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This paper aims to describe a numerical simulation method of ice accretion on BO105 helicopter blades for predicting the effects of trailing edge flap deflection on ice accretion.

A numerical simulation method of ice accretion is established based on Myers model. Next, the shape and location of ice accretion of NACA0012 airfoil are calculated, and a comparison between calculated results and experimental data is made to validate the method. This method is used to investigate the effect of trailing edge flap deflection on ice accretion of a rotor blade.

The numerical method is feasible and effective to study the ice accretion on helicopter rotor blades. The downward deflection of the trailing edge flap affects the shape of the ice.

This method can be further used to predict the ice accretion in actual flights of the helicopters with multielement airfoils.

The numerical simulation method here can lay a foundation of the research about helicopter flight performance in icing condition through predicting the shape and location of ice accretion on rotor blades.

Loitering aerial vehicle (LAV) swarm safety flight control is an unmanned system control problem under multiple constraints, which are derived to prevent the LAVs from suffering risks inside and outside the swarms. The computational complexity of the safety flight control problem grows as the number of LAVs and of the constraints increases. Besides some important constraints, the swarms will encounter with sudden appearing risks in a hostile environment. The purpose of this study is to design a safety flight control algorithm for LAV swarm, which can timely respond to sudden appearing risks and reduce the computational burden.

To address the problem, this paper proposes a distributed safety flight control algorithm that includes a trajectory planning stage using kinodynamic rapidly exploring random trees (KRRT*) and a tracking stage based on distributed model predictive control (DMPC).

The proposed algorithm reduces the computational burden of the safety flight control problem and can fast find optimal flight trajectories for the LAVs in a swarm even there are multi-constraints and sudden appearing risks.

The proposed algorithm did not handle the constraints synchronously, but first uses the KRRT* to handle some constraints, and then uses the DMPC to deal with the rest constraints. In addition, the proposed algorithm can effectively respond to sudden appearing risks by online re-plan the trajectories of LAVs within the swarm.

This paper aims to develop a method for tuning the parameters of the active disturbance rejection controller (ADRC) for fixed-wing unmanned aerial vehicles (UAVs). The bald eagle search (BES) algorithm has been improved, and a cost function has been designed to enhance the optimization efficiency of ADRC parameters.

A six-degree-of-freedom nonlinear model for a fixed-wing UAV has been developed, and its attitude controller has been formulated using the active disturbance rejection control method. The parameters of the disturbance rejection controller have been fine-tuned using the collaborative mutual promotion bald eagle search (CMP-BES) algorithm. The pitch and roll controllers for the UAV have been individually optimized to obtain the most effective controller parameters.

Inspired by the salp swarm algorithm (SSA), the interaction among individual eagles has been incorporated into the CMP-BES algorithm, thereby enhancing the algorithm's exploration capability. The efficient and accurate optimization ability of the proposed algorithm has been demonstrated through comparative experiments with genetic algorithm, particle swarm optimization, Harris hawks optimization HHO, BES and modified bald eagle search algorithms. The algorithm's capability to solve complex optimization problems has been further proven by testing on the CEC2017 test function suite. A transitional function for fitness calculation has been introduced to accelerate the ability of the algorithm to find the optimal parameters for the ADRC controller. The tuned ADRC controller has been compared with the classical proportional-integral-derivative (PID) controller, with gust disturbances introduced to the UAV body axis. The results have shown that the tuned ADRC controller has faster response times and stronger disturbance rejection capabilities than the PID controller.

The proposed CMP-BES algorithm, combined with a fitness function composed of transition functions, can be used to optimize the ADRC controller parameters for fixed-wing UAVs more quickly and effectively. The tuned ADRC controller has exhibited excellent robustness and disturbance rejection capabilities.

The CMP-BES algorithm and transitional function have been proposed for the parameter optimization of the active disturbance rejection controller for fixed-wing UAVs.

The ability to measure cognitive load in the workplace provides several opportunities to improve workplace learning. In recent years, virtual reality (VR) has seen an increase in use for training and learning applications due to improvements in technology and reduced costs. This study aims to focus on the use of simulation task load index (SIM-TLX), a recently developed self-reported measure of cognitive load for virtual environments to measure cognitive load while undertaking tasks in different environments.

The authors conducted a within-subject design experiment involving 14 participants engaged in digit-recall n-back tasks (1-back and 2-back) in two VR environments: a neutral grey environment and a realistic industrial ozone facility. Cognitive load was then assessed using the SIM-TLX.

