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This study employs numerical and statistical approaches to investigate the entropy optimization of steady Casson nanofluid flow over three different geometries subject to boundary conditions induced by convective flow. Multiple linear regression is employed to statistically examine. The present model incorporates several novel elements, such as Arrhenius activation energy, Brownian motion, the Cattaneo-Christov dual flux, thermophoresis, thermal radiation, and so on. Moreover, a comparison is presented between Newtonian and non-Newtonian fluids. By applying the proper similarity transformations, ordinary differential equations (ODEs) are obtained by converting foundational partial differential equations (PDEs). The Runge-Kutta fourth-order method is utilised to solve the obtained ODEs along with the shooting technique. The outcomes are visually depicted via tables and graphs. The velocity drops with increasing Grashof number, and the magnetic field becomes progressively more forceful as the suction parameter increases. The temperature gets reduced with the increase of the suction parameter, solute Grashof number increases with the magnetic field, thermophoresis, and radiation parameters. The entropy is observed to rise with the increase of the effective parameters (magnetic field, Brinkmann number and radiation). The MAD (mean absolute deviation), MSE (mean squared error), and RMSE (root mean square error) values are approaching zero, indicating that the derived outcomes are highly accurate. A lower MAPE (mean absolute percentage error) suggests that the model has a higher level of precision. Therefore, the outcomes of the present model are more precise and reliable. This study has various potential applications such as power plant heat exchangers, material processing industries, and solar thermal energy systems.

Model-based control shows promising potential for engine performance improvement and future aero-propulsion requirements. In this paper, an auto-updating thrust variation mitigation (AuTVM) control approach using on-board model strategies is proposed for gas turbine aero-engines under in-service degradation effects, which aims at active thrust regulation and acceleration protection in a simultaneous way. The AuTVM control is integrated with an on-line block, based on a reliable on-board engine model, and an off-line part for the periodical update of control parameters via post-flight engine monitoring data. The core feature of the AuTVM control is a set of auto-updating loops within the on-line part, including thrust regulation loop, surge margin loop, turbine entry temperature loop, and the steady loop, whose control parameters are periodically adjusted with increasing flight cycles. Meanwhile, an industrial sensor-based baseline controller and two tailored model-based controllers, i.e., a thrust variation mitigation (TVM) controller with fixed gains and a self-enhancing active transient protection (SeATP) controller with pro-active transient protection and passive thrust control, are also developed as comparison bases. Numerical simulations for idle to full-power acceleration tests are carried on a validated aero-thermal turbofan engine model using publicly available degradation data. Simulation results demonstrate that both new engines and severely degraded engines regulated by the AuTVM controller show significant thrust response enhancement, compared to the baseline controller. Moreover, thrust variation at the maximum steady state of degraded engines, which exists within the SeATP controller and the baseline controller, is suppressed by the proposed AuTVM controller. Robustness analysis against degradation uncertainties and sensor accuracy confirms that the AuTVM controller owns a closer maximum steady-state thrust distribution to the desired value than those of the SeATP and the baseline controller while utilizing transient margins of controlled engines more effectively. Hence, the control performance of the AuTVM controller for in-service engines is guaranteed.

This study examines the effect of heat production and radiation absorption on the magnetohydrodynamic Casson fluid flow at the stagnation point in a porous medium. We convert the group of fluid flow equations, which are non-linear partial differential equations with suitable boundary constraints, into a set of non-linear ordinary differential equations using similarity transformations. The homotopy analysis method (HAM) solves the converted system of ordinary differential equations. We draw graphs for numerous values of non-dimensional parameters and tables of surface drag force, rates of heat transfer, and mass transfer to analyze the relationship between velocity field, temperature field, concentration field, and other essential parameters involved in the study. We have proven that the Dufour number, radiation parameter, and heat generation parameter elevate the fluid temperature, whereas the magnetic parameter lowers it. The Casson fluid parameter, buoyancy force parameter, and mixed convection parameter all promote fluid movement throughout the flow field. The presented tabular data allows us to see the trend of heat and mass transfer rates, as well as drag force rates, against important parameters, enhancing our understanding of these rates.

A common denominator between conical-symmetry and conical shock interaction is the spanwise pressure gradient, which perform more non-uniformity and its interaction flow is more complicated than the spanwise-homogeneous planar shock wave. Recent advances in conical-symmetry and conical shock interactions with turbulent boundary layer are reviewed in specific areas: (i) quasi-conical swept interactions due to compression ramps and sharp fins, (ii) impinging conical shock wave with interactions of plate wall, (iii) laminar double cone interactions with consideration of real-gas effects. Substantial success has been achieved in describing the phenomena of the time averaged and instantaneous flow features and the low-frequency unsteadiness, including correlations and coherent structures in the separation bubble, through complementary experimental and numerical studies of swept shock interactions. All available observations are here scrutinized to infer underlying mechanisms of interactions in conical flow, and provide theoretical foundation and hints for fluidic control techniques. Comparison with high-fidelity direct numerical simulations is used to quantified the uncertainty of RANS turbulence models in complex interactions. Regarding heat transfer, extensive studies of hypersonic flow over double cone geometries have shown that those can be predicted with reasonable accuracy, even in the presence of high-temperature effects.

