Abstract
In this paper, according to the analysis of optimum circuits, we present an efficient ventilation network solution based on minimum independent closed loops. Our main contribution is optimizing the circuit dividing strategy to improve the iteration convergence and the efficiency of a single iteration. In contrast to a traditional circuit, a minimum closed loop may contain one or more co-tree branches but fewer high-resistance branches and fan branches. It is helpful in solving the problem of divergence or slow convergence for complex ventilation networks. Moreover, we analyze the dividing rules of closed loops and improve the dividing algorithm of minimum independent closed loops. Compared with the traditional Hardy Cross iteration method, the improved solution method has better iteration convergence and computation efficiency. The experimental results of real-world mine ventilation networks show that the improved solution method converges rapidly within a small number of iterations. We also investigate the influence of network complexity, iterative precision, and initial airflow on the iteration convergence.
Highlights
The effect of mine ventilation mainly depends on the airflow distribution in the ventilation networks
Considering that the truncation error of the Hardy Cross iteration method is not necessarily related to the minimum spanning tree, we propose an improved minimum independent closed loops (MICL) search algorithm to find the optimum circuits in a given minimum spanning tree
The algorithm can be applied in different complex networks, such as a water network, survey control network, wind network and other relevant networks to find a group of minimum independent closed loops
Summary
The effect of mine ventilation mainly depends on the airflow distribution in the ventilation networks. Measuring and computing the natural distribution of airflow is the basis of ventilation regulation optimization [1,2,3,4,5] and the key technology of the real-time ventilation network solution [6,7,8]. The ventilation network simulation is the basis of ventilation system optimization, and it represents, in essence, the solution to a set of large-scale nonlinear equations. A novel and more effective algorithm must be developed to solve the large-scale nonlinear problem of airflow distribution in complex ventilation networks. Another common approach is to convert the nonlinear equations into linear equations and solve the linear equations iteratively. There are many kinds of ventilation network calculation methods, including the airflow-based methods and the air pressure-based methods
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