Yazar "Ekinci, Firat" seçeneğine göre listele

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    A comparison of the performance characteristics of large 2 MW and 3 MW wind turbines on existing onshore wind farms
    (Techno-Press, 2021) Bilgili, Mehmet; Ekinci, Firat; Demirdelen, Tugce
    The aim of the current study is to compare the performance of large 2 MW and 3 MW wind turbines operating on existing onshore wind farms using Blade Element Momentum (BEM) theory and Angular Momentum (AM) theory and illustrate the performance characteristic curves of the turbines as a function of wind speed (U-infinity). To achieve this, the measurement data obtained from two different Wind Energy Power Plants (WEPPs) located in the Hatay region of Turkey was used. Two different horizontal-axis wind turbines with capacities of 2 MW and 3 MW were selected for evaluation and comparison. The hub-height wind speed (U-D), turbine power output (P), atmospheric air temperature (T-atm) and turbine rotational speed (Omega) data were used in the evaluation of the turbine performance characteristics. Curves of turbine power output (P), axial flow induction factor (a), turbine rotational speed (omega), turbine power coefficient (C-P), blade tip speed ratio (lambda), thrust force coefficient (C-T) and thrust force (T) as a function of U-infinity were obtained for the 2 MW and 3 MW wind turbines and these characteristic curves were compared. Results revealed that, for the same wind speed conditions, the higher-capacity wind turbine (3 MW) was operating at higher turbine power coefficient rates, while rotating at lower rotational speed ratios than the lower capacity wind turbine (2 MW).
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    A new method for generating short-term power forecasting based on artificial neural networks and optimization methods for solar photovoltaic power plants
    (Springer Verlag, 2019) Demirdelen, Tugce; Ozge Aksu, Inayet; Esenboga, Burak; Aygul, Kemal; Ekinci, Firat; Bilgili, Mehmet
    In recent times, solar PV power plants have been used worldwide due to their high solar energy potential. Although the PV power plants are highly preferred, the main disadvantage of the system is that the output power characteristics of the system are unstable. As PV power plant system is connected to the grid side, unbalanced power flow effects all systems controls. In addition, the load capacitys is not exactly known. For this reason, it has become an important issue to be known correctly in PV output power and their time-dependent changes. The main aim of this work is to eliminate power plant instability due to the output power imbalance. For the short-term, power prediction is estimated by real-time data of 1 MW PV power plant in use. Estimation power data are compared with real-time data and precision of the proposed method is demonstrated. In the first phase, traditional artificial intelligence algorithms are used. Then, these algorithms are trained with swarm based optimization methods and the performance analyses are presented in detail. Among all the algorithms used, the algorithm with the lowest error is determined. Thus, this study provides useful information and techniques to help researchers who are interested in planning and modeling PV power plants. © Springer Nature Singapore Pte Ltd. 2019.
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    A novel hybrid metaheuristic optimization method to estimate medium-term output power for horizontal axis wind turbine
    (Sage Publications Ltd, 2019) Ekinci, Firat; Demirdelen, Tugce; Aksu, Inayet Ozge; Aygul, Kemal; Esenboga, Burak; Bilgili, Mehmet
    The increasing damage caused by fossil fuels has made it a necessity for new and clean energy sources. In recent years, the use of wind energy from renewable energy sources has increased, which is a new and clean energy source. Wind energy is everywhere in nature. The wind speed changes depending on time. Thus, the wind power is unstable. In order to keep this disadvantage at a minimum level, future power estimation studies have been carried out. In these studies, different methods and algorithms are applied to estimate short and medium term in wind power. In this study, artificial neural network, particle swarm optimization and firefly algorithm (FA) as a new method are used for the first time in predicting wind power. As input data, temperature, wind speed and rotor speed the data recorded in the SCADA in wind turbines are used to predict medium-term wind speed and also wind power. Each method is compared in detail and their performances are revealed.
