Fluid dynamical models and state estimation in process tomography: Effect due to inaccuracies in flow fields

In this paper we consider the reconstruction of rapidly varying objects in process tomography. The evolution of the physical parameters is approximate d with stochastic convection diffusion and fluid dynamics models. The actua l time-varying reconstruction is carried out as a state estimation problem. As the boundary observations we use the voltage data of electrical impedan ce tomography. We have previously shown that state estimation works well in process tomography in the cases in which the fluid dynamics of the system are modeled correctly. In the real case, however, the velocity field cannot usually be determined accurately. This may be caused, for example, by comp lex nature of the flow, the turbulence, discretization, etc. In adopting th e first proposed approach, it is essential to know how much the inaccuracie s in the fluid dynamical model affect the state estimates in process tomogr aphy. In this paper we consider the tolerance of the approach with respect to these inaccuracies. We show that the estimation scheme is relatively tol erant to modeling errors in the flow field. Thus relatively reliable estima tes can be obtained, for example, in a case in which a laminar flow model i s used in turbulent flow conditions. However, the degradation that is due t o incorrect flow fields is not insignificant and it is also conjectured tha t it could be possible that an extension of the proposed method could be us ed to estimate some flow field parameters. (C) 2001 SPIE and IS&T.