Stratified thermal storage promotes energy sustainability by storing excess energy during times of low demand for later use making it possible to integrate renewable energy sources like solar and wind. This communication discusses the significance of triple stratification using the Cattaneo-Christov model in the bioconvective flow of thixotropic fluid coexisting with nanoparticles and gyrotactic microorganisms. The Cattaneo-Christov heat and mass flux is incorporated into the fluid model allowing more accurate prediction of heat and mass phenomena in the fluid system. The governing partial differential equations that describe fluid flow are parametrized to yield an ordinary differential equation system by adopting suitable transformations. The series solutions are obtained by applying the homotopy analysis method (HAM). The effects of relevant parameters on the various profiles are revealed and accurately reported. It is observed that amplified thermal stratification lowers the temperature of the fluid. Also, Brownian motion is used to illustrate the random movement of small particles suspended in liquids, and it is envisioned that the concentration distribution is significantly influenced by the Brownian motion of nanoparticles.

Sweeping jet actuator (SJA) has been widely applied for active flow control in open flows. In this paper, the SJA character in compressor cascade and its performance for separation control in inner flows are discussed. Time-averaged and transient flow field measurement, together with visualization methods are utilized. It is found that endwall effects are important for both SJA behaviors and SJA performance for separation control in compressor cascades. There is a maximum of 12.7% total pressure loss reduction with SJA placed near the separation position, close to the endwall and under appropriate flowrate. The characteristic frequencies in the flow field contribute to the capture of influence regions of vortices and excitation jets. Two concentrated shedding vortices and SJA jets impact region helped to judge that SJA energizes low momentum fluids in a large region and matches the high loss core well. To be concrete, the flow separation control mechanism of SJA lies on the interruption of the blade suction surface boundary layer development and the restriction of the lifting of the boundary layer from endwall towards blade suction surface.

In this paper, by using the bifurcation theory for dynamical system, we construct traveling wave solutions of a high-order nonlinear Schrödinger equation with a quintic nonlinearity. Firstly, based on wave variables, the equation is transformed into an ordinary differential equation. Then, under the parameter conditions, we obtain the Hamiltonian system and phase portraits. Finally, traveling wave solutions which contains solitary, periodic and kink wave solutions are constructed by integrating along the homoclinic or heteroclinic orbits. In addition, by choosing appropriate values to parameters, different types of structures of solutions can be displayed graphically. Moreover, the computational work and it's figures show that this technique is influential and efficient.

Structural modularization, lightweight and functional integration are the urgent development directions for next generation high-performance aeroengines. Heat concentration during aeroengine operation would lead to local high temperature, which tremendously negative impacts on aeroengine structural life and performance. Therefore, the design and optimization of radiator structures are significant for the efficiency and reliability of aeroengine. The structural geometry design and layout optimization of radiators is promising to improve the heat dissipation efficiency and reduce aerodynamic loss. The purpose of this study is to investigate the state of the art and perspectives of aeroengine radiator structural design by a comprehensive literature review. The main contents involve the review on the structural design and layout optimization technologies of radiator structures, the analyses of the structural features, design theory and methods of existed radiator structures, the induction of the theory and method of different radiators structural optimization design, and the discussion on the application perspectives of advanced structures in aeroengine radiators, the report on the current challenges and development directions of the design of radiator structures, including smart materials, lattice structures, variable structures, advanced optimization theories and methods, heat dissipation methods and so forth. The efforts of this study are promising to support the high-performance and lightweight design of aeroengine structures besides radiators, and thermal management system.

This present study is part of the design improvement process of a specified high torque low-speed engine. This work aims at carrying out an in-depth analysis of in-cylinder combustion, mesh sensitivity, and engine performance at supercharge conditions to provide a foundation for the design improvement process of the given engine. The computational fluid dynamic (CFD) simulations are carried out on a 3D sector from −130° to 130° crank angle (CA) by employing appropriate models to represent the different physical and chemical processes and using the finite volume method for solving the governing differential equations. An extensive investigation has been carried out for the choice of base mesh size and the number of local and temporal refinements to capture the phenomena happening in the combustion chamber at diverse temporal and local scales. The present results have been validated against available literature experimental and simulation results. Primary field variables and the well-known four phases of combustion have been studied for gaining in-depth insight into these phenomena. Cylinder average pressure, mean temperature, heat release rate (HRR), integrated heat release rate (IHRR), and emissions of CO2, CO, NOx, HC and soot are presented to assess the quality of combustion. Engine performance analysis has been done in terms of combustion efficiency, gross work, power, torque, and integrated mean effective pressure (IMEP). The base mesh of 1.4 mm may be an appropriate choice during the injection and combustion process spanning throughout around 40° CA from the start of injection while in the remaining simulation duration of around 220° CA base mesh of 2 mm gives a sufficient resolution. It has been found that maximum heat release takes place in Phase-III, the mixing-controlled phase, of the combustion process. More than 98% combustion efficiency has been achieved in all the simulations. Around 99% of the total heat release and emissions production takes place within 60° CA after top dead center (ATDC).

