Browsing by Author "Ata R."

Now showing 1 - 11 of 11
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    Prediction of ratio of mineral substitution in the production of low-clinker factored cement by artificial neural network
    (Association for Scientific Research, 2003) Canpolat F.; Yilmaz K.; Ata R.; Köse M.M.
    Artificial Neural Networks (ANN) has been widely used to solve some of the problems in science and engineering, which requires experimental analysis. Use of ANN in civil engineering applications started in late eighties. One of the important features of the ANN is its ability to learn from experience and examples and then to adapt with changing situations. Engineers often deal with incomplete and noisy data, which is one of the areas where ANN can easily be applied. Dealing with incomplete and noisy data is the conceptual stage of the design process. This paper shows practical guidelines for designing ANN for civil engineering applications. ANN is in cement industry: in the production of low-clinker factored cement, and in the derivation of composition of natural and artificial puzzolans in the production of high performance cement and concrete. By using ANN, a study to find out the optimum ratio of substitution and compression strengths was carried out.
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    Assessment of optimum tip speed ratio of wind turbines
    (Association for Scientific Research, 2005) Çetin N.S.; Yurdusev M.A.; Ata R.; Özdemir A.
    The first thing to do in wind turbine blade design is to select tip speed ratio. Generally speaking, the speed ratio depends on the profile type used and the number of blades. Various speed ratios could be chosen for different types of profiles with different number of blades. Therefore, an optimization procedure should be applied to find the best ratio since this directly affects the energy generated from the turbine and in turn the investment made. This study presents a procedure to assess the optimum speed ratios for various profile types used in practice with various numbers of blades.
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    Assessment of optimum tip speed ratio in wind turbines using artificial neural networks
    (Elsevier Ltd, 2006) Yurdusev M.A.; Ata R.; Çetin N.S.
    [No abstract available]
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    Assessment of optimum tip speed ratio in wind turbines using artificial neural networks
    (Elsevier Ltd, 2006) Yurdusev M.A.; Ata R.; Çetin N.S.
    Wind turbine blade design depends on several factors, such as turbine profile used, blade number, power factor, and tip speed ratio. The key to designing a wind turbine is to assess the optimal tip speed ratio (TSR). This will directly affect the power generated and, in turn, the effectiveness of the investment made. TSR is suggested to be taken between 7 and 8 and in practice generally taken as 7 for a 3-blade network-connected wind turbine. However, the optimal TSR is dependent upon the profile type used and the blade number and could fall out of the boundaries suggested. Therefore, it has to be assessed accordingly. In this study, the optimal TSR and the power factor of a wind turbine are predicted using artificial neural networks (ANN) based on the parameters involved for NACA 4415 and LS-1 profile types with 3 and 4 blades. The ANN structure built is found to be more successful than the conventional approach in estimating the TSR and power factor. © 2005 Elsevier Ltd. All rights reserved.
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    Neural prediction of power factor in wind turbines
    (2007) Ata R.; Cetin N.S.
    The power generated by wind turbines depends on several factors. One of them is the power factor also known as blade efficiency. In this study, the power factor is predicted using Artificial Neural Networks (ANN) and comparisons made with conventional model approach for the selected turbine profiles mostly used in practice. The study has shown that the prediction of power factors from seven input parameters by ANN yields better results than those of the conventional model.
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    Analysis by ann of electricity generation at different height from autonomous wind turbine; [Otonom bi̇r rüzgâr türbi̇ni̇ni̇n farkli yüksekli̇klerdeki̇ enerji̇ eldesi̇ni̇n ysa i̇le anali̇zi̇]
    (2008) Ata R.
    Supplementing our energy based on clean and renewable sources of energy has become imperative due to the present energy crisis and growing environmental consciousness. Wind is one of the potential renewable energy sources, which can be harnessed in a commercial scale for various end-uses. The power generated by wind turbines depends on several factors. Two of these factors are the wind velocity and the tower height of wind turbine. In this study, the annual energy generation based on the tower height is predicted using Artificial Neural Networks(ANN). The predicted ANN data are compared with measured and calculated data from autonomous system which was established in Kirkaǧaç. The accuracy of the results and the speed in reaching the result were proved.
