Liquid‐Liquid Coaxial Swirl Injector Performance Prediction Using General Regression Neural Network | Zendy

Ghorbanian Kaveh | Zendy; Soltani Mohammad R. | Zendy; Ashjaee Mehdi | Zendy; Morad Mohammad R. | Zendy

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Liquid‐Liquid Coaxial Swirl Injector Performance Prediction Using General Regression Neural Network

Author(s) -

Ghorbanian Kaveh,

Soltani Mohammad R.,

Ashjaee Mehdi,

Morad Mohammad R.

Publication year - 2009

Publication title -

particle and particle systems characterization

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.877

H-Index - 56

eISSN - 1521-4117

pISSN - 0934-0866

DOI - 10.1002/ppsc.200701104

Subject(s) - coaxial , injector , sauter mean diameter , artificial neural network , liquid liquid , range (aeronautics) , materials science , position (finance) , regression , mechanics , acoustics , computer science , mechanical engineering , chromatography , physics , engineering , chemistry , mathematics , artificial intelligence , composite material , statistics , nozzle , finance , economics

A general regression neural network technique was applied to design optimization of a liquid‐liquid coaxial swirl injector. Phase Doppler Anemometry measurements were used to train the neural network. A general regression neural network was employed to predict droplet velocity and Sauter mean diameter at any axial or radial position for the operating range of a liquid‐liquid coaxial swirl injector. The results predicted by neural network agreed satisfactorily with the experimental data. A general performance map of the liquid‐liquid coaxial swirl (LLCS) injector was generated by converting the predicted result to actual fuel/oxidizer ratios.

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