Traditional approaches to safety event analysis in autonomous systems have relied on complex machine and deep learning models and extensive datasets for high accuracy and reliability. However, the emerge of multimodal large language models (MLLMs) offers a novel approach by integrating textual, visual, and audio modalities. Our framework leverages the logical and visual reasoning power of MLLMs, directing their output through object-level question–answer (QA) prompts to ensure accurate, reliable, and actionable insights for investigating safety-critical event detection and analysis. By incorporating models like Gemini-Pro-Vision 1.5, we aim to automate safety-critical event detection and analysis along with mitigating common issues such as hallucinations in MLLM outputs. The results demonstrate the framework’s potential in different in-context learning (ICT) settings such as zero-shot and few-shot learning methods. Furthermore, we investigate other settings such as self-ensemble learning and a varying number of frames. The results show that a few-shot learning model consistently outperformed other learning models, achieving the highest overall accuracy of about 79%. The comparative analysis with previous studies on visual reasoning revealed that previous models showed moderate performance in driving safety tasks, while our proposed model significantly outperformed them. To the best of our knowledge, our proposed MLLM model stands out as the first of its kind, capable of handling multiple tasks for each safety-critical event. It can identify risky scenarios, classify diverse scenes, determine car directions, categorize agents, and recommend the appropriate actions, setting a new standard in safety-critical event management. This study shows the significance of MLLMs in advancing the analysis of naturalistic driving videos to improve safety-critical event detection and understanding the interactions in complex environments.

The accurate prediction of tire and brake thermomechanical behavior is crucial for various applications in the automotive industry, including vehicle dynamics analysis, racing performance optimization, and driver assistance system development. The temperature of the brakes plays a crucial role in determining the performance of rubber by altering its temperature. This change impacts the rim and the air within the tire, leading to variations in temperature and tire pressure, which consequently affect the vehicle’s overall performance. Traditionally, these components have been modeled separately, neglecting the crucial thermal interaction between them, thereby losing a lot of important information from the outside that influences the tire. This paper presents a novel method that overcomes this limitation by coupling the thermomechanical models of the tire and brake, enabling a more comprehensive understanding of their combined behavior. Therefore, the present work could be an interesting starting point to understand how a control system can be influenced by the thermodynamic of the wheel–brake system.

Autonomous vehicles frequently encounter performance degradation during high-speed cornering due to excessive speed and lateral acceleration, potentially leading to collisions or rollovers. This paper proposes a novel curve-passing control approach that first estimates the curve radius and then controls the steer and speed for smooth and comfortable handling. In particular, the curve radius is innovatively estimated using a combination of a camera-based lane detection model and a steering wheel dynamic model. The curve-passing control approach is validated on high-speed ramps and curves, demonstrating its robustness and intelligence to adapt to dynamic changes in curve curvature. The model effectively prevents vehicles from entering curves at dangerously high speeds from straight roads and mitigates sudden accelerations or decelerations when entering curves. Experimental results indicate that the vehicle speed is reduced to around 50 km/h and the corresponding acceleration is −0.6 m/s2 upon entering curves with a minimum radius of 150 m. This demonstrates that the proposed control model can ensure a comfortable and safe driving experience by autonomously decelerating the vehicle before entering various types of curves.

Semi-autonomous vehicles (AVs) enable drivers to engage in non-driving tasks but require them to be ready to take control during critical situations. This “out-of-the-loop” problem demands a quick transition to active information processing, raising safety concerns and anxiety. Multimodal signals in AVs aim to deliver take-over requests and facilitate driver–vehicle cooperation. However, the effectiveness of auditory, visual, or combined signals in improving situational awareness and reaction time for safe maneuvering remains unclear. This study investigates how signal modalities affect drivers’ behavior using virtual reality (VR). We measured drivers’ reaction times from signal onset to take-over response and gaze dwell time for situational awareness across twelve critical events. Furthermore, we assessed self-reported anxiety and trust levels using the Autonomous Vehicle Acceptance Model questionnaire. The results showed that visual signals significantly reduced reaction times, whereas auditory signals did not. Additionally, any warning signal, together with seeing driving hazards, increased successful maneuvering. The analysis of gaze dwell time on driving hazards revealed that audio and visual signals improved situational awareness. Lastly, warning signals reduced anxiety and increased trust. These results highlight the distinct effectiveness of signal modalities in improving driver reaction times, situational awareness, and perceived safety, mitigating the “out-of-the-loop” problem and fostering human–vehicle cooperation.

This paper details the conceptualization, design, topology optimization, manufacturing, and validation of a hydraulic brake caliper for Formula SAE race cars made of Scalmalloy®, an innovative Al-Mg-Sc alloy which was never adopted before to manufacture a brake caliper. A monoblock fixed caliper with opposing pistons was developed, focusing on reducing mass for a fixed braking force. The design process began with a theoretical analysis to establish braking force and pressure requirements, followed by preliminary design and topology optimization. The caliper was then manufactured using laser powder bed fusion (LPBF). Comprehensive experimental validation, including testing with static and rotating brake discs on an inertial dynamometer, confirmed the expected caliper’s performance, with the results showing strong alignment with finite element analysis estimations. In particular, strain and displacement measurements showed excellent correlation with numerical estimates, validating the design’s accuracy and effectiveness.

Aerial vehicles are increasingly relying on connectivity to cellular networks, with 5G new radio (NR) and 6G technologies deemed critical for the next generation of indoor and outdoor positioning systems. Conventional time of arrival approaches require time synchronisation between base stations and vehicles, and a clock bias greater than 30 ns can result in a positioning inaccuracy above 10 m. This work, thereby, proposes an integrated positioning technique based on RF fingerprinting using ray-tracing data and reinforced with machine learning. The system leverages advanced sensing technologies, NR communications, and AI-driven random forests to enhance the precision and reliability of air vehicle positioning, contributing to safer and more efficient air travel and autonomous flight operations. The developed solution is evaluated in a representative urban canyon environment, in which the performance of conventional radio-based positioning systems is often degraded. Notably, a supervised learning algorithm based on the received signal strength and time of arrival is shown to exhibit an accuracy of under 3 m in 75% of the areas studied.