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    An experimental determination of the optimum cooling model for dry-type transformers for different cooling configurations
    (Taylor & Francis Inc, 2020) Ekinci, Firat
    Dry-type transformers are superior in terms of safety and environmental protection compared to oil-filled transformers due to their insulating material. Therefore, these transformers are widely used in many critical applications, such as hospitals, schools, shipping, airports, dams, and renewable energy power plants. However, the heat performance of these transformers is relatively poor because of the low thermal conductivity of the epoxy resin material they use that surrounds the core and coil. In addition, electrical losses increase with the rising power capacity of the transformer, so the losses cause overheating in the transformer core and windings. This situation results in thermal aging and insulation deterioration that reduces the service lifetime of the transformers. Thus, thermal analysis and effective cooling systems for the transformers are significant issues in order to increase the life of the transformers. In this study, a 250 VA 380/110 V three-phase, dry-type transformer was modeled and thermally analyzed using the ANSYS/Fluent program. The transformer was also tested under load conditions so that the thermal behavior of the transformer could be observed via a thermal imaging camera. In order to decrease the temperature rise in the transformer core and windings, different cooling configurations were tried by creating a cooling cabinet. The optimum cooling model was determined thanks to experimental studies. This model was also verified by simulation results. In dry-type transformers, the part where the temperature increases the most is the middle leg of the core and coils. Therefore, the air input through the middle leg of the core and the air output through the side legs of the core provide the greatest cooling of the transformer. This configuration results in the middle leg temperature of the core varying between 35 degrees C and 42 degrees C. In all other fan cooling cases, it is seen in both experimental and simulation results that the temperature values do not drop below 47 degrees C. This study showed that the reliability, insulation, and service life of the dry-type transformer can be improved.
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    An experimental investigation of the flow control of a circular cylinder in near wake with parallel plates at Re=7500
    (Elsevier Science Inc, 2024) Durhasan, Tahir; Ekinci, Firat; Firat, Erhan; Akilli, Huseyin
    The vortex street suppression in the near wake region of the cylinder with parallel plates was investigated using particle image velocimetry (PIV) at Reynolds number of Re = 7500. The rigid plates are designed as flat, parallel with the free-stream direction, and located on both sides of the cylinder. Two different plate lengths (0.5D and 1D based on the cylinder diameter, D) were examined at gap ratios of g/D = 0.1, 0.3, 0.5, 0.7, and 0.9, respectively. Proper Orthogonal Decomposition (POD) analyses were also employed to reveal vortex mechanism in the near wake region. The gap ratio significantly influences the interaction of the vortices. At low gap ratios, the plates directly affect the development of shear layers on both side of the cylinder. The shear layers elongate downstream with a reduction of the vortex shedding frequency. Moreover, Reynolds shear stress, vortex shed-ding instabilities and vortex street are suppressed in the near wake region with the help of using parallel plates. On the other hand, vortex shedding reinforces instabilities at higher gap ratios. Besides, the most effective gap ratio is different for each the plate lengths to suppress instabilities.
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    An investigation of the near-wake flow structure of a cylinder with guiding plates
    (E D P Sciences, 2018) Ekinci, Firat; Firat, Erhan; Ozkan, Gokturk M.; Akilli, Huseyin
    this study, the flow behind a circular cylinder with a pair of outer identical guiding plates was investigated using particle image velocimetry (PIV) for various angular positions of the plates (i.e. alpha=+/- 70 degrees, +/- 100 degrees, and +/- 130 degrees). The gaps between these plates and cylinder are equal and are 0.3D. Experiments were carried out at a subcritical Reynolds (Re=rho.U infinity.D/mu) number of 7500, based on the cylinder diameter (D) and the flow velocity (U infinity). The features of the near-wake with and without the guiding plates were interpreted in terms of patterns of time-averaged vorticity and streamlines, time-averaged and fluctuating velocity components. The spectral analysis was also carried out to determine the time-dependent variation of the transverse velocity at given locations in the near-wake. Two-dimensional computations of flow around circular cylinders with and without guiding plates have also been performed to predict the time-averaged and root-mean-square of force coefficients of the various models. It was seen that the guiding plates at an appropriate angular position can lead to substantial attenuation, or retardation, of the process of large-scale vortex formation in the near-wake, thus can lead to vortex-induced vibration (VIV) suppression without any increase in drag.
