Hasil untuk "Motor vehicles. Aeronautics. Astronautics"

Kuo-Chien Liao, Jirayu Lau, Muhamad Hidayat

Aircraft safety is the aviation industry’s primary concern. Inspections must be conducted before each flight to ensure the integrity of the aircraft. To meet the increasing demand for engineers, a system capable of detecting surface defects on aircraft was designed to reduce the workload of the inspection process. The system utilizes the real-time object detection capabilities of the you only look once-version 9 (YOLO v9) algorithm, combined with imagery captured from an unmanned aerial vehicle (UAV)-based aerial platform. This results in a system capable of detecting defects such as cracks and dents on the aircraft’s surface, even in areas that are difficult to reach, such as the upper surfaces of the wings or the higher parts of the fuselage. With the introduction of a Real-Time Messaging Protocol (RTMP) server, the results can be monitored via artificial intelligence (AI) and Internet of Things (IoT) devices in real time for further evaluation. The experimental results confirmed an effective recognition of defects, with a mean average precision (mAP@0.5) of 0.842 for all classes, the highest score being 0.938 for dents and the lowest value 0.733 for the paint-off class. This study demonstrates the potential for developing image detection technology with AI for the aviation industry.

Zhouwei Lou, Weiyi Ge, Ke Xie

With the continuous advancement of artificial intelligence (AI) technology, AI algorithms have demonstrated exceptional aircraft control capabilities in highly dynamic and complex scenarios such as aerial combat. However, the inherent lack of explainability in AI algorithms poses a significant challenge to gaining sufficient trust, presenting potential safety risks that could lead to aircraft loss of control. This limitation hinders the widespread adoption of AI in practical applications. To enhance human–AI trust, improve system stability and safety, and advance the deployment of AI algorithms in practical settings, this study proposes an approach to describe and explain AI decision-making behaviors using natural language. Natural language is a straightforward medium for expressing information, which avoids the need for additional decoding or interpretation, particularly in rapidly changing battlefield environments, enabling pilots to quickly comprehend the intentions of AI algorithms and thereby fostering trust in AI systems. This study constructs a dataset of AI decision behavior description and interpretation based on adversarial temporal data in an aerial combat scenario and introduces an encoder–decoder framework that integrates an attentional mechanism. Findings from the experiments suggest that this approach effectively delineates and elucidates the AI decision-making behaviors, thereby facilitating mutual trust between humans and AI.

Linxiao Han, Peng Zhang, Yingyang Wang et al.

Tailless unmanned aerial vehicles (UAVs) achieve high-agility maneuvers with flight control systems. The attainable moment set (AMS) provides critical theoretical foundations and constraints for their optimization. A computational method is proposed herein to address controllability limitations caused by nonlinear aerodynamic effectiveness. This method incorporates dual constraints on control surface angles and angular rates for the nonlinear AMS, aiming to meet the demands of attitude tracking dynamics in flight control systems. First, a quantitative model is established to correlate dual deflection constraints with aerodynamic moment amplitude and bandwidth limitations. Next, we construct a computational framework for the incremental attainable moment set (IAMS) based on differential inclusion theory. For monotonic nonlinear aerodynamic effectiveness, the vertices of the IAMS are updated using local interpolation, yielding the incremental nonlinear attainable moment set (INAMS). When non-monotonic nonlinearity occurs, stationary points are calculated to adjust the control effectiveness matrix and admissible control set, thereby reducing computational errors induced by non-monotonic characteristics. Furthermore, the effective actions set, derived from a time-varying incremental nonlinear attainable moment set, quantifies the residual moment envelope of tailless UAVs during maneuvers. Comparative simulations indicate that the proposed method achieves correct computation under nonlinear aerodynamic conditions while reliably determining safe flight boundaries during control failure.

Yao Xu, Shaobo Jia, Jichong Guo et al.

