Hasil untuk "Motor vehicles. Aeronautics. Astronautics"

Rodrigo Cassineli Palharini, João Luiz F. Azevedo, Diego Vera Sepúlveda

During atmospheric reentry, a significant number of chemical reactions are produced inside the high-temperature shock wave formed upstream of the spacecraft. Chemical reactions can significantly alter the flowfield structure surrounding the vehicle and affect surface properties, including heat transfer, pressure, and skin friction coefficients. In this scenario, the primary goal of this investigation is to evaluate the Quantum-Kinetic chemistry model for computing rarefied reactive gas flow over simple and complex geometries. The results are compared with well-established reaction models available for the transitional flow regime. The study focuses on two configurations, a sphere and the Orion capsule, analyzed at different altitudes to assess the impact of chemical nonequilibrium across varying flow rarefaction levels. Including chemical reactions led to lower post-shock temperatures, broader shock structures, and significant species dissociation in both geometries. These effects strongly influenced the surface heat flux, pressure, and temperature distributions. Comparison with results from the literature confirmed the validity of the implemented QK model and highlighted the importance of including chemical kinetics when simulating hypersonic flows in the upper atmosphere.

Mihai CAZACU, Alina-Daniela MIHALCEA, Mihai CHITOIU

This paper presents a summary of AERO DAY 2025, a major scientific event in Romanian aerospace research and innovation, jointly organized by CENIT, Dassault Systèmes and INCAS. Held on May 15, 2025, in Bucharest, the event brought together key stakeholders from the aviation industry and academia to explore the impact of digital transformation in aerospace. Central to the event was the concept of the "Virtual Twin Experience", showcasing how advanced simulation tools and collaborative platforms such as the 3DEXPERIENCE ecosystem (CATIA, DELMIA, SIMULIA, ENOVIA) are shaping the future of design, manufacturing, and systems engineering. Highlights included keynote speeches, technical presentations, and workshops focused on emerging technologies, sustainability in aviation, and collaborative R&D initiatives. The event fostered knowledge exchange, networking, and strategic partnerships, positioning Romania as a contributor to innovation in the global aerospace sector.

ZHENG Jinpeng, LI Shaoyuan, ZHU Xiaolin et al.

When training deep neural networks in practical application scenarios， the data used often have various biases， such as long-tailed category distributions， in-distribution noise， and out-of-distribution noise. Most existing methods focus on solving the problem of category imbalance or dealing with noisy labels， but rarely consider both aspects simultaneously， especially when in- and out-of-distribution noises exist at the same time. We propose an imbalanced noisy labels calibration （INLC） method to address this challenge. To handle out-of-distribution samples， we use the model’s consistent predictions to filter them out and assign uniform labels， thereby enhancing the model’s ability to detect out-of-distribution samples. For in-distribution samples， we use the Jensen-Shannon divergence to distinguish noise and reduce misclassification of clean samples， especially in tail categories. To address the problem of category imbalance， we introduce an additional semantic classifier to mitigate the bias of pseudo-labels towards majority categories. Finally， we adopt a consistency regularization method based on strong data augmentation to further improve the model’s generalization performance. We conducted extensive experiments on simulated and real-world datasets， covering different levels of category imbalance from low to high and different proportions of label noise. Experimental results show that INLC significantly alleviates the impact of label noise and category imbalance， and improves the classification accuracy by more than 2% compared with the previous state-of-the-art baseline methods.

YAN Wei, WANG Qimin, LI Jinye et al.

In order to optimize the low-speed characteristics of the high-lift configuration of civil transport aircraft and enhance the take-off and landing performance of the aircraft，it is necessary to control the flow separation of the wings. Through the wind tunnel test，the research on the selection of two passive control technologies，the wingbody fairing and outboard nacelle strake，is carried out. The best combination scheme that can optimize the lowspeed characteristics is found：small size wing-body fairing plus outboard nacelle strake "A"；the area where the flow separation is suppressed by the optimization method is presented through the flow visualization test. The pitch moment characteristics under the conditions of the optimal combination scheme meet the requirements of Boeing criteria.

WEI Kan, LI Ling, LIANG Shilin et al.

