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Öğe Application of ANN to explore the potential use of natural ventilation in buildings in Turkey(Pergamon-Elsevier Science Ltd, 2007) Ayata, Tahir; Arcaklioglu, Erol; Yildiz, OsmanIndoor natural ventilation provides both the circulation of clear air and the decrease of indoor temperature, especially, during hot summer days. In addition to openings, building dimensions and position play a significant role to obtain a uniform indoor air velocity distribution. In this study, the potential use of natural ventilation as a passive cooling system in new building designs in Kayseri, a mid-size city in Turkey, was investigated. First, indoor air velocity distributions with respect to changing wind direction, magnitude and door openings were simulated by the FLUENT package program, which employs finite element methods. Using the simulated data an artificial neural network (ANN) model was developed to predict indoor average and maximum air velocities. The simulations produced by FLUENT show that the average indoor air velocity is generally below 1.0 m/s for the local prevailing wind directions. The simulations results suggest that, in addition to the orientation of buildings in accordance with prevailing wind directions, a proper indoor design of buildings in the area can significantly increase the capability of air ventilation during warm summer days. It was found that a high correlation exists between the simulated and the ANN predicted data indicating a successful learning by the proposed ANN model. Overall, the evaluation of the network results indicated that the ANN approach can be utilized as an efficient tool for learning, training and predicting indoor air velocity distributions for natural ventilation. (C) 2006 Elsevier Ltd. All rights reserved.Öğe Artificial neural network application to the friction-stir welding of aluminum plates(Elsevier Sci Ltd, 2007) Okuyucu, Hasan; Kurt, Adem; Arcaklioglu, ErolAn artificial neural network (ANN) model was developed for the analysis and simulation of the correlation between the friction stir welding (FSW) parameters of aluminium (Al) plates and mechanical properties. The input parameters of the model consist of weld speed and tool rotation speed (TRS). The outputs of the ANN model include property parameters namely: tensile strength, yield strength, elongation, hardness of weld metal and hardness of heat effected zone (HAZ). Good performance of the ANN model was achieved. The model can be used to calculate mechanical properties of welded Al plates as functions of weld speed and TRS. The combined influence of weld speed and TRS on the mechanical properties of welded Al plates was simulated. A comparison was made between measured and calculated data. The calculated results were in good agreement with measured data. (c) 2005 Elsevier Ltd. All rights reserved.Öğe Derivation of empirical equations for thermodynamic properties of a ozone safe refrigerant (R404a) using artificial neural network(Pergamon-Elsevier Science Ltd, 2010) Sozen, Adnan; Arcaklioglu, Erol; Menlik, TayfunThis study, deals with the potential application of the artificial neural networks (ANNs) to represent PVTx (pressure-specific volume-temperature-vapor quality) data in the range of temperature of 173-498 K and pressure of 10-3600 kPa. Generally, numerical equations of thermodynamic properties are used in the computer simulation analysis instead of analytical differential equations. And also analytical computer codes usually require a large amount of computer power and need a considerable amount of time to give accurate predictions Instead of complex rules and mathematical routines, this study proposes an alternative approach based on ANN to determine the thermodynamic properties of an environmentally friendly refrigerant (R404a) for both saturated liquid-vapor region (wet vapor) and superheated vapor region as numerical equations. Therefore, reducing the risk of experimental uncertainties and also removing the need for complex analytic equations requiring long computational time and effort. R-2 values which are errors known as absolute fraction of variance - in wet vapor region are 0.999401, 0 999982 and 0.999993 for specific volume. enthalpy and entropy for training data, respectively. For testing data, these values are 0.998808. 0.999988, and 0 999993 Similarly, for superheated vapor region, they are 0.999967, 0.999999 and 0.999999 for training data, 0.999978, 0.999997 and 0.999999 for testing data. As seen from the results of mathematical modeling, the calculated thermodynamic properties are obviously within acceptable uncertainties. (C) 2009 Elsevier Ltd. All rights reserved.Öğe Determination of residual stresses based on heat treatment conditions and densities on a hybrid (FLN2-4405) powder metallurgy steel using artificial neural network(Elsevier Sci Ltd, 2007) Kafkas, Firat; Karataş, Çetin; Sözen, Adnan; Arcaklioglu, Erol; Saritaş, SüleymanThis paper presents a new approach based on artificial neural networks (ANNs) to determine the residual stresses in PM steel based nickel (FLN2-4405). This study consists of two cases: (i) The experimental analysis: The measurements of residual stresses were carried out by electrochemical layer removal technique. The values and distributions of residual stresses occurring in PM steel processed under various densities (6.8, 7.05, 7.2 and 7.4 g/cm(3)) and heat treatment conditions (sintered at 2050 degrees F, sintered at 2300 degrees F, quenching-tempered, and sinter-hardened) were determined. In most of the experiments, tensile residual stresses were recorded in surface of samples. The residual stress distribution on the surface of the PM steels is affected by the heat treatment conditions and density. Maximum values of residual stresses on the surface were observed sinter hardened condition and 7.4 g/cm(3) density. (ii) The mathematical modeling analysis: The use of ANN has been proposed to determine the residual stresses based on heat treatment conditions and densities in PM steel using results of experimental analysis. The back propagation learning algorithm with two different variants and logistic sigmoid transfer function were used in the network. In order to train the neural network, limited experimental measurements were used as training and test data. The best fitting training data set was obtained with four and five neurons in the hidden layer, which made it possible to predict residual stress with accuracy at least as good as that of the experimental error, over the whole experimental range. After training, it was found the R 2 values are 0.999244, 0.999025, 0.999664 and 0.999322 for sintered at 2050 degrees F, sintered at 2300 degrees F, quenching-tempered, and sinter-hardened, respectively. Similarly, these values for testing data are 0.998354, 0.99706, 0.999607 and 0.999205, respectively. As seen from the results of mathematical modeling, the calculated residual stresses are obviously within acceptable uncertainties. (c) 2006 Elsevier Ltd. All rights reserved.