Effects of Ambient Temperature on the Performance of Turbofan Transonic Compressor by CFD Analysis and Artificial Neural Networks
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.
Keywords:transonic compressor, temperature distortion, CFD, artificial neural networks, deep learning
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