Intelligent interpretation of high‑resolution remote sensing imagery is a fundamental challenge in aerospace information processing. Complex ground environments such as construction and demolition （C&D） waste landfills exemplify the need for robust segmentation models that can handle diverse spatial and spectral patterns. Conventional convolutional neural networks （CNNs） are limited by their local receptive fields， whereas Transformer‑based architectures often lose fine spatial detail， resulting in incomplete delineation of heterogeneous remote sensing targets. To address these issues， we propose a global‑local collaborative network （GLC‑Net）， which is designed for intelligent remote sensing image segmentation. The model integrates an efficient Transformer block to capture global dependencies and a local enhancement block to refine structural details. Furthermore， a multi‑scale spatial aggregation and enhancement （MSAE） module is introduced to strengthen contextual representation and suppress background noise. Deep supervision facilitates hierarchical feature learning. Experiments on two high‑resolution remote sensing datasets （Changping and Daxing） demonstrate that GLC‑Net surpasses state‑of‑the‑art baselines by 1.5%—3.2% in mean intersection over union （mIoU）， while achieving superior boundary precision and semantic consistency. These results confirm that global‑local collaborative modeling provides an effective pathway for intelligent remote sensing image segmentation in aerospace environmental monitoring.

Markus Amann, Malte Probst, Raphael Wenzel et al.

In interactions between automated vehicles (AVs) and crossing pedestrians, modeling implicit vehicle communication is crucial. In this work, we present a combined prediction and planning approach that allows to consider the influence of the planned vehicle behavior on a pedestrian and predict a pedestrian's reaction. We plan the behavior by solving two consecutive optimal control problems (OCPs) analytically, using variational calculus. We perform a validation step that assesses whether the planned vehicle behavior is adequate to trigger a certain pedestrian reaction, which accounts for the closed-loop characteristics of prediction and planning influencing each other. In this step, we model the influence of the planned vehicle behavior on the pedestrian using a probabilistic behavior acceptance model that returns an estimate for the crossing probability. The probabilistic modeling of the pedestrian reaction facilitates considering the pedestrian's costs, thereby improving cooperative behavior planning. We demonstrate the performance of the proposed approach in simulated vehicle-pedestrian interactions with varying initial settings and highlight the decision making capabilities of the planning approach.

Lars Ullrich, Michael Buchholz, Klaus Dietmayer et al.

Research in the field of automated vehicles, or more generally cognitive cyber-physical systems that operate in the real world, is leading to increasingly complex systems. Among other things, artificial intelligence enables an ever-increasing degree of autonomy. In this context, the V-model, which has served for decades as a process reference model of the system development lifecycle is reaching its limits. To the contrary, innovative processes and frameworks have been developed that take into account the characteristics of emerging autonomous systems. To bridge the gap and merge the different methodologies, we present an extension of the V-model for iterative data-based development processes that harmonizes and formalizes the existing methods towards a generic framework. The iterative approach allows for seamless integration of continuous system refinement. While the data-based approach constitutes the consideration of data-based development processes and formalizes the use of synthetic and real world data. In this way, formalizing the process of development, verification, validation, and continuous integration contributes to ensuring the safety of emerging complex systems that incorporate AI.

Dhairya Parekh, Mark L. Franz Ph. D, Sara Zahedian Ph. D et al.

Commercial Motor Vehicle (CMV) safety is crucial in traffic management and public safety. CMVs account for numerous traffic incidents, so monitoring CMV safety and safety inspections is essential for ensuring safe and efficient highway movement. This paper presents the development and real-world application of CMV dashboards designed under the guidance of CMV safety enforcement professionals from the Maryland State Police (MSP), the Maryland Department of Transportation - State Highway Administration (MDOT - SHA), and the Federal Motor Carrier Safety Administration (FMCSA) to enable intuitive and efficient analysis of CMV safety performance measures. First, three CMV safety dashboards enable CMV safety professionals to identify sites with a history of safety performance issues. A supplemental dashboard automates the analysis of CMV enforcement initiatives using the same performance measures. These performance measures are based on CMV probe vehicle speeds, inspection/citation data from Truck Weigh and Inspection Stations (TWIS), patrolling enforcement, and Virtual Weigh Stations (VWS). The authors collaborated with MSP to identify a portion of I-81 in Maryland, susceptible to improvement from targeted CMV enforcement. The supplemental enforcement assessment dashboard was employed to evaluate the impact of enforcement, including the post-enforcement halo effect. The results of the post-enforcement evaluation were mixed, indicating a need for more fine-grained citation data.

Chengdong Wu, Sven Kirchner, Nils Purschke et al.

