New Technologies and Developments in Unmanned Systems

Energy, which is an indispensable part of human life, is one of the most discussed issues on the world agenda today as it was in the past. The largest share of the world’s energy resources belongs to fossil fuels (coal, oil, natural gas). Due to the sustainability and emission problems of fossil fuels, interest in environmentally friendly and renewable energy sources is increasing day by day. In the field of aviation, the search for clean and environmentally friendly energy sources is one of the important issues. The fact that battery technologies cannot yet fully meet the needs of propulsion systems has pushed researchers toward hybrid energy sources. This search has led to the emergence of the concept of hybrid-electric aircraft. Hybrid-electric aircraft are supported by energy sources such as hydrogen, solar, and supercapacitor in addition to batteries. Depending on the purpose and structure of the aircraft, the appropriate energy sources are used at different hybridization rates.

This study aims to determine the material performance of additive manufacturing methods, which enable flexible designs against rapidly changing market needs with a high benefit/cost ratio to meet the increasing defense needs of countries. In addition, the design of an unmanned surface vehicle (USV) resistant to sea conditions has been realized and produced by additive manufacturing. Advantages such as freedom of design, on-site manufacturing, and low cost in low-volume production have made additive manufacturing methods the focus of today’s production method and research topic. Among the additive manufacturing methods, fused filament fabrication (FFF) is the most cost-effective and widely used method. In today’s small craft sector, boats made of thermoplastic material are replacing metal hulls and thermosetting matrix composite hull boats day by day. Although composite boats with thermoplastic or thermosetting matrix have limitations in terms of size, it can be said that they are ideal for small USV. In this study, resistance analysis of the forms of three different boats was made with finite element and empirical methods. The hull form, which exhibits the lowest resistance value, was produced using the FFF method using PETG copolymer developed from environmentally friendly PET material. Cruise and field tests were carried out. Material performances were determined to be used in the production of small-scale USVs with the FFF method, and the mini-scale USV was produced using the PETG material with the FFF method. The suitability of PETG material for marine uses was emphasized.

The unmanned aerial vehicles (UAVs) have been recently employed in medical transportation. However, vibrations can impact negatively on the cargo quality since these medical goods can lose their efficacy for some oscillation intensities. The proposed 7-dof UAV model considers a vibrating payload and an elastic attachment between the UAV and its payload. Proportional derivative (PD) and feedback control based on linear matrix inequalities are then designed to generate UAV trajectory with low payload oscillation amplitudes. Numerical results have shown a high oscillation suppression in both the UAV and its payload trajectories.

In the Free-Form Deformation (FFD) parametrization technique, the choice of number of control points is case-dependent, and its optimum number should be found through trade-off to find its adequate value. In this study, a comparative analysis is performed to show the dependence of the final optimization results to the chosen number of control points. It is shown that there is no considerable difference in the ultimate value of objective function; however, by having 20 control points, the value of objective function and the accuracy of the optimization by considering the feasibility and optimality criteria is deteriorated. Therefore, there should be a trade-off study in the number of control points for free-form deformation block before starting the optimization process.

Additive manufacturing technologies have become the focal point of research and development studies, thanks to their production flexibility, increasing material diversity, and ease of access. Additive manufacturing methods have superior advantages, especially in the manufacture of parts with complex geometry, produced in small numbers, and the possibility of rapid structural change. In areas where there is no project-based and mass production such as the aviation sector, it has started to be not preferred to a significant extent. The fused filament extrusion method is the process of combining thermoplastic materials in layers through a heated nozzle. This study produced propeller designs that allow an unmanned aerial vehicle (UAV) to be used in different missions by fused filament fabrication method using pure polymer and polymer matrix composite filaments. The mechanical properties and force generation performances of the produced propellers were tested. Propellers subject to bending, buckling, and centrifugal forces were manufactured using polyamide 6 and carbon fiber-reinforced polyamide 6 matrix composite filaments. The production of UAV propellers by additive manufacturing using engineering plastics and composite materials is an innovative method, and it is aimed to provide benefits in interaction with UAV and three-dimensional (3D) printers, the number of which is increasing day by day.

