To obtain some basic laws for bird-strike resistance of composite materials in aeronautical application, the high-velocity impact behaviors of composite laminates with different materials were studied by numerical methods. The smoothed particle hydrodynamics (SPH) and finite element method (FEM) coupling models were validated from various perspectives, and the numerical results were comparatively investigated. Results show that the different composite materials have relatively little effect on projectile deformations during the bird impact. However, the impact-damage distributions can be significantly different for different composite materials. The strength parameters and fracture energy parameters play different roles in different damage modes. Lastly, modal frequency was tentatively used to explain the damage behavior of the composite laminates, for it can manifest the mass and stiffness characteristics of a dynamic structure. The dynamic properties and strength properties jointly determine the impact-damage resistance of composite laminates under bird strike. Future optimization study can be considered from these two aspects.

With the extensive application of sensor technology, airlines accumulate a lot of flight data during fleet operations. Quick Access Recorder (QAR) is an important basis for aircraft state estimation and safety assessment, it records the operating status of various aircraft systems. However, the current application of QAR is mainly reflected in the investigation and verification of accidents, but it fails to apply such continuous real-time data to safety monitoring, risk prediction, and early warning. Therefore, this paper proposes a data-driven integrated safety risk warning model based on deep learning. Combined with the characteristics of QAR data and aircraft system failures, the failure mode and impact analysis (FMEA), cause chain analysis (CCA), long and short-term memory (LSTM), and other methods are used to describe the development of risks caused by system failures and predict the trend of flight parameters of each system, improve the ability to predict the risk and risk severity of the aviation system. Taking the Aircraft Refrigeration System (ARS) as the case object, the safety risk warning model is validated to calculate the risk level may face and find out the hidden hazard sources of civil aircraft operation in advance, to improve the safety level of civil aircraft operation

Composite laminates can potentially reduce the weight of aircrafts; however, they are subjected to bird strike hazards in civil aviation. To handle their nonlinear dynamic behaviour, in this study, the impact damage of composite laminates were numerically evaluated and designed by means of smoothed particle hydrodynamics (SPH) and the finite element method (FEM) to simulate the interaction between bird projectiles and the laminates. Attention was mainly focused on the different damage modes in various laminates’ plies induced by bird impact on a square laminated plate. A continuum damage mechanics approach was exploited to simulate damage initiation and evolution in composite laminates. Damage maps were computed with respect to different ply angles, i.e., 0°, 45° and −45°. The damage distributions were comparatively investigated, and then the ply design was considered for crashworthiness improvement. The results aim to serve as a design guideline for future prototype-scale bird strike studies of complex laminated structures.

The application of artificial intelligence in designing the future aircraft cockpit may lead to conflicts especially during the human-computer decision-making process. To harmonize the human-computer relationship, the evolutionary game and collaboration mechanism based on system dynamics (SDs) is proposed in this article. The evolutionary game model is established containing the flight crew and the intelligent decision-making system as players. The profit matrix of different game strategies is constructed, and the time-varying dynamic characteristics of the human-computer evolution game is analyzed. Based on the SDs theory, the human-computer collaboration model is constructed. Its functional component modules are respectively defined by the behavioral characteristics of pilots, decision-making performance, and human-computer evolutionary game. The coupling relationship between them is also presented. With the background of typical task scenarios, a dynamic simulation case of the human-computer collaboration is given. The impacts of task load change rate, task load level, and decision-making performance related parameters are analyzed. The evolutionary laws of human-computer game strategies are thereby revealed. This work can provide modeling methods for human-computer interaction in the aircraft cockpit.

Modern civil aircraft is designed for a greater workload. While the volume of air traffic has grown exponentially over the past two decades, the total number of accidents has been flat or slightly higher than past averages. However, the fatality and property losses caused by aviation accidents are unbearable even if they happen only once. As reported by a Statistical summary from Boeing in 2019, nearly 40% of the deaths occurred during the final approach and landing. A deep learning-based model for accident-type prediction during approach and landing was established, which explores the implied association between accident types and accident factors from the risk factors affecting the occurrence of unsafe events. First, based on the investigation reports of civil aircraft fatal accidents in the recent 43 years, 20 common accident risk factors were extracted by the human factors analysis and classification system (HFACS) method. Implicit association of accident factors was excavated by association rules and 6 key factors were extracted from 20 factors. Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Elman Neural Network (ElmanNN) applicable to the classification were selected for multiple learning and training. The classification results showed that the comprehensive prediction accuracy of accident types reaches 86.7%, which can effectively determine the types of accidents that may occur during the approach and landing phases of civil aircraft, make emergency measures in advance, and incorporate key accident factors into the assessment scope of civil aircraft safety management system to ensure the aviation operation safety.