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Modeling of a mechanical cooling system with variable cooling capacity by using artificial neural network

Yilmaz S. | Atik K.

Article | 2007 | Applied Thermal Engineering27 ( 13 ) , pp.2308 - 2313

Capacity modification in mechanical cooling systems can be performed by various methods. Changing condenser temperature also changes the capacity of the cooling system. In this study, a series of experiments were performed in order to determine the effects of changing cooling water flow rate (changing condenser temperature) in a mechanical heat pump experimental setup on the cooling capacity of the system. Power consumption, thermal efficiency, coefficient of performance (COP) of the system in various cooling capacities were estimated theoretically by using the data acquired from the experiments performed. Performance values obtaine . . .d were used for training Artificial neural network (ANN) whose structure was designed for this operation. The Network, which has three layers as input, output, and hidden layer, has one input and four output cells. Six cells were used in hidden layers. Training was continued until the square error became (? ? 0.005) in this ANN, for which back propagation algorithm was used for training. Desired error value was achieved in ANN and, ANN was tested with both data used for training ANN and data not used. Resultant low relative error value of the test indicates the usability of ANNs in this area. © 2007 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Improving artificial neural networks' performance in seasonal time series forecasting

Hamzaçebi C.

Article | 2008 | Information Sciences178 ( 23 ) , pp.4550 - 4559

In this study, an artificial neural network (ANN) structure is proposed for seasonal time series forecasting. The proposed structure considers the seasonal period in time series in order to determine the number of input and output neurons. The model was tested for four real-world time series. The results found by the proposed ANN were compared with the results of traditional statistical models and other ANN architectures. This comparison shows that the proposed model comes with lower prediction error than other methods. It is shown that the proposed model is especially convenient when the seasonality in time series is strong; howeve . . .r, if the seasonality is weak, different network structures may be more suitable. © 2008 Elsevier Inc. All rights reserved Daha fazlası Daha az

The experimental investigation of the effects of uncoated, PVD- and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks

Nalbant M. | Gökkaya H. | Toktaş I. | Sur G.

Article | 2009 | Robotics and Computer-Integrated Manufacturing25 ( 1 ) , pp.211 - 223

In this study the machining of AISI 1030 steel (i.e. orthogonal cutting) uncoated, PVD- and CVD-coated cemented carbide insert with different feed rates of 0.25, 0.30, 0.35, 0.40 and 0.45 mm/rev with the cutting speeds of 100, 200 and 300 m/min by keeping depth of cuts constant (i.e. 2 mm), without using cooling liquids has been accomplished. The surface roughness effects of coating method, coating material, cutting speed and feed rate on the workpiece have been investigated. Among the cutting tools-with 200 mm/min cutting speed and 0.25 mm/rev feed rate-the TiN coated with PVD method has provided 2.16 µm, TiAlN coated with PVD meth . . .od has provided 2.3 µm, AlTiN coated with PVD method has provided 2.46 µm surface roughness values, respectively. While the uncoated cutting tool with the cutting speed of 100 m/min and 0.25 mm/rev feed rate has yielded the surface roughness value of 2.45 µm. Afterwards, these experimental studies were executed on artificial neural networks (ANN). The training and test data of the ANNs have been prepared using experimental patterns for the surface roughness. In the input layer of the ANNs, the coating tools, feed rate (f) and cutting speed (V) values are used while at the output layer the surface roughness values are used. They are used to train and test multilayered, hierarchically connected and directed networks with varying numbers of the hidden layers using back-propagation scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) algorithms with the logistic sigmoid transfer function. The experimental values and ANN predictions are compared by statistical error analyzing methods. It is shown that the SCG model with nine neurons in the hidden layer has produced absolute fraction of variance (R2) values about 0.99985 for the training data, and 0.99983 for the test data; root mean square error (RMSE) values are smaller than 0.00265; and mean error percentage (MEP) are about 1.13458 and 1.88698 for the training and test data, respectively. Therefore, the surface roughness value has been determined by the ANN with an acceptable accuracy. © 2008 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks

Bekat, Tuğçe | Erdoğan, Muharrem | İnal, Fikret | Genç, Ayten

Article | 2012 | Energy45 ( 1 ) , pp.882 - 887

The amount of bottom ash formed in a pulverized coal-fired power plant was predicted by artificial neural network modeling using one-year operating data of the plant and the properties of the coals processed. The model output was defined as the ratio of amount of bottom ash produced to amount of coal burned (Bottom ash/Coal burned). The input parameters were the moisture contents, ash contents and lower heating values of the coals. The total 653 data were divided into two groups for the training (90% of the data) and the testing (10% of the data) of the network. A three-layer, feed-forward type network architecture with back-propaga . . .tion learning was used in the modeling study. The activation function was sigmoid function. The best prediction performance was obtained for a one hidden layer network with 29 neurons. The learning rate and the tolerance value were 0.2 and 0.05, respectively. R2 (coefficient of determination) values between the actual (Bottom ash/Coal burned) ratios and the model predictions were 0.988 for the training set and 0.984 for the testing set. In addition, the sensitivity analysis indicated that the ash content of coals was the most effective parameter for the prediction of the ratio of bottom ash to coal burned. © 2012 Elsevier Ltd Daha fazlası Daha az

Investigation on Bending over Sheave Fatigue Life Determination of Rotation Resistant Steel Wire Rope

Onur Y.A. | İmrak C.E. | Onur T.Ö.

Article | 2017 | Experimental Techniques41 ( 5 ) , pp.475 - 482

Investigation on theoretical and experimental determination of bending over sheave fatigue lifetimes of rotation resistant steel wire ropes has been conducted. Effects of sheave size and tensile load on bending over sheave fatigue lifetimes of investigated rope have been presented. Bending over sheave fatigue life prediction according to effects of tensile load and sheave diameter has been presented by using artificial neural networks. The results point out that constructed ANN model estimations and experimental results have powerful correlation. © 2017, The Society for Experimental Mechanics, Inc.

Comparison of direct and iterative artificial neural network forecast approaches in multi-periodic time series forecasting

Hamzaçebi C. | Akay D. | Kutay F.

Article | 2009 | Expert Systems with Applications36 ( 2 PART 2 ) , pp.3839 - 3844

Artificial neural network is a valuable tool for time series forecasting. In the case of performing multi-periodic forecasting with artificial neural networks, two methods, namely iterative and direct, can be used. In iterative method, first subsequent period information is predicted through past observations. Afterwards, the estimated value is used as an input; thereby the next period is predicted. The process is carried on until the end of the forecast horizon. In the direct forecast method, successive periods can be predicted all at once. Hence, this method is thought to yield better results as only observed data is utilized in o . . .rder to predict future periods. In this study, forecasting was performed using direct and iterative methods, and results of the methods are compared using grey relational analysis to find the method which gives a better result. © 2008 Elsevier Ltd. All rights reserved Daha fazlası Daha az

Discarding lifetime investigation of a rotation resistant rope subjected to bending over sheave fatigue

Onur Y.A. | İmrak C.E. | Onur T.Ö.

Article | 2019 | Measurement: Journal of the International Measurement Confederation142 , pp.163 - 169

In this paper, theoretical and experimental studies are conducted to exhibit the discarding fatigue lifetime of a rotation resistant rope exposed to alternate bending over sheave (BoS) fatigue. Experimental studies are fulfilled to determine discarding lifetimes of a rotation resistant rope exposed to BoS fatigue. Multiple linear regression analysis is performed and novel theoretical discarding lifetime prediction formula is determined by using the least square method. Furthermore, discarding lifetimes of rotation resistant ropes exposed to BoS fatigue is predicted by using artificial neural network (ANN). There is a vigorous correl . . .ation among the results acquired by regression model, ANN and experimental data. © 2019 Elsevier Lt Daha fazlası Daha az

Non linear vibrations of stepped beam systems using artificial neural networks

Bagdatli S.M. | Özkaya E. | Özyigit H.A. | Tekin A.

Article | 2009 | Structural Engineering and Mechanics33 ( 1 ) , pp.15 - 30

In this study, the nonlinear vibrations of stepped beams having different boundary conditions were investigated. The equations of motions were obtained by using Hamilton's principle and made non dimensional. The stretching effect induced non-linear terms to the equations. Natural frequencies are calculated for different boundary conditions, stepped ratios and stepped locations by Newton-Raphson Method. The corresponding nonlinear correction coefficients are also calculated for the fundamental mode. At the second part, an alternative method is produced for the analysis. The calculated natural frequencies and nonlinear corrections are . . . used for training an artificial neural network (ANN) program which has a multi-layer, feed-forward, back-propagation algorithm. The results of the algorithm produce errors less than 2.5% for linear case and 10.12% for nonlinear case. The errors are much lower for most cases except clamped-clamped end condition. By employing the ANN algorithm, the natural frequencies and nonlinear corrections are easily calculated by little errors, and the computational time is drastically reduced compared with the conventional numerical techniques Daha fazlası Daha az


Küçükali, Serhat

Editorial | 2010 | Energy Policy38 ( 10 ) , pp.6379 - 6380

[No abstract available]

6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve çerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.

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