The snake genus Chrysopelea is notable for its ability to fly without the components we normally associate with flying. In this work, a model to predict the path and glide angle taken by such snakes or aerial robots inspired by such mechanism is described. An earlier work used for such a prediction approximately models the aerodynamic lift force by blade element theory and thin airfoil theory. In this work, the moments around the CG are accounted for. The body of the snake is rotated around appropriate axes according to these moments which vary with time. Further, the forces and moments estimated over multiple time steps are used to continually update parameters like glide angle and free-stream velocity in this study. The calculated data is intended to assess the importance of accounting for the moments in predicting the flight path. The results show that the newly developed approach shows closer agreement with experimental data than the earlier model which neglected the moment. It is intended to use this model for waypoint following algorithms for such robots.

Insect-mimicking flapping-wing micro air vehicles (FWMAVs) are presently of great research interest due to their many practical applications. Studies have shown that using a flexible wing will improve the flight characteristics and the energy efficiency of the FWMAV due to the beneficial influence of the wing deformation on the aerodynamic forces. However, in the opposite direction, the effect of aerodynamic forces on wing deformation is not straightforward. In this study, the insect-like wing is modeled by a body-spring system, combined with an aerodynamic solver based on the unsteady vortex-lattice method. The proposed model is then used for investigating the hovering flight of a hawkmoth wing. Results show that the aerodynamic loads have a considerable influence on the wing deformation.

In order to obtain three-axis attitude information of a nanosatellite, at least two reference directions are needed. Magnetometers and Sun sensors are the most commonly used attitude sensors for measuring these reference directions because they are inexpensive and reliable and require low power consumption. On the other hand, the use of gyroscopes for attitude determination is optional, and having an angular velocity vector measurement may increase the accuracy of the attitude estimation system. This study discusses and compares the performance of a nanosatellite attitude estimation system with and without gyroscope measurements. A nontraditional approach is used for the estimation process where a Sun direction-Earth’s magnetic field-based TRIAD algorithm and an extended Kalman filter (EKF) are integrated to reduce the computational load. Proposed attitude estimation system is simulated with and without gyroscope measurements, and their estimation accuracies are compared. As a result of simulations, it is seen that the use of gyroscopes improves the attitude estimation accuracy by up to 16%.

The shape memory alloy (SMA) wire actuator is desirable to modify the structure properties and shape morphing ability particularly for the aircraft wing application. However, SMA wire embedded directly into a matrix system caused mismatch in coefficient of thermal expansion (CTE) which further led to delamination and structure failure issues. Thus, a soft material that integrated effectively with SMA actuator to achieve shape changing while resisting the external load is required. In this work, SMA wire is inserted through the gaps in corrugated 3D printed thermoplastic polyurethane (TPU) elastomeric structure to analyze the geometrical factors and its flexure performance. The flexure test results indicated that TPU with inactive SMA insertion has sustained higher flexure load with 21% increase in both flexure modulus and maximum load. However, activated SMA wire insertion demonstrated opposite behavior by increasing bending moment. Flexure test on fixed-fixed single SMA wire further supported the above finding. Moreover, SMA inserted into top eccentric from neutral axis of TPU sample has achieved highest flexure modulus and maximum load followed by at bottom and neutral axis. Thus, this design approach is presented to signify the structural integrity of 3D printed TPU with SMA and flexure property modification through SMA eccentric position.

This work presents the coherent structures found in dense and submerged rigid filamentous canopies in a turbulent open-channel flow. The flow structures are characterized by means of the higher-order dynamic mode decomposition (HODMD) method. We represent large-size coherent structures that extend throughout the streamwise direction, and we identify flow instabilities near the canopy edge leading to their breakdown. These structures are present in both the outer and inner regions of the canopy, although they look uncorrelated. We find that dense filamentous canopies stimulate the coherence of the flow, even if the turbulence levels are high.

