Nuclear accident source item inversion method

Abstract

The invention discloses a nuclear accident source item inversion method, which comprises the following main steps: firstly, determining a target signal of a nuclear accident source item inversion andextracting a main influence factor in an environmental influence factor as an input variable by using a PCA method; then, determining a BP neural network topology and normalizing the input variables;after that, determining the number of hidden layer nodes of the BP neural network model and training the neural network model to debug an optimal parameter; finally, using a BP neural network weight and threshold value optimized through an MEA method to establish a PCA-MEA-BP neural network nuclear accident inversion model; and carrying out comparison and analysis on the PCA-MEA-BP neural networknuclear accident inversion model and an unoptimized BP neutral network model result. According to the nuclear accident source item inversion method in the invention, an effective characteristic quantity is extracted by using the PCA method, thereby improving the classification precision, reducing the training time of the neural network and simplifying the reversion model structure; moreover, the MEA algorithm optimizes the neural network weight and threshold value, thereby effectively reducing the network instability and improving the availability of the reversion model.

Description

technical field [0001] The invention relates to an inversion method for an accident source item of a nuclear power plant. Background technique [0002] On March 11, 2011, a magnitude 9.0 earthquake occurred in Japan, followed by a tsunami, which led to a major nuclear accident at the Fukushima nuclear power plant. Serious harm. In the early stage of the accident, the entire plant was powered off, and the damage to the core was unknown. The source term could not be effectively estimated through the operating parameters and damage degree of the core, but the source term could be inverted based on the monitoring data around the nuclear power plant combined with the atmospheric diffusion model. In the case of nuclear accidents, improving the speed and accuracy of source term inversion is helpful to accurately determine the level of nuclear accidents, and to take scientific emergency measures to make accurate accident consequence evaluations. Therefore, it is of great significa...

AI Technical Summary

Problems solved by technology

However, the BP neural network algorithm has certain limitations. It is easy to fall into a local minimum point, it is difficult to search for the global optimum, and it often fails to reach the preset prediction accuracy, and the convergence speed is too slow, which will eventually lead to the failure of the training and learning process.

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