Electrified vehicles (EVs) are increasingly integrated into modern air, road, and water transportation systems [...]

Many of aviation accidents are caused by human-related factors. Despite advancements in various fields related to aviation accidents caused by humanrelated factors continue. There are many unknown areas affecting aviation safety in the human factors of aviation workers, and the main task of aerospace medicine is to ensure aviation safety by appropriately managing these. Recently, with the development of the medical and aviation industries, related environments are rapidly developing and changing. In detail, it is necessary to modernize the aeromedical evaluation standards for air crews in line with international standards. As Korea is also pursuing space development, research on the space environment has also become necessary. Aviation medicine is an essential field that manages human factors in the field of aviation safety, and considering the public nature of aviation-related work, the nation's initial investment in the field of aerospace medicine must be a priority. To this end, it is necessary to establish a state-run aviation medical center and manage it under state leadership.

This study examines the effects of microgravity and space radiation on astronauts’ muscle and bone health during space missions. Microgravity leads to muscle atrophy and alterations in muscle tissue composition, whereas space radiation impacts bone cells by increasing osteoclast activity and decreasing osteoblast activity. The findings underscore the critical need for preventive measures, postmission rehabilitation, and the use of specialized equipment and technology—such as microgravity-adapted treadmills and nutritional supplements—to sustain astronaut health. This study lays the groundwork for developing effective protection and intervention strategies for long-duration space exploration, highlighting the necessity of a thorough understanding of how the space environment affects the human body to ensure optimal health and performance in space.

The basic concept of virtual reality (VR) means ‘an artificial environment that is similar to reality but is not real.’ In general, virtual reality refers to a technology that goes beyond simply implementing a virtual space and directly affects the user's five senses to enable a spatial and temporal experience close to reality. Customer adoption of virtual reality technology is still in its infancy, but as innovation continues to strengthen, more businesses are exploring virtual reality. Several fields have already begun to benefit from the use of virtual reality. Specifically, advancements are being made in games, entertainment, medicine and education, It also enables aeromedical training using simulation techniques. Despite the bright future outlook, there are still problems that need to be resolved, such as high costs and lack of good content. In this article, I will introduce VR and consider its specific use cases in the field of aviation medicine.

The present paper aims to conduct an experiment that compares different methods of detecting objects in images. Programs were developed to evaluate the efficiency of SURF, BRISK, MSER, and ORB object detection methods. Four static gray images with sufficiently different histograms were used. The experiment also highlighted the need for image preprocessing to improve feature extraction and detection. Thus, a programmed method for adjusting pixel groups was developed. This method proved useful when one of the listed algorithms failed to detect the object in the original image, but succeeded after adjustment. The effectiveness of detection methods and the evaluation of their performance depend on the application, image preparation, algorithms used, and their implementation. Results of the detection methods were presented numerically (similarities, gradients, distances, etc.) and graphically.

The main aim of this article is to present a fatigue cycle counting program for a given application. The application is an example from a given case presented in the literature, with the analytical solution discussed herein. Cycle counting for irregular fatigue spectra is important to evaluate for various given load cases, in different industries. Such evaluations are difficult to make without dedicated computer programs. Several specific methods are available in the technical literature to evaluate irregular fatigue spectrums. For this study, one of the most well-known and used methods was chosen, the rainflow-counting algorithm. This method extracts the number of cycles from an irregular fatigue recording which, in turn, is used in the Palmgren-Miner rule to evaluate the fatigue damage for the examined application, considering that the Wohler curve is given. The developed program is written in the Python programming language. The theoretical background is presented for the application and the use of it with the program algorithm.

With the advent of technology, it has become possible to perform direct numerical simulations and the demand for high accuracy computing is increasing. Numerical simulations play an important part in understanding physics of the flow and instability mechanism in flows. For high accuracy, numerical schemes must be chosen that satisfy the physical dispersion relation, should not amplify or attenuate the solution and resolve all possible length and time scales. In the present paper, spectral stability analysis of linear convection equation is performed using first order forward difference (FD1) method and fourth order Runge Kutta (RK4) method, consisting of four stages, for time discretization and a second order central difference (CD2) method for evaluating spatial derivative. The results show that the presence of numerical instability for FD1 method is independent of the CFL number, consistent with the stability analysis which showed FD1 method to be unconditionally unstable. However, for RK4 method, the solution is found to be neutrally stable only for a particular range of CFL number, even stable solution introduced error by attenuating the computed or analytical solution.

The aim of this paper is to analyze the influence of two types of urban morphology existing in the area of Bucharest on the local microclimate, using Landsat satellite data, GIS techniques and numerical modelling. The two types of urban design analyzed are differentiated by the period in which they were constructed, one being built in the communist era and the other in the current period. The phases of the study are: i) geospatial analysis of urban morphology focusing on the urban design features of each period; ii) spatial analysis of the land surface temperature in relation with urban geometry, using Landsat 9 satellite images; iii) simulation of microclimate using ENVI-met software to highlight the local thermal environment. Satellite data analysis highlighted higher values for the land surface temperature in neighborhoods built in the current period compared to the areas built in the communist era. The results of the simulation highlight the distinctions in the thermal environment between the two categories examined, lower values of air and surface temperatures for the neighborhoods built in the communist era.