In this study, a PD controller is used as an attitude controller with using only magnetic actuator. Gain of the PD controller is tuned by a particle swarm optimization to increase the pointing accuracy of an Earth-oriented satellite. While deterministic approach is used, increase of the performance of the satellite controller is observed.

In this work, a predictive reduced order model based on a combination of proper orthogonal decomposition and deep learning architectures is analyzed to predict the evolution in time of the thermodynamic states of a reacting flow database. The complexity of this type of flow resides in the multiscale nature and the transient behavior of the physical states. For solving the first problem, different scaling methods have been applied and compared to the case without scaling the variables. The results show that the scaling methods used improve the prediction error for all the variables studied. The temporal modes related to the periodic behavior of the flow are better predicted by the algorithm than the transient ones, as expected. Methods based on deep learning architectures, as the one presented in this paper, can be suitable to generate high-fidelity databases with a low computational cost. These databases can be useful to improve the efficiency in combustion in aircraft, as well as reducing the pollutants produced in combustion systems.

Today, technology in the field of space is developing rapidly. In this direction, the scope and importance of studies in space is gradually expanding. Expanding space studies have created high spacecraft capabilities and precision operational processing requirements, creating the curiosity of reaching other planets and objects. In this context, rendezvous and docking problems have emerged. In this study, the docking problem was tried to be solved for two objects moving in the same orbit, and the amount of thrust required for docking was calculated using a global navigation satellite system (GNSS)-based system. With this study, the basic logic for the solutions and simulations of more complex problems to be created in the future has been tried to be examined.

Birds can reduce the induced drag using an elongation of the wings called primary feathers. In this paper, a numerical analysis of a biomimetic unmanned aerial vehicle (UAV) that imitates that elongation at its wings is presented. Specifically, the UAV has a rectangular wing and three grids at the tip of the wing that changes the lift distribution over the wing. The effect of the grid span has been compared using computational fluid dynamics (CFD) for the UAV without grids and for 1/3, 2/3, and fully extended grids. The aerodynamic forces (lift and drag) have been obtained under different flight conditions in order to compare the differences in aerodynamic efficiency for each grid span configuration. In general, an increase of grid span translates into a significant increase of more than 20% in lift and aerodynamic efficiency of the UAV during cruise flight.

Air-assisted atomization is one of the dominant forcing used to induce breakup in liquid jets. This process is defined by the simultaneous injection of a low-momentum liquid jet and a high-momentum air stream along the same direction, separated only by a thin solid plate. The interaction between the two phases generates an instability that manifests itself in the generation of droplets following the breakup of these liquid sheets. In this work, we study the performance of a reduced order model (ROM), which combines dimensionality reduction techniques with deep learning architectures, to predict the flow dynamics once trained on simulation data.

Understanding fluid dynamic problems in the aerospace industry is a challenging endeavor: complex physical phenomena need to be described and quantified. In the latter years, data-science techniques have arisen as a powerful tool to tackle these problems. In this contribution, we focus on a fully data-driven technique, the higher order dynamic mode decomposition (HODMD). We describe first the technique, departing from its solid mathematical foundations, and justify its capabilities. We show next the applicability of HODMD to a complex fluid dynamic problem, a compressible, turbulent jet. Finally, we exploit the fully data-driven nature of the tool to show the general applicability of the HODMD method to other complex data types, specifically medical dataset.

Hydropneumatic suspensions are widely used, especially in heavy vehicles. In this study, a double-acting version of hydropneumatic suspension is studied. A dynamic model of two degrees of freedom (DOF) quarter car is created with the help of MATLAB/Simulink software. The suspension structure of a 6x6 vehicle is used as the basis for kinematic relationships. The tire properties are considered in the model as an additional DOF to achieve more realistic results. Parameter set of a 14.00 R20 tire with 160G load index is used in the tire model. The resulting dynamic model was analyzed for a road profile representing a bump.

This work deals with the detection of defects in welding joints of steel plates. We will process measured data at a set of receivers obtained in non-destructive tests by using a very powerful mathematical tool called the topological derivative. The performance of the method will be illustrated in a simplified model in two dimensions covering highly demanding situations that include defects of different sizes and a reduced number of emitters and receivers.

