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. According to an existing dam state prediction method, the calculation precision is extremely dependent on correct description of a concrete material constitutive model, generally, model parameters can only be obtained from prototype observation or twinborn experiments, and determination and calibration of the parameters are complex. According to the method, the nonlinear evolution characteristic of a concrete material along with time is fully considered, a data assimilation model is constructed by utilizing a dam body virtual displacement field and the displacement amount of an actual dam measurement point at the same moment, and displacement back analysis is performed on linear elastic constitutive parameters of the concrete material of the dam; observation data is introduced into the data assimilation model for model updating, the influence of data errors on the model is fully considered, and the evolution direction of the data assimilation model is adjusted in real time, so that the estimation precision of the data assimilation model is improved, the prediction capability of a prediction model is effectively improved, and important reference data can be provided for real-time early warning analysis of a dam.

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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