Coupling of clearance joint and harsh aerodynamic heating environment is an inevitable nonlinear factor in folding mechanism of the fin of high-speed aircrafts that remarkably modifies natural frequencies and modes of vibration from the initial design state. However, accurately predicting dynamic properties of deployable fin with full consideration of these effects is not common industry practice. A practical semi-analytical model based on Hertz contact theory and ESDU-78035 model is proposed in this study to investigate high-temperature connection stiffness of local hinged–locked mechanisms. Material property degradation and clearance variation caused by thermal expansion are comprehensively considered and quantified in this model. Vibration characteristics of the assembled deployable fin are then solved using finite element method (FEM). The real-time evolutionary process of thermal mode of the fin is discussed. And natural frequencies of fixed-value and time-varying connection stiffness are compared. The simulation results of this study demonstrate that the relative error of structure temperature between the sequential approach and fully coupled simulations is less than 6.98%. The connection stiffness (slope of the load-displacement curve) of the folding mechanism under high temperature conditions decreases by 3.52%, and the variation is mainly caused by the degradation of the elastic modulus of the material, while the clearance change due to the thermal expansion has no significant effect on the slope. The natural frequency of the deployable fin exhibits an inverse correlation with the temperature change trend, and the first three frequencies decrease by 1.67, 7.75, and 16.28 Hz compared to the initial value, respectively.

Tight budgets often limit the scope of test campaigns within the development programmes of small uncrewed air vehicles (UAVs). This paper explores a range of combinations of instrumentation suites and protocols for both wind tunnel and flight evaluation, focusing on the key aspect of drawing up the drag curve of the airframe. Through extensive testing of a 5kg maximum take-off mass, fixed wing, twin motor, richly instrumented test platform, we show that automated glides over a range of airspeeds and the slow down manoeuvre are effective ways of determining power-off drag, while estimating thrust from propeller speed, and voltage and current sensing based methods work well for the power-on case. We also seek the most time-efficient and robust mix of the above manoeuvres to yield a given drag curve accuracy level and we find wind condition impacts the manoeuvre makeup of the optimal strategy.

The flow characteristics of the plume ejected from a micro-jet engine’s rectangular exhaust nozzle have been studied by conducting experimental and numerical analyses. The radiated infrared signature of a plume ejected from a rectangular exhaust nozzle with a large aspect ratio in a jet propulsion engine is known to be significantly lower than that of a plume ejected from a circular exhaust nozzle. The velocity and temperature distributions, which are the flow characteristics of the jet, were measured to investigate this phenomenon. For this purpose, we installed a circular nozzle and a rectangular exhaust nozzle with an aspect ratio of five to a micro-jet engine. The results showed that the plume spreads wider as it moves away from the nozzle exit and that the velocity rapidly decreases in the case of the rectangular nozzle, contrary to the case of the circular nozzle. Similar tendencies were observed for the temperature distribution and magnitude of the ejected plume. Thus, we concluded that the flow distribution caused by the nozzle shape induces a greater drop in the radiated infrared signature of the plume ejected from the rectangular nozzle than the circular nozzle. Flow analysis was conducted to evaluate the flow in and outside the exhaust nozzle; results similar to those of the experiment were obtained. These results show that the ejecting jet has a greater mixing effect on the air outside when using the rectangular nozzle than the circular nozzle.

The ground delay program (GDP) is a commonly used tool in air traffic management. Developing a departure flight delay prediction model based on GDP can aid airlines and control authorities in better flight planning and adjusting air traffic control strategies. A model that combines the improved sparrow search algorithm (ISSA) and Multilayer Perceptron (MLP) has been proposed to minimise prediction errors. The ISSA uses tent chaotic mapping, dynamic adaptive weights, and Levy flight strategy to enhance the algorithm’s accuracy for the sparrow search algorithm (SSA). The MLP model’s hyperparameters are optimised using the ISSA to improve the model’s prediction accuracy and generalisation performance. Experiments were performed using actual GDP-generated departure flight delay data and compared with other machine learning techniques and optimisation algorithms. The results of the experiments show that the mean absolute error (MAE) and root mean square error (RMSE) of the ISSA-MLP model are 16.8 and 24.2, respectively. These values are 5.61%, 6.3% and 1.8% higher in MAE and 4.4%, 5.1% and 2.5% higher in RMSE compared to SSA, particle swarm optimisation (PSO) and grey wolf optimisation (GWO). The ISSA-MLP model has been verified to have good predictive and practical value.

In this study a representative sandwich panel is investigated statically in two different configurations under similar bending loads. In one configuration serrations are introduced in the honeycomb core while the other one has un-modified core. Three-point bend test (TPBT) has been performed on both configurations through Finite Element Analysis (FEA) technique using ANSYS Workbench considering American Society for Testing and Materials (ASTM) standards. In both configurations the same aluminium honeycomb core is modelled having an adhesive layer in between adjacent foils to simulate actual scenario instead of relying on the block properties. Honeycomb core offers highest strength in its thickness (T) direction or the z-direction by virtue of its shape. Any distortion in the shape of the honeycomb adversely affects its strength. During bending the honeycomb core witnesses multidirectional forces consequently leading to distortion or crumpling. The serrations in the structure allow bending of the honeycomb core with minimal loss of strength by limiting the deformation to a specific region consequently preserving the shape as well as the strength of the honeycomb core. The results of both samples are compared with respect to deflection, strain and reaction force. It proves that serrated core is more favourable to be used in bent or curved sandwich panels.

