Source Inversion in Time-Variant Flow Field Based on Bayesian Inference

Abstract:

In this paper， a numerical simulation of pollutant diffusion was conducted at five different locations and the results were taken as the measurement. The adjoint equation was used to calculate the simulated concentration of the sensors in time-variant flow field. The likelihood function was constructed by measurement and simulated concentration and the posterior probability of source parameters in time-varying flow field was calculated based on Bayesian inference. The results show that the errors of inversion of source parameters depend on the error between the measurement and the simulated concentration. When the distance between the source and the sensors is greater， the posterior probability of source parameters shows a wider distribution， indicating the larger uncertainty of the inversion result. When the source is closer to the sensors， the uncertainty of the inversion result is significantly reduced. In addition， the influence of measured data at different stages of pollutant diffusion in the process of inversion was also discussed. The inversion errors and the standard deviation of posterior probability are found to be smaller by using the measurement in the initial stage of diffusion than by using the data in the stable stage， but the improvement is not obvious.