Effects of Ambient Temperature on the Performance of Turbofan Transonic Compressor by CFD Analysis and Artificial Neural Networks

  • M. U. Sohail Department of Aerospace & Aeronautics, Institute of Space Technology, Pakistan http://orcid.org/0000-0003-0429-749X
  • M. Hassan Department of Electrical Engineering, University of Paderborn, Paderborn, Germany
  • S. H. R. Hamdani Department of Aerospace & Aeronautics, Institute of Space Technology, Pakistan
  • K. Pervez Department of Aerospace & Aeronautics, Institute of Space Technology, Pakistan
Keywords: transonic compressor, temperature distortion, CFD, artificial neural networks, deep learning


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.


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H. Zhou, F. Yu, K. Yang, “Study on Design Compliences of Civil Turbofan Engine with the requirements defined in FAR 33.65”, Procedia Engineering, Vol. 80, pp. 183-192, 2014

W. Balicki, P. Glowacki, S. Szczecinski, R. Chachurski, J. Szczecinsk, “Effect of the Atmosphere on the Performances of Aviation Turbine Engine”, Acta Mechanica et Automatica, Vol. 8, No. 2, pp. 70-73, 2014

T. K. Ibrahim, M. M. Rahman, O. M. Ali, F. Basrawi, R. Mamat, “Optimum Performance Enhancing Strategies of the Gas Turbine Based on the Effective Temperatures”, MATEC Web of Conferences, Vol. 38, Article ID 01002, 2016

N. R. Smith, R. A. Berdanier, J. C. Fabian, N. L. Key, “Reconciling Compressor Performance Differences for Varying Ambient Inlet Conditions”, Journal of Engineering for Gas Turbines & Power, Vol. 137, No. 12, Article ID 122603, 2015

S. L. Dixon, C. A. Hall, Fluid Mechanics and Thermodynamics of Turbomachinery, Elsevier, 2005

R. Stasyshan, N. Breedlove, “How Inlet conditions impact on Centrifugal Air compressor”, available at: www.airbestpractices.com/technology/air-compressors/how-inlet-conditions-impact-centrifugal-air-compressor-performance

T. K. Ibrahim, M. M. Rahman, M. K. Mohammed, F. Basrawi, “Statistical analysis and optimum performance of the gas turbine power plant”, International Journal of Automotive and Mechanical Engineering, Vol. 13, No. 1, pp. 3215-3225, 2016

A. Razak, “Simulating the effect of change in ambient pressure on engine performance”, in: Industrial Gas Turbines, Woodhead Publishing, pp. 293-322, 2007

Z. Liu, X. Liu, K. Wang, Z. Liang, J. A. F. O. Correia, A. M. P. De Jesus, “GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades”, Energies, Vol. 12, No. 6, Article ID 1026, 2019

A. Samad, K. Y. Kim, “Shape optimization of an axial compressor blade by multi-objective genetic algorithem”, Proceedings of the Institution of Mechanical Engineers Part A: Journal of Power and Energy, Vol. 222, No. 5, pp. 599-611, 2008

A. Jokar, R. Zomorodian, M. G. Ghofrani, P. Khodaparast, “Active control of surge in centrifugal compressors using a brain emotional learning-based intelligent controller”, Proceesing of the Institution of Mechanical Engineers Part C: Journal of Mechanical Engineering Science, Vol. 230, No. 16, pp. 2828-2839, 2016

Y. Zhao, H. D. Akolekar, J. Weatheritt, V. Michelassi, R. D. Sandberg, “Turbulence Model Development using CFD-Driven Machine Learning”, available at: https://arxiv.org/abs/1902.09075, 2019

S. A. Gandhi, C. V. Kulkarni, “Why SSIM? - A Full Reference Image Quality Assessment”, International Journal of Electronics and Communication Engineering, Vol. 2, No. 2, pp. 135-142, 2013

I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016


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