In this work, two different interpolation methods are compared with the aim at reconstructing complete aeronautical fields. The first method consists on applying singular value decomposition to the database, combined with linear interpolation. In the second method, the linear interpolation will be replaced by a neural network with the aim at improving the results. The two methods have been applied to reconstruct an atmospheric boundary layer field. The results show that using neural networks improves the error made in the interpolation by two orders of magnitude. In addition, the neural network provides quite accurate results, reconstructing the stationary variables of the atmospheric boundary layer.

Synthetic jets are devices of increasing importance in industry due to applications such as reducing drag in aircraft acting as an active flow control device. This work presents three different reduced-order models of synthetic jets to identify modes and instabilities in different ways. First, higher order dynamic mode decomposition (HODMD) extracts information related to frequencies, while vector quantization principal component analysis (VQPCA) and autoencoders extract the modes with locally linear and nonlinear approaches, respectively. In addition, VQPCA distinguishes regions with similar features. All these techniques are found to extract information with similarities and differences, due to their different algorithms, which may be highly relevant for future applications of flow control.

The emerging literature on AI-assisted autonomous weapons focuses more on the technical aspects of the debate and ignores the ethical issues. This article discusses whether ethical and moral values can have an impact on autonomous weapons. The thesis of the article is that the ethical double standard inherent in war may cause more global problems with unethically used autonomous weapons in the future. The issue is evaluated using just war theory and normative ethics, and potential long-term outcomes are discussed. In the first part of the article, the concepts of ethics and just war are explained. The next section discusses the connection of autonomous weapons with normative ethics and the principle of just war. It is explained how the basic ethical principles for autonomous weapons can be determined with the help of normative ethics and just war theory. It is pointed out that lethal technologies supported by artificial intelligence are prone to bias and data dependence, and legal issues in the use of autonomous weapons are discussed. The next section explains the potential consequences of unethical weapon use and points out the importance of ethical use for democratic states. The final chapter highlights the critical importance of surveillance in increasing autonomy in war within the framework of just war theory and proposes solutions.

This study investigates the strategies used by honeybees (Apis mellifera) to avoid unfamiliar obstacles encountered during flight. Bees were trained in behavioural experiments to fly in a tunnel that contained a solitary vertically oriented cylindrical obstacle placed along the midline of the tunnel. Flight trajectories of bees were recorded in two conditions where the diameter of the obstructing cylinder was 60 mm and 165 mm, respectively. The digitised trajectories of the bees were analysed to identify visual cues that could play a role in mediating obstacle avoidance: such as retinal angle (angular subtense of the obstacle), retinal expansion velocity (REV) and relative retinal expansion velocity (RREV). Our findings, based on analysing salient events during flight, such as the point of deceleration before the obstacle, suggest an obstacle avoidance response that is based on the RREV of the obstacle when the bee is on a collision course. This study paves the way for the design of biologically inspired unmanned aerial systems by systematically identifying some of the cues that might be used by honeybees for initiating obstacle avoidance.

This work analyzes the transonic flow around a rotating circular cylinder by using direct numerical simulations. The combination of the uniform freestream flow with the flow due to the rotation of the cylinder generates a lift force (Magnus effect) that, for transonic flows, can be conditioned by the presence of a shock wave on the suction side. In addition, the dependence of lift and drag forces with the dimensional parameters of the problem has been tested. The numerical simulations have been performed with the SU2 code, which solves the Navier-Stokes equations for compressible flow using a second-order finite volume scheme combined with different convective flow reconstruction schemes: JST and HLLC. Finally, an implicit time integration scheme combined with the dual time-stepping method has been used.

CubeSat missions are changing, and their number is increasing day by day due to their high efficiency. Their small size makes it easier for CubeSats to reach space. In this study, a new launch vehicle was designed to fit the CubeSat dimensions. The first design and analysis of a stable launch vehicle that will carry the 3U-sized CubeSat to the stratosphere layer has been carried out. In future studies, studies of the launch vehicle that will reach the Karman line, which is the space limit, will be carried out.