Öğe Forecasting based on sectoral energy consumption of GHGs in Turkey and mitigation policies(Elsevier Sci Ltd, 2007) Sözen, Adnan; Gülseven, Zafer; Arcaklioglu, ErolRecently, global warming and its effects have become one of the most important themes in the world. Under the Kyoto Protocol, the EU has agreed to an 8% reduction in its greenhouse gas (GHG) emissions by 2008-2012. The GHG emissions (total GHG, CO2, CO, SO2, NO2, E (emissions of non-methane volatile organic compounds)) covered by the Protocol are weighted by their global warming potentials (GWPs) and aggregated to give total emissions in CO2 equivalents. The main subject in this study is to obtain equations by the artificial neural network (ANN) approach to predict the GHGs of Turkey using sectoral energy consumption. The equations obtained are used to determine the future level of the GHG and to take measures to control the share of sectors in total emission. According to ANN results, the maximum mean absolute percentage error (MAPE) was found as 0.147151, 0.066716, 0.181901, 0.105146, 0.124684, and 0.158157 for GHG, SO2, NO2, CO, E, and CO2, respectively, for the training data with Levenberg-Marquardt (LM) algorithm by 8 neurons. R-2 values are obtained very close to 1. Also, this study proposes mitigation policies for GHGs. (C) 2007 Elsevier Ltd. All rights reserved.Öğe Modelling of yield length in the mould of commercial plastics using artificial neural networks(Elsevier Sci Ltd, 2007) Karataş, Çetin; Sözen, Adnan; Arcaklioglu, Erol; Ergüney, SamiAccording to the yield length of a plastic, whether a mould is filled fully or not can be estimated. In this study, a new formula based on various injection parameters was developed to determine the yield length in plastic moulding of the commercial plastics, the most widely used ones are low-density polyethylene, high-density polyethylene, polystyrene and polypropylene, by artificial neural network (ANN). This study covers two main objectives: (i) the yield properties of plastics have been investigated at various injection parameters (cylinder temperature, injection pressure, injection flow rate and mould temperature) in a mould including spiral chutes as experimental; (ii) the yield length of the commercial plastics based on various measured injection parameters (cylinder temperature, injection pressure, injection flow rate and mould temperature) in a mould was described by ANN using experimental data. Some experimental data were used as test data, and these values were not used for training. Scaled conjugate gradient and Levenberg-Marquardt (LM) learning algorithms and logistic sigmoid transfer function were used in the network. The results show that the maximum mean absolute percentage error (MAPE) was found to be 1.574969% and R-2 values 0.99964. The best approach was found in the LM algorithm with six neurons. The MAPE and R-2 for testing data were 1.849 and 0.9995, respectively, similar to algorithm and neurons. This study is considered to be helpful in predicting the yield length in the mould whose injection parameters are known. The present method greatly reduces the time required by design engineers to find the optimum solution and in many cases reaches a solution that could not be easily obtained from simple modeling programs. (c) 2005 Elsevier Ltd. All rights reserved.Öğe Predictions of temperature distributions on layered metal plates using artificial neural networks(Pergamon-Elsevier Science Ltd, 2006) Ayata, Tahir; Cavusoglu, Abdullah; Arcaklioglu, ErolThe temperature distribution influences the amount of energy needed to heat a body. The benefits of using multi-layered metal plates (NIMP) are due to the requirement of a regular temperature distribution on the opposite side with one side heated irregularly. The factors that affect the regular distribution of the temperature in such a structure are the thickness of the layers and the materials themselves, since for different materials, heat conduction coefficients, density and specific heat values change. In this study, the main objective is to find a neural network solution for the problem of the non-regular distribution of temperature on the non-heated side of an irregularly heated NIMP consisting of two layers of Cu/CrNi and Al/CrNi in order to obtain the optimum thickness levels for the layers. To achieve this aim, the results of the finite elements method (FEM) produced by the program package ANSYS have been used to train and test the network. They are the coefficient of heat conduction (K), specific heat (C), density (D), temperature (T) and layer thickness (L), which are used as the input layer, while the outputs are the maximum,minimum and mean temperature values of the materials. The back propagation learning algorithm with three different variants, single layer and logistic sigmoid transfer function have been used in the network. By using the weights of the network, formulations have been given for each output. The network has yielded R-2 values of 0.999 and the mean percent errors are smaller than 0.8 for the training data, while the R-2 values are about 0.999 and the mean percent errors are smaller than 0.7 for the test data. The analysis has been extended for different materials and for the different temperature values that have been applied. The Al/CrNi laminated plate has a lower temperature gradient distribution on the upper (or non-heated) surface due to its lesser heat conductivity compared to the Cu/CrNi steel. The thickness of 8 mm provides the best results among the alloys that have been considered. (c) 2005 Elsevier Ltd. All rights reserved.