The present study highlights the benefits of using vortex generators as a passive boundary layer control method to delay the flow separation. Using high fidelity CFD analysis, a parametric study regarding the optimum position of vortex generators is carried out using an in-house automation procedure. After that, relevant quantities such as wall shear stress, turbulent kinetic energy, pressure coefficient distribution and oil flow visualization are analyzed and corresponding conclusions are drawn. The use of turbulent and energy quantities allows to quantify the impact of the generated vortices on the boundary layer and to identify the optimal positioning of vortex generators for improved aerodynamic performance.

This paper presents an analytical method to describe the shape of an airplane wing. The analytical shape of the wing section implies analytical expressions for the wing parameters: maximum thickness, maximum camber, maximum camber position, minimum thickness position, and so on. The analytical expressions of the wings in a first representation allow simple relationships with the geometric parameters of the wing. The defined expressions for the wings parameters relevant for aircraft design are versatile.

The paper presents a wireless system integrated with a machine learning (ML) model for structural health monitoring (SHM) of carbon fiber reinforced polymer (CFRP) structures, primarily targeting aerospace applications. The system collects data via carbon nanotube (CNT) piezoresistive sensors embedded within CFRP coupons, wirelessly transmitting these data to a central server for processing. A deep neural network (DNN) model predicts mechanical properties and can be extended to forecast structural failures, facilitating proactive maintenance and enhancing safety. The modular design supports scalability and can be embedded within digital twin frameworks, offering significant benefits to aircraft operators and manufacturers. The system utilizes an ML model with a mean absolute error (MAE) of 0.14 on test data for forecasting mechanical properties. Data transmission latency throughout the entire system is less than one second in a LAN setup, highlighting its potential for real-time monitoring applications in aerospace and other industries. However, while the system shows promise, challenges such as sensor reliability under extreme environmental conditions and the need for advanced ML models to handle diverse data streams have been identified as areas for future research.

In order to obtain important knowledge for raising the performance and efficiency of propulsion systems, in this paper we investigate the dynamics process of pressure gain combustion in the Pulse Detonation Engine. Through comprehensive experimentation, we analyse the intricate mechanisms governing the pressure gain combustion in PDEs. Our findings present innovative methods for increasing combustion efficiency, boosting thrust generation, and addressing operational difficulties in PDE designs. Additionally, we discuss creative strategies for utilizing pressure gain effects to enhance engine performance metrics.

The present paper details the recent advances in three schlieren visualization methods, considering only those applied to obtain values of gas-dynamic parameters of the phenomenon, namely SIV (schlieren image velocimetry), CCS (color-calibrated schlieren) and BOS (background-oriented schlieren). The above-mentioned advances refer to innovative optical configurations - introducing of new optical components or new optical alignments, state-of-the art or improved calibration methods, references to various post-processing algorithms, and present the possibility to improve system efficiency or validate the resulting schlieren data by coupling the schlieren systems with other optical methods.

This article explores the feasibility of generating velocity maps for a flame and its plume from a burning candle using Schlieren Image Velocimetry (SIV). Currently, SIV is predominantly employed in the literature to track highly turbulent flows by using the developing eddies as flow markers. The novelty of this study lies in evaluating the quality of a nearly laminar flame, which exhibits significant background density variation due to its own plume. In addition to the SIV technique, a brief assessment is conducted using a segmented color filter, specially designed and manufactured to evaluate the potential for measuring the temperature range of the flame.

For the doctoral study on the influence of the aircraft external stores over the aeroelasticity behavior of the IAR-99 HAWK, we have developed a new Finite Elements Model (FEM) with new state-of-the-art methods and technologies available at the end of 2020. After creating a new Finite Elements Model (FEM) of the aircraft IAR-99 HAWK using special elements, the team decided to validate the results by performing a free-free vibrations analysis on this model. In this presentation we have described the finite elements used and their peculiarities, the finite elements model obtained for the aircraft IAR-99 HAWK and the results of the free-free vibrations analysis obtained in the empty equipped configuration. The results of the theoretical free-free vibrations obtained for the IAR-99 HAWK in the empty equipped configuration are presented and compared to those obtained from ground tests, thus confirming the accuracy of the new Finite Element Model (FEM). The new Finite Element Model (FEM) of the IAR-99 HAWK will be versatile enough to provide insight into the structural behavior of the aircraft, regardless of the phenomenon for which it is analyzed: static stress, aerodynamic simulations, flutter, etc.

Abstract The general synthetic iterative scheme (GSIS) has proven its efficacy in modeling rarefied gas dynamics, where the steady-state solutions are obtained after dozens of iterations of the Boltzmann equation, with minimal numerical dissipation even using large spatial cells. In this paper, the fast convergence and asymptotic-preserving properties of the GSIS are harnessed to remove the limitations of the direct simulation Monte Carlo (DSMC) method. The GSIS, which leverages high-order constitutive relations derived from DSMC, is applied intermittently, which not only rapidly steers the DSMC towards steady state, but also eliminates the requirement that the cell size must be smaller than the molecular mean free path. Several numerical tests have been conducted to validate the accuracy and efficiency of this hybrid GSIS-DSMC approach.

