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Öğe Analysis of the effect of arrhenius activation energy and temperature dependent viscosity on non-newtonian maxwell nanofluid bio-convective flow with partial slip by artificial intelligence approach(Elsevier B.V., 2022) Çolak, Andaç BaturThis study focused on the analysis of partial slip effect of Arrhenius activation energy and temperature dependent viscosity on non-Newtonian Maxwell nanofluid bio-convective flow using artificial intelligence approach. Local Nusselt number, local Sherwood number and local density number values, which are dimensionless flow parameters, have been used to examine the said effect. Three different artificial neural network models have been developed using the numerically obtained data sets. Each of the feed forward back propagation multilayer perceptron network models has been developed with different input parameters. 80% of the data set has been used for training the model and 20% for the testing phase. The estimation performance of the network models developed with the Bayesian regularization training algorithm has been extensively analyzed, and the compatibility between the estimation values and the target data has been examined. The findings have shown that artificial neural network models have been developed to make predictions with high accuracy. In addition, artificial neural networks have also proven to be an ideal engineering tool that can be used to analyze the partial slip effect of non-Newtonian Maxwell nanofluids on bio-convective flow. © 2022Öğe Bir hibrit nanosıvının termofiziksel özelliklerinin yapay sinir ağı ile modellenmesi ve deneysel olarak incelenmesi(Niğde Ömer Halisdemir Üniversitesi / Fen Bilimleri Enstitüsü, 2020) Çolak, Andaç Batur; Bayrak, MustafaBu doktora çalışmasında, öncelikle Al2O3 ve Cu nanopartikülleri kullanılarak, iki aşamalı yöntemle 0.0125, 0.025, 0.05, 0.1 ve 0.2’lik hacimsel yoğunluklarda beş farklı Al2O3-Cu/Su hibrit nanosıvısı hazırlanmıştır. Hibrit nanosıvılar, manyetik karıştırıcı ve ultrasonik homojenizatör ile hazırlanmış, aglomerasyonu önlemek ve stabiliteyi sağlamak maksadı ile süzey aktif madde olarak Arabic Gum kullanılmıştır. Hazırlanmış olan her bir Al2O3-Cu/Su hibrit nanosıvısının ısıl iletkenlik, özgül ısı ve viskozite gibi termofiziksel özellikleri, deneysel olarak ölçülmüştür. Elde edilen deneysel veriler, araştırmacılar tarafından geliştirilmiş olan ve literatürde sıklıkla kullanılan model korelasyonlarla karşılaştırılmıştır. Ardından, elde edilmiş olan veriler ile, Al2O3-Cu/Su hibrit nanosıvısının termofiziksel özelliklerinin tahmin edilmesi amacıyla yapay sinir ağı modellenmiş ve yapay sinir ağından elde edilen değerler, deneysel sonuçlar, literatürde kullanılmış olan model korelasyonlar ve bu çalışmada türetilmiş matematiksel korelasyonlarla karşılaştırılmıştır. Anahtar Sözcükler: Hibrit nanosıvı, özgül ısı, ısıl iletkenlik, viskozite, yapay sinir ağıÖğe Experimental study for artificial neural network (ANN) based prediction ofelectric energy production of diesel engine based cogeneration power plant(2021) Çolak, Andaç BaturIn this study artificial neural network (ANN) has been developed in order to estimate the electricity production of cogeneration power plant, which produces a total of 11.52 MW electric power, consisting of two V type and 12 cylinders each of which is 5.760 kW diesel engines running with heavy fuel oil no 6. In the ANN which was developed for the estimation of electric power generation of cogeneration, power plant(W), Time period (t), working hours (h), fuel consumption (m) and internal power consumption (Wp) values were used as input variables. After evaluating the performance of different ANNs, an ANN, consisting of one hidden layer and 10 neurons, was considered to be the most ideal one. As a result of the comparison with experimental data, it is concluded that this model estimates the electricity generation values of the cogeneration power plant with an R-value of 0,99073 and mean square error 4.734e-8Öğe Prediction of Infection and Death Ratio of COVID-19 Virus in Turkey by Using Artificial Neural Network (ANN)(Bentham Science Publishers, 2021) Çolak, Andaç BaturBackground: For the first time in December 2019, as reported in the Wuhan city of China, COVID-19 deadly virus spread rapidly around the world and the first cases were seen in Turkey on March 11, 2020. On the same day, a pandemic was declared by the World Health Organization due to the rapid spread of the disease throughout the world. Methods: In this study, a multilayered perception feed-forward back propagation neural network has been designed for predicting the spread and mortality rate of the COVID-19 virus in Turkey. COVID-19 data from six different countries were used in the design of the artificial neural network, which has 15 neurons in its hidden layer. 70% of these optimized data were used for training, 20% for validation, and 10% for testing. Results: The simulation results showed that the COVID-19 virus in Turkey, between day 20 and 37, was the fastest to rise. The number of cases for the 20th day was predicted to be 13.845. Conclusion: As for the death rate, it was predicted that a rapid rise would start on the 20th day and a slowdown around the 43rd day and progress towards the zero case point. The death rate for the 20th day was predicted to be 170 and for the 43rd day it was 1,960s. © 2021 Bentham Science Publishers.Öğe Prediction of viscous dissipation effects on magnetohydrodynamic heat transfer flow of copper-poly vinyl alcohol Jeffrey nanofluid through a stretchable surface using artificial neural network with Bayesian Regularization(Elsevier B.V., 2022) Çolak, Andaç BaturIn this study, the viscous dissipation effects of copper-polyvinyl alcohol (Cu-PVA) Jeffrey nanofluid on magnetohydrodynamic (MHD) heat transfer flow across a stretchable surface have been analyzed with an artificial intelligence approach. The flow parameters, skin friction and Nusselt number, are numerically obtained with a closed Keller-box and partial differential equations converted to a non-linear ordinary differential equation system using the appropriate similarity transformation. Using the obtained data set, two different artificial neural network (ANN) models have been developed. In the multi-layer perceptron (MLP) network model developed with Bayesian Regularization training algorithm, solid volume fraction (?), Deborah number (?), magnetic parameter (M), Prandtl number (Pr) and Eckert number (Ec) values have been defined as input parameters and skin friction and Nusselt number values ??have been obtained in the output layer. R values ??for skin friction and Nusselt number have been calculated as 0.99020 and 0.99394, respectively. The study findings show that the developed ANN model can predict with high accuracy and is a high-performance engineering tool that can be used in modeling viscous dissipation effects. © 2022
