On the Design of a Class of Rotary Compressors Using Bayesian Optimization

Abstract

The optimization process of compressors is usually regarded as a ‘black-box’ problem, in which the mathematical form underlying the relationship between design parameters and the design objective is impractical and costly to be obtained. To solve the ‘black-box’ problem, Bayesian optimization has been proven as an accurate and efficient method. However, the application of such a method in the design of compressors is rarely discussed, particularly no work has been reported in terms of the positive displacement type compressor. Therefore, this paper aims to introduce the Bayesian optimization to the design of positive displacement compressors through the optimization process of the novel limaçon compressor. In this paper, a two-stage optimization process is presented, in which the first stage optimizes the geometric parameters as per design requirements and the second stage focuses on revealing an optimum setting of port geometries that improves machine performance. A numerical illustration is offered to prove the validity of the presented approach.

A Literature Review of the Positive Displacement Compressor: Current Challenges and Future Opportunities

“…There are some other machine learning methods applied in the modelling and optimisation of positive displacement compressors, albeit to a limited level. Lu et al [114,115] used the Bayesian optimisation method to optimise the port geometries of the limaçon rotary compressor. According to the results, the authors discovered that most of the obtained outcomes fall within the desired region, and the average isentropic and volumetric efficiencies obtained from the optimisation were 93.81% and 83.62%, respectively.…”

Nested Optimization of Oil-Circulating Hydro-Pneumatic Energy Storage System for Hybrid Mining Trucks

“…For complex nonlinear optimization problems, the appropriate optimization algorithm that could search out the global optimal solution quickly and efficiently is the key issue [21]. Genetic algorithm (GA), as a classical global optimization algorithm, has been widely used in many applications [22].…”