Abstract: Multi-lump and distributed parameter models are used to analyze the frequency behavior of a pressurized water reactor (PWR). The distributed parameter model is built upon the partial differential equations describing heat transfer and fluid flow in the reactor core. For comparison, a lumped parameter reactor core model with multiple fuel and coolant lumps is employed. The features of the transfer functions for both models are evaluated. The distributed parameter model has the ability to offer an accurate transfer function at any location throughout the reactor core. In contrast, the multi-lump parameter model only provides an average value in a given region (lump). Comparisons show that the multi-lump model results are only most favorable for frequencies less than ~0.1 Hz.

Abstract: This paper empirically shows that the combined effect of applying the selected feature subsets and optimized parameters on machine learning techniques significantly improves the accuracy for solar power prediction. To provide evidence, experiments are carried on in two phases. For all the experiments the machine learning techniques namely Least Median Square (LMS), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) are used. In the first phase five well-known wrapper feature selection methods are used to obtain the prediction accuracy of machine learning techniques with selected feature subsets and default parameter settings. The experiments from the first phase demonstrate that holding the default parameters, LMS, MLP and SVM provides better prediction accuracy (i.e. reduced MAE and MASE) with selected feature subsets rather than without selected feature subsets. After getting improved prediction accuracy from the first phase, the second phase continues the experiments to optimize machine learning parameters and the prediction accuracy of those machine learning techniques are re-evaluated through adopting both the optimized parameter settings and selected feature subsets. The comparison between the results of two phases clearly shows that the later phase (i.e. machine learning techniques with selected feature subsets and optimized parameters) provides substantial improvement in the accuracy for solar power prediction than the earlier phase (i.e. machine learning techniques with selected feature subsets and default parameters). Experiments are carried out using reliable and real life historical meteorological data. The machine learning accuracy of solar radiation prediction is justified in terms of statistical error measurement and validation metrics. Experimental results of this paper facilitate to make a concrete verdict that providing more attention and effort towards the feature subset selection and machine learning parameter optimization (e.g. combined effect of selected feature subsets and optimized parameters on prediction accuracy which is investigated in this paper) can significantly contribute to improve the accuracy of solar power prediction.

Abstract: The paper reports the results of pilot test on the cooling performance of a direct cross flow mechanical cooling tower located in the Kebili region in the southern part of Tunisia. In this study heat and mass transfer data are measured within the tower over a period of one year and compared with external weather data collected over the same period. The data enabled the influence of different weather conditions on the performance of the cooling tower to be analyzed. The results obtained show that ambient humidity has a greater influence on performance than external temperature. In fact, significantly better cooling performance of about 80% was obtained during the high temperature, low humidity summer months than during the winter period, less than 40%, with relatively low external temperature and high humidity. These results indicate the relative importance of evaporative cooling as compared to convective cooling. The effect of wind on cooling performance was found to be considerable but was confined to those periods when wind direction coincided with the orientation of the louvers of the tower. This was observed to occur only during the summer period when compared to winter period, thus attesting the benefits of the use of proper cooling tower design for improving efficiency and conserve energy.