Hasil untuk "Motor vehicles. Aeronautics. Astronautics"

Robin Dehler, Dominik Authaler, Aryan Thakur et al.

Grid mapping is a fundamental approach to modeling the environment of intelligent vehicles or robots. Compared with object-based environment modeling, grid maps offer the distinct advantage of representing the environment without requiring any assumptions about objects, such as type or shape. For grid-map-based approaches, the environment is divided into cells, each containing information about its respective area, such as occupancy. This representation of the entire environment is crucial for achieving higher levels of autonomy. However, it has the drawback that modeling the scene at the cell level results in inherently large data sizes. Patched grid maps tackle this issue to a certain extent by adapting cell sizes in specific areas. Nevertheless, the data sizes of patched grid maps are still too large for novel distributed processing setups or vehicle-to-everything (V2X) applications. Our work builds on a patch-based grid-map approach and investigates the size problem from a communication perspective. To address this, we propose a patch-based communication pipeline that leverages existing compression algorithms to transmit grid-map data efficiently. We provide a comprehensive analysis of this pipeline for both intra-vehicle and V2X-based communication. The analysis is verified for these use cases with two real-world experiment setups. Finally, we summarize recommended guidelines for the efficient transmission of grid-map data in intelligent transportation systems.

Jean-Pierre Busch, Lukas Ostendorf, Guido Linden et al.

As highly automated driving is transitioning from single-vehicle closed-access testing to commercial deployments of public ride-hailing in selected areas (e.g., Waymo), automated driving and connected cooperative intelligent transport systems (C-ITS) remain active fields of research. Even though simulation is omnipresent in the development and validation life cycle of automated and connected driving technology, the complex nature of public road traffic and software that masters it still requires real-world integration and testing with actual vehicles. Dedicated vehicles for research and development allow testing and validation of software and hardware components under real-world conditions early on. They also enable collecting and publishing real-world datasets that let others conduct research without vehicle access, and support early demonstration of futuristic use cases. In this paper, we present karl., our new research vehicle for automated and connected driving. Apart from major corporations, few institutions worldwide have access to their own L4-capable research vehicles, restricting their ability to carry out independent research. This paper aims to help bridge that gap by sharing the reasoning, design choices, and technical details that went into making karl. a flexible and powerful platform for research, engineering, and validation in the context of automated and connected driving. More impressions of karl. are available at https://karl.ac.

Valentin Ioan Remus NICULESCU, Mihnea BUTIURCA, Dumitru POPESCU et al.

An important part of the construction of an aircraft is the shape of the wings. Their aerodynamics involve numerous evaluations and simulations through fluid dynamics. The reduction of the evaluation times is achieved by a parametric function. This is represented by the ratio of two polynomials. This representation will be important in order to reduce computation time for artificial network applications. The number of parameters is reduced. The analytical shape of the wing section implies analytical expression for wing parameters: maximum thickness, maximum camber, maximum camber position, minimum thickness position, and so on. These characteristics have algebraic expressions that involve arithmetic operations. We have constructed a simple mathematical wing model. We replace the discrete wing shape with a continuous form, which is described by lacunary polynomials.

Casey D. Majhor, Jeremy P. Bos

We present a multifractal artificial terrain generation method that uses the 3D Weierstrass-Mandelbrot function to control roughness. By varying the fractal dimension used in terrain generation across three different values, we generate 60 unique off-road terrains. We use gradient maps to categorize the roughness of each terrain, consisting of low-, semi-, and high-roughness areas. To test how the fractal dimension affects the difficulty of vehicle traversals, we measure the success rates, vertical accelerations, pitch and roll rates, and traversal times of an autonomous ground vehicle traversing 20 randomized straight-line paths in each terrain. As we increase the fractal dimension from 2.3 to 2.45 and from 2.45 to 2.6, we find that the median area of low-roughness terrain decreases 13.8% and 7.16%, the median area of semi-rough terrain increases 11.7% and 5.63%, and the median area of high-roughness terrain increases 1.54% and 3.33%, all respectively. We find that the median success rate of the vehicle decreases 22.5% and 25% as the fractal dimension increases from 2.3 to 2.45 and from 2.45 to 2.6, respectively. Successful traversal results show that the median root-mean-squared vertical accelerations, median root-mean-squared pitch and roll rates, and median traversal times all increase with the fractal dimension.

