This article presents a new efficient method of determining values of gas flow parameters (e.g. axial dispersion coefficient, DL and Pèclet number, Pe). A simple and very fast technique based on the pulse tracer response is proposed. It is a method which combines the benefits of a transfer function, numerical inversion of the Laplace transform and optimization allows estimation of missing coefficients. The study focuses on the simplicity and flexibility of the method. Calculations were performed with the use of the CAS-type program (Maple®). The correctness of the results obtained is confirmed by good agreement between the theory and experimental data for different pressures and temperature. The CAS-type program is very helpful both for mathematical manipulations as a symbolic computing environment (mathematical formulas of Laplace-domain model are rather sophisticated) and for numerical calculations. The method of investigations of gas flow motion is original. The method is competitive with earlier methods.
The BeiDou navigation satellite system (BDS) is one of the four global navigation satellite systems. More attention has been paid to the positioning algorithm of the BDS. Based on the study on the Kalman filter (KF) algorithm, this paper proposed a novel algorithm for the BDS, named as the minimum dispersion coefficient criteria Kalman filter (MDCCKF) positioning algorithm. The MDCCKF algorithm adopts minimum dispersion coefficient criteria (MDCC) to remove the influence of noise with an alpha-stable distribution (ASD) model which can describe non-Gaussian noise effectively, especially for the pulse noise in positioning. By minimizing the dispersion coefficient of the positioning error, the MDCCKF assures positioning accuracy under both Gaussian and non-Gaussian environment. Compared with the original KF algorithm, it is shown that the MDCCKF algorithm has higher positioning accuracy and robustness. The MDCCKF algorithm provides insightful results for potential future research.