An AI-Based Fast Design Method for New Centrifugal Compressor Families

Limiting global warming's effects requires a sudden reduction of greenhouse gas emissions to pursue a net-zero carbon growth in the next decades. Along with this energy transition, drastic and rapid changes in demand are expected in many sectors, including the one for centrifugal compressors. In this context, new aerodynamic design processes exploiting the know-how of existing impeller families to generate novel centrifugal compressors could quickly react to demand variations and ensure companies' success. Modifying the characteristics of existing compressors using a 1D single-zone model is a fast way to exploit this know-how. Besides, artificial intelligence could be useful to highlight relationships between geometrical parameters and performance, thus facilitating the achievement of optimized machines for new applications. Although the scientific literature shows several studies on mono-dimensional approaches, the joint use of a 1D single-zone model with an artificial neural network for designing new impellers from pre-engineered ones remains understudied. Such a model was provided in this paper. An application to the case study of an expander-compressor impeller family derived from other existing natural gas liquefaction one was presented. Results proved that the proposed model enabled developing a new family from an existing one, improving the performance while containing design time and computational efforts.