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Balcilar M.,Yildiz Technical University | Dalkilic A.S.,Yildiz Technical University | Agra O.,Yildiz Technical University | Atayilmaz S.O.,Yildiz Technical University | And 2 more authors.
International Communications in Heat and Mass Transfer | Year: 2012

This paper predicts the condensation and evaporation pressure drops of R32, R125, R410A, R134a, R22, R502, R507a, R32/R134a (25/75 by wt%), R407C and R12 flowing inside various horizontal smooth and micro-fin tubes by means of the numerical techniques of artificial neural networks (ANNs) and non-linear least squares (NLS). In its analyses, this paper used experimental data from the National Institute of Standards and Technology (NIST) and Eckels and Pate, as presented in Choi et al.'s study provided by NIST. In their experimental setups, the horizontal test sections have 1.587, 3.78, 3.81 and 3.97. m long countercurrent flow double tube heat exchangers with refrigerant flowing in the inner smooth (8, 8.01 and 11.1. mm i.d.) and micro-fin (4.339, 5.45, 7.43 and 8.443. mm i.d.) copper tubes and cooling water flowing in the annulus. Their test runs cover a wide range saturation temperatures, vapor qualities and mass fluxes. The pressure drops are calculated with 1485 measured data points, together with analyses of artificial neural networks and non-linear least squares numerically. Inputs of the ANNs of the best correlation are the measured values of the test sections, such as mass flux, tube length, inlet and outlet vapor qualities, critical pressure, latent heat of condensation, mass fraction of liquid and vapor phases, dynamic viscosities of liquid and vapor phases, hydraulic diameter, two-phase density and the outputs of the ANNs, which comprise the experimental total pressure drops of the evaporation and condensation data from independent laboratories. The total pressure drops of in-tube condensation and in-tube evaporation tests are modeled using the artificial neural network (ANN) method of multi-layer perceptron (MLP) with 12-40-1 architecture. Its average error rate is 7.085%, which came from the cross validation tests of 1485 evaporation and condensation data points. Dependency of the output of the ANNs from 12 numbers of input values is also shown in detail, and new ANN based empirical pressure drop correlations are developed separately for the conditions of condensation and evaporation in smooth and micro-fin tubes as a result of the analyses. In addition, a single empirical correlation for the determination of both evaporation and condensation pressure drops in smooth and micro-fin tubes is proposed with an error rate of 14.556%. © 2012 Elsevier Ltd. Source

Aroonrat K.,King Mongkuts University of Technology Thonburi | Jumpholkul C.,King Mongkuts University of Technology Thonburi | Leelaprachakul R.,Thai German Products Public Company Ltd | Dalkilic A.S.,Yildiz Technical University | And 3 more authors.
International Communications in Heat and Mass Transfer | Year: 2013

This study investigates heat transfer and flow characteristics of water flowing through horizontal internally grooved tubes. The test tubes consisted of one smooth tube, one straight grooved tube, and four grooved tubes with different pitches. All test tubes were made from type 304 stainless steel. The length and inner diameter of the test tube were 2m and 7.1mm, respectively. Water was used as working fluid, heated by DC power supply under constant heat flux condition. The test runs were performed at average fluid temperature of 25°C, heat flux of 3.5kW/m2, and Reynolds number range from 4000 to 10,000. The effect of grooved pitch on heat transfer and pressure drop was also investigated. The performance of the grooved tubes was discussed in terms of thermal enhancement factor. The results showed that the thermal enhancement factor obtained from groove tubes is about 1.4 to 2.2 for a pitch of 0.5in.; 1.1 to 1.3 for pitches of 8, 10, and 12in., respectively; and 0.8 to 0.9 for a straight groove. © 2012 Elsevier Ltd. Source

Yiamsawas T.,King Mongkuts University of Technology Thonburi | Mahian O.,Islamic Azad University at Mashhad | Dalkilic A.S.,Yildiz Technical University | Kaewnai S.,King Mongkuts University of Technology Thonburi | And 2 more authors.
Applied Energy | Year: 2013

