基于性能数值模拟的凝汽器预测模型OA

As a key component of cold-end system in water cooled power generation unit,the condenser's operational status is critical to the unit's safety.Current numerical simulation methods have limitations in predicting condenser performance.To optimize condenser performance in power plants,a deep learning prediction model was developed by integrating computational fluid dynamics(CFD)simulations with neural network technology.Numerical simulations were conducted to calculate thermal performance parameters under four load points and four circulating water temperatures using the data of a power plant.The resulting data were then used to train a neural network,and a prediction model was thereby established for the internal flow field and thermal parameters.A comparison between the prediction model and the numerical simulation results demonstrated that the developed neural network model could offer high computational efficiency and prediction accuracy,enabling real-time performance prediction for the condenser.This research can provide reference for constructing deep learning models in engineering applications.