This paper quantifies the impacts of the airframe configuration change on the performance differences between a tube-and-wing and a blended wing body aircraft. Both are sized for a 5,000 nmi design range carrying 225 passengers, initially using the same engine. Parametric geometry is created for both concepts based on relevant public information. The tube-and-wing notional geometry is derived from the existing Boeing 767-300ER, whereas JetZero’s concept inspires the blended wing body. These geometries are optimised using computational fluid dynamics and gradient-free approaches. Drag polars for each optimised model, spanning the expected operating envelope, are generated using computational fluid dynamics simulations and multi-fidelity surrogate models. Mission analysis is performed for the blended wing body, a conventional tube-and wing variant with metallic structures, and an advanced tube-and-wing with composite structures. The results show that the blended wing body operates with 15-20% higher lift-over-drag during the cruise, 24% lower fuel burn for the design mission, and 15% reduction in ramp weight relative to the conventional tube-and-wing. These differences drop to 20% for the design mission fuel burn and 10% for the ramp weight relative to the advanced tube-and-wing. When the engines are re-sized and optimised separately for each configuration, the blended wing body demonstrates a 25% improvement in block fuel and 16% reduction in ramp weight relative to the conventional tube-and-wing, which decreases to 21% and 10% relative to the advanced tube-and-wing. In both comparisons, the fuel efficiency advantage of the blended wing body decreases as the mission range is reduced.

Rapid and comprehensive fighter optimisation is an important part of modern combat decision-making. However, due to the numerous influencing factors, it is difficult for decision-makers to consider comprehensively and specify the optimal decision, and it is highly subjective, which leads to different decision conclusions from person to person. Therefore, to solve the above deficiencies in fighter selection, this paper proposes a sequential decision-making framework that comprehensively considers the effectiveness, maintenance, support capability and health status of the fighter aircraft. Based on the multi-dimensional state, it provides comprehensive and credible auxiliary support for commanders. The sequential decision-making framework (called GRA-VIKOR-IFNs) uses the combination of equation and fuzzy multi-criteria decision-making (FMCDM) to evaluate the effectiveness, support capability and health in turn, to complete the step-by-step selection of fighter models, troops and sorties. The evaluation equation is for the effectiveness evaluation and a hybrid method using the extended grey correlation analysis (GRA) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method based on intuitionistic fuzzy numbers (IFNs) is for the support capability and health evaluation. The proposed strategy is in line with the logic and demand of actual combat and training decision-making and takes into account the influence of uncertain factors. Finally, a comparison with some classical methods is carried out, such as the full consistency method (FUCOM), the technique for order of preference by similarity to ideal solution (TOPSIS) and so on. The GRA-VIKOR-IFNs method is consistent with the results of other methods and the result sort resolution is 0.0619 and at least 40% higher than other methods, which can lead the commanders to a more reliable and clear decision.

The energy efficiency of emerging aircraft designs plays a key role, not only in reducing environmental impact, but also in reducing operating costs in the anticipated rise in fuel prices. The European Clean Sky 2 project HLFC-Win is investigating the feasibility of hybrid laminar flow control (HLFC) technology integrated into the outer wing leading edge for a long-haul aircraft. HLFC technology reduces aerodynamic friction drag by means of suction of the boundary layer through a micro-perforated skin to achieve laminarity and thereby improving aircraft performance. However, integrating such a system is not without its drawbacks, as the integration has an impact on the geometry, mass, aerodynamics and engine offtakes that need to be considered. Therefore, the aim of this current work is to assess the HLFC system based on a fair, objective and transparent comparison between the HLFC aircraft and an aircraft of the same technology level without HLFC. The assessment of the HLFC system is twofold, firstly estimating the mission-based performance at the overall aircraft level and secondly performing a lifecycle simulation with three scenarios to determine realistic fuel and cost savings. The mission-based performance assessment indicates a block fuel reduction of over 3 % for the design mission which averages 1.6 to 2.5 % considering a realistic route scenario and expected degradation. The economic assessment suggests a dependency on the scenario chosen, ranging from a 0.7 % increase in total cost (in an unfavourable scenario) to almost a 1 % reduction in total cost (in a favourable scenario), equivalent to $15 million saved per HLFC aircraft over its lifetime. These results support the commercial viability of HLFC technology, which offers significant aerodynamic and fuel efficiency improvements and operating cost savings to the aviation industry. Importantly, no critical barriers were identified for the integration of HLFC technology, further underscoring its potential to improve aircraft performance.

