GA-based dynamical correction of dispersion coefficients in Lagrangian puff model

摘要: In atmospheric dispersion models of nuclear accident, the dispersion coefficients were usually obtained by tracer experiment, which are constant in different atmospheric stability classifications. In fact, the atmospheric wind field is complex and unstable. The dispersion coefficients change even in the same atmospheric stability, hence the great errors brought in. According to the regulation, the air concentration of nuclides around nuclear power plant should be monitored during an accident. The monitoring data can be used to correct dispersion coefficients dynamically. The error can be minimized by correcting the coefficients. This reverse problem is nonlinear and sensitive to initial value. The property of searching the optimal solution of Genetic Algorithm (GA) is suitable for complex high-dimensional situation. In this paper, coupling with Lagrange dispersion model, GA is used to estimate the coefficients. The simulation results show that GA scheme performs well when the error is big. When the correcting process is used in the experiment data, the GA-estimated results are numerical instable. The success rate of estimation is 5% lower than the one without correction. Taking into account the continuity of the dispersion coefficient, Savitzky-Golay filter is used to smooth the estimated parameters. The success rate of estimation increases to 75.86%. This method can improve the accuracy of atmospheric dispersion simulation.