The findings revealed higher task difficulty for the 2-back task due to higher mental demand. Furthermore, a notable interaction emerged between cognitive load and different virtual environments.

This study relied solely on an n-back task and SIM-TLX self-report measure to assess cognitive load. Future studies should consider including ecologically valid tasks and physiological measurement tools such as eye-tracking to measure cognitive load.

Identifying cognitive workload sources during VR tasks, especially in complex work environments, is considered beneficial to the application of VR training aimed at improving workplace learning.

This study provides unique insights into measuring cognitive load from various sources as defined by the SIM-TLX sub-scales to investigate the impact of simulated workplace environments.

The objective of this research is to propose an immersive framework that integrates virtual reality (VR) technology with directives international safety training certification bodies to enhance construction safety training, which eventually leads to safer construction sites.

The adopted methodology combines expert insights and experimentation to maximize the effectiveness of construction safety training. The first step was identifying key considerations for VR models such as motion sickness prevention and adult learning theories. The second step was developing a game-like VR model for safety training, with multiple hazards and scenarios based on the considerations of the previous step. After that, safety experts evaluated the model and provided valuable feedback on its alignment with international safety training practices. Finally, the developed model is tested by senior students, where the testing format followed the Institution of Occupational Safety and Health (IOSH) working safely exam structure.

An advanced immersive VR safety training model was developed based on extensive lessons learned from the literature, previous work and psychology-informed adult learning theories. Model testing – through focus groups and hands-on experimentation – demonstrated significant benefit of VR in upgrading and complementing traditional training methods.

The findings presented in this paper make a significant contribution to the field of safety training within the construction industry and the broader context of immersive learning experiences. It also fosters further exploration into immersive learning experiences across educational and professional contexts.

Unmanned aerial vehicles (UAVs), known for their exceptional flexibility and maneuverability, have become an integral part of mobile edge computing systems in edge networks. This paper aims to minimize system costs within a communication cycle. To this end, this paper has developed a model for task offloading in UAV-assisted edge networks under dynamic channel conditions. This study seeks to efficiently execute task offloading while satisfying UAV energy constraints, and validates the effectiveness of the proposed method through performance comparisons with other similar algorithms.

To address this issue, this paper proposes a task offloading and trajectory optimization algorithm using deep deterministic policy gradient, which jointly optimizes Internet of Things (IoT) device scheduling, power distribution, task offloading and UAV flight trajectory to minimize system costs.

The analysis of simulation results indicates that this algorithm achieves lower redundancy compared to others, along with reductions in task size by 22.8%, flight time by 34.5%, number of IoT devices by 11.8%, UAV computing power by 25.35% and the required cycle for per-bit tasks by 33.6%.

A multi-objective optimization problem is established under dynamic channel conditions, and the effectiveness of this approach is validated.

To ensure the stability of the flying wing layout unmanned aerial vehicle (UAV) during flight, this paper uses the radial basis function neural network model to analyse the stability of the aforementioned aircraft.

This paper uses a linear sliding mode control algorithm to analyse the stability of the UAV's attitude in a level flight state. In addition, a wind-resistant control algorithm based on the estimation of wind disturbance with a radial basis function neural network is proposed. Through the modelling of the flying wing layout UAV, the stability characteristics of a sample UAV are analysed based on the simulation data. The stability characteristics of the sample UAV are analysed based on the simulation data.

The simulation results indicate that the UAV with a flying wing layout has a short fuselage, no tail with a horizontal stabilising surface and the aerodynamic focus of the fuselage and the centre of gravity is nearby, which is indicative of longitudinal static instability. In addition, the absence of a drogue tail and the reliance on ailerons and a swept-back angle for stability result in a lack of stability in the transverse direction, whereas the presence of stability in the transverse direction is observed.

The analysis of the stability characteristics of the sample aircraft provides the foundation for the subsequent establishment of the control model for the flying wing layout UAV.

This study aims to validate the linear flow theory with computational fluid dynamics (CFD) simulations and to propose a novel shape for the airfoil that will improve supersonic aerodynamic performance compared to the National Advisory Committee for Aeronautics (NACA) 64a210 airfoil.