Due to continuous scientific and technological progress, more and more sophisticated algorithms are being developed, which allow to solve difficult problems quite easily and with a high accuracy. In this paper, a very complex aeronautical problem will be treated, which consists of predicting the velocity at which a destructive phenomenon such as flutter appears, using with that aim an algorithm based on high order dynamic mode decomposition (HODMD) and another one based on convolutional neural networks (CNN). The introduction will be about the importance of developing new and more efficient methods to predict flutter, as well as a brief explanation of this phenomenon; in the method point, the HODMD algorithm and its advantages versus the conventional DMD will be discussed, and convolutional neural network (CNN)-based algorithms will be introduced; then, the results obtained for both algorithms will be shown; to end up, some conclusions will be obtained.

An invariant feature space has been used within a probabilistic clustering approach to identify the boundary layer, wake region, and outer flow regions for the flow past a circular cylinder at Re = 3900. The feature space has been constructed to be independent of the coordinate frame used to generate the data, and the methodology has been validated using data obtained from a high-order numerical simulation. Our methodology reveals satisfactory clustering of the viscous dominated flow regions (boundary layer and wake). The identified region will be used in the future to locally adapt the mesh.

The direct or indirect affection of the disaster is a severe issue in the analysis of smart cities. The behavior of public information is vast, and the detection of victims and potential risks is time limited. Social networks provide live information within the disaster region where the emergency and rescue organizations would reach the critical zones. However, the disaster knowledge with critical insights is generally flooded with non-rescue information which is overwhelmed through different modalities in big data analytics. Therefore, the guidance of big data represents the foundations in smart disaster management. In this study, we have focused on the 2020 Izmir earthquake which is classified as a severe earthquake in the intensity scale. The earthquake information has been retrieved using microblogs from Twitter. The dataset has been preprocessed manually and automatically. The manual labels have been trained as vector embeddings in order to generate automatic labels as a semi-supervised approach. Naive Bayes, support vector machines, and BERT transformer networks have been applied on two classes. All approaches scored relevant evaluation values for disaster knowledge. Our findings presented the efficacy of big data approaches for rescue and non-rescue classes in disaster management. We conclude that smart rescue strategies would rely on big data analytics where the civil rescue teams outnumber the emergency and rescue organizations.

This paper investigates the factors which affect the packing ratio of deployable articulated truss masts and investigates the necessary design criteria for new designs with enhanced packing ratio. First, the available deployable articulated truss masts are examined, and the design parameters of these structures are worked out. Then a novel design called HiDAM is proposed with superior packing ratio compared to similar ones in the literature.

The guidance, navigation, and control (GNC) is one of the most prominent subsystems in weapon design. Smart munitions, as new trend in artillery shell development, need trajectory propagators as a core part of this subsystem. These propagators are deployed inside the electronic fuse and use advanced and precise models for the calculation of the point of impact. This prediction is an input for the guidance and control loops and governs the design of the navigation part. The objective of this work is to study the feasibility of using neural networks (NN) as artillery projectile trajectory propagators to replace the calculation performed on board by means of an artificial neural network, which predicts the point of impact based on initial launch conditions. For this, in this work, a trajectory propagator is created within MATLAB and Simulink with a four-degree-of-freedom (DOF) and five-degree-of-freedom nonlinear models. With this dataset, a neural network is trained to learn the mechanics of outdoor ballistics inherent in the results of the propagator. Finally, the results of the predictions of the neural network are compared with the results of the propagator, showing a promising low error in the prediction of the neural network, and their viability is analyzed as possible artillery shell trajectory propagators.

Unmanned aerial vehicle technologies have gained serious momentum in recent years. Despite these developments, the production of a UAV still requires expensive equipment. Recently, with the developments in rapid prototyping technologies, some researchers printed and flew 3D printed UAVs, successfully. In this study, we investigate the possibility of using 3D printer technology for the manufacturing of a 3.8-m wingspan hybrid UAV. It has been observed that it is possible to manufacture bigger UAVs with this approach, which is promising in making the production of UAVs accessible to the general public.

