The industrial grinding devices, which work in the high-energetic fluidized bed conditions make it possible to obtain guaranteed particle size distribution of product and decrease of consumption energy. The matrix model for transformation of particle size distribution in the fluidized bed opposed jet mill is presented in the part IV of article. The proposed model contains the mass population balance of particle equation, in which are block matrices: the matrix of circuit M, the matrix of inputs F and the matrix of feed F0. The matrix M contains blocks with the transition matrix P, the classification matrix C, the identity matrix I and the zero matrix 0. The matrix was marked using with discrete forms of the selection and breakage functions, mean while the matrices of classification - using the equation, describing classification of grains in the grinding chamber of mill. In paper was discussed this model in details (part 2.1). The correctness of received form of the selection and breakage functions was confirmed. The method determination of the transition matrix for fluidized-jet grinding of grains (part 2.2) and the classification matrix for gravitational and centrifugal zones of grains (part 2.3) are presented. The verification of model obtained on basis results with experimental investigations, which were performed on a laboratory fluidized bed opposed jet mill. The experiment contained grinding of selected narrow size fractions of limestone in turbulent fluidized layer conditions, what in part I and part II of article (Zbroński, Górecka-Zbrońska 2007a, b) are presented. The parameters of parametric identification were: factor of proportionality - contained in the equation on the discrete form of selection function and sizes of limiting grains - contained in equation on the diagonal elements of classification matrix for stage of gravitational and centrifugal (part 3). The classic Fisher-Snedecor test was applied for estimation of prediction particle size distribution of grinding product (part 4). The significant divergences between numerical and experimental results of particle size distribution weren't affirmed. The experimental verification, parametric identification and statistical estimation of the proposed model showed that this model make it possible to forecasting particle size distribution of grinding product.
A navigation complex of an unmanned flight vehicle of small class is considered. Increasing the accuracy of navigation definitions is done with the help of a nonlinear Kalman filter in the implementation of the algorithm on board an aircraft in the face of severe limitations on the performance of the special calculator. The accuracy of the assessment depends on the available reliable information on the model of the process under study, which has a high degree of uncertainty. To carry out high-precision correction of the navigation complex, an adaptive non-linear Kalman filter with parametric identification was developed. The model of errors of the inertial navigation system is considered in the navigation complex, which is used in the algorithmic support. The procedure for identifying the parameters of a non-linear model represented by the SDC method in a scalar form is used. The developed adaptive non-linear Kalman filter is compact and easy to implement on board an aircraft.
This article investigates identification of aircraft aerodynamic derivatives. The identification is performed on the basis of the parameters stored by Flight Data Recorder. The problem is solved in time domain by Quad-M Method. Aircraft dynamics is described by a parametric model that is defined in Body-Fixed-Coordinate System. Identification of the aerodynamic derivatives is obtained by Maximum Likelihood Estimation. For finding cost function minimum, Lavenberg-Marquardt Algorithm is used. Additional effects due to process noise are included in the state-space representation. The impact of initial values on the solution is discussed. The presented method was implemented in Matlab R2009b environment.