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    Comparison of transesterification and thermal cracking methods on fuel specifications of castor oil biodiesel
    (Elsevier Science Bv, 2017) Serin, Hasan; Akar, Mustafa Atakan; Yildizhan, Safak; Ekinci, Firat; Ozcanli, Mustafa
    [Abstract Not Available]
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    Design and Finite Element Analysis of Permanent Magnet Synchronous Generator for Wind Turbine Application
    (Springer Science and Business Media Deutschland GmbH, 2021) Yavuzdeger, Abdurrahman; Esenboga, Burak; Ekinci, Firat; Demirdelen, Tugce
    Today, the demand for renewable energy sources is increasing day by day in order to reduce fossil fuels and meet the increasing energy demand. The fact that wind energy is suitable for energy production at continuous or low wind speed depending on geographical conditions increases its importance among eco-friendly energy sources. However, energy efficiency is one of the most important issues in the renewable energy field because energy production from these energy sources is constantly changing due to climate changes. Therefore, it is very important to use renewable energy sources efficiently and to enable innovative developments that will increase energy efficiency. In this chapter, a more efficient wind turbine alternator is modeled and analyzed in detail by using the ANSYS/Maxwell software program. The main objective of this chapter is to create an efficient alternator model used in both vertical and horizontal wind turbines. This alternator model is selected as a permanent magnet synchronous generator (PMSG) since there is no need for external excitation, smaller in size and easy to control. Firstly, the parameters are determined by using the mathematical model of the alternator. Secondly, the alternator is modeled and designed with the help of the design parameters such as pole pair, magnetizing inductance, the stator leakage, winding properties, number of turns and slots, etc. During the design process, all materials of the alternator are designed by taking into consideration of characteristic features of them. Finally, the designed alternator is electromagnetically analyzed thanks to ANSYS/Maxwell Electromagnetic Suit program which uses the Finite Element Method (FEM). Therefore, the electrical efficiency of the wind turbine alternator at different wind speeds is performed and the optimum design of the alternator is obtained. It is hoped that this study will guide for wind power plant operators and researchers interested in wind turbine design parameters. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Effect of partial shading conditions on off-grid solar PV/Hydrogen production in high solar energy index regions
    (Pergamon-Elsevier Science Ltd, 2019) Mert, Basak Dogru; Ekinci, Firat; Demirdelen, Tugce
    In present work, the effect of partial shading on off-grid solar PV/hydrogen production in solar energy has been studied. The study was designed to stimulate future work in this area and to help demonstrate PV/hydrogen production. Four different electrodes in the study were coated and used in PV/Hydrogen Production. Pt anode and four different cathode materials which were Cu, Cu/Ni, Cu/NiBi and Cu/NiMo were used in the study. Data obtained from 105 W PV panel via automation system installed at ATU University, Adana, in Turkey were used for data of days representing different seasons by electrolysis experiment. The experiments were carried out between 08:00 and 16:00. The main contribution of this study is to produce hydrogen by using a part of the electrical energy gained from the solar panels, and at the same time to reveal the effect of the electrical energy produced by the partial shading of the panels on the hydrogen production. Furthermore, the effect of cathode material type was investigated for the impact of partial shading on hydrogen production. Results showed that Cu/NiMo has better hydrogen production efficiency than Cu/Ni, Cu/NiBi. The lowest efficiency was observed in the bare Cu electrode. (C) 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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    Experimental investigation on solar PV panel dust cleaning with solution method
    (Pergamon-Elsevier Science Ltd, 2022) Ekinci, Firat; Yavuzdeger, Abdurrahman; Nazligul, Hueseyin; Esenboga, Burak; Mert, Basak Dogru; Demirdelen, Tugce
    The efficiency of solar PV panels varies depending on various factors; the type of material used to generate electrical energy, the quality of workmanship in the solar PV panel installation, environmental factors, dirt on the PV panel and design. Dust and dirt formed according to environmental conditions adhere to the solar PV panels and prevent the solar radiation from penetrating the surface. Thus, the solar PV panels need to be cleaned. In this study, three different chemical solutions prepared in laboratory conditions are applied to solar PV panels with a solar PV panel cleaning robot, which is manufactured using 3D printer technology to remove dust and dirt accumulated on solar PV panels for the first time in the literature. Thus, the effectiveness of chemical solutions to increase solar PV panel efficiency is demonstrated. The penetration of chemical solutions on the PV panel surface is ensured by the solar PV panel cleaning robot. The experimental set is realized under natural dust and dirt conditions. The effectiveness of the chemical solutions and electrical performance analysis results of solar PV panels are demonstrated by measurements and tests. The amount of power harvested from the PV panel cleaned using proposed Solution 1 (2-propanol) has been increased by 15%.