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    An adaptive neuro-fuzzy inference system approach for prediction of power factor in wind turbines
    (2009) Ata R.
    This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) model for predicting the power factor of a wind turbine. This model based on the parameters involved for NACA 4415 and LS- 1 profile types with 3 and 4 blades. In model development, profile type, blade number, Schmitz coefficient, end loss, profile type loss, and blade number loss were taken as input variables, while the power factor was taken as output variable. After a successful learning and training process the proposed model produced reasonable mean errors. The results on a testing data indicate that the ANFIS model is found to be more successful than the ANN approach in estimating the power factor.
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    An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines
    (2010) Ata R.; Kocyigit Y.
    This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) model to predict the tip speed ratio (TSR) and the power factor of a wind turbine. This model is based on the parameters for LS-1 and NACA4415 profile types with 3 and 4 blades. In model development, profile type, blade number, Schmitz coefficient, end loss, profile type loss, and blade number loss were taken as input variables, while the TSR and power factor were taken as output variables. After a successful learning and training process, the proposed model produced reasonable mean errors. The results indicate that the errors of ANFIS models in predicting TSR and power factor are less than those of the ANN method. © 2010 Elsevier Ltd. All rights reserved.
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    Analysis of height affect on average wind speed by ann
    (Association for Scientific Research, 2011) Ata R.; Çetin N.S.
    The power generated by wind turbines depends on several factors. Two of them are the wind speed and the tower height of wind turbine. In this study, the annual average wind speed based on the tower height is predicted using Artificial Neural Networks (ANN) and comparisons made with conventional model approach. The backpropagation multi layer ANNs were used to estimate annual average wind speed for three locations in Turkey. The Model has been developed with the help of neural network methodology. It involves four input variables-wind speed of measured location, desired height on measured location, height above ground level of measured location and Hellmann coefficient and one output variables-annual average wind speed. The model accuracy is evaluated by comparing the conventional model results with the actual measured and calculated values. © Association for Scientific Research.
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    Artificial neural networks applications in wind energy systems: a review
    (Elsevier Ltd, 2015) Ata R.
    Neural networks approaches are becoming useful as an alternate way to classical methods. As a computation and learning paradigm, they are presented as a different modeling approach to solve complicated problems. They have been used to solve complicated practical problems in various areas, such as engineering, medicine, business, manufacturing, military etc. They have also been applied for modeling, identification, optimization, prediction, forecasting, evaluation, classification, and control of complex systems. During the last three decades, artificial neural network have been extensively employed in numerous fields of science and technology. They are not programmed in the conventional procedure but they are trained using data exemplifying the behaviour of a system. This study presents various applications of neural networks used in wind energy systems. The applications of neural networks in wind energy systems could be grouped in three major categories: forecasting and prediction, prediction and control, identification and evaluation. The main purpose of this paper is to present an overview of the neural network applications in wind energy systems. Published literature presented in this study indicate the potential of ANN as a useful tool for wind energy systems. Author strongly believes that this survey will be very much useful to the researchers, scientific engineers working in this area to find out the relevant references and current state of the field. © 2015 Elsevier Ltd
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    Retraction notice to “Artificial neural networks applications in wind energy systems: A review” [Renew Sustain Energy Rev (2015) 534−62](S1364032115004360)(10.1016/j.rser.2015.04.166)
    (Elsevier Ltd, 2018) Ata R.
    This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This article has been retracted at the request of the Editor-in-Chief. This article bears substantial similarity to a previously published paper: Artificial neural networks in renewable energy systems applications: a review, Soteris A. Kalogirou, Renewable and Sustainable Energy Reviews, Volume 5, Issue 4, December 2001, Pages 373–401. DOI: 10.1016/S1364-0321(01)00006-5. One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original and has not been submitted to nor appeared in another publication elsewhere. As such this article represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission or review process. © 2018, Elsevier Ltd