Unmanned aerial vehicle (UAV) communication using non-orthogonal multiple access-based coordinated direct and relay transmission (NOMA-CDRT) supports both massive connectivity and wide-area coverage, becoming a key technology for future emergency rescue communications. However, relay forwarding and high-quality line-of-sight links may subject UAV-aided NOMA-CDRT to multiple eavesdropping attempts by saboteurs. Therefore, we propose a multi-node joint jamming scheme using artificial noise (AN) for the UAV-assisted NOMA-CDRT to improve the system’s physical layer security. In the proposed scheme, the base station directly serves a nearby user while using a UAV relay to serve a disaster-affected user, and both the users and the UAV relay utilize AN to jointly interfere with eavesdroppers around the users. To accurately characterize and maximize the ergodic secrecy sum rate (ESSR) of the proposed scheme, we derive the corresponding closed-form expressions and design a joint power allocation and interference control (JPAIC) algorithm using particle swarm optimization. Simulations verify the correctness of the theoretical analysis, the ESSR advantage of the proposed scheme compared with the conventional NOMA-CDRT, and the effectiveness of the proposed JPAIC.

Jianmiao Yi, Feng Deng

An airfoil shape parameterization that can generate a compact design space is highly desirable in practice. In this paper, a compact airfoil parameterization is proposed by incorporating deep learning into the PAERO parameterization method based on the thin-airfoil theory. Following the PAERO parameterization, the mean camber line is represented by a number of aerodynamic performance parameters, which can be used to narrow down the design space according to the thin-airfoil theory. In order to further reduce the design space, the airfoil thickness distribution is represented by data-driven generative models, which are trained by the thickness distributions of existing airfoils. The trained models can automatically filter out the physically unreasonable airfoil shapes, resulting in a highly compact design space. The test results show that the proposed method is significantly more efficient and more robust than the widely used CST parameterization method for airfoil optimization.

Mai Xin, Zhifeng Ye, Tong Zhang et al.

After many years of development, the technology of analyzing the working condition of power units based on vibration signals has received relatively stable applications, but the accuracy and the degree of automation and intelligence for fault diagnosis are still inadequate due to the limitations in the ongoing development of key technologies. With the development of big data and artificial intelligence technology, the involvement of new technologies will be an important boost to the development of this field. In this study, in order to support subsequent research, bibliometrics is used as a tool to sort the development of the technology in this field at the macro level. At the micro level, key publications in the literature are studied to better understand the development status at the technical level and prepare for the selection of entry points to facilitate in-depth innovation in the future.

Aleksandra Cvetković, Vesna Blagojević, Jelena Manojlović

The performance analysis of an energy constrained Internet of Things (IoT) system with unmanned aerial vehicle (UAV) is provided in this paper. In the considered system, a power beacon is used for the energy supply of a sensor node that has no other power sources, while the UAV is used for the collection of sensor data. The outage and capacity performances are analyzed under the assumption of a Nakagami-<i>m</i> fading environment, for the case when the power and information transfer are performed based on the time-switching protocol and the UAV is randomly positioned at a certain height. Based on the provided analysis we derive the exact closed-form expressions for the outage probability, the outage capacity and the ergodic capacity of the power beacon assisted IoT system. The analytical results are confirmed using an independent simulation method. The performed analysis demonstrates the impact of various system and channel parameters on system performances.

Juwon Lim

Purpose: Aviation medical exams are pivotal for health management in aviation professionals, ensuring safety. Despite their importance, a gap exists in literature detailing the dynamics of these exams, especially during the COVID-19 era. Methods: Longitudinal data assessed distribution and trends based on sex, age, and qualification. A segment analyzed the pandemic’s influence, and a correlation between pilot age and disqualification rate was evaluated. Results: Males represented 95.5% (124,751) of total applicants; females 4.5% (5,861). Age distribution: under 40 (53.2%), 40s (26.4%), 50s (16.1%), and over 60 (4.2%). The majority (94.7%) had class 1 type. The fit rate was 87.4%, with conditional fit at 11.9%. Exams increased from 2,529 in 2000 to 15,149 in 2019, then decreased during COVID-19, with an expected recovery in 2023. Pilots’ trend mirrored this, with projections to exceed 12,000 exams in 2023. Of the pilots, 0.15% were deemed unfit, with age correlating with disqualification. Conclusion: This study illuminates the evolution and impact of aviation medical examinations over 24 years, accentuating the effects of the COVID-19 pandemic.

Hong Zhang, Quanming Li, Jiachen Wang et al.

Unmanned aerial vehicle (UAV) tilt photography technology has gradually become a new technical means of disaster risk identification. This technology combines UAVs, satellite remote sensing, and ground online monitoring systems to establish an integrated space–sky–Earth system that can be used for tailings pond risk identification. With the use of this system for visual interpretation, water body identification, and monitoring data analysis, multiple types of monitoring parameters of a typical tailings pond in China, such as the seepage line and surface deformation, were obtained. Moreover, intelligent fusion analysis was performed of multisource data to outline the problems affecting tailings safety in the process of elevation expansion and irregular ore discharge of the tailings pond. Warning values of different levels were obtained to assess the overall safety condition of the tailings pond, and the proposed technology was verified. The research results could provide a new basis for accurate evaluation of the running state of tailings ponds and offer an effective remote monitoring means for tailings pond enterprises and supervisory departments.

Lin Chen, Xiaoyu Fu, Santos Ramil et al.

Fuzzy logical control is a robust and effective control method in industrial fields, which renders it applicable to the attitude control of a solar sail. However, it is hard to apply in black-box and time-varying problem as real solar sail attitude control. Considering the lack of a priori knowledge and the unacceptable manual workload in the design of the fuzzy logical controller (FLC), an intelligent FLC designer (IFLCD) is developed by introducing neural network modelling and automatic design method. Besides, IFLCD also supports self-adaption for better control accuracy. By applying the proposed IFLCD in the attitude stabilization of a solar sail with individually controllable elements (SSICE), an effective solution of unmanned, time-varying, and complex system control method is offered without any mathematical model, which also overcomes the difficulties in FLC design Considering the performance degradation, accident, and distance problems faced by spacecraft, IFLCD can help with more practical problems that are hard be solved by traditional control theory.

Chen Chai, Ziyao Zhou, Weiru Yin et al.

Purpose – The presentation of in-vehicle warnings information at risky driving scenarios is aimed to improve the collision avoidance ability of drivers. Existing studies have found that driver’s collision avoidance performance is affected by both warning information and driver’s workload. However, whether moderation and mediation effects exist among warning information, driver’s cognition, behavior and risky avoidance performance is unclear. Design/methodology/approach – This purpose of this study is to examine whether the warning information type modifies the relationship between the forward collision risk and collision avoidance behavior. A driving simulator experiment was conducted with waring and command information. Findings – Results of 30 participants indicated that command information improves collision avoidance behavior more than notification warning under the forward collision risky driving scenario. The primary reason for this is that collision avoidance behavior can be negatively affected by the forward collision risk. At the same time, command information can weaken this negative effect. Moreover, improved collision avoidance behavior can be achieved through increasing drivers’ mental workload. Practical implications – The proposed model provides a comprehensive understanding of the factors influencing collision avoidance behavior, thus contributing to improved in-vehicle information system design. Originality/value – The significant moderation effects evoke the fact that information types and mental workloads are critical in improving drivers’ collision avoidance ability. Through further calibration with larger sample size, the proposed structural model can be used to predict the effect of in-vehicle warnings in different risky driving scenarios.

Wuhua Wang, Jiakui Tang, Na Zhang et al.

<i>Pedicularis</i> has adverse effects on vegetation growth and ecological functions, causing serious harm to animal husbandry. In this paper, an automated detection method is proposed to extract <i>Pedicularis</i> and reveal the spatial distribution. Based on unmanned aerial vehicle (UAV) images, this paper adopts logistic regression, support vector machine (SVM), and random forest classifiers for multi-class classification. One-class SVM (OCSVM), isolation forest, and positive and unlabeled learning (PUL) algorithms are used for one-class classification. The results are as follows: (1) The accuracy of multi-class classifiers is better than that of one-class classifiers, but it requires all classes that occur in the image to be exhaustively assigned labels. Among the one-class classifiers that only need to label positive or positive and labeled data, the PUL has the highest F score of 0.9878. (2) PUL performs the most robustly to change features in one-class classifiers. All one-class classifiers prove that the green band is essential for extracting <i>Pedicularis</i>. (3) The parameters of the PUL are easy to tune, and the training time is easy to control. Therefore, PUL is a promising one-class classification method for <i>Pedicularis</i> extraction, which can accurately identify the distribution range of <i>Pedicularis</i> to promote grassland administration.

