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The purpose of this paper is to assess the risk of controlled flight into terrain (CFIT) for airlines and to develop a practical method for evaluating and predicting CFIT risk to ensure safe and efficient airline operations.

In accordance with the monitoring project specification issued by the Flight Standards Department of the Civil Aviation Administration of China (CAAC), a preliminary draft of evaluation indicators for CFIT risk was developed based on the literature review and semi-structured interviews. Fifteen aviation experts were then selected and invited to participate in a Delphi method to revise the draft. Analytic hierarchy process (AHP) and entropy weight method were used to determine the combined weight of the indicators. The variable fuzzy set model and quick access recorder (QAR) data were applied to evaluate the CFIT risk of an airline from 2007 to 2018, and the classification results were compared with actual operational data.

The research findings reveal that the six most significant monitoring items affecting CFIT risk are incorrect configuration settings during landing, loss of altitude during climbing, ground proximity warning, G/S deviation, flap extension delay during landing and incorrect takeoff configuration. The CFIT risk of airlines has shown an increasing trend since 2015. The values in 2010, 2017 and 2018 were greater than 2 and less than 2.5, indicating that the CFIT risk is at Level 2, close to Level 3, and the risk is low but approaching medium.

Using the combination weight determined by AHP and entropy weight method to rank the weight of 15 monitoring items, airlines can take necessary measures (simulator training, knowledge training) to reduce the occurrence of monitoring items with high weight to reduce CFIT risk. This risk assessment method can quantitatively evaluate the CFIT risk of airlines and provide theoretical guidance and technical support for airlines to formulate safety management measures and flight training programs, enabling the interconnection between QAR data and flight quality.

The proposed method in this study differs from traditional approaches by offering a quantitative assessment of CFIT risk for airlines and enabling the interconnection between QAR data and flight quality.

Semantic modelling is an essential prerequisite for designing the intelligent human–computer interaction in future aircraft cockpit. The purpose of this paper is to outline an ontology-based solution to this issue.

The scenario elements are defined considering the cognitive behaviours, system functions, interaction behaviours and interaction situation. The knowledge model consists of a five-tuple array including concepts, relations, functions, axioms and instances. Using the theory of belief-desire-intention, the meta-model of cognitive behaviours is established. The meta-model of system functions is formed under the architecture of sub-functions. Supported by information flows, the meta-model of interaction behaviours is presented. Based on the socio-technical characteristics, the meta-model of interaction situation is proposed. The knowledge representation and reasoning process is visualized with the semantic web rule language (SWRL) on the Protégé platform. Finally, verification and evaluation are carried out to assess the rationality and quality of the ontology model. Application scenarios of the proposed modelling method are also illustrated.

Verification results show that the knowledge reasoning based on SWRL rules can further enrich the knowledge base in terms of instance attributes and thereby improve the adaptability and learning ability of the ontology model in different simulations. Evaluation results show that the ontology model has a good quality with high cohesion and low coupling.

The approach presented in this paper can be applied to model complex human–machine–environment systems, from a semantics-driven perspective, especially for designing future cockpits.

Different from the traditional approaches, the method proposed in this paper tries to deal with the socio-technical modelling issues concerning multidimensional information semantics. Meanwhile, the constructed model has the ability of autonomous reasoning to adapt to complex situations.

The purpose of this paper is to analyze the bird impact damage of fuselage composite stiffened structures by numerical method and to evaluate the damage and the bird impact resistance of different structures.

The deformation and damage of composite stiffened plates during bird impact are numerically analyzed by the explicit finite element software LS-DYNA. A comparative study on the numerical calculation results was conducted by using SPH (Smoothed Particle Hydrodynamics)-FEM (Finite Element Method) modeling and simulation. First, the I-shaped, T-shaped, straight stiffened plates and unstiffened plate were designed. Second, the accuracy of the bird model was verified and further used to evaluate bird strikes on composite stiffened plate. Third, the results of damage modes as well as displacements of the stiffened plates were compared.

The stiffeners can increase the local stiffness of the composite panel, which can effectively inhibit the bird’s movement along the impact direction. Adding stiffeners can change the panel matrix tension damage from global distribution to local distribution mode; however, the impact damage distribution and the ability to inhibit damage propagation can differ for different stiffened panels. Especially, the I-stiffened panel exhibits a better anti-bird strike performance.

The analysis of geometric parameters of structural components by numerical methods can reduce the cost of the design phase and has been widely used in aircraft design. The present study evaluated the bird impact damage of composite stiffened plates with different structures, which provides a guideline for selecting the stiffened plate structure in the fuselage skin.

About 70% of all aircraft accidents are caused by human–machine interaction, thus identifying and quantifying performance shaping factors is a significant challenge in the study of human reliability. An information flow field model of human–machine interaction is put forward to help better pinpoint the factors influencing performance and to make up for the lack of a model of information flow and feedback processes in the aircraft cockpit. To enhance the efficacy of the human–machine interaction, this paper aims to examine the important coupling factors in the system using the findings of the simulation.

