DSpace Arşivi :: by Yazar "Bilgili, Mehmet" değerine göre listeleniyor

Yazar "Bilgili, Mehmet" seçeneğine göre listele

Listeleniyor 1 - 20 / 21

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).
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.
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.
A Strategic Solution to Turkey’s Intermediate Goods Problem: Ceyhan Energy Specialized Industrial Zone
(2019) Ekinci, Fırat; Bilgili, Mehmet
There is an on-going demand for energy from hydrocarbon compounds and intermediate goods in meetingthe need for both production and intermediate products. The European Union and Turkey’s need for energyand intermediate goods are provided by petroleum and natural gas imported from geographically nearestenergy regions through pipelines. In this study, a strategic solution to demand for energy in Turkey isproposed. Furthermore, information about the energy status, the chemical sector and intermediate goodsimports of Turkey is provided. Considerations are proposed on the capabilities of petrochemical plants tobe established in Ceyhan Energy Specialized Industrial Zone (CESIZ) as an outlet of petroleum fromAzerbaijan and Iraq and a potential logistic energy base in the Eastern Mediterranean region. CeyhanEnergy Specialized Industrial Zone, as the first energy zone in Turkey and the nearby region, is introducedand the investors are briefed on the petrochemical facilities planned to be established in the zone. Theimportance of intermediate chemical products produced by petrochemical plants to be established inCeyhan Energy Specialized Industrial Zone in reducing the country’s foreign trade deficit is revealed. Dueto lack of studies conducted on CESIZ in the literature, this study would contribute to potential investorsand academicians.
ENERGY AND EXERGY ANALYSIS OF A VAPOR ABSORPTION REFRIGERATION SYSTEM IN AN INTERCITY BUS APPLICATION
(2019) Kurtulmuş, Nazım; Bilgili, Mehmet; Şahin, Beşir
A Vapor Absorption Refrigeration (VAR) system driven by the exhaust gas waste heat received from the internal combustion engine of an intercity bus is modeled and analyzed for air conditioning the intercity bus cabin under different operating parameters. Initially, the hourly comfort cooling load of the intercity bus is calculated for a cooling season spanning five months between May and October in Turkey. After determining the capacity of heat source sufficiency for air conditioning the intercity bus, energy and exergy analyses of the VAR system are conducted, then designed and compared with the vapor compression refrigeration system in respect to the effect of fuel consumption. The results show that approximately 4,489 kg/year of fuel can be saved by using the VAR system driven by an exhaust gas waste heat in an intercity bus. The maximum coefficient of performance (COP) of the VAR system is obtained as 0.78 at 5 a.m. in May, and the maximum total exergy destruction for the VAR system is obtained as 15.25 kW at 4 p.m. in July. Lastly, the specific time is selected to investigate the effect of operating and environmental parameters on the VAR system.
Evaluation of heat transfer characteristics of a rectangular grooved heat exchanger under magnetic field using artificial neural network
(Elsevier Science Inc, 2025) Tumse, Sergen; Tantekin, Atakan; Bilgili, Mehmet; Sahin, Besir
This study presents the application of an artificial neural network (ANN) model to predict the Nusselt number of CuO-water nanofluid in a rectangular grooved channel under the effect of a magnetic field. In the developed ANN model, while Reynolds number (250 <= Re <= 1250), volume fraction of nanofluids (0 <=Phi <= 5), and Hartmann numbers (0 <= Ha <= 28) were taken as input parameters, Nusselt number was selected as the output parameter. Data were generated from a computational fluid dynamics (CFD) code by discretizing equations using the finite difference method. Therefore, the outcomes acquired from numerical simulations using CFD code were used for training and testing the generated ANN model. According to the results the generated ANN model can accurately predict the Nusselt number with a mean absolute percentage error (MAPE) of 0.4288 %, mean absolute error (MAE) of 0.0351, and root mean square error (RMSE) of 0.0540 in testing and of 0.3177 % MAPE, 0.0249 MAE and 0.0328 RMSE in the training. Furthermore, the correlation coefficient (R) values are observed as 0.9998 and 0.9988 in training and testing phases, which demonstrate the prediction success of the generated ANN model. Notably, the ANN model reduced computational time from 8 h, using CFD methods, to just 10 min for testing cases, showcasing its efficiency in handling nonlinear flow cases where traditional CFD methods may struggle. This study represents a novel contribution to the field as one of the first to apply ANN techniques for predicting heat transfer in grooved channels under magnetic fields and nanofluid flow, offering potential applications in the design of thermal systems in industries such as electronics cooling, nuclear reactors, and metallurgy.
