Use of Artificial Neural Network in Rotorcraft Cooling System

  • Altuğ Akın Aerospace Engineering Department, Middle East Technical University (METU)
  • Harika S. Kahveci Aerospace Engineering Department, Middle East Technical University (METU)
Keywords: artificial neural network, computational fluid dynamics, heat transfer, rotorcraft


In this study, an Artificial Neural Network (ANN) is used to determine the surface temperatures of the avionics equipment located in an avionics bay of a rotorcraft. The bay is cooled via a system of a fan that supplies ambient air to the interior of the bay and an exhaust. A Feedforward Multi-Layer ANN is used with the input parameters of the fan and exhaust locations and the air mass flow rate of the fan. For training of the network, the results obtained by a large number of Computational Fluid Dynamics (CFD) analyses are used. An analysis on the accuracy of the ANN algorithm through the use of different ANN architectures revealed that an ANN with fifteen neurons in the hidden layer provides the best accuracy among the considered options. The size of the training data is increased progressively and its effect on the prediction accuracy of the ANN algorithm is also observed. The regression capability of the ANN is later compared with a response surface built by a commonly used full quadratic linear model. The comparison shows that the ANN predicts the avionics surface temperatures with much better accuracy.

Author Biographies

Altuğ Akın, Aerospace Engineering Department, Middle East Technical University (METU)

Altug Akin is a graduate student at the Aerospace Engineering Department at METU, Turkey. He received his bachelor’s degree in Mechanical Engineering in 2013 from the same university. His research focuses on the avionics bay cooling of rotorcraft. He is also an employee of the Turkish Aerospace Company where he works as an Environmental Control Systems Design Engineer.

Harika S. Kahveci, Aerospace Engineering Department, Middle East Technical University (METU)

Dr. Harika S. Kahveci is an Assistant Professor at the Aerospace Engineering Department at METU, Turkey. She received her Ph.D in Mechanical Engineering from The Ohio State University in 2010, her M.S. degree in Aerospace Engineering from Penn State University in 2004, and her B.S. degree in 2002 from the same department of METU where she is currently teaching. She worked at General Electric Company for 11 years undertaking various responsibilities and worked on the design of aerodynamics, heat transfer and blade cooling of gas turbines. She is the recipient of the UTSR Gas Turbine Industrial Fellowship Award (2003), the Critical Difference for Women Fellowship (2008), and the ASME Best Technical Paper Award (2013), and received several company awards at GE. She was awarded the ASME Gas Turbine Award in 2015. Her research   interests   include   design   of   gas turbines, engine aerothermodynamics and cooling systems, computational fluid dynamics and experimental techniques.


[1] U. K. Mallela and A. Upadhyay, “Buckling Load Prediction of Laminated Composite Stiffened Panels Subjected to In-Plane Shear Using Artificial Neural Networks,” Thin-Walled Structures, 102: 158-164, 2016.
[2] A. Z. Al-Garni, “Neural Network-Based Failure Rate for Boeing-737 Tires,” Journal of Aircraft, 34: 771-777, 1997.
[3] F. Mazhar, A. M. Khan, I. A. Chaudhry, and M. Ahsan, “On Using Neural Networks in UAV Structural Design for CFD Data Fitting and Classification,” Aerospace Science and Technology, 30: 210-225, 2013.
[4] S. J. Schreck, W. E. Faller, and M. W. Luttges, “Neural Network Prediction of Three-Dimensional Unsteady Separated Flowfields,” Journal of Aircraft, 32: 178-185, 1995.
[5] J. Yu and J. S. Hesthaven, “Flowfield Re-construction Method Using Artificial Neural Network,” AIAA Journal, 57: 482-498, 2019.
[6] Z. Li, X. Sun, C. Hu, G. Liu, and B. He, “Neural Network Based Online Predictive Guidance for High Lifting Vehicles,” Aerospace Science and Technology, 82-83:149-160, 2018.
[7] L. Huang, C. Ma, Y. Li, J. Gao, and M. Qi, “Applying Neural Networks (NN) to the Improvement of Gasoline Turbocharger Heat Transfer Modeling,” Applied Thermal Engineering, 141: 1080-1091, 2018.
[8] R. G. Peyvandi and S. Z. I. Rad, “Precise Prediction of Radiation Interaction Position in Plastic Rod Scintillators Using a Fast and Simple Technique: Artificial Neural Network,” Nuclear Engineering and Technology, 50: 1154-1159, 2018.
[9] K. Ye, Y. Zhang, L. Yang, Y. Zhao, N. Li, and C. Xie, “Modeling Convective Heat Transfer of Supercritical Carbon Dioxide Using an Artificial Neural Network,” Applied Thermal Engineering, 150: 686-695, 2019.
[10] A. Mitra, A. Majumdar, P. K. Majumdar, and D. Bannerjee, “Predicting Thermal Resistance of Cotton Fabrics by Artificial Neural Network Model,” Experimental Thermal and Fluid Science, 50:172-177, 2013.
[11] G. D. Nicola, M. Pierantozzi, G. Petrucci, and R. Stryjek, “Equation for the Thermal Conductivity of Liquids and an Artificial Neural Network,” Journal of Thermophysics and Heat Transfer, 30: 651-660, 2016.
[12] M. H. Esfe, M. R. H. Ahangar, D. Toghraie, M. H. Hajmohammad, H. Rostamian, and H. Tourang, “Designing Artificial Neural Network on Thermal Conductivity of Al2O3-Water-EG (60-40%) Nano-fluid Using Experimental Data,” Journal of Thermal Analysis and Calorimetry, 126: 837-843, 2016.
[13] ANSYS Fluent User’s Guide, Release 18.1, Canonsburg, PA: ANSYS, Inc., 2017.
[14] SAE International,“Aerothermodynamics Systems Engineering and Design”, Warrendale, PA, SAE Aerospace Information Report, 1168/3, 1990.
[15] S. Marsland, Machine Learning: An Algorithmic Perspective, 2nd ed. Boca Raton, FL: CRC Press, 2015.
[16] D. Svozil, V. Kvasnicka, and J. Pospichal, “Introduction To Multi-Layer Feed-Forward Neural Networks,” Chemometrics and Intelligent Laboratory Systems, 39: 43-62, 1997.
[17] D. C. Montgomery, Design and Analysis of Experiments, 8th ed. Hoboken, NJ: John Wiley & Sons, 2009.
[18] MATLAB User Guide, Release 2016a, Natick, MA: The MathWorks Inc., 2016.
[19] D. J. C. Mackay, “A Practical Bayesian Framework for Backpropagation Networks,” Neural Computation, 4: 448-472, 1992.
[20] A. T. Goh, “Some Civil Engineering Applications of Neural Networks,” Proceedings of the Institution of Civil Engineers - Structures and Buildings, 104:463–469, 1994.
How to Cite
A. Akın and H. Kahveci, “Use of Artificial Neural Network in Rotorcraft Cooling System”, JAST, vol. 12, no. 2, pp. 157-170, Jul. 2019.