Abstract
The unfavorable effects of non-uniform temperature inlet flow on gas turbine engine operations have always been a hindrance on the performance of turbo-fan engines. The propulsive efficiency is a function of the overall efficiency of turbofan engine which itself is dependent on other ambient parameters. Variation of inlet compressor temperature due to increase or decrease of aircraft altitude, air density, relative humidity, and geographical climate conditions affects the compressor performance. This research focuses on the turbofan transonic compressor performance due to ambient temperature distortion. A novel predictive approach based on neural network model has been implemented to predict the compressor performance and behavior at different ambient temperature conditions. The model produces substantially accurate results when compared to the results of CFD analysis. Computational results from CFD analysis show that engine thrust decreases at higher altitude, lower density and lower pressure regions.
Highlights
Among the air breathing jet engines, turbofans are widely used in civil and military aircraft propulsion systems
This paper presents a study which uses artificial neural networks (ANNs) to predict pressure ratio, temperature ratio, efficiency, and mass flow rate distribution of three-dimensional compressor rotors, as part of a larger project called “System Identification Development for Analysis of Transonic Axial Compressor Rotor 67”
Besides high altitude, www.etasr.com humidity, low air pressure and density, ambient temperature play a vital role in its performance
Summary
Among the air breathing jet engines, turbofans are widely used in civil and military aircraft propulsion systems. Compressor performance and stability deteriorated by inlet airfoil distortion, velocity distortion, reduced airflow, temperature and pressure circumferential distribution, while high ambient temperature, high absolute humidity, and low air density decrease compressor stall margin, and increase fuel volatility [1-4]. A gene expression programming to train CFD based machine learning neural network was developed for RANS turbulence model of high-pressure turbine blades. This explicit framework model could deduce wake prediction of the CFD based model [12]. To the best of our knowledge there is no published work that relates the transonic axial compressor and artificial neural networks (ANNs) to predict the performance and behavior of the compressor at variable ambient temperature. This paper presents a study which uses ANNs to predict pressure ratio, temperature ratio, efficiency, and mass flow rate distribution of three-dimensional compressor rotors, as part of a larger project called “System Identification Development for Analysis of Transonic Axial Compressor Rotor 67”
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