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In this study, a numerical and empirical scheme for increasing cooling tower performance is developed by combining the particle swarm optimization (PSO) algorithm with a neural network and considering the packing’s compaction as an effective factor for higher accuracies. An experimental setup is used to analyze the effects of packing compaction on the performance. The neural network is optimized by the PSO algorithm in order to predict the precise temperature difference, efficiency, and outlet temperature, which are functions of air flow rate, water flow rate, inlet water temperature, inlet air temperature, inlet air relative humidity, and packing compaction. The effects of water flow rate, air flow rate, inlet water temperature, and packing compaction on the performance are examined. A new empirical model for the cooling tower performance and efficiency is also developed. Finally, the optimized performance conditions of the cooling tower are obtained by the presented correlations. The results reveal that cooling tower efficiency is increased by increasing the air flow rate, water flow rate, and packing compaction.

In this contribution, we have investigated the effects of magnetoelastic loads on free vibration characteristics of the magnetorheological-based sandwich beam. The considered sandwich beam consists of a magnetorheological core with elastic top and base layers. For these means, the structural governing equations are derived using the Hamilton principle and solved by the finite element method. The results are validated in comparison with the existing results in the literature. The effects of variation in the parameters such as magnetic field intensity and the thickness of the core and top layers on the deviation of the first natural frequency and the corresponding loss factor are studied as well. Finally, in order to provide deep insight, the effects of magnetoelastic loads on the dynamic behavior of the three-layered sandwich beam are examined through a comprehensive survey.

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