Numerical approaches of industrial aeronautical problems have become very popular, but a great computational cost is attached to them. A good alternative in fluid dynamics is developing reduced order models using, i.e., modal decomposition techniques such as singular value decomposition (SVD), proper orthogonal decomposition, or dynamic mode decomposition, which reduce the dimensionality of large databases extracting the most relevant features of the flow. Other techniques, like resolvent analysis, predict the flow response to some external driving force, which is suitable for flow control applications. This work briefly reviews the performance of these methodologies for the analysis of fluid dynamic databases and introduces the possibility of combining these techniques with a parallel SVD algorithm to deal with large databases, which are often encountered when solving industrial aeronautical problems.

The use of UAV systems for military reconnaissance and surveillance activities is one of the most frequently used methods. However, problems related to flight time cause restrictions in the use of UAV systems on the field of defense and security. With today’s developing technologies, TUAV (Tethered Unmanned Aerial Vehicle) systems have been started to be designed to overcome the flight time limitations. TUAV systems in the field of defense are used to ensure border security, protect forward operational bases, and establish telecommunication ports. TUAV systems offer significant advantages to military bases in many aspects in reconnaissance and observation activities. They have important advantages in terms of flight time, safe flight, ease of use, autonomy, legislations, secure data transfer, and deterrence. In this study, usage examples of TUAV systems in the field of defense were examined, and examples of use in the field of defense were presented.

Power electronics and battery management systems required to guide and control unmanned air vehicles produce considerable amounts of heat. To improve the endurance and reliability of UAVs, it is critical to maintain and manage the operating temperature of the airframe, power electronics, and batteries regardless of the thermal load generated from the multiple heat sources in the airframe. The UAVs have internal cooling systems that use the surrounding air or exploit internal fans to mitigate the thermal load. However, in both situations, pulling ambient air and using cooling fans, its usage implies higher power demand, due to the increased aerodynamic drag or the electric consumption of the coolers, which ultimately reduces the operational time of the airframe. In this line, to mitigate the impact of the cooling requirements on the airframe range or operational envelope, it is crucial to have accurate measurements of the current heat flux and evaluate the amount of cooling required to control the thermal load. In this paper, we define an approach to identify the optimum location of thermal sensors based on Kriging interpolation and simulated annealing optimizations to minimize the number of sensors required and ensure correct heat transfer characterization.

In this paper, a basic consensus formation control (CFC) algorithm has been proposed for a group of four-rotor unmanned aerial vehicle (UAV) called as quadrotor to realize a cooperative fly in formation. The main idea is to apply the CFC algorithm on five quadrotors to realize a pentagonal formation shape from any initial positions throughout a time-varying trajectory. Also, the interconnection between quadrotors is provided by a predefined Laplacian matrix. Moreover, in order to avoid collisions between quadrotors, a collision avoidance algorithm has been added to main hybrid control equation. The simulation results show the effectiveness of the proposed control algorithm.

The tandem airfoil configuration (TAC) has various notable advantages such as reduction in span of wing, structural weight, and parasitic drag. It also increases the total lift of the aircraft. Experimental or computational study observations were made in earlier works at low Reynolds number associated with low Mach numbers or high Reynolds number in the transonic regime. In this work, a 2D CFD analysis is conducted where the TAC is studied at transonic, low-Reynolds-number flows. Such flows occur at high altitudes of around 30 km for stealth UAVs planned by the military. Such situations also occur at low altitudes in the Martian atmosphere. The aerodynamic effects of airfoil shape on TAC are observed primarily using two airfoils, Wortmann fx63137-il (low-Reynolds-number airfoil) and RAE 2822 (transonic airfoil). Our simulations show that low-Reynolds-number effects are found to be dominant in the low transonic Mach regime (0.7–0.8) where Wortmann FX 63-137 shows good aerodynamic efficiency, but as we move to higher transonic Mach numbers (>0.8), RAE 2822 showed better aerodynamic efficiencies for the cases tested.