The performance-shaping factors were retrieved from the model, which was created to thoroughly describe the information flow. The coupling degree between the performance shaping factors was calculated, and simulation and sensitivity analysis are based on system dynamics.

The results show that the efficacy of human–computer interaction is significantly influenced by individual important factors and coupling factors. To decrease the frequency of accidents after seven hours, attention should be paid to these factors.

The novelty of this work lies in proposing a theoretical model of cockpit information flow and using system dynamics to analyse the effect of the factors in the human–machine loop on human–machine efficacy.

The use of aviation incident data to carry out aviation risk prediction is of great significance for improving the initiative of accident prevention and reducing the occurrence of accidents. Because of the nonlinearity and periodicity of incident data, it is challenging to achieve accurate predictions. Therefore, this paper aims to provide a new method for aviation risk prediction with high accuracy.

This paper proposes a hybrid prediction model incorporating Prophet and long short-term memory (LSTM) network. The flight incident data are decomposed using Prophet to extract the feature components. Taking the decomposed time series as input, LSTM is employed for prediction and its output is used as the final prediction result.

The data of Chinese civil aviation incidents from 2002 to 2021 are used for validation, and Prophet, LSTM and two other typical prediction models are selected for comparison. The experimental results demonstrate that the Prophet–LSTM model is more stable, with higher prediction accuracy and better applicability.

This study can provide a new idea for aviation risk prediction and a scientific basis for aviation safety management.

The innovation of this work comes from combining Prophet and LSTM to capture the periodic features and temporal dependencies of incidents, effectively improving prediction accuracy.

The purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.

This paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors.

The global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models.

Unlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management.

This study aims to solve the problem that the traditional hierarchically performed hazard origin and propagation studies (HiP-HOPS) cannot make dynamic model for the complex system such as integrated modular avionics (IMA) system.

A new combination method that combines HiP-HOPS with architecture analysis and design language (AADL) is proposed.

The combination method potentially reduces the amount of rework required for safety analysis and modelling of a modified design.

Modelling the IMA system with the combination method can just make qualitative analysis but cannot make quantitative analysis.

The static model depicts the fault propagation among the components while the dynamic model describes the composite fault with AADL for IMA system.

The results of the case study show that the proposed method not only keeps model consistency but also makes safety analysis and modelling for IMA system efficiently.

The purpose of this paper is to introduce the multidisciplinary design optimization method using approximation model for the aircraft engine fan blade based on the airworthiness compliance such as stress, vibration, and bird impact.

Firstly, the airworthiness analysis of the typical fan blade was carried out based on the numerical simulation. Secondly, the design of experiment (DOE) was utilized to construct the approximation model of the fan blade. Finally, the airworthiness optimization of fan blade was carried out based on Kriging approximation model.

The numerical simulation result shows that the analysis method can show the airworthiness compliance in the design stage. And the optimization result shows that structure, bird impact and vibration characteristics improve obviously, satisfying the constraints conditions of optimization.

The multidisciplinary design optimization method of fan blade based on the airworthiness and approximation model is presented and achieved.

The purpose of this paper is to introduce parametered modeling technology for the civil aircraft engine fan blade, to design the fan blade rapidly and accurately.

The entire fan blade consists of three crucial parts: blade airfoil, tenon and airfoil root. Blade airfoil with a free surface feature is formed through the blade profiles from the hub to tip in the radial direction. The non‐uniform rational basis spline (NURBS) is utilized to describe the blade profile. The geometry model of fan blade tenon is generated by extruding the sketch of the tenon. And the fillet section is designed to achieve the smooth transition of the up surface and the bottom surface of the blade root. Furthermore, the fan blade of a typical commercial engine is redesigned by the above method.

The stress analysis of the fan blade shows that the fan blade model designed in this work is reasonable.

The parametered fan blade model is presented on the basics of feature‐based modeling technology.

This paper aims to propose a method of hazards identification of uncontained engine rotor failure (UERF) based on collision detection between geometric models. UERF is a typical particular risk that imposes threat on flight safety of an aircraft. Aircraft systems are made up of many parts and components; therefore, it is difficult to identify hazards caused by UERF early in the design cycle.

The methodology involves the following steps: the mapping relationship of input information is established; the parametric models are used to simulate the uncontained fragments of different categories; the parts and components that the uncontained fragment may collide with are determined by uniform space decomposition and precise collision detection; and the catastrophic hazards are identified with the comparison of the collision detection result sets and the minimum cut sets.

An application case, which takes the hydraulic system of a certain type of civil aircraft in design as a study object, shows that the method proposed in this paper is suitable and efficient for hazards identification of UERF.

The method proposed herein is useful for acquiring the minimum cut sets that will be triggered by the uncontained fragment in the design phase.

A novel and effective method of hazards identification of UERF for an aircraft with large and complex systems is presented, which is helpful to the optimization of the layout design of parts and components of the aircraft.