To design the new airfoil shape, this study uses a convex optimization approach to obtain a global optimal shape for an airfoil. First, modeling is conducted using linear flow theory, and then numerical verification is done by CFD simulations using ANSYS Fluent. The optimization process ensures that the new airfoil maintains the same cross-sectional area and thickness as the NACA 64a210 airfoil. This study found that an efficient way to obtain the ideal airfoil shape is by using linear flow theory, and the numerical simulations supported the assumptions inherent in the linear flow theory.

This study’s findings show notable improvements (from 4% to 200%) in the aerodynamic performance of the airfoil, especially in the supersonic range, which points to the suggested airfoil as a potential option for several fighter aircraft. Under various supersonic conditions, the optimized airfoil exhibits improved lift-over-drag ratios, leading to improved flight performance and lower fuel consumption.

This study was conducted mainly for supersonic flow, whereas the subsonic flow is tested for a Mach number of 0.7. This study would be extended for both subsonic and supersonic flights.

Convex optimization and linear flow theory are combined in this work to create an airfoil that performs better in supersonic conditions than the NACA 64a210. By closely matching the CFD results, the linear flow theory's robustness is confirmed. This means that the initial design phase no longer requires extensive CFD simulations, and the linear flow theory can be used quickly and efficiently to obtain optimal airfoil shapes.

The proposed airfoil can be used in different fighter aircraft to enhance performance and reduce fuel consumption. Thus, lower carbon emission is expected.

The unique aspect of this work is how convex optimization and linear flow theory were combined to create an airfoil that performs better in supersonic conditions than the NACA 64a210. Comprehensive CFD simulations were used for validation, highlighting the optimization approach's strength and usefulness in aerospace engineering.

Multi-unmanned aerial vehicle (UAV) systems have succeeded in gaining the attention of researchers in diversified fields, especially in the past decade, owing to their capability to operate in complex scenarios in a coordinated manner. Path planning for UAV swarms is a challenging task depending upon the environmental conditions, the limitations of fixed-wing UAVs and the swarm constraints. Multiple optimization techniques have been studied for path-planning problems. However, there are local optimum and convergence rate problems. This study aims to propose a multi-UAV cooperative path planning (CoPP) scheme with four-dimensional collision avoidance and simultaneous arrival time.

A new two-step optimization algorithm is developed based on multiple populations (MP) of disturbance-based modified grey-wolf optimizer (DMGWO). The optimization is performed based on the objective function subject to multi constraints, including collision avoidance, same minimum time of flight and threat and obstacle avoidance in the terrain while meeting the UAV constraints. Comparative simulations using two different algorithms are performed to authenticate the proposed DMGWO.

The critical features of the proposed MP-DMGWO-based CoPP algorithm are local optimum avoidance and rapid convergence of the solution, i.e. fewer iterations as compared to the comparative algorithms. The efficiency of the proposed method is evident from the comparative simulation results.

A new algorithm DMGWO is proposed for the CoPP problem of UAV swarm. The local best position of each wolf is used in addition to GWO. Besides, a disturbance is introduced in the best solutions for faster convergence and local optimum avoidance. The path optimization is performed based on a newly designed objective function that depends upon multiple constraints.

The small air transport (SAT) domain is gaining increasing interest over the past decade, based on its perspective relevance in enabling efficient travel over a regional range, by exploiting small airports and fixed wing aircraft with up to 19 seats (EASA CS-23 category). To support its wider adoption, it is needed to enable single pilot operations.

An integrated mission management system (IMMS) has been designed and implemented, able to automatically optimize the aircraft path by considering trajectory optimization needs. It takes into account both traffic scenario and weather actual and forecasted condition and is also able to select best destination airport, should pilot incapacitation occur during flight. As part of the IMMS, dedicated evolved tactical separation system (Evo-TSS) has been designed to provide elaboration of both surrounding and far located traffic and subsequent traffic clustering, to support the trajectory planning/re-planning by the IMMS.

The Clean Sky 2-funded project COAST (Cost Optimized Avionics SysTem) successfully designed and validated through flight demonstrations relevant technologies enabling affordable cockpit and avionics and supporting single pilot operations for SAT vehicles. These technologies include the TSS in its baseline and evolved versions, included in the IMMS.

This paper describes the TSS baseline version and the basic aspects of the Evo-TSS design. It is aimed to outline the implementation of the Evo-TSS dedicated software in Matlab/Simulink environment, the planned laboratory validation campaign and the results of the validation exercises in fast-time Matlab/Simulink environment, which were successfully concluded in 2023.