Iranian Journal of Chemical Engineering (IJChE)

Abstract The artificial neural network (ANN) approach was applied to develop simple correlations for predicting the thermal conductivity of nitrogen-methane and carbon dioxide-methane mixtures. The genetic algorithm method was used to obtain global optimum parameters (weights and biases) of the ANNs. The methane mole fraction, temperature, pressure, and density as effective parameters on thermal conductivity were network input variables. 171 and 180 data points related to the nitrogen-methane and carbon dioxide-methane gas mixtures, respectively, divided to test and train datasets. Simple correlations were obtained due to the small number of optimal neurons in the ANN structures. The mean relative errors of 0.206% and 0.199% for the testing dataset indicate the high accuracy and validation of the correlations. The work indicates that artificial intelligence approaches are very useful for thermal conductivity modeling in natural gases. A sensitivity analysis was performed on all input variables that indicates that the gas mixture density has the greatest impact on the thermal conductivity.

Abstract In the study, the thermal-hydraulic performance of the zigzag channels with circular cross-section was analyzed by Computational Fluid Dynamics (CFD). The standard K-Ꜫ turbulent scalable wall functions were used for modeling. The wall temperature was assumed constant 353 K and water was used as the working fluid. The zigzag serpentine channels with bend angles of 5 - 45° were studied for turbulent flow from 4000 to 40,000 Reynolds number (Re). The thermal performance of the zigzag 45° channel was better than the other channels and also it had the highest friction factor (f). The bends caused secondary flow, and as the bend angle increased, the secondary flow increased. This Phenomenon had a positive effect on thermal performance and a negative effect on hydraulic performance by increasing the friction factor. The obtained CFD data used to develop correlations for predicting the Nu and f as the functions of Re and bend angles. The correlation constants were optimized by the genetic algorithm method which leads to the mean relative errors of 3.32% and 6.94% for Nu and f estimation, respectively.

Abstract In the present study, Adaptive Neuro–Fuzzy Inference System (ANFIS) approach was applied for predicting the heat transfer and air flow pressure drop on flat and discontinuous fins. The heat transfer and friction characteristics were experimentally investigated in four flat and discontinuous fins with different geometric parameters including; fin length (r), fin interruption (s), fin pitch (p), and fin thickness (t). Two ANFIS models were developed using the Computational Fluid Dynamic (CFD) results which validated by the experimental data. The ANFIS models were applied for prediction of Nusselt number (Nu) and friction factor (f) as functions of Reynolds number (Re), and fin geometric parameters including, spanwise spacing ratio (p/t), and streamwise spacing ratio (s/r). The low error values for testing data set, which were not employed in the training of the ANFIS, proved the precise and validity of the model. The root mean square error (RMSE) of 0.7343 and mean relative error (MRE) of 1.33% were resulted for prediction Nu. In addition, these values for estimation of the f were resulted 0.0158, 3.32%, respectively.