scholarly journals A study of stability of difference scheme of Markov counting chain method for fluidization modeling

Vestnik IGEU ◽  
2021 ◽  
pp. 65-74
Author(s):  
A.V. Mitrofanov ◽  
O.V. Sizova ◽  
N.S. Shpeynova ◽  
A.A. Zhemchugov ◽  
S.M. Mikhailova

Devices with a fluidized bed of granular material are applied in many energy power technology processes. The fluidized bed is a heterogeneous system, so mathematical models assuming its spatial discretization are necessary for its proper description. Markov chain theory is one of the most effective tools for the mathematical description of the fluidized bed structure. Many research papers are devoted to the issues of the theory application when developing mathematical models of various technological processes in the fluidized bed. At the same time, much less attention is paid to the issue of stability analysis of the proposed algorithms. Thus, it is a highly topical issue to analyze the computational stability of models of fluidized bed based on the mathematical principles of the Markov chain theory. The Markov chain approach is used as a mathematical basis for modeling of the flow patterns in a fluidized bed. The parametric identification of the model is performed using the dependencies known from the scientific papers, and the transition matrices are aligned with the physical parameters of the mass flows, which makes the proposed model nonlinear. The mixed criterion of the stability algorithm is formulated. It shows the influence of the spatiotemporal parameters of the problem sampling on the stability of computational procedures. The stability of the difference scheme to calculate formation of a fluidized bed of a monodisperse granular material is studied. The influence of the time sampling frequency on the stability of the resulting solution is considered. The effect of various parameters of the model on the loss of computational stability is estimated. It is proved that the time and spatial sampling frequencies should be chosen as a result of a mixed stability criterion. The study proves that the methodology of the Markov chain theory is an acceptable tool to describe the structure of such particle systems as a fluidized bed. It is established that macro-diffusion parameter of particle motion is the most influential in the process of computational stability loss. Thus, on the one hand, it is relevant to conduct further comparative studies of existing models of macrodiffusion, and on the other hand, it is possible to use models based on the theory of Markov chains considering the proposed stability criterion.

2017 ◽  
Vol 2017 (13) ◽  
pp. 2026-2031
Author(s):  
Shenzhi Xu ◽  
Xiaomeng Ai ◽  
Jiakun Fang ◽  
Jinyu Wen ◽  
Pai Li ◽  
...  

2019 ◽  
Vol 28 (06) ◽  
pp. 1950045
Author(s):  
Kyle Leland Chapman

The first provably ergodic algorithm for sampling the space of thick equilateral knots off-lattice, as a function of thickness, will be described. This algorithm is based on previous algorithms of applying random reflections. It is an off-lattice generalization of the pivot algorithm. This move to an off-lattice model provides a huge improvement in power and efficacy in that samples can have arbitrary values for parameters such as the thickness constraint, bending angle, and torsion, while the lattice forces these parameters into a small number of specific values. This benefit requires working in a manifold rather than a finite or countable space, which forces the use of more novel methods in Markov–Chain theory. To prove the validity of the algorithm, we describe a method for turning any knot into the regular planar polygon using only thickness non-decreasing moves. This approach ensures that the algorithm has a positive probability of connecting any two knots with the required thickness constraint which is used to show that the algorithm is ergodic. This ergodic sampling allows for a statistically valid method for estimating probability distributions of arbitrary functions on the space of thick knots.


1964 ◽  
Vol 86 (4) ◽  
pp. 383-387 ◽  
Author(s):  
H. T. McAdams

Profiles of abrasive surfaces are analyzed by means of Markov chain theory. The Chapman-Kolmogorov equations, together with recurrent-event theory, are used to deduce theoretical distributions for such important statistics as the distances between effective cutting points and the lengths of lands on a worn grinding surface. Both first-order and second-order Markov chains are examined for their applicability to a stochastic model of the grinding process.


1977 ◽  
Vol 14 (01) ◽  
pp. 89-97 ◽  
Author(s):  
S. Chatterjee ◽  
E. Seneta

The problem of tendency to consensus in an information-exchanging operation is connected with the ergodicity problem for backwards products of stochastic matrices. For such products, weak and strong ergodicity, defined analogously to these concepts for forward products of inhomogeneous Markov chain theory, are shown (in contrast to that theory) to be equivalent. Conditions for ergodicity are derived and their relation to the consensus problem is considered.


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