Improved Model for Predicting Total Dissolved Gas Generation With the Residence Time of the Water in the Stilling Phase - Peng, Yiyun; Lin, Yuqing; Zeng, Chenjun; Zha, Wei; Mao, Feijian; Chen, Qiuwen; Mo, Kangle; Yao, Siyang (Frontiers in Environmental Science, 2022)
Quantitative predictions of total dissolved gas (TDG) super-saturation are essential for developing operation schemes for high dams. Most TDG generation prediction models have various shortcomings that affect the accuracy of TDG super-saturation estimates, such as oversimplification of influencing factors and uncertainty in parameter values. In this study, the TDG generation process was divided into three parts, gas-liquid mass transfer process in the stilling phase, dilution resulting from the water jet plunging into the stilling phase, and outflow of TDG–super-saturated water from the stilling phase, while considering the water body and bubbles in the stilling phase as a whole. The residence time of the water in the stilling phase (Tr) was introduced to estimate mass transfer time, along with dimensional analysis methods. The properties of TDG generation were evaluated experimentally under varying Tr values. Based on the theoretical analysis and experimental results, a basic water renewal model was proposed and was validated using experimental measurements. Furthermore, prediction results of this model were compared with those of a classical empirical model and mechanical model based on observed data from a field survey at Xiluodu Dam. The results show that the relative errors between the predicted and experimental measurements were all less than 5%, indicating that the developed prediction model has a good performance. Compared with the mechanism model, the developed model could reduce the standard error (SE), normalized mean error (NME), and error of maximum (REMAX) by 60%, 96%, and 15%, respectively. Meanwhile, the developed model could reduce the SE, NME, REMAX by 17.4%, 36%, and 23%, respectively, compared with the experience model. Considering all the error indexes, it can be concluded that the prediction performance of the water renewal model is the best among the three models. The proposed model was also more generically applicable than the existing models. Prediction results of water regeneration model for TDG could aid the drafting of governing measures to minimize the risk of super-saturated TDG.