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    Green touch for hydrogen production via alkaline electrolysis: The semi-flexible PV panels mounted wind turbine design, production and performance analysis
    (Pergamon-Elsevier Science Ltd, 2020) Demirdelen, Tugce; Ekinci, Firat; Mert, Basak Dogru; Karasu, Ilyas; Tumay, Mehmet
    The novel solar-wind integrated system has been firstly used for hydrogen production in literature with validating theoretical, simulated and experimental studies. This integrated system consists of two main parts; solar-assisted wind turbine and alkaline electrolysis cell. In the first part of this system, the semi-flexible PV panels are smoothly integrated on the vertical axis wind turbine blade. This is a unique design in literature, unlike the hybrid systems that include wind turbines and solar PV panels in published literature. The production and testing of the hybrid integrated system in a single structure were performed both in laboratory conditions and also the system was set up the roof of ATU (Adana Alparslan Turkes Science and Technology University) in Adana. The second part includes hydrogen production via alkaline electrolysis system. The cathodes consist of nickel-coated copper (Cu/Ni) and nickel-vanadium binary coated copper (Cu/NiV), that was produced via electrodeposition technique by self-supporting. The performance of electrodes was compared in 1 M KOH solution via I-V behavior, electrochemical impedance spectroscopy, and long term cathodic polarization analysis. Results showed that polarization resistance was decreased almost 4 times by NiV when comparing the Ni. The surface inhomogeneity values were 0.91 and 0.81 for Cu/Ni and Cu/NiV respectively. The hydrogen gas evolved at the cathodes was also measured and higher volumes were detected for NiV binary coating. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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    Long short-term memory (LSTM) neural network and adaptive neuro-fuzzy inference system (ANFIS) approach in modeling renewable electricity generation forecasting
    (Taylor & Francis Inc, 2021) Bilgili, Mehmet; Yildirim, Alper; Ozbek, Arif; Celebi, Kerimcan; Ekinci, Firat
    Renewable energy sources are developing rapidly worldwide because they are unlimited and permanent, available in every country and also eliminate foreign dependency. In this respect, accurate renewable electricity generation (REG) forecasting is essential in a country's energy planning in relation to its development. In this study, two different data-driven methods such as adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM) and long short-term memory (LSTM) neural network were applied to perform one-day ahead short-term REG forecasting. In addition, short-term hydropower electricity generation (HEG), geothermal electricity generation (GEG), and bioenergy electricity generation (BEG) forecasting were also made using these methods. The correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used as evaluation criteria. The values predicted by the ANFIS-FCM and LSTM models were compared with the actual values by evaluating their errors. According to the test results obtained in terms of MAPE evaluation criteria, the best estimation model was obtained for GEG. The lowest MAPE values were found to be 7.20%, 7.46%, 1.63%, and 2.46% for REG, HEG, GEG, and BEG estimates, respectively. The results showed that both ANFIS and LSTM models presented satisfying performances in daily REG prediction, and the ANFIS and LSTM models gave almost identical results.
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    Modeling and experimental validation of dry-type transformers with multiobjective swarm intelligence-based optimization algorithms for industrial application
    (Springer London Ltd, 2022) Demirdelen, Tugce; Esenboga, Burak; Aksu, Inayet Ozge; Ozdogan, Alican; Yavuzdeger, Abdurrahman; Ekinci, Firat; Tumay, Mehmet
    In recent years, the optimum and efficient design of the transformer core and conductive materials is the most significant issues to overcome the high-temperature problems. The temperature increases on the transformer materials are directly related to the energy efficiency of it. The overheating of the core and coils of the transformer reduces the amount of energy to be obtained from the transformer. However, copper, core, eddy current and other losses can be minimized by obtaining an optimum design of the transformer for maximum efficiency. Thus, the transformer life and the energy efficiency to be obtained from the transformer are maximized. The temperature rise and temperature distribution of the windings can be monitored by computer technology and the transformer can be safely overloaded and the production cost can be minimized. Also, the operating life of the transformers can be further increased by specifying hot spot temperatures on the transformer coils and core. In this study, 3 kVA and 5 kVA Dyn 11 connected 380/220-V dry-type transformers are optimized by multiobjective swarm intelligence-based optimization methods. The main contribution of this study is to prevent the overheating of the transformers by reducing the losses in the transformer core and coils and to reduce the costs of the transformer. The thermal and electromagnetic analyses of the transformers are realized by ANSYS/Maxwell software program which utilizes the industry-leading ANSYS/Fluent computational fluid dynamics and finite element method solvers. Finally, the experimental analyses are realized under the loaded conditions for the transformers. The experimental results are verified with the simulation results. The optimization, modeling, thermal/electromagnetic analysis and experimental processes are carried out step by step in this study. The transformer manufacturers will realize the optimum cost, efficiency and thermal analysis before transformers are manufactured.
