AUTHOR=Pakatchian Mohammad Reza , Ziamolki Alireza , Alhuyi Nazari Mohammad 

TITLE=Applications of machine learning approaches in aerodynamic aspects of axial flow compressors: A review

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1135055

DOI=10.3389/fenrg.2023.1135055

ISSN=2296-598X

ABSTRACT=Compressor is one of the key gas turbine engine components and its performance and characteristics significantly affect the overall performance of the engine. Axial flow compressors are one of the most conventional types of compressors that are widely used in the turbine engines applied for power generation in large-scale. In addition to numerical simulation, intelligent techniques are applicable for characterization of axial compressors and predicting their performance. The present work reviews the studies applied different intelligent methods for performance forecasting and modeling different aerodynamic aspects of axial compressors. Corresponding to the outcomes of the considered research works it can be expressed that by using these methods, axial compressors can be characterized properly with acceptable exactness. In addition, these techniques are useful for performance prediction of the compressors. The accuracy and performance of these methods is impacted by several elements namely the employed method and applied input variables. Finally, some suggestions are mentioned for the future studies in the similar fields of study.