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Öğe Application of artificial intelligence techniques for heat exchanger predictions in food industry(Elsevier, 2024) Öztuna Taner, Öznur; Mercan, Hatice; Çolak, Andaç Batur; Radulovic, Jovana; Taner, Tolga; Dalkılıç, Ahmet SelimHeat exchangers (HEXs) are deployed in diverse engineering applications, such as cooling and refrigeration systems; power plants; and automotive, chemical, textile, and food industries. Understanding the principles and fluid-to-fluid heat exchange geometry can be complex. Researchers usually apply the first and second laws of thermodynamics to conduct numerical, analytical, and experimental techniques on HEXs. Experimental approaches tend to be costlier due to setup expenses, while theoretical and numerical analyses rely heavily on assumptions and complex equations. To address these challenges, artificial intelligence (AI) models have emerged as a promising solution for modeling, optimization, and performance estimation of thermal systems employing HEXs. In the last 30 years, AI-based approaches have gained widespread adoption in thermal analysis of HEXs, building upon past research. Three main types of thermal analysis have been reported: single-phase flow, two-phase flow, and machine learning-based physical property evaluation. AI approaches have proven effective in estimating crucial HEX parameters like pressure drop (?P), heat transfer coefficient (h), friction factor (f), and Nusselt number (Nu). They have also demonstrated success in assessing phase change characteristics during fluid boiling and condensation processes, as well as identifying two-phase flows. Despite these advancements, it is emphasized that more work remains to fully harness AI’s potential for thermal analysis of HEXs. As AI gains traction, it presents itself as a valuable technology for enhancing the study of HEXs with satisfactory results.Öğe Determination of optimum insulation thickness in submarines(Yildiz Technical University, 2023) Durmaz, Savaş; Çolak, Andaç Batur; Mercan, Hatice; Dalkiliç, Ahmet SelimOne of the most effective ways to save energy for cooling and heating applications is thermal insulation. Because of this, determining the ideal insulation thickness is a popular topic for publications. The purpose of this study is to determine the appropriate insulation thickness needed for a submarine’s external construction while it is cruising in various locations. Since seawater makes up a submarine’s external environment, situations involving five distinct sea-water temperatures from around the globe have been studied. There are five of them: the Med-iterranean, Marmara, Aegean, Black Sea, and Sakhalin, which is in the North Pacific Ocean and has the coldest seawater on earth. By using the idea of degree-days, the annual cooling and heating needs of submarines in various regions have been computed. Based on life cycle cost analysis, optimization has been accomplished. In the beginning, the results of a study published in the literature supported the calculation methods utilized. The use of insulation materials such as rock wool, glass wool, polyurethane, expanded polystyrene, fiberglass, and foam glass, as well as fuel oil to run the generator, has been taken into account in a number of calculations, including the best insulation thickness, annual savings value, annual energy cost, and payback period. The findings indicate that depending on seawater temperatures and insulation materials, the ideal insulation thicknesses range between 2 and 12 cm, energy savings between 8.5% and 90%, and payback periods between 1.1 and 10 years.Öğe 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, SomchaiShell 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.Öğe 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, SomchaiThe 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.