A new method for predicting multiphase flows via neural networks is presented. The idea underlying this method is to use single-phase flow data to avoid performing multiphase flow simulations and therefore reducing drastically the computation time by approximately a factor of three. Numerical simulations have been carried out using the volume of fluid method to model the flow in two concentric jets. The physical mechanisms involved are analyzed using higher order dynamic mode decomposition (HODMD) and show a large number of similarities between the single-phase and the multiphase flow cases. Flow predictions are done using a recurrent neural network, and the results show that it is possible to predict the temporal evolution of multiphase flows from single-phase flow databases.

The aim of this project is to install a ram air turbine (RAT) in a fixed-wing UAV using wind energy as a power source for its avionics in emergency situations. For this, the flight conditions in a glide performance and the power parameters necessary to control the descent have been defined. Once these data have been calculated, a turbine has been designed to provide the necessary energy. The QBlade software has been applied to design the blades and their subsequent simulation based on the BEM theory. Several airfoils and turbine configurations have been analyzed, obtaining as a result the optimal profile for the RAT, the SG6043 with three blades, this being the one that offers the least resistance and reaches the required energy more efficiently. Finally, the impact of the turbine assembly on the drone system has been studied, presenting the advantages and disadvantages of the application of said technology and how it would affect the actions of the aircraft.

The emergence of unmanned aerial vehicle (UAV) is widely used in commercial and military settings. There is a need for skilled UAV operators due to rising accident rates compared to manned aircraft operations. These reasons negatively influence UAV applicability and availability. The human factor analysis and classification system (HFACS) is a well-known approach for accident risk assessments that aggregates the accident causation to four primary sources: (i) organizational influences, (ii) supervision, (iii) precondition, and (iv) acts. This research introduces a novel method implementing the HFACS model combined with an analytical hierarchical decision-making model (AHP). In this research, a questionnaire has been developed to investigate the critical factors from the UAV operators’ point of view on accidents. Sixteen UAV operators participated in a two-level hierarchal model with four main criteria and 15 sub-criteria in the present research.

Propellers/rotors are usually indispensable parts of unmanned air vehicles that enable them to hover, fly, and maneuver. It is therefore extremely important to understand and resolve various flow structures that are present during flight. Here, the flow around a hovering, small-scale, custom-made propeller is numerically investigated by wall-modeled large eddy simulation (WMLES). Different operating regimes, achieved by changing RPMs, were both measured and computed, and the two sets of results are compared. Additional flow visualizations in the form of instantaneous flow fields are presented. While the computed thrust and power curves follow the expected trends, slight discrepancy between the experimental and numerical values is observed. It can be attributed to differences in the two setups (i.e., some simplifications of the real geometry in the numerical experiment) as well as the complexity of flow transition processes (present at such small Reynolds number flows).

Morphing wing applications have been used to change various dimensional properties of aircraft. Various 2D and 3D parameters can be changed on the aircraft’s wings, tail surfaces, or fuselage with these applications. Two primary schools are becoming widespread today in these application areas: mechanisms school and smart surfaces that employ shape-memory materials and smart actuators. The fishbone active camber approach is a research field that focuses on controlling the deflection on the wing’s trailing edge. In this approach, a tendon-like structure is designed that can be brought into desired shapes by creating tension on the wing structure. In this study, previous studies on fishbone active camber were revisited, the present situation was evaluated, and a roadmap was put forward for future studies from the authors’ perspective.

In this paper, a novel data-driven algorithm is designed for fault-tolerant aircraft trajectory prediction (ATP). The ATP model is based on autoencoder architecture due to its excellent performance when input data provided by the GPS is deficient. The resiliency of our designed autoencoder is examined in case of adversarial attacks. Peer-to-peer (P2P) blockchain is utilized in order to ensure predicted trajectories for the GPS data failures and adversarial attacks. Validation studies were done using a database provided by OpenSky Network, and trajectory prediction accuracy was evaluated for a variety of data failures. Performance index confirmed the autoencoder excellent efficiency, while it was secured with P2P blockchain in case of adversarial attacks.

