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...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Turkish Air Force Academy
2019-07-01
|
| Series: | Havacılık ve Uzay Teknolojileri Dergisi |
| Subjects: | |
| Online Access: | http://www.jast.hho.edu.tr/JAST/index.php/JAST/article/view/373/298 |
| _version_ | 1797910506167599104 |
|---|---|
| author | Altuğ Akın Harika S. Kahveci |
| author_facet | Altuğ Akın Harika S. Kahveci |
| author_sort | Altuğ Akın |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T11:25:27Z |
| format | Article |
| id | doaj.art-543aa22036a94db8a48fc6ba9636023d |
| institution | Directory Open Access Journal |
| issn | 1304-0448 1304-0448 |
| language | English |
| last_indexed | 2024-04-10T11:25:27Z |
| publishDate | 2019-07-01 |
| publisher | Turkish Air Force Academy |
| record_format | Article |
| series | Havacılık ve Uzay Teknolojileri Dergisi |
| spelling | doaj.art-543aa22036a94db8a48fc6ba9636023d2023-02-15T16:18:25ZengTurkish Air Force AcademyHavacılık ve Uzay Teknolojileri Dergisi1304-04481304-04482019-07-01122157170Use of Artificial Neural Network in Rotorcraft Cooling SystemAltuğ Akın0Harika S. Kahveci1Middle East Technical UniversityMiddle East Technical UniversityIn 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.http://www.jast.hho.edu.tr/JAST/index.php/JAST/article/view/373/298artificial neural networkcomputational fluid dynamicsheat transferrotorcraft |
| spellingShingle | Altuğ Akın Harika S. Kahveci Use of Artificial Neural Network in Rotorcraft Cooling System Havacılık ve Uzay Teknolojileri Dergisi artificial neural network computational fluid dynamics heat transfer rotorcraft |
| title | Use of Artificial Neural Network in Rotorcraft Cooling System |
| title_full | Use of Artificial Neural Network in Rotorcraft Cooling System |
| title_fullStr | Use of Artificial Neural Network in Rotorcraft Cooling System |
| title_full_unstemmed | Use of Artificial Neural Network in Rotorcraft Cooling System |
| title_short | Use of Artificial Neural Network in Rotorcraft Cooling System |
| title_sort | use of artificial neural network in rotorcraft cooling system |
| topic | artificial neural network computational fluid dynamics heat transfer rotorcraft |
| url | http://www.jast.hho.edu.tr/JAST/index.php/JAST/article/view/373/298 |
| work_keys_str_mv | AT altugakın useofartificialneuralnetworkinrotorcraftcoolingsystem AT harikaskahveci useofartificialneuralnetworkinrotorcraftcoolingsystem |