Forecasting near-surface air temperature via SARIMA and LSTM: A regional time-series study
(Pergamon-Elsevier Science Ltd, 2025) Aksoy, Muhammed M.; Mowla, Najmul; Bilgili, Mehmet; Pinar, Engin; Durhasan, Tahir; Asadi, Davood
Accurate modeling of near-surface air temperature (AT) trends is critical for assessing global and regional climate risks, particularly in light of the intensifying warming signals observed across the northern hemisphere and the tropics. This study proposes a robust and computationally efficient framework for forecasting near-surface AT across the global, the northern hemisphere, the southern hemisphere, and the tropics using two complementary time-series modeling techniques: seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) networks. The models are trained to capture both structured seasonal patterns and nonlinear temporal dynamics by leveraging the ERA5 reanalysis dataset (1970-2024) and incorporating preprocessing steps such as detrending and Z-score normalization. SARIMA consistently outperformed LSTM across most domains, particularly in the global region, achieving lower RMSE (0.0967 degrees C) and higher correlation (R = 0.9975), reflecting its superior capacity for linear and seasonal signal extraction. Quantitatively, SARIMA demonstrates 5%-10% lower RMSE and slightly higher correlation than LSTM across all domains, underscoring the statistical significance of its performance advantage. Projected near-surface AT anomalies by 2050 reveal a marked warming trend, with the SARIMA model estimating a global anomaly of +1.078 degrees C and a northern hemisphere anomaly of +1.474 degrees C, closely aligning with IPCC-reported trajectories and exceeding CMIP5 RCP4.5 projections. The findings underscore SARIMA's reliability for short-to mid-term near-surface AT forecasting and LSTM's potential for future hybrid modeling schemes. This work fills a critical methodological gap by integrating statistical rigor with scalable deep learning, offering enhanced fidelity for regional climate adaptation planning.
Future forecast of global mean surface temperature using machine learning and conventional time series methods
(Springer Wien, 2025) Durhasan, Tahir; Pinar, Engin; Uluocak, Ihsan; Bilgili, Mehmet
One of the most important indicators of climate change and, consequently, global warming is the rise in the average surface temperature of the entire world. As observed in this light, forecasting the global mean surface temperature is an essential issue that must be addressed to develop adaptation measures for climate change. Many models have been developed to forecast the temperature of the air; however, these models often concentrate on local areas or use a restricted amount of station data. In this study, seasonal autoregressive integrated moving average (SARIMA), long-short-term memory (LSTM), and gated recurrent unit (GRU) models are used to predict global mean surface temperature (GMST) data. The data sets used in the study are GISTEMP and HardCRUT datasets and consist of land surface air temperature and water surface temperature. An evaluation of the performance of the models is carried out using various error measures to guarantee a high level of prediction accuracy. All models' results indicate that the yearly GMST value increase relative to 1961-1990 will be between 0.94 oC and 1.45 oC in 2050. In addition, the yearly GMST value, measured as approximately 14.8-15.00 oC in 2022, will be between 15.15 oC and 15.43 oC in 2050, according to the obtained models.
Global monthly sea surface temperature forecasting using the SARIMA, LSTM, and GRU models
(Springer Heidelberg, 2025) Bilgili, Mehmet; Pinar, Engin; Durhasan, Tahir
Global warming has become one of the world's most pressing problems in recent years, accompanied by rising sea surface temperature (SST). The SST time series data are an essential component in balancing the energy at the planet's surface. It is of the utmost importance to forecast future SSTs to assist us in better comprehending the climate dynamics and identifying catastrophic circumstances in advance based on historical observations received from earth observation systems. In this sense, monitoring and forecasting SST has become vital for better understanding future climate trends. In this regard, this study proposes a gated recurrent units (GRUs) model, a long short-term memory (LSTM) neural network technique, and a seasonal auto-regressive integrated moving average (SARIMA) statistical model to predict global monthly SST data. According to the findings from the testing procedure, the MAPE values were 0.1377% for the SARIMA model, 0.1374% for the LSTM model, and 0.1390% for the GRU model. All models were found to have MAE, RMSE, and R values within the ranges of 0.0250-0.0253 oC, 0.032-0.0323 oC, and 0.9772-0.9775, respectively. The results of the proposed SARIMA, LSTM, and GRU models showed that they could accurately and satisfactorily predict the global monthly SST time series.