The controller is one of the most important modules in the autonomous driving pipeline, ensuring the vehicle reaches its desired position. In this work, a reinforcement learning based lateral control approach, despite the imperfections in the vehicle models due to measurement errors and simplifications, is presented. Our approach ensures comfortable, efficient, and robust control performance considering the interface between controlling and other modules. The controller consists of the conventional Model Predictive Control (MPC)-PID part as the basis and the demonstrator, and the Deep Reinforcement Learning (DRL) part which leverages the online information from the MPC-PID part. The controller's performance is evaluated in CARLA using the ground truth of the waypoints as inputs. Experimental results demonstrate the effectiveness of the controller when vehicle information is incomplete, and the training of DRL can be stabilized with the demonstration part. These findings highlight the potential to reduce development and integration efforts for autonomous driving pipelines in the future.

Xiaobin Shen, Jingyu Zhao, Zekun Ye et al.

Computational results of aircraft icing and predictions of ice shape are not only determined by the solutions of air-supercooled droplet two-phase flow and icing thermodynamic models of surface water film, but are also influenced by the growth mode of the ice layer. Two ice growth modes were established in a two-dimensional (2D) icing process simulation framework to calculate the ice thickness and ice shape, depending on whether surface deformation of the icing process was considered. Ice accretion simulations were performed with the two ice growth modes for an NACA0012 airfoil under rime ice and mixed ice conditions, and the results of ice amount, ice thickness, and ice shape were compared and analyzed. Under the same amount of ice formation, the ice thickness and ice shape obtained using different ice growth modes vary. The ice thickness and the ice shape size are relatively large without considering surface deformation, whereas the results with growth correction show a certain degree of reduction, which is more noticeable around the leading edge and the ice horns. However, the degrees of difference in ice thickness and ice shape are not the same, and the deviation in ice thickness is more obvious. Furthermore, the ice thickness and ice shape obtained using the ice growth correction mode are more consistent with experimental data and commercial software results, verifying the accuracy of the ice simulation method and the necessity of considering ice surface deformation. This paper is an essential guide for understanding the icing mechanism and accurately predicting two-dimensional ice shape.

Sanghyeon Nam, Hyejin Lee, Youngki Kim et al.

This study proposes a novel climate control strategy for electric vehicles (EVs) by addressing door-opening interruptions, an overlooked aspect in EV thermal management. We create and validate an EV simulation model that incorporates door-opening scenarios. Three controllers are compared using the simulation model: (i) a hierarchical non-linear model predictive control (NMPC) with a unique coolant dividing layer and a component for cabin air inflow regulation based on door-opening signals; (ii) a single MPC controller; and (iii) a rule-based controller. The hierarchical controller outperforms, reducing door-opening temperature drops by 46.96% and 51.33% compared to single layer MPC and rule-based methods in the relevant section. Additionally, our strategy minimizes the maximum temperature gaps between the sections during recovery by 86.4% and 78.7%, surpassing single layer MPC and rule-based approaches, respectively. We believe that this result opens up future possibilities for incorporating the thermal comfort of passengers across all sections within the vehicle.

Zhiyu Shao, Qiong Wu, Pingyi Fan et al.

This work aims to investigate semantic communication in high-speed mobile Internet of vehicles (IoV) environments, with a focus on the spectrum sharing between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. We specifically address spectrum scarcity and network traffic and then propose a semantic-aware spectrum sharing algorithm (SSS) based on the deep reinforcement learning (DRL) soft actor-critic (SAC) approach. Firstly, we delve into the extraction of semantic information. Secondly, we redefine metrics for semantic information in V2V and V2I spectrum sharing in IoV environments, introducing high-speed semantic spectrum efficiency (HSSE) and semantic transmission rate (HSR). Finally, we employ the SAC algorithm for decision optimization in V2V and V2I spectrum sharing based on semantic information. This optimization encompasses the optimal link of V2V and V2I sharing strategies, the transmission power for vehicles sending semantic information and the length of transmitted semantic symbols, aiming at maximizing HSSE of V2I and enhancing success rate of effective semantic information transmission (SRS) of V2V. Experimental results demonstrate that the SSS algorithm outperforms other baseline algorithms, including other traditional-communication-based spectrum sharing algorithms and spectrum sharing algorithm using other reinforcement learning approaches. The SSS algorithm exhibits a 15% increase in HSSE and approximately a 7% increase in SRS.

Xiaoyuan Wang, Junyan Han, Hui Xiang et al.

