporation, correspondingly.