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    Modelling of wind turbine power output by using ANNs and ANFIS techniques
    (Institute of Electrical and Electronics Engineers Inc., 2017) Ekinci, Firat; Demirdelen, Tu?çe; Bilgili, Mehmet
    In this study, artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) were applied to estimate the wind turbine power output of a horizontal axis wind turbine. Hub-height wind speed, atmospheric air temperature and rotational speed values obtained from an operating wind power plant (WPP) were employed as input data in the model. According to the derived results, the mean absolute percentage error (MAPE) and correlation coefficient (R) values for the ANN model were determined as 4.41% and 0.9850, respectively, whereas the corresponding values for the ANFIS model were found as 2.19% and 0.9971, respectively. The obtained results showed that ANN and ANFIS models can be used to predict wind turbine power output in a simple, reliable and accurate way. © 2017 IEEE.
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    Modelling of Wind Turbine Power Output by Using ANNs and ANFIS Techniques
    (IEEE, 2017) Ekinci, Firat; Demirdelen, Tugce; Bilgili, Mehmet
    In this study, artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) were applied to estimate the wind turbine power output of a horizontal axis wind turbine. Hub-height wind speed, atmospheric air temperature and rotational speed values obtained from an operating wind power plant (WPP) were employed as input data in the model. According to the derived results, the mean absolute percentage error (MAPE) and correlation coefficient (R) values for the ANN model were determined as 4.41% and 0.9850, respectively, whereas the corresponding values for the ANFIS model were found as 2.19% and 0.9971, respectively. The obtained results showed that ANN and ANFIS models can be used to predict wind turbine power output in a simple, reliable and accurate way.
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    Performance Assessment of a Novel Eco-Friendly Solar Panel Mounted Hybrid Rotating Energy System with Renewable Energy Applications
    (Taylor and Francis Ltd., 2023) Yavuzdeger, Abdurrahman; Ekinci, Firat
    It is a known fact that fossil fuels will be depleted in the near future owing to the negative effects on the environment. The number of applications using renewable energy sources instead of fossil fuels to obtain energy has increased significantly. It is aimed to provide effective energy production with the proposed rotary energy system (RES) installation for regions with high wind energy and solar energy potential. In the paper, the design, manufacturing process, installation, and output power prediction of a novel RES are presented. In the proposed system, a hybrid system whose energy is derived from solar and wind energy is envisaged. The electrical characteristics of the solar panels with dimensions 140 × 60 × 2.5 mm mounted on the prototype are 6 V 100 mA. The prototype has been tested at different rotation speeds to evaluate the effect of wind energy. Moreover, the output power prediction based on Feedforward Neural Network (FFNN) and Particle Swarm Optimization trained Feedforward Neural Network (PSO-FFNN) has been performed with the data obtained from the prototype system. The three quantitative standard statistical performance evaluation measures, root mean square error (RMSE), mean absolute percentage error (MAPE) and Theil's inequality coefficient (TIC) are employed to compare the performances of these architectures. FFNN architecture, the RMSE, MAPE and TIC values are calculated as 0.0690, 0.0455 and 0.0278, respectively. For the PSO-FFNN architecture, RMSE, MAPE and TIC values are 0.0530, 0.0383, and 0.0213, respectively. It has been proved that it will be produced energy more effectively thanks to the hybrid RES in meeting energy demand. © 2023 IETE.
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    The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence
    (Mdpi, 2019) Demirdelen, Tugce; Tekin, Piril; Aksu, Inayet Ozge; Ekinci, Firat
    In order to produce more efficient, sustainable-clean energy, accurate prediction of wind turbine design parameters provide to work the system efficiency at the maximum level. For this purpose, this paper appears with the aim of obtaining the optimum prediction of the turbine parameter efficiently. Firstly, the motivation to achieve an accurate wind turbine design is presented with the analysis of three different models based on artificial neural networks comparatively given for maximum energy production. It is followed by the implementation of wind turbine model and hybrid models developed by using both neural network and optimization models. In this study, the ANN-FA hybrid structure model is firstly used and also ANN coefficients are trained by FA to give a new approach in literature for wind turbine parameters' estimation. The main contribution of this paper is that seven important wind turbine parameters are predicted. Aiming to fill the mentioned research gap, this paper outlines combined forecasting turbine design approaches and presents wind turbine performance in detail. Furthermore, the present study also points out the possible further research directions of combined techniques so as to help researchers in the field develop more effective wind turbine design according to geographical conditions.
