Use of Artificial Neural Network in Rotorcraft Cooling System
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.
 A. Z. Al-Garni, “Neural Network-Based Failure Rate for Boeing-737 Tires,” Journal of Aircraft, 34: 771-777, 1997.
 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.
 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.
 J. Yu and J. S. Hesthaven, “Flowfield Re-construction Method Using Artificial Neural Network,” AIAA Journal, 57: 482-498, 2019.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 ANSYS Fluent User’s Guide, Release 18.1, Canonsburg, PA: ANSYS, Inc., 2017.
 SAE International,“Aerothermodynamics Systems Engineering and Design”, Warrendale, PA, SAE Aerospace Information Report, 1168/3, 1990.
 S. Marsland, Machine Learning: An Algorithmic Perspective, 2nd ed. Boca Raton, FL: CRC Press, 2015.
 D. Svozil, V. Kvasnicka, and J. Pospichal, “Introduction To Multi-Layer Feed-Forward Neural Networks,” Chemometrics and Intelligent Laboratory Systems, 39: 43-62, 1997.
 D. C. Montgomery, Design and Analysis of Experiments, 8th ed. Hoboken, NJ: John Wiley & Sons, 2009.
 MATLAB User Guide, Release 2016a, Natick, MA: The MathWorks Inc., 2016.
 D. J. C. Mackay, “A Practical Bayesian Framework for Backpropagation Networks,” Neural Computation, 4: 448-472, 1992.
 A. T. Goh, “Some Civil Engineering Applications of Neural Networks,” Proceedings of the Institution of Civil Engineers - Structures and Buildings, 104:463–469, 1994.
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