Abstract Steady-state numerical simulations were conducted to capture the aerodynamic characteristics and flow patterns resulting from a tubercled and non-tubercled wing subjected to various combined pitch and yaw conditions at $$Re=1.8 \times 10^{5}$$ R e = 1.8 × 10 5 . Pitch angle ranged from $$0^{\circ }$$ 0 ∘ to $$25^{\circ }$$ 25 ∘ , while two different yaw angles of $$10^{\circ }$$ 10 ∘ and $$30^{\circ }$$ 30 ∘ were used. Results show that $$10^{\circ }$$ 10 ∘ yaw angle does not impact upon the lift and drag characteristics significantly, while a $$30^{\circ }$$ 30 ∘ yaw angle leads to substantial lift and drag losses. Additionally, the tubercled wing continues to confer favourable stall-mitigating characteristics even for the larger yaw angle. Finally, despite skewing the flow structures significantly, the $$30^{\circ }$$ 30 ∘ yaw angle also reduces the formations of bi-periodic flow structures, flow separations and recirculating regions along the leading-edge tubercles, suggesting potentially better flow stability and controllability.

The weapon-target assignment problem is the key to the combat mission of the UAV against the enemy in the battlefield environment. The purpose is to find a reasonable weapon target assignment scheme based on the threat, value and damage probability of the target, so as to improve the combat efficiency. Aiming at the problem that the current multi-objective optimization algorithm has slow convergence speed and poor convergence stability when solving the static weapon-target assignment problem, and it is difficult to adapt to the high real-time performance of the current battlefield, an improved non-dominated sorting genetic algorithm based on reference points is proposed. The initial population is optimized by binary coding attack scheme, and adaptive mutation and crossover strategy as well as population optimization update strategy is introduced. Based on the threat matrix and advantage matrix obtained by evaluating the battlefield situation, the target attack scheme is generated after multiple iterations of the population. Finally, the Pareto solution set satisfying the constraint condition is calculated, and the relative optimal solution in the Pareto frontier is taken as the attack scheme of multi-UAV. Multiple experiments show that under good conditions, the improved algorithm reduces convergence time by 46.74%, reduces target threat value by 50.5%, reduces total flight range by 26.46%, and increases the number of killing targets by 11.76% compared with the original algorithm. It is proved that the algorithm is reasonable and efficient in solving the problem of target assignment of multi-UAV air-to-ground strike mission.

With the rapid development and application of computer technology and machine vision technology, the exploration of unexploded submunition search technology based on “artificial intelligence +” model has received extensive attention. However, due to the danger of unexploded submunitions and the particularity of military applications, data set construction is a bottleneck problem that needs to be solved urgently. Based on this, the paper discusses the construction methods and processes of real physical image data sets and three-dimensional reconstruction data sets using physical images. It focuses on the analysis of the key technologies and their advantages and disadvantages in the construction process of the two data sets. A multi-camera is used to collect the target image and geographic coordinate information, and then the deep learning algorithm is used to extract the target feature, generate the three-dimensional point cloud and fuse the three-dimensional image. The experimental results show that the three-dimensional data set constructed by this method can effectively solve the problem of insufficient data volume of the existing data set of unexploded submunitions. Finally, the future development direction of the data set construction method is prospected.

Interrupted-sampling repeater jamming (ISRJ) generates a number of controllable false targets in the range dimension of radar signal by using the principle of signal undersampling and the characteristics of matched filtering. Based on the characteristics of ISRJ sampling function, this paper presents a design method of phase coded waveform and mismatched filter for ISRJ. According to the parameters of pulse width and repetition frequency of interference, a phase coded signal of mismatched filtering suitable for suppressing ISRJ is designed before the radar transmits the signal. According to the ISRJ sampling function and the coded signal designed before transmission, a mismatched filter to suppress ISRJ is constructed to reduce the interference energy after receiving the echo with interference. The simulation results show that compared with traditional unprocessed phase coded signals, the optimized signals of the proposed method exhibit better anti-jamming capability.

The attack capabilities and potential advantages of thrust adjustable air-to-air missiles in complex combat scenarios is explored. This paper envisions a concept of thrust adjustable air-to-air missiles and establishes a three-dimensional trajectory dynamics model, then employs trajectory optimization methods to study the attack capabilities of thrust adjustable air-to-air missiles. Numerical simulation is conducted for two envisioned combat scenarios: target loss recapture scenario and target change scenario after launch, and their capabilities are compared with traditional powered air-to-air missile. Simulation results indicate that in the target loss and recapture scenario, when the initial missile-to-target distance is between 70 to 100 km, the maximum allowable target loss duration of thrust adjustable air-to-air missiles is increased 20 to 40 s approximately than that of traditional solid-propellant air-to-air missile, and the latest allowed target loss time is about 20 s later than traditional air-to-air missile. In the target change after launch scenario, when the initial missile-to-target distance is between 70 to 100 km and the lateral distance between the new and the old targets is 30 km, the tolerance margin of latest target change time for thrust adjustable air-to-air missiles is delayed by approximately 10 to 70 s compared to traditional solid-propellant air-to-air missile.

In a single UAV-assisted mobile edge computing system, in order to enable the UAV to serve all user devices in a large area, the large area can be divided into a plurality of sub-areas and the UAV can be set to fly between the sub-areas with a fixed route to provide computing services for the user devices. Considering the scarcity of computational resources for user devices and the fact that users outside the coverage area of the UAV may choose to move to the coverage area for task offloading in order to maximize their own utility, the partial offloading problem of user devices can be transformed into the problem of maximizing the utility of each user device. The mixed-strategy game and the submodular game are used to determine the movement probability of user devices and the amount of offloaded data, so as to derive the optimal offloading strategy, and the existence of mixed-strategy Nash equilibrium and pure-strategy Nash equilibrium is proved, respectively. Simulation results show that the proposed scheme can effectively improve the utility of user device compared with classical schemes such as MBO (Binary Offloading Based on Mixed Strategy Game), and its convergence and stability are verified.