In this paper, a new approach to modeling and controlling the problems associated with a morphing unmanned aerial vehicle (UAV) is proposed. Within the scope of the study, a dataset was created by obtaining a wide range of aerodynamic parameters for the UAV with Ansys Fluent under variable conditions using the computational fluid dynamics approach. For this, a large dataset was created that considered 5 different angles of attack, 14 different swept angles, and 5 different velocities. While creating the dataset, the analyses were verified by considering studies that have been experimentally validated in the literature. Then, an artificial intelligence-based model was created using the dataset obtained. Metaheuristic algorithms such as the artificial bee colony algorithm, ant colony algorithm and genetic algorithms are used to increase the modeling success of the adaptive neuro-fuzzy inference system (ANFIS) approach. A novel modeling approach is proposed that constitutes a new decision support system for real-time flight. According to the results obtained, all the ANFIS models based on metaheuristic algorithms were more successful than the traditional approach, the multilinear regression model. The swept angle that meets the minimum lift needed by the UAV for different flight conditions was estimated with the help of the designed decision support system. Thus, the drag force is minimised while obtaining the required lift force. The performance of the UAV was compared with the nonmorphing configuration, and the results are presented in tables and graphs.

Lateral jets are used to control the missiles and re-entry vehicles at high altitudes. The objective of the research paper is to investigate the effect of lateral jet interaction with the external flow on a blunted nose cone re-entry vehicle configuration and its flight speed. Structured mesh is used for the simulations, and the computational analysis is carried out by Ansys Fluent solver. The simulation results are validated, and the same methodology used for the parametric analysis. Simulations have been carried out at an external flow Mach number 6, 8.1, 12 and 16 at five degree angle-of-attack for jet-off and jet-on conditions. At 8.1 Mach number, the normal force coefficient is decreased by 45.6% due to jet interaction. The lateral jet interaction effectively reduces the nose down pitching moment. At 8.1 Mach number, the pitching moment coefficient was reduced by 48% with the jet-on condition compared to the jet-off condition.

To study the performance of the main windshield of a commercial aircraft that has been verified to be airworthy by bird-strike tests against unmanned aerial vehicle (UAV) impact at high-speed, a typical light UAV with various possible flight postures and the main windshield of a commercial aircraft are considered. The transient impact responses at critical moments, energy change and contact force of a multi-layer windshield impacted by a UAV with different postures are investigated using a simulation method based on the models verified by the high-speed impact test between the whole UAV and the full-size nose. This study shows that the flight posture of the UAV has a significant effect on the damage to the windshield. When the abdomen of a typical light UAV maintains a posture parallel to the plane of the windshield, the high-speed impact would cause catastrophic damage to the windshield and no longer be airworthy. Simultaneously, the damage to the aircraft windshield caused by UAV collision is far more serious than that caused by bird strikes under similar collision conditions. The mass-concentrated components of the UAV and their high-hardness characteristics are the main factors of affecting multi-layer glass of windshield damage. The degree of damage to the windshield is positively related to the absorbed energy rather than the impact contact force. In this study, the impact simulation results between the windshield and UAV with different flight postures are verified qualitatively by testing, which provides a rational understanding and technical pre-research support for emerging and increasingly frequent potential safety hazards in air transport practice.

In the present work, a folding wing system (FWS) was developed for guided ammunitions, so that the swept-back angle could be adjusted during both gliding and diving phases. Unlike previous designs, the FWS does not have any fixing mechanisms or brake elements, and it provides folding functionality to reduce the drag force during the terminal phase. We conducted mechanism design, manufactured the FWS, performed system identification and designed various controllers including linear quadratic regulator (LQR), linear quadratic integrator (LQI), sliding mode control (SMC) and second-order sliding mode control (SOSMC) to adjust and hold the desired swept-back angles. Then, the performance of the FWS was tested experimentally under two different flight scenarios, with and without aerodynamic loads. While all controllers operated with almost zero steady-state error (SSE) in the absence of aerodynamic loads, the SOSMC was the most effective controller under aerodynamic loads, considering SSE, delay, chattering, and energy consumption.

There are many deviation sources in the assembly process of aircraft complex thin-walled structures. To get important factors that affect quality, it is crucial to diagnose the key deviation resources. The deviation transfer between deviation sources and assembly parts has the characteristics of small sample size, nonlinearity, and strong coupling, so it is difficult to diagnose the key deviation sources by constructing assembly dimension chains. Therefore, based on the deviation detection data, transfer entropy and complex network theory are introduced. Integrating the depth-first traversal algorithm with degree centrality theory, a key deviation diagnosis method for complex thin-walled structures is proposed based on weighted transfer entropy and complex networks. The application shows that key deviation sources that affect assembly quality can be accurately identified by the key deviation source diagnosis method based on complex networks and weighted transfer entropy.