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.
Long-term projections of global, northern hemisphere, and arctic sea ice concentration using statistical and deep learning approaches
(Pergamon-Elsevier Science Ltd, 2025) Bilgili, Mehmet; Pinar, Engin; Mowla, Md. Najmul; Durhasan, Tahir; Aksoy, Muhammed M.
The accelerating decline in sea ice concentration (SIC) poses significant challenges for global climate regulation, maritime navigation, and arctic ecosystem stability. This study develops and evaluates two advanced time-series forecasting models, seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) networks, to project SIC trends through 2050 across three spatial domains: the globe, the northern hemisphere, and the arctic. Utilizing the ERA5 reanalysis dataset (1970-2024) from the European center for medium-range weather forecasts (ECMWF), the models capture seasonal cycles and complex temporal dependencies to enable robust long-term projections. Comparative analysis demonstrates that SARIMA effectively models periodic fluctuations, while LSTM excels at learning nonlinear dependencies inherent in SIC dynamics. Performance metrics, including mean absolute percentage error (MAPE), root mean square error (RMSE), and correlation coefficient (R), confirm the high accuracy of both models, with SARIMA showing superior capability in representing structured seasonal patterns. Projections indicate a persistent decline in SIC, with arctic concentrations decreasing from 55.60% in 2023 to approximately 46.84% by 2050, underscoring the pronounced effects of arctic amplification. These results provide valuable insights for climate modeling, arctic policy formulation, and the development of adaptive navigation strategies in a rapidly changing polar environment.
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.
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.
Multimodal deep learning for estimating mean precipitable water vapor
(Wiley, 2026) Mowla, Md. Najmul; Aksoy, Muhammed M.; Pinar, Engin; Bilgili, Mehmet; Durhasan, Tahir
Precipitable water vapor (PWV) is a crucial atmospheric variable that influences weather systems, climate variability, and hydrological processes. Accurate PWV estimation is essential for improving numerical weather prediction, climate modeling, and remote-sensing applications. However, existing methods often rely on extensive meteorological inputs or computationally intensive architectures, limiting their applicability in data-sparse regions. This study introduces a novel hybrid framework, EMMA-NN-BiGRU-XGBoost, designed to forecast monthly mean PWV across Turkey using only four physically meaningful inputs: latitude, longitude, altitude, and seasonal indicators. The framework integrates an enhanced multimodal attention (EMMA) mechanism that disentangles spatial, altitudinal, and seasonal influences, improving interpretability and physical consistency. Bidirectional gated recurrent units (BiGRU) capture temporal dependencies, and XGBoost models nonlinear feature interactions within a weighted stacking ensemble. Hyperparameters are optimized via particle swarm optimization and Bayesian optimization, with particle swarm optimization demonstrating superior tuning efficiency. Extensive benchmarking against traditional machine-learning models, using grid search and random search with fivefold cross-validation, as well as deep-learning baselines, demonstrates significant improvements in predictive accuracy, achieving an root-mean-square error of and an of 0.92, representing a 15%-20% reduction in error compared with state-of-the-art methods. The model also exhibits robustness across diverse climatic zones in Turkey. Shapley additive explanations further elucidate feature importance, aligning model outputs with climatological principles. Beyond methodological advances, this work provides a scalable, interpretable, and data-efficient baseline for PWV forecasting, thereby facilitating enhanced climate diagnostics, hydrological risk assessments, and early warning systems, particularly in regions with limited meteorological observations.
