Estimativa de estado no armazenamento térmico em materiais de mudança de fase contendo nanopartículas

Abstract

This work deals with the solution of state estimation problems by using Bayesian techniques, in applications related to the energy storage in phase change materials containing metallic nanoparticles. Physical situations modelled by one-dimensional and two-dimensional problems were analyzed. The one-dimensional model was solved analytically by using Rubinstein’s solution and numerically by the Finite Volume Method. For the two-dimensional case, the state evolution model was formulated in discrete form by the Finite Volume Method. However, for the reduction of the computational cost associated with the state estimation problem, a reduced model was proposed in terms of Proper Orthogonal Decomposition and Radial Basis Functions. The Approximation Error Model was implemented to deal with the inherent errors related to the reduced model. The state estimation problem was solved with the Particle Filter technique, for nonlinear models with additive and Gaussian uncertainties, for cases involving simulated transient measurements. The focus of this work was the estimation of the energy stored in the phase change material under direct solar irradiance, with nonintrusive temperature measurements. The results obtained here demonstrated the robustness of the approach used, even for large uncertainties in the evolution and observation models, as well as in the measurements.