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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2021, Vol. 42 ›› Issue (4): 524353-524353.doi: 10.7527/S1000-6893.2020.24353

• Solid Mechanics and Vehicle Conceptual Design • Previous Articles     Next Articles

Sensitivity analysis of key design parameters of commercial aircraft using deep neural network

FAN Zhouwei1, YU Xiongqing1, WANG Chao2, ZHONG Bowen2   

  1. 1. Key Laboratory of Fundamental Science for National Defense-Advanced Design Technology of Flight Vehicle, College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
    2. Beijing Aeronautical Science & Technology Research Institute, COMAC, Beijing 102211, China
  • Received:2020-06-01 Revised:2020-08-18 Published:2020-09-02
  • Supported by:
    Funded by Beijing Key Laboratory of Civil Aircraft Design and Simulation Technology, Beijing Aeronautical Science & Technology Research Institute, Commercial Aircraft Corporation of China

Abstract: The sensitivity analysis of key design parameters of aircraft reveals the relationship between the key design parameters and aircraft characteristics, facilitating the decision making in aircraft preliminary design. Aiming at the key design parameter sensitivity of wide-body commercial aircraft, we establish a deep neural network model based on the features of key design parameters and aircraft characteristics and the coupling relationship among multiple disciplines, taking the key design parameters as input to predict the aircraft characteristics. In this model, multiple input layers, multiple output layers, and multiple blocks of hidden layers are set to simulate the effects of key design parameters on aircraft characteristics and the interactions among different aircraft characteristics. Comparisons with traditional surrogate models reveal that the deep neural network model has higher prediction accuracy and better adaptability to the aircraft characteristics. The proposed model is then used to analyze the sensitivity of the commercial aircraft primary parameters. The analysis results show that a lower maximum takeoff weight and a shorter takeoff balanced field length can be achieved when the wing sweep at 1/4 chord is between 30° to 31.5°. The maximum static thrust of engines at sea level and the wing area have the most significant influence on the direct operation cost, maximum takeoff weight, and other characteristics.

Key words: aircraft conceptual design, sensitivity analysis, deep neural network, commercial aircraft, surrogate model

CLC Number: