Open AccessTheory and practice of neural networks application to building mathematical model of centrifugal compressor vane diffusers
Author(s) -
А. Г. Никифоров,
Andrei Rekovetc,
Yu. B. Galerkin,
Evgeniy Petukhov,
A. F. Rekstin,
V. B. Semenovsky,
Olga Solovyeva
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1180/1/012025
Subject(s) - centrifugal compressor , diffuser (optics) , gas compressor , artificial neural network , computer science , set (abstract data type) , computational fluid dynamics , impeller , centrifugal pump , mechanical engineering , control engineering , simulation , engineering , artificial intelligence , aerospace engineering , physics , optics , light source , programming language
Optimal design of centrifugal compressor stages needs special computational and experimental methods, both of them could be costly enough. So new advanced design methods which can provide optimal solution faster are needed. Authors developed the set of mathematical models – Universal modeling method - for describing compressor stages characteristics. Its models are being widened and improved. One the most advanced approaches of model building is based on machine learning. A neural network based method for predicting centrifugal compressor vane diffuser characteristics was developed. Input data for network training was obtained from CFD simulations. The resulting model for diffuser loss coefficient shows good approximation quality and can be used for improvement of VD model in Universal modeling method.