The axisymmetric nozzle mechanism is the core part for thrust vectoring of aero engine, which contains complex rigid-flexible coupled multibody system with joints clearance and significantly reduces the efficiency in modeling and calculation, therefore the kinematics and dynamics analysis of axisymmetric vectoring nozzle mechanism based on deep neural network is proposed. The deep neural network model of the axisymmetric vector nozzle is established according to the limited training data from the physical dynamic model and then used to predict the kinematics and dynamics response of the axisymmetric vector nozzle. This study analyses the effects of joint clearance on the kinematics and dynamics of the axisymmetric vector nozzle mechanism by a data-driven model. It is found that the angular acceleration of the expanding blade and the driving force are mostly affected by joint clearance followed by the angle, angular velocity and position of the expanding blade. Larger joint clearance results in more pronounced fluctuations of the dynamic response of the mechanism, which is due to the greater relative velocity and contact force between the bushing and the pin. Since axisymmetric vector nozzles are highly complex nonlinear systems, traditional numerical methods of dynamics are extremely time-consuming. Our work indicates that the data-driven approach greatly reduces the computational cost while maintaining accuracy, and can be used for rapid evaluation and iterative computation of complex multibody dynamics of engine nozzle mechanism.

A demonstration of a fully onboard method for generating background oriented schlieren (BOS) data on a jet exhaust is presented. Readily available commercial camera equipment is used to capture in-flight imagery of a miniature jet engine exhaust mounted on a custom-built model aircraft. The setup for image acquisition and processing algorithms are described. A new process for registration of images to reduce the degrading effects of vibration and flexure of the airframe are developed and presented along with the underpinning BOS algorithm. Results show that jet flows can be visualised using this technique using a contained system on a single aircraft and demonstrate how a simple technique, such as BOS, can be democratised to such an extent that the cost of conducting in-flight jet measurements can be reduced to the budget of any model aircraft flyer.

To investigate the flame acceleration to detonation in 2.0 and 0.5 mm planar glass combustion chambers, the experiments have been conducted utilising ethylene/oxygen mixtures at atmospheric pressure and temperature. The high-speed camera has been used to record the revolution of flame front and pressure inside the combustion chamber. Different equivalence ratios and ignition locations have been considered in the experiments. The results show that the detonation pressure in the 2 mm thick chamber is nearly three times of Chapman-Jouguet pressure, while detonation pressure in the 0.5 mm thick chamber is only 45.7% of the Chapman-Jouguet value at the stoichiometric mixture. This phenomenon is attributed to the larger pressure loss in the thinner chamber during the detonation propagation. As the value of equivalence ratio is 2.2, the detonation cannot be produced in the 2 mm thick chamber, while the detonation can be generated successfully in the 0.5 mm thick chamber. This phenomenon indicates that the deflagration is easily to be accelerated and transformed into the detonation, due to a larger wall friction and reflection. Besides, the micro-obstacle has been added into the combustor can shorten the detonation transition time and reduces the distance of the detonation transition.

Effusion cooling is the state-of-the-art cooling technology for gas turbine hot-gas path components. Typically, effusion cooling holes across the entire combustor liner are aligned with the combustor axis, rendering a nominal zero compound angle between highly directional miniature effusion cooling jets and the main flow direction. The pitch of effusion cooling holes is optimised accordingly. However, the swirling main flow results in a non-zero compound angle and an effectively different pitch from the design. The directional effect of effusion cooling as a result of swirling main flow on the adiabatic film cooling effectiveness (AFE) is a combined effect of a non-zero compound angle and a varied pitch. The current experimental study aims to investigate the isolated effects of compound angle on AFE by excluding the influences of varying pitch. With an improved understanding of the sole effects of non-zero compound angles on AFE, the roles that a varied pitch plays in modifying AFE are further discussed to guide future effusion cooling designs under swirling main flow conditions. Binary pressure sensitive paint (PSP) was used to determine AFE experimentally.

The experimental 7AD rotor blade is assessed for flutter stability in hover to identify the influence of aerodynamic contributions related to blade aerofoil, rotor inflow and wake periodicity on flutter onset. For the aeroelastic analyses, the multibody model is tightly coupled with an unsteady aerodynamic model based on Wagner’s function and related enhancements for the general motion of an aerofoil section considering heave and pitch. The mathematical setup of the approximated Wagner function in state space is extended for axial flow to include unsteady effects related to rotor inflow and wake periodicity. Since the aerodynamic model is based on indicial response functions, a separation of these contributions is possible and allows for the study of their impact on rotor blade flutter. The according flutter results are extracted in terms of frequency and damping behaviour for three test cases that differ in the unsteady aerodynamic model for circulation comprising blade aerofoil, rotor inflow and wake periodicity. As known for articulated rotor blades, also the 7AD blade exhibits a classical bending-torsion coupling. The lowest flutter onset is found for unsteady aerodynamics limited to blade aerofoil, whilst the cases with added rotor inflow and wake periodicity show both the same flutter onset at a 5% larger rotor speed. Here, the influence of rotor inflow plays the major role, since it increases the torsion damping within the critical flutter coupling. Added wake periodicity neither changes frequency nor damping and, hence, does not affect the aeroelastic coupling.

Unmanned aerial vehicles (UAVs) with fully electric propulsion systems mainly use lithium-based batteries. However, using fuel cells, hybrid propulsion systems are created to improve the flight time and payload capacity of the UAVs. Energy management and energy-aware path planning are important aspects to be explored in hybrid-propulsion powered UAV configurations. These facilitate optimal power distribution among energy sources and motion planning considering energy consumption, respectively. In the literature, although there are many studies on the energy management of hybrid-powered UAVs and path planning of only battery-powered UAVs, there are research gaps in energy-aware path planning of hybrid-powered UAVs. Additionally, the energy management of hybrid-powered UAVs is usually considered independent of path planning in the literature. This paper thoroughly reviews recent energy-aware path planning for small UAVs to address the listed critical challenges above, providing a new perspective and recommendations for further research. Firstly, this study evaluates the recent status of path planning, hydrogen-based UAVs, and energy management algorithms and identifies some challenges. Later, the applications of hydrogen-powered UAVs are summarised. In addition, hydrogen-based hybrid power system topologies are defined for small UAVs. Then, the path-planning algorithms are classified, and existing studies are discussed. Finally, this paper provides a comprehensive and critical assessment of the status of energy-aware path planning of UAVs, as well as detailed future work recommendations for researchers.

Aerodynamic investigations are crucial for the efficient design of Lighter-than-Air (LTA) systems. This study explores the aerodynamic characteristics of conventional and multi-lobed airships, motivated by the growing interest in LTA systems due to advancements in materials science, energy sources, aerodynamics, propulsion technology and control systems. The study employs the k-epsilon turbulence model, which is well-suited for turbulent flow simulations, and the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) algorithm, known for its effectiveness in pressure-velocity coupling in fluid dynamics simulations. The results indicate that multi-lobed airships offer enhanced aerodynamic efficiency over conventional designs. Detailed analyses of lift and drag coefficients provide insights into aerodynamic performance, guiding the optimisation of airship designs for improved efficiency. The findings of this study support the development of more aerodynamically efficient airship designs, which can serve as cost-effective, energy-efficient and quieter alternatives to traditional aircraft, particularly for applications such as surveillance, cargo transport and scientific research.

Damage initiation hotspots around features, such as bolts and ply drops, must be investigated during the preliminary design phase of large composite structures, such as composite airframes. A global-local modelling approach is commonly employed to perform this investigation, whereby a global low-fidelity model is used to drive high-fidelity local models around the features of interest. However, this methodology is slow, repetitive and expert-dependent. In this investigation, we address these issues by applying machine learning techniques to this global-local modelling framework and demonstrate the time-saving benefit when predicting damage initiation of bolted composite joints. Feature engineering of model inputs and outputs, and appropriate customisation of machine learning methods enables damage initiation prediction. Special consideration is given to the boundary conditions that must be varied to simulate the response of the bolted composite joints. Results show over three orders of magnitude time-saving benefit and satisfactory accuracy of the proposed methodology. This indicates its potential to be developed further into a rapid design and optimisation tool.

