City Research Online

Abstract

Screw machines are widely used in industrial applications for gas compression and transport, including refrigeration systems, compressed air supply, heat pumps, and process industries. Among them, conventional twin screw compressors are a mature technology with a long history of development and analysis. Internally geared screw machines (IGSMs) are a more recent alternative in which one rotor operates inside another on parallel offset axes. This arrangement differs fundamentally from the side-by-side rotor layout of conventional twin screw machines and may offer new opportunities for machine design and performance improvement. However, research on IGSMs remains limited and has mainly focused on geometric feasibility and simplified performance predictions.

This thesis contributes to the development of IGSM technology through three main objectives. The first is the development of a detailed numerical geometry model for IGSMs, coupled with an established performance prediction framework. The second is the use of this modelling capability to carry out an initial assessment of IGSM performance through parametric studies and comparison with conventional screw machine designs. The third is the investigation of new rotor profiling methods, including extensions of the traditional rack generation method and the use of deep neural networks for rotor profile generation.

The main outcome of the thesis is a computationally efficient modelling framework that enables lower-cost simulation of IGSMs, together with initial design guidance and feasible rotor design pathways identified through systematic parametric studies. Overall, the work helps extend current understanding of IGSMs from early geometric concepts towards more practical tools for design and
performance evaluation.

Publication Type:	Thesis (Doctoral)
Subjects:	Q Science > QA Mathematics > QA76 Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery
Departments:	School of Science & Technology > School of Science & Technology Doctoral Theses Doctoral Theses

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