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

Abstract

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.

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Published
2019-07-12
How to Cite
[1]
A. Akın and H. Kahveci, “Use of Artificial Neural Network in Rotorcraft Cooling System”, JAST, vol. 12, no. 2, pp. 157-170, Jul. 2019.
Section
Articles