Investigation of Thermo-Flow Characteristics of Natural Draft Dry Cooling Systems Designed with Only One Tower in 2 × 660 MW Power Plants

Abstract

In recent years, natural draft dry cooling systems with only one tower have been adopted in some 2 × 660 MW power-generating units owing to the advantage of lower construction costs. The operating cases of two power-generating units and one power-generating unit will both appear based on the power load requirement, which may lead to very different flow and heat transfer performances of this typical cooling system. Therefore, this research explores the local thermo-flow characteristics of air-cooled heat exchangers and sectors, and then analyzes the overall cooling performance of the above two operating cases under various wind conditions. Using the numerical modeling method, the results indicate that the flow and heat transfer performance of this cooling system decreases significantly in the case of one unit with half sectors dismissed. At wind speeds lower than 8 m/s, the difference in turbine back pressure between two units and one unit appears obviously higher than in other wind conditions, even reaching 4.37 kPa. Furthermore, the air-cooled heat exchanger in the lower layer always has better cooling capability than that in the upper layer, especially in conditions where there is an absence of wind and under low wind speeds. The operating case of one unit is not recommended for this dry cooling system because of the highly decreased energy efficiency. In conclusion, this research could provide theoretical support for the engineering operation of this typical natural draft dry cooling system in 2 × 660 MW power plants.

Efficient Stochastic Model for Operational Availability Optimization of Cooling Tower Using Metaheuristic Algorithms

“…The thermodynamical and optimization characteristics of this system are also analyzed. Thermo-flow measures of the dry cooling system designed with only one tower in power plants have been investigated [7]. An experimental study has been carried out to investigate the efficiency of the wet cooling tower having diverse packing compaction using the artificial neural network and particle swarm optimization algorithm [8].…”