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    The role of hydropower installations for sustainable energy development in Turkey and the world
    (Pergamon-Elsevier Science Ltd, 2018) Bilgili, Mehmet; Bilirgen, Harun; Ozbek, Arif; Ekinci, Firat; Demirdelen, Tugce
    Hydropower has the largest share among renewable energy sources in the world, supplying more than 16.6% of total global electricity to over 160 countries around the world. Global hydropower capacity increased to approximately 1096 GW with the addition of 25 GW of new hydropower capacity in 2016. With a 216 TWh per year generation capacity, Turkey's hydropower potential is the largest in Europe. The increased rate of installed capacity in Turkey was ranked 7th in the world in 2016 with an annual installed hydroelectric capacity of 0.8 GW. The main objective of this paper is to review the developments of hydropower installations around the world and in Turkey with an emphasis on the potential of small scale hydropower systems such as waterwheels in utilizing low head water flow for household electricity usage. In the first part of this study, the growth of worldwide hydropower capacity is reviewed and the countries with the largest installed and new built hydropower capacities are reported. In the second part of this study, the current status of Turkey's hydropower plants is discussed in detail with respect to annual regional rainfall, gross water mass flow and potential of Turkey's major water basins to demonstrate the potential energy output that can be harnessed from small-scale systems implemented in low-head water sources. In addition, the most recent information on Turkey's electricity generation and consumption rates are reported. (C) 2018 Elsevier Ltd. All rights reserved.
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    Thermodynamic analysis of the human body in different climate regions of Turkey to determine the comfort conditions with exergy method
    (Inderscience Enterprises Ltd, 2018) Ekinci, Firat; Bilgili, Mehmet
    Thermodynamic analysis of human body is studied to determine the thermal comfort conditions. In this context, exergy and energy analysis in the air conditioning of buildings is necessary for efficient use of energy. Thermodynamic analysis of human body is carried out for seven different climate regions of Turkey based on the use of meteorological parameters such as minimum and maximum and average monthly temperatures, atmospheric pressure and average relative humidity by implementing energy and exergy analysis. Human body, which is the subject of this study, was in the light activity level quantified as 58.2 W/m(2). Analysis results have indicated that the major energy loss with 39.28 W/m(2) is due to heat transfer with radiation, convection and conduction. Furthermore, the energy loss rates by water vapour diffusion from the skin, respiration, temperature difference and sweating were determined as 11.13 W/m(2), 4.29 W/m(2), 0.73 W/m(2) and 0.02 W/m(2), respectively. The maximum exergy consumption rate by the human body was 2.33 W/m(2) for the cold and semi dry - less humid climate region (CR-7), while the minimum exergy consumption rate was obtained as 0.91 W/m(2) for the hot and semi dry climate region (CR-1). Information presented in this study is expected to contribute to the design of air conditioning systems in order to choose more efficient energy systems.
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    Thermodynamic analysis of the human body in different climate regions of Turkey to determine the comfort conditions with exergy method
    (Inderscience Publishers, 2018) Ekinci, Firat; Bilgili, Mehmet
    Thermodynamic analysis of human body is studied to determine the thermal comfort conditions. In this context, exergy and energy analysis in the air conditioning of buildings is necessary for efficient use of energy. Thermodynamic analysis of human body is carried out for seven different climate regions of Turkey based on the use of meteorological parameters such as minimum and maximum and average monthly temperatures, atmospheric pressure and average relative humidity by implementing energy and exergy analysis. Human body, which is the subject of this study, was in the light activity level quantified as 58.2 W/m2. Analysis results have indicated that the major energy loss with 39.28 W/m2 is due to heat transfer with radiation, convection and conduction. Furthermore, the energy loss rates by water vapour diffusion from the skin, respiration, temperature difference and sweating were determined as 11.13 W/m2, 4.29 W/m2, 0.73 W/m2 and 0.02 W/m2, respectively. The maximum exergy consumption rate by the human body was 2.33 W/m2 for the cold and semi dry - less humid climate region (CR-7), while the minimum exergy consumption rate was obtained as 0.91 W/m2 for the hot and semi dry climate region (CR-1). Information presented in this study is expected to contribute to the design of air conditioning systems in order to choose more efficient energy systems. Copyright © 2018 Inderscience Enterprises Ltd.