Starting with the actual operational requirements of active defense, this paper analyzes the advantages, disadvantages, and usage scenarios of different trajectory prediction methods, also analyzes the differences between the active defense combat scenarios of air platforms and other aircraft trajectory prediction scenarios that are currently mainly studied. A state estimation scheme based on extended Kalman filtering is proposed for the special attack intent and motion law of the trajectory prediction object in this scenario. It simulates the process of the attack missile guided with proportional guidance law attacking aircraft based on simulation software, to establish an observation model under conditions of constant proportional guidance coefficient, and performs linearization simulation using Kalman filter nonlinear model. The motion states of the attack missile at different times are observed and the short-term trajectory prediction is performed. The simulation results show that the model can significantly reduce state estimation errors in the active defense combat scenario.

Multiple aircraft formation has strong multi operation capability, high reliability and high overall efficiency, which is an important direction for the future development of aviation field. High-precision relative navigation is crucial for multiple aircraft to achieve formation flight. In order to achieve high-precision relative navigation, the existing methods usually increase the relative navigation accuracy by adding the types of navigation sensors or improving the measurement accuracy of navigation sensors. However, this not only leads to an increase in the cost and volume of the relative navigation system, but also leads to an increase in the system complexity. Therefore, based on the overall geometry topology structure of the formation, a formation geometric topology constraint model is established and introduced into the estimation of the relative inertial navigation errors in this paper. A collaborative relative navigation method based on geometric topology constraints is proposed. By introducing the geometric topology information of the whole formation, the proposed method can improve the relative navigation accuracy without increasing the configuration of navigation sensors. On this basis, a high-precision collaborative relative navigation scheme for multiple aircraft is designed. The simulation results show that the proposed method has higher relative navigation accuracy than the existing method, and the mean squared errors of relative attitude determination, relative positioning and relative velocity measurement are reduced by more than 66.48%, 48.73% and 69.53%, respectively. The proposed method can achieve high-precision relative navigation for multiple aircraft.

In order to improve the penetration and survival performance of hypersonic vehicles in the unpowered glide section under incomplete guidance and detection information or interference of detection equipment, this paper establishes a dynamic model of hypersonic vehicles in the glide section. The trajectory is optimized using sequential convex optimization method, which has better control energy consumption while satisfying terminal constraints, as well as ensuring terminal acceleration convergence and good computational performance. The trajectory tracking part adopts a linear LQR controller design to stably track and plan the trajectory. Compared with the traditional optimal control gui-dance law with angle constraints, the sliding phase adopts a trajectory planning-trajectory tracking method without the need for line of sight angle information. The relative angle information of the target can be introduced after the sliding phase ends, which has stronger survival performance in complex electromagnetic situations

In the field of hyperspectral target detection, algorithms based on collaborative representation have shown excellent performance. However, the pollution of background dictionaries by anomaly has always been a problem, which affects the detection performance of this algorithm partly. This paper proposes an improved collaborative representation algorithm for hyperspectral anomaly detection, and designs a double-layer collaborative representation structure. Firstly, the first layer collaborative representation algorithm is used to detect most of the anomaly. Its neighborhood pixels are used for background purification to eliminate the pollution of the detected anomaly on the background dictionary. Then, the purified background dictionary is used to predict the background, and in the second layer, the collaborative representation is used for anomaly detection. Simulation experiments show that a simple double layer collaborative representation structure can effectively alleviate the background pollution caused by anomaly. The detection performance of this algorithm is significantly improved compared to the basic collaborative representation algorithm, and it also has relatively good detection performance.

The online generation method of nonlinear optimal terminal guidance command with the overload constraint and autopilot delay is studied. Firstly, the optimality conditions of the nonlinear optimal guidance problem with stationary target are established based on the Pontryagin’s maximum principle, and the overload constraint is embedded into the optimality conditions by the saturation function. Secondly, the parametric method is used to generate the flight trajectory data set that satisfies the optimality conditions by numerical integration. Then, the neural network is trained using the data set to fit the mapping relationship between the relative motion state of the missile-target and the optimal guidance command, so as to generate the guidance command under the overload constraint within milliseconds. For the delay response of autopilot, the differential compensation method is used to estimate the optimal guidance command output by the neural network at the next moment to achieve fast tracking. Finally, the simulation results show that the proposed method can generate optimal guidance command online for both stationary targets and small maneuvering targets.

In order to enhance the precision of missile strikes on targets, controlling the three-dimensional gui-dance problem involving attack time and attack angle is of significant importance in practical applications. For addressing this problem, this paper proposes a control law for attack time and attack angle under field-of-view constraints based on the three-dimensional vector guidance model. Firstly, by extending the planar vector guidance law to three-dimensional space, it introduces a three-dimensional vector attack angle constrained guidance law. Secondly, incorporating a residual time bias term into the interception component of the above guidance command, it designs a three-dimensional vector guidance law under field-of-view constraints, and performs the stability analysis. This design ensures that the missile can hit the target with the desired attack time and attack angle within the constraints of the field of view, with errors less than 0.01. Finally, the correctness and effectiveness of the designed guidance law are validated through numerical simulations.

