Parameter identification of a pressure regulator with a nonlinear structure using a particle filter based on the nonlinear state space model

Fusion of a simulation model and observation data has been investigated extensively for the purpose of data assimilation in geophysics. The inaccuracy of the parameters, initial conditions, or boundary conditions causes a discrepancy in the simulation results and the actual phenomenon. The present paper describes the parameter identification of a pressure regulator with a nonlinear structure by sequential Bayes estimation in the framework of data assimilation. A damping coefficient of feedback system in the pressure regulator that cannot be observed directly is estimated using a particle filter and a nonlinear state space model. The data assimilation concept is demonstrated using a pressure regulator as an engineering application.