Traffic lights detection and recognition (TLDR) is one of the necessary abilities of multi-type intelligent mobile platforms such as drones. Although previous TLDR methods have strong robustness in their recognition results, the feasibility of deployment of these methods is limited by their large model size and high requirements of computing power. In this paper, a novel lightweight TLDR method is proposed to improve its feasibility to be deployed on mobile platforms. The proposed method is a two-stage approach. In the detection stage, a novel lightweight YOLOv5s model is constructed to locate and extract the region of interest (ROI). In the recognition stage, the HSV color space is employed along with an extended twin support vector machines (TWSVMs) model to achieve the recognition of multi-type traffic lights including the arrow shapes. The dataset, collected in naturalistic driving experiments with an instrument vehicle, is utilized to train, verify, and evaluate the proposed method. The results suggest that compared with the previous YOLOv5s-based TLDR methods, the model size of the proposed lightweight TLDR method is reduced by 73.3%, and the computing power consumption of it is reduced by 79.21%. Meanwhile, the satisfied reasoning speed and recognition robustness are also achieved. The feasibility of the proposed method to be deployed on mobile platforms is verified with the Nvidia Jetson NANO platform.

M. V. Seleznev, K. I. Gryadunov, K. E. Balyshin

The development of modern heat-stressed aircraft engines is a complex process based on the advanced achievements of various branches of science and technology, including chemmotology. Each new generation of aircraft engines imposes stricter requirements on the quality of the aviation oils used to ensure the reliable operation, including engine oil systems, rotor bearings and other components. One of the important factors in reducing friction and wear-out of modern gas turbine engines is the use of high-quality oils with a high level of anti-wear and anti-friction properties which allow engines to operate under various relubrication intervals. In the domestic regulatory and technical documentation, the anti-wear properties of aviation oils are evaluated using a four-ball friction machine according to GOST 9490, and the anti-friction properties are not taken into account. The specified friction machine has a variety of disadvantages. In this regard, the authors evaluated the anti-wear and anti-friction properties of domestic aviation oils using a versatile vibro-tribometer which allows for the operational properties of oils to be researched under the modes that are the most characteristic for the actual operation of aircraft engines compared with parameters of oil tests by a four-ball friction machine. Unlike the four-ball friction machine, the vibro-tribometer design implements a contact - interaction scheme in a “ball-plate plane” friction pair. At the same time, a thermal chamber is installed on this application that provides constant heating of the friction pair and the tested lubricating oils to the required temperature (from 0 to 150 ℃). It has been found that IPM-10 aviation oil possesses the best anti-wear and anti-friction properties, and with an increase in the tested oil temperature, a proportional increase in wear-out in the “ball-plate plane” friction pair occurs.

Qing Wu, Xiaohua Ge, Qing-Long Han et al.

This paper provides a systematic review of emerging control techniques used for railway Virtual Coupling (VC) studies. Train motion models are first reviewed, including model formulations and the force elements involved. Control objectives and typical design constraints are then elaborated. Next, the existing VC control techniques are surveyed and classified into five groups: consensus-based control, model prediction control, sliding mode control, machine learning-based control, and constraints-following control. Their advantages and disadvantages for VC applications are also discussed in detail. Furthermore, several future studies for achieving better controller development and implementation, respectively, are presented. The purposes of this survey are to help researchers to achieve a better systematic understanding regarding VC control, to spark more research into VC and to further speed-up the realization of this emerging technology in railway and other relevant fields such as road vehicles.

Xingshuai Dong, Max Cappuccio, Hamad Al Jassmi et al.

Personal autonomous vehicles are cars, trucks and bikes capable of sensing their surrounding environment, planning their route, and driving with little or no involvement of human drivers. Despite the impressive technological achievements made by the industry in recent times and the hopeful announcements made by leading entrepreneurs, to date no personal vehicle is approved for road circulation in a 'fully' or 'semi' autonomous mode (autonomy levels 4 and 5) and it is still unclear when such vehicles will eventually be mature enough to receive this kind of approval. The present review adopts an integrative and multidisciplinary approach to investigate the major challenges faced by the automative sector, with the aim to identify the problems that still trouble and delay the commercialization of autonomous vehicles. The review examines the limitations and risks associated with current technologies and the most promising solutions devised by the researchers. This negative assessment methodology is not motivated by pessimism, but by the aspiration to raise critical awareness about the technology's state-of-the-art, the industry's quality standards, and the society's demands and expectations. While the survey primarily focuses on the applications of artificial intelligence for perception and navigation, it also aims to offer an enlarged picture that links the purely technological aspects with the relevant human-centric aspects, including, cultural attitudes, conceptual assumptions, and normative (ethico-legal) frameworks. Examining the broader context serves to highlight problems that have a cross-disciplinary scope and identify solutions that may benefit from a holistic consideration.