As a hypersonic aerodynamic configuration that can break through the lift-drag ratio barrier, waverider configuration is an important candidate for wide-speed reusable aircraft. Therefore. It has become a hot research topic that improving the low speed performance of the waverider vehicle and realizing wide-speed flight. Due to the idea of designing the flow field first and then constructing the shape of the waverider vehicle, the basic flow field will affect its performance. This paper analyzes and compares the calculation methods of the basic flow field, and then introduces the design methods of the combined, variable Mach number and vorticity lift wide-speed waverider vehicle, compares the advantages and disadvantages of each method. Finally, some suggestions for the future development of wide-speed waverider vehicle are put forward from two perspectives of basic flow field and design methods.

Behavioral reasoning of communication radiation sources refers to inferring their behavioral patterns and target intentions through observing and analyzing the signals emitted by communication radiation sources. Especially under non-cooperative conditions, the reasoning behind the behavior of communication radiation sources plays an essential role in the fields of electronic reconnaissance and signal intelligence. This article first clarifies and summarizes the definition and classification of the behavior of communication radiation sources. It then elucidates the meaning of behavioral reasoning of communication radiation sources and further distinguishes the differences and connections between behavioral analysis and behavioral recognition. Subsequently, it reviews the key technologies of behavioral reasoning for communication radiation sources and compares the latest research based on clustering algorithms, classification algorithms, deep learning, and behavioral characteristics. Finally, it analyzes and forecasts the challenges and development of the technology for behavioral reasoning of communication radiation sources.

In response to the strong coupling between line-of-sight angular rate and attitude error in the output of the strapdown rotating projectile, a method based on adaptive fading volume covariance Kalman filtering (AFCKF) is proposed. To achieve the decoupling of line-of-sight angular rate, the relative motion characteristics of missile and target are considered. A state model is constructed, which includes an estimation model of line-of-sight angular rate in the terminal guidance phase, and a measurement model including line-of-sight angle and attitude angular is established based on geometric relationships. To solve the problem of filter failure caused by the divergence of the line-of-sight angular rate estimation results in traditional CKF for rotating projectile, a fading factor based on residual sequence is introduced to adjust the predicted state covariance for quick convergence of the estimation results. To verify the effectiveness of AFCKF, typical interferences of attitude angle and measurement angle are considered. Simulation results show that the mean estimation errors of line-of-sight elevation rate and azimuth rate of the proposed method are 30.41% and 42.18% of the traditional EKF, respectively, effectively improving the accuracy of line-of-sight angular rate estimation for rotating projectil

Aiming at the difficulty of tracking and recognizing ballistic targets and active multi-false targets in the presence of countermeasures, a joint tracking and recognition method for ballistic targets and false targets based on the robust interacting multiple model (RIMM) strategy is proposed. This method develops the interacting multiple model (IMM) strategy based on the deduced true target and false target motion pattern set and the fine difference features within the set, using the extended Kalman filter (EKF) as sub filters. Additionally, this method introduces probability adjustment factors and time-varying factors into the IMM strategy to update the probability transition matrix in real time and amplify the fine feature difference of the motion pattern set effectively, which not only achieves stable tracking of ballistic targets and false targets, improves the tracking accuracy, but also identifies them online in real time, achieving integrated tracking and identification. Simulation results show that the proposed method has better performance than traditional single model EKF algorithm and classical IMM+EKF algorithm, and it can track and recognize ballistic targets and false targets in real time, which is conducive to improving the efficiency of radar resource scheduling.

With the rapid development and application in practical battlefields of hypersonic weapons, the demand for hypersonic defense technologies has become increasingly urgent in various countries. Due to the different target characteristics of hypersonic weapons from traditional missiles, existing missile defense systems are difficult to achieve good interception effects, and related equipment and technologies need to be redeveloped. In order to sort out the future demand directions of hypersonic defense technologies, this paper summarized the current status of hypersonic defense capabilities in countries such as the United States, Russia, and Japan from the aspects of early warning detection, interception and strike and so on, and analyzed relevant key technologies. It is proposed that future hypersonic defense should focus on increasing interception distance, reducing combat costs, and dealing with system attack.

In the realm of supply chain logistics, the Multi-Depot Multi-Supplier Vehicle Routing Problem (MDMSVRP) poses a significant challenge in optimizing the transportation process to minimize costs and enhance operational efficiency. This problem involves determining the most cost-effective routes for a fleet of vehicles to deliver goods from multiple suppliers to multiple depots, considering various constraints and non-linear relationships. The routing problem (RP) is a critical element of many logistics systems that involve the routing and scheduling of vehicles from a depot to a set of customer nodes. One of the most studied versions of the RP is the Vehicle Routing Problem with Time Windows (VRPTW), in which each customer must be visited at certain time intervals, called time windows. In this paper, it is considered that there are multiple depots (supply centers) and multiple suppliers, along with a fleet of vehicles. The goal is to efficiently plan routes for these vehicles to deliver goods from the suppliers to various customers while considering relaxed time windows. This research is intended to establish a new relaxation scheme that relaxes the time window constraints in order to lead to feasible and good solutions. In addition, this study develops a discrete optimization model as an alternative model for the time-dependent VRPTW involving multi-suppliers. This research also develops a metaheuristic algorithm with an initial solution that is determined through time window relaxation.