دورية أكاديمية

Structural Optimization of the Aircraft NACA Inlet Based on BP Neural Networks and Genetic Algorithms

التفاصيل البيبلوغرافية
العنوان: Structural Optimization of the Aircraft NACA Inlet Based on BP Neural Networks and Genetic Algorithms
المؤلفون: Zhimao Li, Changdong Chen, Houju Pei, Benben Kong
المصدر: International Journal of Aerospace Engineering, Vol 2020 (2020)
بيانات النشر: Hindawi Limited, 2020.
سنة النشر: 2020
المجموعة: LCC:Motor vehicles. Aeronautics. Astronautics
مصطلحات موضوعية: Motor vehicles. Aeronautics. Astronautics, TL1-4050
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1687-5966
1687-5974
Relation: https://doaj.org/toc/1687-5966; https://doaj.org/toc/1687-5974
DOI: 10.1155/2020/8857821
URL الوصول: https://doaj.org/article/908d0e7103284fa480b0be6736cc2010
رقم الأكسشن: edsdoj.908d0e7103284fa480b0be6736cc2010
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:16875966
16875974
DOI:10.1155/2020/8857821