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Öğe Artificial neural network-based cooling capacity estimation of various radiator configurations for power transformers operated in ONAN mode(Elsevier, 2024) Koca, Aliihsan; Şenturk, Oğuzkan; Çolak, Andaç Batur; Bacak, Aykut; Dalkılıç, Ahmet SelimPower transformers submerged in oil are universally acknowledged as very useful elements in electrical power networks. A fraction of the electrical energy involved in the conversion from high to low voltages is lost as thermal energy, which is produced inside the transformer's windings and core. The effective dissipation of heat is of paramount significance and can be accomplished through the strategic installation of radiators on the tank. This study aims to examine the total cooling capacity of a radiator employed for cooling a power transformer operating in oil, natural air, and natural mode. The investigation is conducted by varying the design parameters, specifically the number of fins per radiator and the radiator length. The study also utilizes computational fluid dynamics results to achieve a substantial convergence with experimental findings. In the context of the verification study conducted for a specific study point, it was found that the simulation outcomes at a mass flow rate of 0.15 kg/s corresponded to about 7.4 % of the experimental results. Additionally, the cooling capacity value obtained was 7.2 %. In the context of machine learning, the performance of the numerical approach was assessed by employing the Bayesian regularization method. The evaluation revealed that the margin of deviation, mean squared error, and coefficient of determination (R2) metrics yielded values of ?0.001 %, 1.32E-02, and 0.99930, respectively.Öğe Experimental and numerical investigations on the heat transfer characteristics of a real-sized radiant cooled wall system supported by machine learning(Elsevier Masson s.r.l., 2023) Çolak, Andaç Batur; Açıkgöz, Özgen; Karakoyun, Yakup; Koca, Aliihsan; Dalkılıç, Ahmet SelimDespite the extensive utilization of radiant air conditioning units in rooms, challenging points of design associated with the calculation of cooling load are still present. Except for the radiant wall cooling studies aimed at conducting heat transfer-focused analyses carried out by the authors of this investigation, neither experimental nor computational studies exist in the related literature. The current experimental and computational study aims to address the deficiencies in the radiant-cooled wall problem. Differing from other conditioned rooms, the heat is exposed through the back surface of the analyzed wall, whose heat flux range lies between 1.60 and 10.84 W/m2. The total, radiative, and convective heat transfer coefficients of 7.78, 5.13, and 2.52 W/m2.K are acquired as values for use in building energy simulation programs. Seven different artificial neural network models are designed to estimate the total, radiative, and convective heat transfer coefficients and heat transfer rates. Dependency analyses are also performed using various inputs in the investigated numerical models. The margin of deviation values computed for six different output factors are found below ?1.80%, the mean square error values are less than 1.51E-04, the R values are greater than 0.98, and the data points do not surpass the 10% deviation line. Artificial neural networks have been found to outperform well-known correlations in estimating experimental results. Extensive measured experimental data are presented for the sake of other researchers numerical modelling and validation issues. Building energy simulation software designers and engineers in the field of thermal comfort are thought to benefit from these findings.Öğe Improving pressure drop predictions for R134a evaporation in corrugated vertical tubes using a machine learning technique trained with the Levenberg-Marquardt method(Springer, 2024) Çolak, Andaç Batur; Bacak, Aykut; Karakoyun, Yakup; Koca, Aliihsan; Dalkılıç, Ahmet SelimThe 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%.