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Cooling towers play a vital role in many large-scale process applications, and any decline in their performance has a considerable effect on the underlying process. It is known that the efficiency of a power plant is greatly affected by the temperature difference of the condenser. The objective of this paper was to produce cooling tower design recommendations and considerations that would prevent negative impacts and ensure stable and efficient operation. Computational fluid dynamics (CFD) was used to examine the components that contribute to cooling tower performance, using steady-state simulations and average weather data from the Egyptian Meteorological Authority. Air flow patterns in and around cooling towers were predicted using computational fluid dynamics. The current study includes a numerical analysis of the performance of the cooling tower at different wind speeds and heights of the cooling tower above the ground. This study found that some wind speeds have a negative effect and others have a positive effect, and the height of the cooling tower above the ground has a positive effect on the performance of the tower.

In this paper, we study non-Bayesian and Bayesian estimation of parameters for the Kumaraswamy distribution based on progressive Type-II censoring. First, the maximum likelihood estimates and maximum product spacings are derived. In addition, we derive the asymptotic distribution of the parameters and the asymptotic confidence intervals. Second, Bayesian estimators under symmetric and asymmetric loss functions (Squared error, linear exponential, and general entropy loss functions) are also obtained. The Lindley approximation and the Markov chain Monte Carlo method are used to derive the Bayesian estimates. Furthermore, we derive the highest posterior density credible intervals of the parameters. We further present an optimal progressive censoring scheme among different competing censoring scheme using three optimality criteria. Simulation studies are conducted to evaluate the performance of the point and interval estimators. Finally, one application of real data sets is provided to illustrate the proposed procedures.