A dam state prediction method and system based on data assimilation

Abstract

The invention discloses a dam state prediction method and system based on data assimilation, and belongs to the technical field of dam state prediction. The calculation accuracy of the existing dam state prediction methods is extremely dependent on the correct description of the concrete material constitutive model. Generally, model parameters can only be obtained from prototype observations or twin experiments, and the determination and calibration of parameters are complicated. The invention fully considers the nonlinear evolution characteristics of the concrete material with time, uses the virtual displacement field of the dam body and the displacement of the actual measuring point of the dam at the same time, builds a data assimilation model, and performs displacement on the linear elastic constitutive parameters of the concrete material of the dam. Inverse analysis; update the data assimilation model by introducing observational data to update the model, fully consider the impact of data errors on the model, and adjust the evolution direction of the data assimilation model in real time to improve the estimation accuracy of the data assimilation model and effectively improve the prediction ability of the prediction model. It can provide important reference data for real-time early warning analysis of dams.

Description

technical field [0001] The invention relates to a dam state prediction method and system based on data assimilation, and belongs to the technical field of dam state prediction. Background technique [0002] A dam is a special building including a reservoir and a hydropower station, which plays the role of water storage, flood control and power generation, and has significant social and economic benefits. However, domestic dam projects have the characteristics of many points, wide areas and large quantities. Once a serious accident such as a dam failure occurs, it will cause immeasurable damage to the downstream people and the environment. Therefore, it is necessary to ensure the construction quality of dam projects. Maintain operational safety. [0003] However, dam projects have the characteristics of large construction scale, complex design structure, long construction and service period, and diverse operating environment. The main material concrete also has dynamic physi...

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Problems solved by technology

[0004] Prediction methods based on statistical models, such as neural networks, regression models, and genetic algorithms, are all empirical models or empirical analysis methods, which ignore the dynamic structural mechanics evolution in the process of dam load and temperature rise and fall, and lack real physical model support and interpretation basis, it is impossible to accurately predict extreme weather and other historically unprecedented situations

[0005] The prediction method based on simulation analysis has sufficient basis for physical explanation, but the dam structure presents a time-space nonlinear behavior, and its matrix decomposition and solution calculation scale is large and time-consuming; the second difficulty is that the calculation accuracy is extremely dependent on the correct description of the concrete material constitutive model , generally only model parameters can be obtained from prototype observations or twin experiments, and the determination and calibration of parameters are relatively complicated

Therefore, it is difficult to predict in real time and accurately

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