Hybrid optimization method applied to Roots type power machine

Abstract

The invention discloses a hybrid optimization method applied to a Roots type power machine, which optimizes blade parameters of the Roots type power machine by using a particle swarm and simulated annealing hybrid optimization algorithm, and comprises the following steps of: (1) initialization: setting dimensions of an initialization population according to simulation data, and then defining and assigning a constant value; by adopting the optimization method designed by the invention, a hybrid optimization algorithm of a particle swarm optimization algorithm and a simulated annealing algorithm is selected by adopting an optimization model discriminator to optimize blade parameters, all particles are enabled to find an optimal solution in a multi-dimensional superbody through the particle swarm optimization algorithm, global optimum is found by following a currently searched optimal value, and the blade parameters are optimized. Secondary optimization is carried out through a simulated annealing algorithm to make up for the defect that the particle swarm optimization is caught in a local optimal solution, and therefore model parameters such as the number, size and position of pressure relief openings, the distance between a blade meshing point and the axis center, the number of blade teeth and the gap between blade teeth are optimized.

Description

technical field [0001] The invention relates to the technical field of Roots-type power machines, in particular to a hybrid optimization method applied to Roots-type power machines. Background technique [0002] The blade parameters are optimized using a hybrid optimization algorithm. In the field of Roots-type power machines, optimize the blade parameters of the power machine to improve the performance indicators of the power machine; in the field of energy recovery, design a new type of steam power machine, which is conducive to improving the efficiency of energy recovery. [0003] The traditional waste heat power generation technology and its power equipment are mainly power generation systems composed of waste heat boilers and steam turbines. This system is too large and suitable for the recovery of medium and high temperature waste heat, but has low economic benefits for the recovery of low temperature waste heat. At present, the waste heat power generation systems of ...

AI Technical Summary

Problems solved by technology

The goal of the PSO algorithm is to make all particles find the optimal solution in the multi-dimensional hyper-volume (Multi-dimensional Hyper-volume). It searches for the global optimal solution by following the optimal value currently searched, but the algorithm will produce premature convergence and It is proved that the algorithm is not globally convergent without weighting, the convergence speed is fast and it is easy to fall into the local optimal solution. Therefore, the SA algorithm is used for secondary optimization. The SA algorithm is used to make up for the lack of the PSO algorithm falling into the local optimal solution.

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