Improving accuracy is cornerstone for ballistic rockets. Using inertial navigation systems (INS) and global navigation satellite systems (GNSS), accuracy becomes independent of range. However, during the terminal phase of flight, when movement is governed by nonlinear and highly changing forces and moments, guidance strategies based on these systems provoke enormous errors in attitude and position determination. Employing additional sensors, which are independent of cumulative errors, such as the quadrant photodetector, can mitigate these effects. This research presents a new nonlinear hybridization algorithm to feed navigation and control systems, which is based on neural networks to accurately predict the line of sight vector from multiple sensor measurements. Simulation results demonstrate the performance of the presented approach in a six-degree-of-freedom (DOF) simulation environment showing high accuracy and robustness against parameter uncertainty.

In this study, a model predictive control (MPC) approach for the longitudinal dynamics of a fixed-wing unmanned aerial vehicle (UAV) is proposed. The main purpose of the proposed method is to reduce inaccuracy in the control of a UAV’s longitudinal movement more quickly. A simulation environment is created in MATLAB/Simulink using the mathematical model of the GTM (generic transport model) with tail number T-2 developed by NASA. The problem is defined as multi-input, multi-output system. In order to show efficiency of proposed method, linear quadratic regulator (LQR) is also used as a comparison. Tests were carried out for speed, reliability, and robustness. Obtained results demonstrate that MPC outperformed LQR.

In this study, the reinforcement learning-based controller algorithm design is developed to control the pitch angle of the vertical takeoff and landing (VTOL) system model. The Deep Q Network (DQN) algorithm is chosen to control the VTOL system because control algorithms such as proportional-integral-derivative (PID) controllers are insufficient to control the system since they do not have a structure that acts by learning about environmental effects. The pitch angle of the VTOL system is controlled with sinusoidal reference and constant reference signals. These reference signals are applied to the DQN algorithm, which uses discrete action space to maximize reward value with a predetermined reward function. The DQN algorithm is tested in the MATLAB/Simulink environment using the VTOL system’s mathematical model. The tracking performances of the DQN-based controller algorithm are compared with traditional PID controllers whose parameters are tuned by PID tuner in terms of the integral absolute error, mean square error, integral square error criteria, overshoot, and settling time. The simulation results are shown via simulation studies.

Process and measurement noise covariance matrices are tuned for an adaptive attitude estimation of a nanosatellite at low Earth orbit based on extended Kalman filter (EKF) that is added by singular value decomposition (SVD) method. The tuning procedure compensates the measurement and process noise covariance variations. The tuning of the R matrix is simply processed in SVD, one of the single-frame methods. The tuning of the Q matrix is defined in the second stage of the Kalman-based estimator design. The tuning rules are run at the same time, so the filter is capable of being robust against initialization errors, system noise uncertainties, and measurement malfunctions without an additional filter design usage.

This study discusses a nontraditional attitude estimation algorithm for a nanosatellite using an adaptive fading Kalman filter (AFKF) to prevent filter divergence in case of attitude sensor malfunctions. The presented algorithm integrates the TRIAD algorithm with an adaptive filter to estimate the attitude accurately. The TRIAD algorithm uses magnetometer and Sun sensor measurements and provides a coarse attitude estimate as the first step of the algorithm. Then, this coarse estimate is filtered via an adaptive Kalman filter which is capable of maintaining the estimation accuracy in case of a sensor malfunction. In order to verify the performance of the proposed system, two simulations are performed where the magnetometer and Sun sensor measurement noises are increased, respectively.

UAV applications in defense industry are increasing in recent years, and most of these applications such as collision avoidance systems, target tracking, navigation, and guidance algorithms are based on stochastic dynamic equations (SDEs) which are suitable to be analyzed by Monte Carlo simulations. The purpose of this paper is to define a strategy to select the number of Monte Carlo runs of each simulation. For this reason, a Monte Carlo simulator for UAV’s trajectory estimation algorithms is used, and the computational load and accuracy of the population parameters are considered to select the number of Monte Carlo runs.