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Öğe Analysis of the thermalization dynamics of two-layer thin films irradiated by femtosecond laser(Elsevier Gmbh, 2020) Tunc, K. M. Murat; Gunay, M. Erdem; Bayata, FatmaIn this work, ultrafast thermalization dynamics was examined for a variety of two layer-thin films (Au/Si, Au/Ni, Au/W, Au/Al and Au/Pb). Non-equilibrium energy transport under laser irradiation was formulated for the electron and lattice sub-systems of the thin films. A significant reduction in the temperature of the electron and the lattice of the gold surface was observed especially for Au/Si and Au/Ni thin films due to their large G values. Next, the effects of laser power intensity and laser heating duration on the temperature distributions were examined for Au/Ni two-layer thin film. It was found that, as the laser intensity increased, the maximum electron temperature increased dramatically; on the other hand, as the pulse heating duration increased, the electron temperature gradually decreased. It was then concluded that thermal damage threshold of the gold surface can be improved by depositing gold layer on a substrate material with high electron-phonon coupling factor. Hence the thermal failure of thin films used in optical components of ultrafast laser systems or micro/nano electro mechanical systems can be prevented.Öğe Direct air capture system: a feasibility study for Izmir(Taylor & Francis Ltd, 2022) Tunc, K. M. Murat; Cagla, Levon; Kasikci, Mustafa; Caliskan, Rafet Yagiz; Korkmaz, ZeynepThis study aims to reduce 1% of the annual emissions in the city of Izmir. In line with this target, this study worked on the integration of a liquid solvent-based DACS system within Izmir. Primarily, the emissions within the city boundaries were calculated based on the calculation method of each sector. For the mentioned target, each component's energy requirement of the KOH solvent-based DACS system was calculated in detail. According to these calculations, the system's total energy requirement, economic, and area calculations were obtained. As a result, the total emission amount was calculated as 11,809,362 tCO(2). Total needed energy, area, and cost for DACS system resulted to be 1,169 kJ kg(-1), 12,527 m(2), and $136.6 million, respectively. In addition to numerical results, DACS is the most logical solution apart from increasing environmental awareness for creating a better, stronger, and environmentally friendly city for the near future.Öğe Forecasting annual natural gas consumption using socio-economic indicators for making future policies(Pergamon-Elsevier Science Ltd, 2019) Sen, Doruk; Gunay, M. Erdem; Tunc, K. M. MuratNatural gas is a foreign-dependent source of energy in many countries and a rapid increase of its consumption is mainly associated with the increase of living standards and needs. In this work, Turkey was taken as a case study with high degree of foreign dependence of energy, and the future natural gas consumption was predicted by several different multiple regression models using socio-economic indicators as the descriptor variables. Among these, gross domestic product and inflation rate were found to be the only significant ones for this prediction. Next, three different projections for the future values of the significant descriptor variables were tested, and the natural gas consumption was predicted to rise gradually in the range 1.3 +/- 0.2 billion m(3) per year reaching to a consumption of 64.0 +/- 3.5 billion m(3) in the year 2025. It was then discussed that this additional natural gas can be compensated by utilizing local lignite sources or by starting a nuclear energy program although these two methods to reduce the future natural gas consumption have some conflictions with the general European energy matrix and environmental politics. Thus, it was concluded that resuming the wind and solar-based electricity generation programs can be considered as a more reasonable option. (C) 2019 Elsevier Ltd. All rights reserved.Öğe Forecasting electricity consumption of OECD countries: A global machine learning modeling approach(Elsevier Sci Ltd, 2021) Sen, Doruk; Tunc, K. M. Murat; Gunay, M. ErdemElectricity is a critical utility for social growth. Accurate estimation of its consumption plays a vital role in economic development. A database that included past electricity consumption data from all OECD countries was prepared. Since national trends may be transferable from one country to another, the entire database was modeled and simulated via machine learning techniques to forecast the energy consumption of each country. Understanding similarities among the profiles of different countries could increase predictive accuracy and improve associated public policies.