Experimental investigations are performed to determine the viscosity of TiO2 and Al2O3 nanoparticles suspended in a mixture of ethylene glycol/water (EG-water, 20/80wt%). The experiments are conducted at various volume fractions between 0% and 4% and a temperature range of 15-60°C. Some comparisons are made between the experimental results and the theoretical models and correlations presented for viscosity in the literature. The results indicate that the theoretical models are not suitable to predict the viscosity of nanofluids. Finally, using the experimental data, a useful correlation is presented to predict the viscosity. To estimate the required pumping power in an energy device, in the first, the viscosity should be determined. Therefore, the results of the present work may be helpful in the design of energy devices. © 2013 Elsevier Ltd. Source

Balcilar M.,Yildiz Technical University | Dalkilic A.S.,Yildiz Technical University | Suriyawong A.,King Mongkuts University of Technology Thonburi | Yiamsawas T.,King Mongkuts University of Technology Thonburi | And 2 more authors.
International Communications in Heat and Mass Transfer | Year: 2012

The nucleate pool boiling heat transfer characteristics of TiO 2 nanofluids are investigated to determine the important parameters' effects on the heat transfer coefficient and also to have reliable empirical correlations based on the neural network analysis. Nanofluids with various concentrations of 0.0001, 0.0005, 0.005, and 0.01vol.% are employed. The horizontal circular test plate, made from copper with different roughness values of 0.2, 2.5 and 4μm, is used as a heating surface. The artificial neural network (ANN) training sets have the experimental data of nucleate pool boiling tests, including temperature differences between the temperatures of the average heater surface and the liquid saturation from 5.8 to 25.21K, heat fluxes from 28.14 to 948.03kWm -2. The pool boiling heat transfer coefficient is calculated using the measured results such as current, voltage, and temperatures from the experiments. Input of the ANNs are the 8 numbers of dimensional and dimensionless values of the test section, such as thermal conductivity, particle size, physical properties of the fluid, surface roughness, concentration rate of nanoparticles and wall superheating, while the outputs of the ANNs are the heat flux and experimental pool boiling heat transfer coefficient from the analysis. The nucleate pool boiling heat transfer characteristics of TiO 2 nanofluids are modeled to decide the best approach, using several ANN methods such as multi-layer perceptron (MLP), generalized regression neural network (GRNN) and radial basis networks (RBF). Elimination process of the ANN methods is performed together with the copper and aluminum test sections by means of a 4-fold cross validation algorithm. The ANNs performances are measured by mean relative error criteria with the use of unknown test sets. The performance of the method of MLP with 10-20-1 architecture, GRNN with the spread coefficient 0.7 and RBFs with the spread coefficient of 1000 and a hidden layer neuron number of 80 are found to be in good agreement, predicting the experimental pool boiling heat transfer coefficient with deviations within the range of ±5% for all tested conditions. Dependency of output of the ANNs from input values is investigated and new ANN based heat transfer coefficient correlations are developed, taking into account the input parameters of ANNs in the paper. © 2012 Elsevier Ltd. Source

Tavakol M.M.,Shiraz University | Yaghoubi M.,Shiraz University | Yaghoubi M.,The Academy of Science | Masoudi Motlagh M.,Shiraz University
Experimental Thermal and Fluid Science | Year: 2010

Air flow field around a surface-mounted hemisphere of a fixed height for two different turbulent boundary layers (thin and thick) are investigated experimentally and numerically. Flow measurements are performed in a wind tunnel using hot-wire anemometer and streamwise component of velocity fluctuation are calculated using a special developed program of the hardware system. Mean surface pressure coefficients and velocity field for the same hemisphere are determined by the numerical simulation. Turbulent flow field and intensity are measured for two types of boundary layers and compared at various sections in both streamwise and spanwise directions. Numerical scheme based on finite volume and SIMPLE algorithm is used to treat pressure and velocity coupling. Studies are performed for Reynolds number, ReH = 32,000. Based on the numerical simulation using RNG k-ε turbulence model, flow pathlines, separation region and recirculation area are determined for the two types of turbulent boundary layer flows and complex flow field and recirculation regions are identified and presented graphically. © 2009 Elsevier Inc. All rights reserved. Source

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