Geometric optimisation of a coupled vane compressor

• Main Author: Teo, Jing Chung
• Other Authors: Ooi Kim Tiow
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2022
• Subjects: Engineering::Mechanical engineering::Motors, engines and turbines
• Online Access: https://hdl.handle.net/10356/158571
• Institution: Nanyang Technological University

There exist many variations of positive displacement rotary compressors that are currently utilised globally for their respective applications. The ongoing demand for compressors seeks a more efficient, compact and optimised compressor that can replace the current version to better enhance its optimality in energy saving, compactness and usage of raw material during manufacturing. In view of the need for improvement, a rotary compressor named Coupled Vane Compressor (CVC) was invented in 2019 which highlights the usage of two coupled vanes that slides diametrically through the rotor. The CVC was designed to reduce material usage during fabrication while having a higher energy efficient rate as compared to the other rotary compressors in the market. As such, the CVC is currently the most compact, material saving and energy efficient compressor to date. The study links a genetic algorithm optimisation technique, named NSGA-II, with the existing mathematical models which were used to determine the operational characteristics of the CVC in a simulation program. With a predetermined set of constraints, the NSGA-II can search for an optimal set of geometrical parameters to give rise an improved mechanical and volumetric efficiency during operation. Seven design variables namely rotor radius, cylinder radius, cylinder height, vane length, vane thickness, valve reed, suction port length, suction port diameter and discharge port diameter were varied to search for an optimised set of geometrical parameters. This paper will detail the execution of the genetic algorithm paired with the simulation program, showing the utilisation of different operators and its ability to handle defined constraints. The result is presented in comparison to previous known studies to show improvement made using the NSGA-II