Öğe Forecasting long-term world annual natural gas production by machine learning(Elsevier Sci Ltd, 2023) Sen, Doruk; Hamurcuoglu, K. Irem; Ersoy, Melisa Z.; Tunc, K. M. Murat; Gunay, M. ErdemThe goal of this study is to model the global annual natural gas production using a variety of machine learning models in order to predict future production and determine a peak production date. World gross domestic product (GDP) based on purchasing power parities (at PPPs), inflation percentage, Henry Hub Price, Eum Price, cumulative natural gas resources, and annually discovered new resources were taken as descriptor variables, and Shapley analysis was conducted to observe the importance of features on the dataset. It was revealed according to this analysis that, Henry Hub price, inflation percentage, and newly discovered resources had minor effects on natural gas production, so they were left out. Then, a variety of machine learning algorithms were employed and the one with the highest prediction ability was found to be the stochastic gradient descent (SGD) algorithm. Next, this model was tested under four different scenarios, each with different GDP and natural gas price projections. Finally, natural gas production was found to reach its peak sometime between 2034 and 2046. It was then concluded that rather than relying on a traditional approach based on the Hubbert Curve, a machine learning model that takes into account all relevant factors can be used to accurately forecast natural gas production and its peak time, allowing governments and policymakers to make the necessary preparations.Öğe Hydropower Plants Tailwater Energy Production And Optimization(IEEE, 2015) Tunc, K. M. MuratWater is the first renewable source used to generate electricity. After converting water's power to electricity in hydro power stations water falls down back to the river. Water leaving water turbines is called tailwater. Tailwater still has energy to be converted. In this study to produce electric energy from the tail water of the plant has been investigated. The use of low head Kaplan turbines which are placed in hydromatrix form, are feasible for this type of applications. For an existing water head and flow rate, the number and geometrical properties of hydromatrix turbines are investigated. In the case study Sanibey water dam's tail water data has been used to determine optimum size and number of hydromatrix turbines to be installed to maximize electrical energy production.Öğe Machine Learning Analysis of Thermal Performance Indicator of Heat Exchangers with Delta Wing Vortex Generators(Mdpi, 2024) Aksoez, Zafer Yavuz; Guenay, M. Erdem; Aziz, Muhammad; Tunc, K. M. MuratIn this work, the design features of delta wing vortex generators (DWVGs) on the thermo-hydraulic performance of heat exchangers are investigated using machine learning. Reynolds numbers, attack angle, length, wing-to-width ratio, and relative pitch ratio of DWVGs were used as descriptor variables, with Nusselt numbers, friction factors, and performance evaluation criterion (PEC) serving as target variables. Decision tree classification revealed the pathways leading to high or low values of the performance variables. Among many of those pathways, it was found that high Reynolds numbers (between 8160 and 9800) and high attack angles (greater than or equal to 47.5 degrees) lead to high Nusselt numbers. On the other hand, an attack angle between 41 degrees and 60 degrees, a Reynolds number less than 8510, and a wing-to-width ratio greater than or equal to 0.4 causes a high friction factor. Finally, the PEC is likely to enhance when the Reynolds number is higher than or equal to 10,300 and the attack angle is between 47.5 degrees and 60 degrees. In addition to the decision tree analysis, SHapley Additive exPlanations (SHAP) analysis (a part of explainable machine learning) was also applied to reveal the importance of design features and their positive and negative effects on the target variables. For example, for a Nusselt number as the target variable, the Reynolds number was found to be the most influential variable, followed by the attack angle and the relative pitch ratio, all of which had a positive impact on the target. It was then concluded that machine learning methods could help provide strong insights into the configuration design features of heat exchangers in DWVGs to improve their efficiency and save energy.Öğe Simulation of electricity generation by marine current turbines at Istanbul Bosphorus Strait(Pergamon-Elsevier Science Ltd, 2016) Yazicioglu, Hasan; Tunc, K. M. Murat; Ozbek, Muammer; Kara, TolgaIn this work, several simulations and analyses are carried out to investigate the feasibility of generating electricity from underwater sea currents at Istanbul Bosphorus Strait. Bosphorus is a natural canal which forms a border between Europe and Asia by connecting Black Sea and Marmara Sea. The differences in elevation and salinity ratios between these two seas cause strong marine currents. Depending on the morphology of the canal the speed of the flow varies and at some specific locations the energy intensity reaches to sufficient levels where electricity generation by marine current turbines becomes economically feasible. In this study, several simulations are performed for a 10 MW marine turbine farm/cluster whose location is selected by taking into account several factors such as the canal morphology, current speed and passage of vessels. 360 different simulations are performed for 15 different virtual sea states. Similarly, 8 different configurations are analyzed in order to find the optimum spacing between the turbines. Considering the spatial variations in the current speed within the selected region, the analyses are performed for three different flow speeds corresponding to +/- 10% change in the average value. For each simulation the annual energy yield and cluster efficiency are calculated. (C) 2015 Elsevier Ltd. All rights reserved.