Municipal solid waste to biomass energy in Türkiye: A life cycle assessment approach for circular economy integration
(Pergamon-Elsevier Science Ltd, 2025) Tokmakci, Muhammet; Ozdil, N. Filiz (Tumen); Bilgili, Mehmet
This study assesses the potential of biomass energy derived from municipal solid waste (MSW) in T & uuml;rkiye, focusing on its contribution to the national energy portfolio and the circular economy. T & uuml;rkiye, facing increasing energy demand and environmental challenges, has a growing need to diversify its energy sources. By utilizing MSW, the country can simultaneously address waste management issues and generate renewable energy. The analysis, based on data from 2010 to 2020, reveals that T & uuml;rkiye's theoretical biomass potential from MSW was approximately 31,789 kt, with an electricity generation potential of 379,698 GWh, representing 7.81 % of the country's electricity demand. This study uses a Life Cycle Assessment (LCA) approach to evaluate the environmental impacts of different WtE technologies, including pyrolysis, gasification, and anaerobic digestion. The LCA results show that adopting these technologies could significantly reduce greenhouse gas emissions, particularly carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Furthermore, regional analysis highlights the Marmara region as having the highest biomass energy potential, contributing over 35 % of T & uuml;rkiye's total MSW production. Projections for 2030 suggest that T & uuml;rkiye's annual waste generation could exceed 35 million tons, offering even greater potential for biomass energy production. In addition, this study compares T & uuml;rkiye's WtE potential with that of other countries, particularly in the European Union, and suggests that by adopting similar technologies and policy frameworks, T & uuml;rkiye can enhance its energy independence and meet its renewable energy targets. The results underscore the importance of integrating MSW-derived biomass energy into T & uuml;rkiye's national energy strategy, contributing to a sustainable and circular economy model.
Performance Analysis of Solar Powered Absorption Refrigeration System for Mersin Province
(2016) Şahin, Beşir; Bilgili, Mehmet; Çetingöz, Altan; Kurtulmuş, Nazım
Bu çalışmada, Mersin iline ait saatlik atmosfer hava sıcaklığı ve güneş ışınım verileri kullanılarak güneş enerji destekli absorpsiyonlu soğutma (SPAR) sisteminin performans analizi yapılmıştır. Güneş enerji destekli absorpsiyonlu soğutma sisteminin tasarımında soğutucu-soğurgan çifti olarak amonyak-su (NH3-H2O) ve vakum tüplü güneş kollektörü tercih edilmiştir. İlk olarak, seçilen binanın saatlik soğutma yükü hesaplanmıştır. Sonra, bu soğutma yüküne göre sistemin termodinamik analizi yapılmış ve soğutulacak bölge için gerekli güneş kollektör alanı hesaplanmıştır. Elde edilen sonuçlara göre maksimum etkinlik katsayısı, (COP)Mayıs ayında, minimum etkinlik katsayısı, (COP) ise Temmuz ve Ağustos aylarında elde edilmiştir. Birim alana gelen maksimum güneş ışınım değeri, 23 Haziran günü saat 13.00'de 0,878 kW/m2olduğunda, COP değeri 0,434 olarak hesaplanmıştır. Sonuç olarak, 30 m2'lik soğutulacak bölge için optimum güneş kollektör alanı 50 m2 olarak hesaplanmıştır
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.
Thermodynamic Analysis of an Intercity Bus Air-Conditioning System Working with HCFC, HFC, CFC and HC Refrigerants
(2019) Bilgili, Mehmet; Çardak, Ediz; Aktaş, Arif Emre; Ekinci, Fırat
The purpose of this study is to perform thermodynamic analysis of an intercitybus air-conditioning (ICBAC) system working with hydrochlorofluorocarbon(HCFC), hydrofluorocarbon (HFC), chlorofluorocarbon (CFC) andhydrocarbon (HC) refrigerants. For this aim, an intercity bus with a busload of56 passengers, which is thought to motion in the Adana province of Turkey,was selected. R404A, R410A, R502, R507, R22, R290, R134a, R500 andR600a were selected as different HCFC/HFC/CFC/HC refrigerant types in theICBAC system. Useful and reversible works of compressor, coefficient ofperformance (COP), exergy efficiency and exergy destructions of the entireICBAC system and its each sub-unit were obtained. The results showed thatthe performance of the ICBAC system was significantly influenced bychanging the refrigerants. However, R600a refrigerant based on hydrocarbonwas found as a refrigerant to have minimum total exergy destruction,maximum exergy efficiency and maximum COP compared to otherrefrigerants used in the ICBAC system with the same amount of cooling loadand the same climatic condition.
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.
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.

Yazar "Bilgili, Mehmet" seçeneğine göre listele

Sayfa Başına Sonuç

Sıralama seçenekleri