Cooling tower is crucial equipment in the cool-end system of power plant and the natural draft counter-flow wet cooling tower(NDWCT)gets wide application.The artificial neural network(ANN)technique is becoming an effe...Cooling tower is crucial equipment in the cool-end system of power plant and the natural draft counter-flow wet cooling tower(NDWCT)gets wide application.The artificial neural network(ANN)technique is becoming an effective method for the thermal performance investigation of cooling towers.However,the neural network research on the energy efficiency performance of NDWCTs is not sufficient.In this paper,a novel approach was proposed to predict energy efficiency of various NDWCTs by using Back Propagation(BP)neural network:Firstly,based on 638 sets of field test data within 36 diverse NDWCTs in power plant,a three-layer BP neural network model with structure of 8-14-2 was developed.Then the cooling number and evaporation loss of water of different NDWCTs were predicted adopting the BP model.The results show that the established BP neural network has preferable prediction accuracy for the heat and mass transfer performance of NDWCT with various scales.The predicted cooling number and evaporative loss proportion of the testing cooling towers are in good agreement with experimental values with the mean relative error in the range of 2.11%–4.45%and 1.04%–4.52%,respectively.Furthermore,the energy efficiency of different NDWCTs can also be predicted by the proposed BP model with consideration of evaporation loss of water in cooling tower.At last,a novel method for energy efficiency prediction of various NDWCTs using the developed ANN model was proposed.The energy efficiency index(EEI)of different NDWCTs can be achieved readily without measuring the temperature as well as velocity of the outlet air.展开更多
Based on the heat and mass transfer theory and the characteristics of general-purpose software FLUENT, a three-dimensional numerical simulation platform, composed of lots of user defined functions(UDF), has been devel...Based on the heat and mass transfer theory and the characteristics of general-purpose software FLUENT, a three-dimensional numerical simulation platform, composed of lots of user defined functions(UDF), has been developed to simulate the thermal performance of natural draft wet cooling towers(NDWCTs). After validation, this platform is used to analyse thermal performances of a 220m high super large cooling tower designed for inland nuclear plant under different operational conditions. Variations of outlet temperature of the cooling tower caused by changes of water flow rates, inlet water temperatures are investigated. Effects of optimization through non-uniform water distributions on outlet water temperature are discussed, and the influences on the flow field inside the cooling tower are analyzed in detail. It is found that the outlet water temperature will increase as the water flow rate increases, but the air flow rate will decrease. The outlet water temperature will decrease 0.095K and 0.205K, respectively, if two non-uniform water distribution approaches are applied.展开更多
基金supported by the National Key R&D Program of China(Grant No.2017YFF0209803)。
文摘Cooling tower is crucial equipment in the cool-end system of power plant and the natural draft counter-flow wet cooling tower(NDWCT)gets wide application.The artificial neural network(ANN)technique is becoming an effective method for the thermal performance investigation of cooling towers.However,the neural network research on the energy efficiency performance of NDWCTs is not sufficient.In this paper,a novel approach was proposed to predict energy efficiency of various NDWCTs by using Back Propagation(BP)neural network:Firstly,based on 638 sets of field test data within 36 diverse NDWCTs in power plant,a three-layer BP neural network model with structure of 8-14-2 was developed.Then the cooling number and evaporation loss of water of different NDWCTs were predicted adopting the BP model.The results show that the established BP neural network has preferable prediction accuracy for the heat and mass transfer performance of NDWCT with various scales.The predicted cooling number and evaporative loss proportion of the testing cooling towers are in good agreement with experimental values with the mean relative error in the range of 2.11%–4.45%and 1.04%–4.52%,respectively.Furthermore,the energy efficiency of different NDWCTs can also be predicted by the proposed BP model with consideration of evaporation loss of water in cooling tower.At last,a novel method for energy efficiency prediction of various NDWCTs using the developed ANN model was proposed.The energy efficiency index(EEI)of different NDWCTs can be achieved readily without measuring the temperature as well as velocity of the outlet air.
基金the National Natural Science Foundation of China (No. 51176170)Foundation for the Author of National Excellent Doctoral Dissertation of PR China (2007B4) are gratefully acknowledged
文摘Based on the heat and mass transfer theory and the characteristics of general-purpose software FLUENT, a three-dimensional numerical simulation platform, composed of lots of user defined functions(UDF), has been developed to simulate the thermal performance of natural draft wet cooling towers(NDWCTs). After validation, this platform is used to analyse thermal performances of a 220m high super large cooling tower designed for inland nuclear plant under different operational conditions. Variations of outlet temperature of the cooling tower caused by changes of water flow rates, inlet water temperatures are investigated. Effects of optimization through non-uniform water distributions on outlet water temperature are discussed, and the influences on the flow field inside the cooling tower are analyzed in detail. It is found that the outlet water temperature will increase as the water flow rate increases, but the air flow rate will decrease. The outlet water temperature will decrease 0.095K and 0.205K, respectively, if two non-uniform water distribution approaches are applied.
