The International Civil Aviation Organisation (ICAO) audits its Member States for safety oversight and monitors its Effective Implementation (EI). The global average EI was 68.83% in 2019, with 46% audited Member States achieving 75% EI (2022 target); however, an ICAO (2016 – 2018) audit highlighted six Annex 13 non-compliance issues. One issue was that more than 60% of Member States do not have a comprehensive and detailed investigator training program, contributing to many shortcomings that include a lack of essential and volatile evidence preservation, investigation management, investigation reporting and/or safety recommendations. This paper proposes an Expert System that captures knowledge in aircraft accident investigation generated over many years and allows aircraft investigators to share, access and interrogate accumulated knowledge to support the aircraft accident investigation process. The Expert System will improve the evidence analysing timeline, conclusion consistency and accuracy and support the on-the-job “field” training of evidence analysis through self-discovery.

With the development of China’s economy, China’s aviation market has expanded, and related industries have also developed rapidly. For the long-term development of the industry, many countries and enterprises began to make demand forecasts with different levels for the product market. The same is true for China’s civil aircraft-related industries. There are a variety of predictive models, but not all of them are appropriate for the prediction of civil aircraft market demand. This paper introduces a variety of modelling methods for forecasting models, including time series forecasting models and causal analysis forecasting models. The contribution of our work is the adoption of a new coefficient determination method to establish a variable-weight combination forecasting model, which greatly improves the forecasting accuracy. In addition, we also propose a new and more stable prediction model, the chain prediction model. Simulation prediction is carried out for each model in this work. Through the analysis and comparison of the prediction results, we conclude that the prediction effects of the variable weight combination prediction model and the chain prediction model are superior to those of other single prediction models. The chain prediction model in particular has better performance in medium- and long-term prediction, compared with the other prediction models. Finally, the model is applied to predict the demand of Chinese civil aircraft in the next 20 years, which confirms that the Chinese civil aircraft market will expand in the future.

Unmanned Combat Aerial Vehicles (UCAVs) are believed by many to be the future of aerial strike/reconnaissance capability. This belief led to the design of the UCAV 1303 by Boeing Phantom Works and the US Airforce Lab in the late 1990s. Because UCAV 1303 is expected to take on a wide range of mission roles that are risky for human pilots, it needs to be highly adaptable. Geometric morphing can provide such adaptability and allow the UCAV 1303 to optimise its physical feature mid-flight to increase the lift-to-drag ratio, manoeuvrability, cruise distance, flight control, etc. This capability is extremely beneficial since it will enable the UCAV to reconcile conflicting mission requirements (e.g. loiter and dash within the same mission). In this study, we conduct several modifications to the wing geometry of UCAV 1303 via Computational Fluid Dynamics (CFD) to analyse its aerodynamic characteristics produced by a range of different wing geometric morphs. Here we look into two specific geometric morphing wings: linear twists on one of the wings and linear twists at both wings (wash-in and washout). A baseline CFD of the UCAV 1303 without any wing morphing is validated against published wind tunnel data, before proceeding to simulate morphing wing configurations. The results show that geometric morphing wing influences the UCAV-1303 aerodynamic characteristics significantly, improving the coefficient of lift and drag, pitching moment and rolling moment.

During early phases of wing design, analytic and low-fidelity methods are often used to identify promising design concepts. In many cases, solutions obtained using these methods provide intuition about the design space that is not easily obtained using higher-fidelity methods. This is especially true for aerostructural design. However, many analytic and low-fidelity aerostructural solutions are limited in application to wings with specific planforms and weight distributions. Here, a numerical method for minimising induced drag with structural constraints is presented that uses approximations that apply to unswept planar wings with arbitrary planforms and weight distributions. The method is applied to the National Aeronautics and Space Administration (NASA) Ikhana airframe to show how it can be used for rapid aerostructural optimisation and design-space exploration. The design space around the optimum solution is visualised, and the sensitivity of the optimum solution to changes in weight distribution, structural properties, wing loading and taper ratio is shown. The optimum lift distribution and wing-structure weight for the Ikhana airframe are shown to be in good agreement with analytic solutions. Whereas most modern high-fidelity solvers obtain solutions in a matter of hours, all of the solutions shown here can be obtained in a matter of seconds.

Stratospheric airships are lighter-than-air vehicles that work at an altitude of 20km in the lower calm portion of the stratosphere. They can be used as real-time surveillance platforms for environment monitoring and civil communication. Solar energy is the ideal power choice for long-endurance stratospheric airships. Attitude control is important for airships so that they can point at a target for observation or adjust the attitude to improve the output performance of solar panels. Stratospheric airships have a large volume and semi-flexible structure. The typical actuators used are aerodynamic surfaces, vectored thrust and ballonets. However, not all these actuators can work well under special working conditions, such as low density and low speed. In this study, moving-mass control is introduced to stratospheric airships because its control efficiency is independent of airspeed and atmospheric density. A nonlinear feedback controller based on generalised inverse with a nonlinear mapping module is designed to implement moving-mass control. Such a new station keeping scheme with moving masses is proposed for airships with different working situations.

In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.

The use of a submerged inlet is advantageous in modern aircrafts because of its low drag resistance, small radar cross section and ease of maintenance. Although it is well known that the forebody boundary layer deteriorates the aerodynamic performance of a submerged inlet, the level of impact has not yet been fully quantified. To quantify the forebody boundary-layer effect, a submerged diverter was designed to remove a portion of the low-energy boundary flow. The flow pattern and aerodynamic performance of a submerged inlet, with and without the diverter, were investigated by wind-tunnel experimentation and numerical simulations. The effects of mass flow, free stream speed, angle-of-attack and sideslip angle on the aerodynamic characteristics of the inlet with and without the submerged diverter were studied, over an operating envelope of M0 = 0.3 ∼ 0.6, $\alpha$ = –6$^{\circ}$ ∼ 8$^{\circ}$ and $\beta$ = 0$^{\circ}$ ∼ 4$^{\circ}$. The results indicate that both the total pressure loss and the circumferential distortion can be significantly reduced with the removal of the forebody boundary-layer low-energy flow. Meanwhile, the main mechanisms for losses in the submerged inlet were also analysed.