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.
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