Urban air mobility (UAM) utilising novel transportation tools is gradually being recognised as a significant means to alleviate ground transportation pressures, vertiports which serve as pivotal nodes in UAM require efficient methods for assessing its operational capacity to develop an appropriate operational strategy and help to design vertiport ground infrastructure scientifically. This study proposes a multi-dimensional assessment method for the capacity of vertiports considering throughput and quality of service based on genetic algorithm (CEGA). The method comprehensively considers constraints such as unmanned aerial vehicle (UAV) safety separation, battery endurance, number of landing vertipads and UAV speed. The experimental results indicate that the vertiport with the scheduling algorithm proposed by this study has a larger capacity and experiences fewer delay than the vertiport with first-come-first-served (FCFS) algorithm when the vertiport has the same limited number of vertipads. Different proportions of UAVs significantly affect the quality of service and the degree of operation delays. The weights of vertiport throughput and customer satisfaction are the parameters that represent the importance of throughput and customer satisfaction in the objective function of the capacity assessment model. When the weights of throughput and customer satisfaction are set to 0.8 and 0.2 respectively, the performance of this optimisation model is optimal. This study provides a novel solution for capacity assessment and operation scheduling of vertiports, laying the foundation for improving the efficiency of UAM operations.

In this paper, a brand-new adaptive fault-tolerant non-affine integrated guidance and control method based on reinforcement learning is proposed for a class of skid-to-turn (STT) missile. Firstly, considering the non-affine characteristics of the missile, a new non-affine integrated guidance and control (NAIGC) design model is constructed. For the NAIGC system, an adaptive expansion integral system is introduced to address the issue of challenging control brought on by the non-affine form of the control signal. Subsequently, the hyperbolic tangent function and adaptive boundary estimation are utilised to lessen the jitter due to disturbances in the control system and the deviation caused by actuator failures while taking into account the uncertainty in the NAIGC system. Importantly, actor-critic is introduced into the control framework, where the actor network aims to deal with the multiple uncertainties of the subsystem and generate the control input based on the critic results. Eventually, not only is the stability of the NAIGC closed-loop system demonstrated using Lyapunov theory, but also the validity and superiority of the method are verified by numerical simulations.

A three-dimensional robust nonlinear cooperative guidance law is proposed to address the challenge of multiple missiles intercepting manoeuvering targets under stringent input constraints and thruster failure. The finite-time convergence theory is used to design a distributed nonlinear sliding mode guidance law, ensuring that the system converges in finite time, with the upper limit of convergence time related to the initial state. A nonlinear sliding surface is adopted to mitigate actuator saturation issues. Then, considering thruster failure, a robust cooperative guidance law is further introduced, ensuring mission completion through the reconstruction of the guidance law. The closed-loop system is proven to be stable using Lyapunov theory, and the influence of hyperparameters on the cooperative guidance law is analysed. Additionally, the results of numerical simulations and hardware-in-the-loop experiments demonstrate the effectiveness and robustness of the proposed algorithm in dealing with stringent input saturation and various disturbances.

In this paper, we designa robust Successive Generalised Dynamic Inversion (SGDI) flight control system for high-performance trajectory tracking of target sun-synchronous orbit Satellite Launch Vehicles (SLVs). The robust SGDI control system is designed to track an optimal reference trajectory such that the desired orbital terminal conditions of the ascent flight phase are achieved. The proposed SGDI is composed of two loops. The attitude control loop employs Dynamically Scaled Generalised Inversion (DSGI) of Servo Constraint Dynamics (SVD) in the deviations of Euler attitude angles from their desired optimal trajectories. The inner-dynamics control loop employs DSGI of an SVD in the SLV angular velocity components. Robustification control elements are augmented within the two loops of the baseline SGDI control system to overcome control performance degradation due to dynamic scaling of the Moore-Penrose generalised inverse, modeling and parametric uncertainties, and exogenous disturbances. The robust SGDI control system works to enforce global convergence of the SLV attitude trajectories to the reference trajectories. The high-performance attributes of the robust SGDI control system are verified via comparisons with a classical sliding mode control system, and by performing numerous runs of Monte Carlo simulations under various types of uncertainties and external wind disturbances.

Low-flying aircraft are susceptible to attacks by ground-launched infrared (IR)-guided man portable air defence system (MANPADS) and surface-to-air missiles (SAM). When seen from direct below, a dual band sensor can lock on to either exhaust plume or aircraft surfaces. Based on the magnitude of the IR signature, the missile can use any one source for the terminal guidance. In this study, the IR signature of the aircraft surface and potential plume core is analysed and compared from direct bottom view in different IR bands. In the Long Wave Infrared (LWIR) band, the surface emission is higher and in the Medium Wave Infrared (MWIR) band the plume emission is higher. The plume (MWIR) emission is higher than the surface (LWIR) emission for low Mach numbers, but as the Mach number increases the plume (MWIR) to surface (LWIR) emission ratio decreases, and at supersonic Mach numbers the surface LWIR signature is higher than the plume MWIR signature. The plume MWIR to surface LWIR ratio further depends on the engine power, altitude of operation and the emissivity of the aircraft surface. In the reheat mode, plume MWIR emission is always higher than the surface LWIR emission. The dual band IR detector can be a combination of short wave infrared (SWIR)-MWIR, SWIR-LWIR, and the MWIR-LWIR band. The MWIR-LWIR dual band combination is the best suited combination of IR windows for a dual band IR sensor/detector for aircraft application.

In this article, a genetic algorithm (GA) is proposed as a solution for the path planning of unmanned aerial vehicles (UAVs) in 3D, both static and dynamic environments. In most cases, genetic algorithms are utilised for optimisation in offline applications; however, this work proposes an approach that performs real-time path planning with the capability to avoid dynamic obstacles. The proposed method is based on applying a genetic algorithm to find optimised trajectories in changing static and dynamic environments. The genetic algorithm considers genetic operators that are employed for path planning, along with high mutation criteria, the population of convergence, repopulation criteria and the incorporation of the destination point within the population. The effectiveness of this approach is validated through results obtained from both simulations and experiments, demonstrating that the genetic algorithm ensures efficient path planning and the ability to effectively avoid static and dynamic obstacles. A genetic algorithm for path planning of UAVs is proposed, achieving optimised paths in both static and dynamic environments for real-time tasks. In addition, this path planning algorithm has the properties to avoid static and moving obstacles in real-time environments.

The rising demand for air traffic will inevitably result in a surge in both the number and complexity of flight conflicts, necessitating intelligent strategies for conflict resolution. This study addresses the critical challenges of scalability and real-time performance in multi-aircraft flight conflict resolution by proposing a comprehensive method that integrates a priority ranking mechanism with a conflict resolution model based on the Markov decision process (MDP). Within this framework, the proximity between aircraft in a multi-aircraft conflict set is dynamically assessed to establish a conflict resolution ranking mechanism. The problem of multi-aircraft conflict resolution is formalised through the MDP, encompassing the design of state space, discrete action space and reward function, with the transition function implemented via simulation prediction using model-free methods. To address the positional uncertainty of aircraft in real-time scenarios, the conflict detection mechanism introduces the aircraft’s positional error. A deep reinforcement learning (DRL) environment is constructed incorporating actual airspace structures and traffic densities, leveraging the Actor Critic using Kronecker-factored Trust Region (ACKTR) algorithm to determine resolution actions. The experimental results indicate that with 20–30 aircraft in the airspace, the success rate can reach 94% for the training set and 85% for the test set. Furthermore, this study analyses the impact of varying aircraft numbers on the success rate within a specific airspace scenario. The outcomes of this research provide valuable insights for the automation of flight conflict resolution.

In order to improve the performance of $k - \omega $ SST model in turbomachinery, previous studies have used the machine-learning (ML) technique to obtain turbulence models (for example, the ML-RANS EQ model). However, these models do not lead to satisfactory results in complex flows in turbomachinery. In this study, we use non-equilibrium training dataset to obtain a new turbulence model (i.e., the ML-RANS TR-NE-EQ model). Calculations in various cases of turbine cascade flows show that ML-RANS TR-NE-EQ model performs obviously better than ML-RANS EQ model as well as $k - \omega $ SST model.

Aircraft play a major role in meeting the fast and efficient transportation needs of modern society, thanks to their advanced features. However, gas turbine engines used in aircraft have many negative effects on human health. One of the negative effects is the exhaust gases released by these engines to nature. In this study, it is discussed to present alternative models based on heuristic methods to reduce the emission values of the synthetic fuel mixture used in the combustion chamber of gas turbine engines. For this purpose, a model based on artificial neural networks (ANN) based on the back-tracking search optimisation (BSO) algorithm is proposed by using experimentally obtained emission values found in the literature. In the proposed model, the parameters of the optimum ANN structure are first determined by the BSO algorithm. Then, by using the optimum ANN structure, the most appropriate input values were found with the BSO algorithm, and the emission values were reduced. The simulation results have shown that the proposed method will be a fast and safe alternative method for reducing emission values.