Hiroshi Yamashita, Feijia Yin, Volker Grewe et al.

Climate-optimized routing is an operational measure to effectively reduce the climate impact of aviation with a slight increase in aircraft operating costs. This study examined variations in the flight characteristics among five aircraft routing strategies and discusses several characteristics of those routing strategies concerning typical weather conditions over the North Atlantic. The daily variability in the North Atlantic weather patterns was analyzed by using the European Center Hamburg general circulation model (ECHAM) and the Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model in the specified dynamics mode from December 2008 to August 2018. All days of the ten complete winters and summers in the simulations were classified into five weather types for winter and into three types for summer. The obtained frequency for each of the weather types was in good agreement with the literature data; and then representative days for each weather type were selected. Moreover, a total of 103 North Atlantic flights of an Airbus A330 aircraft were simulated with five aircraft routing strategies for each representative day by using the EMAC model with the air traffic simulation submodel AirTraf. For every weather type, climate-optimized routing shows the lowest climate impact, at which a trade-off exists between the operating costs and the climate impact. Cost-optimized routing lies between the time- and fuel-optimized routings and achieves the lowest operating costs by taking the best compromise between flight time and fuel use. The aircraft routing for contrail avoidance shows the second lowest climate impact; however, this routing causes extra operating costs. Our methodology could be extended to statistical analysis based on long-term simulations to clarify the relationship between the aircraft routing characteristics and weather conditions.

Alessandro Giuseppi, Roberto Germanà, Federico Fiorini et al.

Fire monitoring and early detection are critical tasks in which Unmanned Aerial Vehicles (UAVs) are commonly employed. This paper presents a system to plan the drone patrolling schedule according to a real-time estimation of a fire propagation index that is derived from satellite data, such as the Normalized Difference Vegetation Index (NDVI) measurement and the Digital Elevation Model (DEM) of the surveilled area. The proposed system employs a waypoint scheduling logic, derived from a dynamic Voronoi Tessellation of the area, that combines characteristics of the territory (e.g., vegetation density) with real-time measurements (e.g., wind speed and direction). The system is validated on a case study in Italy, in the municipality of the city of L’Aquila, on three different fire scenarios. In normal situations, the designed waypoint-based navigation system provided an effective monitoring of the area, enabling the early detection of starting fires. The developed solution also demonstrated good performance in tracking and anticipating the fire front advance, potentially providing a better situational awareness to emergency operators and support their response policies. Both the test environment and the simulator have been made open-source.

Eric S. Hendricks, Justin S. Gray

Aviation researchers are increasingly focusing on unconventional vehicle designs with tightly integrated propulsion systems to improve overall aircraft performance and reduce environmental impact. Properly analyzing these types of vehicle and propulsion systems requires multidisciplinary models that include many design variables and physics-based analysis tools. This need poses a challenge from a propulsion modeling standpoint because current state-of-the-art thermodynamic cycle analysis tools are not well suited to integration into vehicles level models or to the application of efficient gradient-based optimization techniques that help to counteract the increased computational costs. Therefore, the objective of this research effort was to investigate the development a new thermodynamic cycle analysis code, called pyCycle, to address this limitation and enable design optimization of these new vehicle concepts. This paper documents the development, verification, and application of this code to the design optimization of an advanced turbofan engine. The results of this study show that pyCycle models compute thermodynamic cycle data within 0.03% of an identical Numerical Propulsion System Simulation (NPSS) model. pyCycle also provides more accurate gradient information in three orders of magnitude less computational time by using analytic derivatives. The ability of pyCycle to accurately and efficiently provide this derivative information for gradient-based optimization was found to have a significant benefit on the overall optimization process with wall times at least seven times faster than using finite difference methods around existing tools. The results of this study demonstrate the value of using analytic derivatives for optimization of cycle models, and provide a strong justification for integrating derivatives into other important engineering analyses.

