It is difficult to manually process and analyze large amounts of data. Therefore, to solve a given problem, it is easier to reach the solution by studying the data obtained from the environment of the problem with computational intelligence methods. In this study, pool boiling heat flux was estimated in the isolated bubble regime using two optimization methods (genetic and artificial bee colony algorithm) and three machine learning algorithms (decision tree, artificial neural network, and support vector machine). Six boiling mechanisms containing eighteen different parameters in the genetic and the artificial bee colony (ABC) algorithms were used to calculate overall heat flux of the isolated bubble regime. Support vector machine regression (SVMReg), alternating model tree (ADTree), and multilayer perceptron (MLP) regression only used the heat transfer equation input parameters without heat transfer equations for prediction of pool boiling heat transfer over a horizontal tube. The performance of computational intelligence methods were determined according to the results of error analysis. Mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) error were used to calculate the validity of the predictive model in genetic algorithm, ABC algorithm, SVMReg, MLP regression, and alternating model tree. According to the MAPE error analysis, the accuracy values of MLP regression (0.23) and alternating model tree (0.22) methods were the same. The SVMReg method used for pool boiling heat flux estimation performed better than the other methods, with 0.17 validation error rate of MAPE.

In the present work, we have conducted thermodynamic analysis of an organic Rankine cycle (ORC) using waste heat from intercooler and regenerator in Brayton cycle with intercooling, reheating, and regeneration (BCIRR). First of all, the first law analysis is used in this combined cycle. Several outputs are revealed in this study such as the cycle efficiencies in Brayton cycle which is dependent on turbine inlet temperature, intercooler pressure ratios, and pinch point temperature difference. For all cycles, produced net power is increased because of increasing turbine inlet temperature. Since heat input to the cycles takes place at high temperatures, the produced net power is increased because of increasing turbine inlet temperature for all cycles. The thermal efficiency of combined cycle is higher about 11.7% than thermal efficiency of Brayton cycle alone. Moreover, the net power produced by ORC has contributed nearly 28650 kW. The percentage losses of exergy for pump, turbine, condenser, preheater I, preheater II, and evaporator are 0.33%, 33%, 22%, 23%, 6%, and16% respectively. The differences of pinch point temperature on ORC net power and efficiencies of ORC are investigated. In addition, exergy efficiencies of components with respect to intercooling pressure ratio and evaporator effectiveness is presented. Exergy destructions are calculated for all the components in ORC.

The most important parameter which affects the results of building energy analysis is the weather data and it can be obtained by different methods for the same location. Although lots of studies have been conducted for Türkiye, it was seen that the impact of different weather data for the same location has never been investigated. The aims of this study were to compare the heating and cooling demands of the buildings with respect to different weather files. Building loads were calculated using five different meteorological source data. Calculations are made for eight cities which represent heating and cooling dominated climates of Türkiye. Calculation procedure of internal heat gain was explained in detail. All simulations were performed using Energyplus v9.2. The findings of the comparison showed that although some results are similar to each other for some weather files, they could have great variances in the energy analysis also. A common missing meteorological data-filling algorithm may be developed in order to reduce the deviations in energy analysis results.