Browsing by Author "Ugurer, Doruk"
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Article Citation Count: 8Enhanced photovoltaic panel energy by minichannel cooler and natural geothermal system(Wiley, 2021) Jafari, Rahim; Erkilic, Kaan T.; Ugurer, Doruk; Kanbur, Yunus; Yildiz, Murat o.; Ayhan, Ege B.; Automotive EngineeringCommercial photovoltaic (PV) solar panels convert the solar energy directly to electricity but their efficiency is low. The rest of the energy is mostly converted to heat. Although the conversion efficiency of PV panels is low, getting hot causes increase in the temperature of the PV cells which results in further dramatic decrease of their efficiency and the technical lifetime. In the present study, a PV panel with cooling system was made in which a polymer minichannel heat exchanger was fully integrated with the PV cells during the fabrication of the panel. Heat exchangers containing minichannels and microchannels have higher heat transfer capability than pipes and channels as they have a higher ratio of area to volume. Besides, since the heat exchanger is adhered to the solar cells during the panel fabrication, the thermal contact resistance drops to minimum. Circulated coolant dissipates the extracted heat from the panel to the ground by buried long life and low-price plastic tubes. Since the earth temperature beyond a depth of 4 m is relatively constant, 10 degrees C to 16 degrees C, the earth acts as a cooling medium for free. The experimental results show that the cooling system is capable to dispose of 570 W heat from the PV panel in the ground. The daily electricity generation rises about 10%. The levelized cost of energy (LCOE) is minimum compared to the available PV panels with active cooling techniques in the literature.Article Citation Count: 0Prediction of the onset of shear localization based on machine learning(Cambridge Univ Press, 2023) Akar, Samet; Ayli, Ece; Ulucak, Oguzhan; Ugurer, Doruk; Department of Mechanical EngineeringPredicting the onset of shear localization is among the most challenging problems in machining. This phenomenon affects the process outputs, such as machining forces, surface quality, and machined part tolerances. To predict this phenomenon, analytical, experimental, and numerical methods (especially finite element analysis) are widely used. However, the limitations of each method hinder their industrial applications, demanding a reliable and time-saving approach to predict shear localization onset. Additionally, since this phenomenon largely depends on the type and parameters of the constitutive material model, any change in these parameters requires a new set of simulations, which puts further restrictions on the application of finite element modeling. This study aims to overcome the computational efficiency of the finite element method to predict the onset of shear localization when machining Ti6Al4V using machine learning methods. The obtained results demonstrate that the FCM (fuzzy c-means) clustering ANFIS (adaptive network-based fuzzy inference system) has given better results in both training and testing when it is compared to the ANN (artificial neural network) architecture with an R-2 of 0.9981. Regarding this, the FCM-ANFIS is a good candidate to calculate the critical cutting speed. To the best of the authors' knowledge, this is the first study in the literature that uses a machine learning tool to predict shear localization.