Sharif Digital Repository

Past experiences have shown that a local wind can considerably affect the performances of powerplant cooling towers and factory chimneys. In thermal powerplants, the performance of Rankin cycles would reduce if the temperature of its condenser increases. This issue is very important to powerplants located in countries with strong local winds. To remedy the malperformance of a natural cooling tower in windy conditions, it is required to understand the physics of flow around cooling towers more clearly. One adverse physics is known as the wind covering problem which can drastically affect the natural draught through a cooling tower in windy conditions. In this paper, we focus on wind-covering... 

Increasing concerns about the lack of energy resources, global warming and environmental pollution have pushed the relevant organizations to develop energy saving strategies such as energy labeling programs. In this research, a new energy labeling program is developed and evaluated for the wet cooling towers in Iran. The cooling tower parameters, including its cooling capacity and the fan power consumption, are measured using an experimental setup. The experimental data are standardized to compensate the effect of the ambient wet bulb temperature on the cooling tower performance which cannot be controlled during the experiments. The results show that the cooling tower capacity and the fan... 

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,... 

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,...