Andrea Tagliabue, Y. Hsiao, Urban Fasel et al.

Accurate and agile trajectory tracking in sub-gram Micro Aerial Vehicles (MAVs) is challenging, as the small scale of the robot induces large model uncertainties, demanding robust feedback controllers, while the fast dynamics and computational constraints prevent the deployment of computationally expensive strategies. In this work, we present an approach for agile and computationally efficient trajectory tracking on the MIT SoftFly [1], a sub-gram MAV (0.7 grams). Our strategy employs a cascaded control scheme, where an adaptive attitude controller is combined with a neural network (NN) policy trained to imitate a trajectory tracking robust tube model predictive controller (RTMPC). The NN policy is obtained using our recent work [2], which enables the policy to preserve the robustness of RTMPC, but at a fraction of its computational cost. We experimentally evaluate our approach, achieving position Root Mean Square Errors (RMSEs) lower than 1.8 cm even in the more challenging maneuvers, obtaining a 60% reduction in maximum position error compared to [3], and demonstrating robustness to large external disturbances.

S. Chen, Yu. M. Zabolotnov

The problem of controlling a rotating hub-spoke tethered formation system in low Earth orbit is considered, in which microsatellites are located radially around the central spacecraft (hub) and connected to it by tethers (spokes). To analyze the dynamics of the tethered system, a mathematical model is developed in the orbital coordinate system by Lagrange method, in which the central spacecraft is regarded as a rigid body. In the proposed control scheme, the spin motion of the central body is regulated by the control moment, and tether deployment control law is proposed by robust approach, which is carried out by regulating the tether tensions and low thrusts acting on the microsatellites. The robustness and stability of the system are investigated using Lyapunov theory and Hamilton-Jacobi inequality, which is used to determine the robustness index of the control system. The results of numerical calculations are presented, which confirm that the proposed control scheme is effective when taking into account periodic gravitational perturbations, external perturbations and perturbations associated with uncertainty in the initial states of the system and with the rotation of the central body.

Pierluigi Della Vecchia, Massimo Mandorino, Vincenzo Cusati et al.

Aircraft retrofitting is a challenging task involving multiple scenarios and stakeholders. Providing a strategy to retrofit an existing platform needs detailed knowledge of multiple aspects, ranging from aircraft performance and emissions, development and conversion costs to the projected operating costs. This paper proposes a methodology to account for retrofitting costs at an industrial level, explaining the activities related to such a process. Costs are mainly derived from three contributions: development costs, conversion costs and equipment acquisition costs. Different retrofitting packages, such as engine conversion and onboard systems electrification, are applied in the retrofitting of an existing 90 PAX regional turbofan aircraft, highlighting the impact on both aircraft performance and industrial costs. Multiple variables and scenarios are considered regarding trade-offs and decision-making, including the number of aircraft to be retrofitted, the heritage of an aircraft and its utilization, the fuel price and the airport charges. The results show that a reduction of 15% in fuel demand and emissions are achievable, considering a fleet of 500 platforms, through a conspicuous investment of around EUR 20 million per aircraft (50% of the estimated price). Furthermore, depending on the scenarios driven by the regulatory authorities, governments or airlines, this paper provides a useful methodology to evaluate the feasibility of retrofitting activities.

Arled Papa, Jörg von Mankowski, Hansini Vijayaraghavan et al.

While 5G delivers high quality services mostly in a two dimensional terrestrial area covering our planet's surface, with 6G we aim at a full exploitation of three dimensions. In this way, 6G includes all kinds of non-terrestrial networks. In particular, Unmanned Aerial Vehicles (UAVs), High-Altitude Platforms (HAPs), (self-)flying taxis and civil aircrafts are new additions to already existing satellite networks complementing the cellular terrestrial network. Their integration to 6G is promising with respect to service coverage, but also challenging due to the so far rather closed systems. Emerging technology concepts such as Mobile Edge Computing (MEC) and Software-Defined Networking (SDN) can provide a basis for a full integration of aeronautical systems into the terrestrial counterpart. However, these technologies render the management and orchestration of aeronautical systems complex. As a step towards the integration of aeronautical communication and services into 6G, we propose a framework for the collection, monitoring and distribution of resources in the sky among heterogeneous flying objects. This enables high-performance services for a new era of 6G aeronautical applications. Based on our aeronautical framework, we introduce emerging application use-cases including Aeronautical Edge Computing (AEC), aircraft-as-a-sensor, and in-cabin networks.