Yazar "Okbaz, Abdulkerim" seçeneğine göre listele

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    Enhanced performance of a microchannel with rectangular vortex generators
    (Yildiz Technical Univ, 2023) Gonul, Alisan; Okbaz, Abdulkerim
    Microchannel heat sinks and heat exchangers are widely used in the cooling of electronic systems. However, it is still important to enhance the heat transfer in the microchannel so that the intense heat generated can be removed. Vortex generators (VGs) create secondary flow s tructures i n t he fl ow, in creasing th e flu id mix ing, thi nning the the rmal bou ndary layer, and ultimately boosting heat transfer. Here, we have controlled the flow structure and improved the heat transfer with the lowest possible pressure loss by placing VGs of different sizes, numbers, and angles of attack in a microchannel. The improvement in heat transfer is accelerated as vortex intensity increases. The angle of attack has a significant impact on vortex formation lengths, which reach high dimensions around 90 & DEG;. Furthermore, increasing the VG length significantly increases the vortex formation lengths. The number of VG pairs has a significant impact on heat transfer and pressure losses. As the number of VG pairs increases, so does the area occupied by the secondary flow regions in the microchannel, increasing the fluid mixture and boosting heat transfer. The highest enhancement in heat transfer using VGs is obtained at around 230%, while the corresponding increase in pressure loss is 950%. According to the JF factor which we consider a performance evaluation criteria, the best result is around 1.38. The G enetic A ggregation R esponse S urface Methodology has been applied to numerical results. The related method i s realized t o produce results that are consistent with the numerical results within a & PLUSMN;5% error interval. All the input parameters considered in the sensitivity analysis have an impact of at least 10% on the output parameters.
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    Flow optimization in a microchannel with vortex generators using genetic algorithm
    (Pergamon-Elsevier Science Ltd, 2022) Gonul, Alisan; Okbaz, Abdulkerim; Kayaci, Nurullah; Dalkilic, Ahmet Selim
    In this study, delta winglet-type vortex generators, widely used in conventional macro channels and proven to be effective, are used in microchannels to increase their heat transfer capacities. The effects of vortex generators on heat transfer and pressure loss characteristics are studied numerically for different angles of attack, vortex generator arrangement type, the transverse and longitudinal distance between vortex generators, vortex generator length and height, and different Reynolds numbers. The thermal and hydraulic characteristics are presented as the Nusselt number, the friction factor, and the performance evaluation criteria number (PEC) that takes into account the heat transfer enhancement and the corresponding increase in pressure loss. The variation of Nu/Nu0, f/f0, and PEC are found to be in the range of 1.03-1.87, 1.04-1.8, and 0.92-1.62, respectively. A multi-objective optimization study are performed with the response surface methodology analysis to see how different parameters affect heat transfer and pressure loss and to determine the most optimum design. Besides, local sensitivity analysis study is carried out through the RSM, and water inlet velocity for heat transfer enhancement is found to be the most effective parameter. Among the geometric parameters, vortex generator height is determined as the most effective factor. Finally, practical Nusselt number and friction factor correlations taking many parameters into account are proposed to be able to compare the results of other researchers, and for engineers designing microchannel cooling systems.
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    Prediction of heat transfer characteristics in a microchannel with vortex generators by machine learning
    (Walter De Gruyter Gmbh, 2023) Gonul, Alisan; Colak, Andac Batur; Kayaci, Nurullah; Okbaz, Abdulkerim; Dalkilic, Ahmet Selim
    Because of the prompt improvements in Micro-Electro-Mechanical Systems, thermal management necessities have altered paying attention to the compactness and high energy consumption of actual electronic devices in industry. In this study, 625 data sets obtained numerically according to the change of five different geometric parameters and Reynolds numbers for delta winglet type vortex generator pairs placed in a microchannel were utilized. Four dissimilar artificial neural network models were established to predict the heat transfer characteristics in a microchannel with innovatively oriented vortex generators in the literature. Friction factor, Nusselt number, and performance evaluation criteria were considered to explore the heat transfer characteristics. Different neuron numbers were determined in the hidden layer of each of the models in which the Levethenberg-Marquardt training algorithm was benefited as the training algorithm. The predicted values were checked against the target data and empirical correlations. The coefficient of determination values calculated for each machine learning model were found to be above 0.99. According to obtained results, the designed artificial neural networks can provide high prediction performance for each data set and have higher prediction accuracy compared to empirical correlations. All data predicted by machine learning models were collected within the range of +/- 3% deviation bands, whereas the majority of the estimated data by empirical correlations dispersed within & PLUSMN;20% ones. For that reason, a full evaluation of the estimation performance of artificial neural networks versus empirical correlations data is enabled to fill a gap in the literature as one of the uncommon works.