Structural Optimization of the Aircraft NACA Inlet Based on BP Neural Networks and Genetic Algorithms
With the development of the increasing demand for cooling air in cabin and electronic components on aircraft, it urges to present an energy-efficient optimum method for the ram air inlet system. A ram air performance evaluation method is proposed, and the main structural parameters can be extended t...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8857821 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832546959481634816 |
|---|---|
| author | Zhimao Li Changdong Chen Houju Pei Benben Kong |
| author_facet | Zhimao Li Changdong Chen Houju Pei Benben Kong |
| author_sort | Zhimao Li |
| collection | DOAJ |
| description | With the development of the increasing demand for cooling air in cabin and electronic components on aircraft, it urges to present an energy-efficient optimum method for the ram air inlet system. A ram air performance evaluation method is proposed, and the main structural parameters can be extended to a certain type of aircraft. The influence of structural parameters on the ram air performance is studied, and a database for the performance is generated. A new method of integrating the BP neural networks and genetic algorithm is used for structure optimization and is proven effective. Moreover, the optimum result of the structure of the NACA ram air inlet system is deduced. Results show that (1) the optimization algorithm is efficient with less prediction error of the mass flow rate and fuel penalty. The average relative error of the mass flow rate is 1.37%, and the average relative error of the fuel penalty is 1.41% in the full samples. (2) Predicted deviation analysis shows very little difference between optimized and unoptimized design. The relative error of the mass flow rate is 0.080% while that of the fuel penalty is 0.083%. The accuracy of the proposed optimization method is proven. (3) The mass flow rate after optimization is increased to 2.506 kg/s, and the fuel penalty is decreased by 74.595 Et kg. The BP neural networks and genetic algorithms are studied to optimize the design of the ram air inlet system. It is proven to be a novel approach, and the efficiency can be highly improved. |
| format | Article |
| id | doaj-art-908d0e7103284fa480b0be6736cc2010 |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-908d0e7103284fa480b0be6736cc20102025-02-03T06:46:38ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742020-01-01202010.1155/2020/88578218857821Structural Optimization of the Aircraft NACA Inlet Based on BP Neural Networks and Genetic AlgorithmsZhimao Li0Changdong Chen1Houju Pei2Benben Kong3College of Aerospace Engineering, Key Laboratory of Aircraft Environmental Control and Life Support, MIIT, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaAVIC Nanjing Servo Control System Co., Ltd., Nanjing 210032, ChinaCollege of Aerospace Engineering, Key Laboratory of Aircraft Environmental Control and Life Support, MIIT, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Aerospace Engineering, Key Laboratory of Aircraft Environmental Control and Life Support, MIIT, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaWith the development of the increasing demand for cooling air in cabin and electronic components on aircraft, it urges to present an energy-efficient optimum method for the ram air inlet system. A ram air performance evaluation method is proposed, and the main structural parameters can be extended to a certain type of aircraft. The influence of structural parameters on the ram air performance is studied, and a database for the performance is generated. A new method of integrating the BP neural networks and genetic algorithm is used for structure optimization and is proven effective. Moreover, the optimum result of the structure of the NACA ram air inlet system is deduced. Results show that (1) the optimization algorithm is efficient with less prediction error of the mass flow rate and fuel penalty. The average relative error of the mass flow rate is 1.37%, and the average relative error of the fuel penalty is 1.41% in the full samples. (2) Predicted deviation analysis shows very little difference between optimized and unoptimized design. The relative error of the mass flow rate is 0.080% while that of the fuel penalty is 0.083%. The accuracy of the proposed optimization method is proven. (3) The mass flow rate after optimization is increased to 2.506 kg/s, and the fuel penalty is decreased by 74.595 Et kg. The BP neural networks and genetic algorithms are studied to optimize the design of the ram air inlet system. It is proven to be a novel approach, and the efficiency can be highly improved.http://dx.doi.org/10.1155/2020/8857821 |
| spellingShingle | Zhimao Li Changdong Chen Houju Pei Benben Kong Structural Optimization of the Aircraft NACA Inlet Based on BP Neural Networks and Genetic Algorithms International Journal of Aerospace Engineering |
| title | Structural Optimization of the Aircraft NACA Inlet Based on BP Neural Networks and Genetic Algorithms |
| title_full | Structural Optimization of the Aircraft NACA Inlet Based on BP Neural Networks and Genetic Algorithms |
| title_fullStr | Structural Optimization of the Aircraft NACA Inlet Based on BP Neural Networks and Genetic Algorithms |
| title_full_unstemmed | Structural Optimization of the Aircraft NACA Inlet Based on BP Neural Networks and Genetic Algorithms |
| title_short | Structural Optimization of the Aircraft NACA Inlet Based on BP Neural Networks and Genetic Algorithms |
| title_sort | structural optimization of the aircraft naca inlet based on bp neural networks and genetic algorithms |
| url | http://dx.doi.org/10.1155/2020/8857821 |
| work_keys_str_mv | AT zhimaoli structuraloptimizationoftheaircraftnacainletbasedonbpneuralnetworksandgeneticalgorithms AT changdongchen structuraloptimizationoftheaircraftnacainletbasedonbpneuralnetworksandgeneticalgorithms AT houjupei structuraloptimizationoftheaircraftnacainletbasedonbpneuralnetworksandgeneticalgorithms AT benbenkong structuraloptimizationoftheaircraftnacainletbasedonbpneuralnetworksandgeneticalgorithms |