Ryuichi Umeno, Makoto Itoh, Satoshi Kitazaki

Purpose - Level 3 automated driving, which has been defined by the Society of Automotive Engineers, may cause driver drowsiness or lack of situation awareness, which can make it difficult for the driver to recognize where he/she is. Therefore, the purpose of this study was to conduct an experimental study with a driving simulator to investigate whether automated driving affects the driver’s own localization compared to manual driving. Design/methodology/approach - Seventeen drivers were divided into the automated operation group and manual operation group. Drivers in each group were instructed to travel along the expressway and proceed to the specified destinations. The automated operation group was forced to select a course after receiving a Request to Intervene (RtI) from an automated driving system. Findings - A driver who used the automated operation system tended to not take over the driving operation correctly when a lane change is immediately required after the RtI. Originality/value - This is a fundamental research that examined how the automated driving operation affects the driver's own localization. The experimental results suggest that it is not enough to simply issue an RtI, and it is necessary to tell the driver what kind of circumstances he/she is in and what they should do next through the HMI. This conclusion can be taken into consideration for engineers who design automatic driving vehicles.

Ya Lin Pan, Jun Huang

Poor lateral-directional stability due to the absence of vertical stabilizer is a great risk to the aircraft with a flying wing layout. In this paper, an unmanned aerial vehicle with this kind of configuration is chosen as the research object. A three-dimensional model of the unmanned aerial vehicle is established, and then the sensitivity analysis is performed to obtain the effects of main aerodynamic shape parameters on lateral-directional flying quality. The results show that the roll mode and spiral mode of the aircraft meets the requirements of Level 1 flying quality in MIL-F-8785C. But the Dutch roll mode is generally divergent, which means that the flying quality of the aircraft is unacceptable. Thus it can be seen that the Dutch roll mode is the key to the dynamic stability of the aircraft. Further studies show that increasing the value of the wing aspect ratio or decreasing the values of the dihedral angle and torsion angle is useful for improving the Dutch roll mode. It is valuable to reveal the influence mechanism of aerodynamic shape parameters on lateral-directional flying quality for the design of flying wing aircraft.

S. A. Ishkov, G. A. Filippov, P. V. Fadeenkov

We study the problem of determining time-optimal control of in-plane rendezvous transfer of spacecraft with low transversal thrust. We use the Pontryagin maximum principle to determine the optimal control program. Motion is considered in the vehicle centric system with linearized equations. We recognize secular and periodic components of relative motion. Motion control is accomplished by the reversal of the thrust acceleration component. We study the general problem controlling the periodic and secular components at the same time (joint optimal control program). Also we study partial problems determining separate control programs for secular and periodic components of planar motion. Solving partial problems made it possible to determine the structure of the joint optimal control program. We found that the adjustment of secular motion components contains no more than two phases of constant acceleration. The adjustment of periodic motion components consists of a sequence of boost and deceleration phases, the number of which in a single pass does not exceed three.

For exploring the relationship between the mental or cognitive state and metric of vigilance test for unmanned aerial vehicle (UAV), a vigilance state evaluation method and sphere of application based on behavior signals is established. A classical vigilance test avoiding to crash is set. During the experiments, the subjective ratings as well as behavior signals (Response Time, Lapse) are recording. The dynamic changing of behavior signals is analyzed using statistical analysis. The results demonstrate that compared with continuous PVT test, the subject's mental workload in rest PVT test decreases dramatically. Compared other metrics, the speed of response time can reflect the dynamic changing of subject's mental state. The metric of Q-50 has a strong robustness for outlier of subject. Considering that the metrics have strong correlation with operator's cognitive state, they can effectively analyze the different workload.

P. S. Levochkin, V. K. Chvanov, V. S. Vasiliev et al.

Nowadays the improvement of the design of liquid-propellant rocket engines (LPREs) depends on a variety of factors and activities, among which we can distinguish, for example, the improvement of energy characteristics of units and assemblies forming part of the LPREs. The article compares the operation of LPRE turbo-pump turbines using double-sided and single-sided labyrinth seals used in Energomash's engines. The key geometrical parameters of each seal variant, images of 3D-models of seals constructed for the calculations, the resulting computational grid for each seal variant, as well as graphs of the pressure distribution and velocity fields are presented for visual comparison. Sectoral leakage was calculated for each variant of the seal, the possibility of reducing the temperature of the turbine inlet working gas was assessed. In the future attention should be paid to a more detailed study of this type of seals with the use of modern computing power