Yurii Shyrokyi, Iurii Sysoiev, Yevhen Fesenko

The subject matter of the article is the thermophysical and mechanical properties of surface layers of structural materials using a quantum-mechanical approach. The aim of the article is to adjust the parameters of the heat conductivity and thermoelasticity problem, considering all possible external and internal thermal effects and the quantum-mechanical description of the material structure, for electrodes in vacuum-arc nanostructuring. The task to be solved is to perform calculations using the developed model for a copper cathode considering the energy spent on the formation of nanoparticles during ion-plasma processing with oxygen ions. The methods used are methods for solving nonlinear problems. The following results were obtained. 1. The nature of the dependencies of the maximum temperature, the expected volume of nanostructures, and the maximum depth of their formation on the energy of oxygen ions with charges z = 1 and z = 2 matches previously known dependencies obtained by the classical model, but under quantum-mechanical consideration, the maximum temperature values increase by 15%, the volume of the nanocluster increases by 50%, and the maximum depth of its occurrence increases by 1.5 times. 2. When selecting the parameters of ion-plasma processing for obtaining nanostructures with ion energies 100...500 eV, the previously proposed model with general thermophysical and mechanical properties of structural materials can be used. 3. For technologies with ion energies in the range of 103...2∙103 eV, the previously proposed model can be used but with quantum-mechanical effects of structural materials considered. 4) For technologies with ion energies above 104 eV, calculations should be performed using both approaches (the classical approach and the approach considering the quantum-mechanical properties of structural materials), and after comparison, the variant whose calculation results are closest to the experimental results should be used. Conclusions. The proposed theoretical model using the thermophysical, mechanical, and quantum-mechanical properties of structural materials can be used to adjust the technological parameters of ion-plasma processing to assess the formation of nanostructures in protective and strengthening coatings.

Felipe Mogollon, Zaloa Fernandez, Angel Martin et al.

Vehicles are sophisticated machines equipped with sensors that provide real-time data for onboard driving assistance systems. Due to the wide variety of traffic, road, and weather conditions, continuous system enhancements are essential. Connectivity allows vehicles to transmit previously unknown data, expanding datasets and accelerating the development of new data models. This enables faster identification and integration of novel data, improving system reliability and reducing time to market. Data Spaces aim to create a data-driven, interconnected, and innovative data economy, where edge and cloud infrastructures support a virtualised IoT platform that connects data sources and development servers. This paper proposes an edge-cloud data platform to connect car data producers with multiple and heterogeneous services, addressing key challenges in Data Spaces, such as data sovereignty, governance, interoperability, and privacy. The paper also evaluates the data platform's performance limits for text, image, and video data workloads, examines the impact of connectivity technologies, and assesses latencies. The results show that latencies drop to 33ms with 5G connectivity when pipelining data to consuming applications hosted at the edge, compared to around 77ms when crossing both edge and cloud processing infrastructures. The results offer guidance on the necessary processing assets to avoid bottlenecks in car data platforms.

Haoji Liu, Fatemeh Jahedinia, Zeyu Mu et al.

Cooperative on-ramp merging control for connected automated vehicles (CAVs) has been extensively investigated. However, they did neglect the connected vehicle identification process, which is a must for CAV cooperations. In this paper, we introduced a connected vehicle identification system (VIS) into the on-ramp merging control process for the first time and proposed an evaluation framework to assess the impacts of VIS on on-ramp merging performance. First, the mixed-traffic cooperative merging problem was formulated. Then, a real-world merging trajectory dataset was processed to generate dangerous merging scenarios. Aiming at resolving the potential collision risks in mixed traffic where CAVs and traditional human-driven vehicles (THVs) coexist, we proposed on-ramp merging strategies for CAVs in different mixed traffic situations considering the connected vehicle identification process. The performances were evaluated via simulations. Results indicated that while safety was assured for all cases with CAVs, the cases with VIS had delayed initiation of cooperation, limiting the range of cooperative merging and leading to increased fuel consumption and acceleration variations.

Keyvan Salehi Paniagua, Pablo García-Fogeda, Félix Arévalo

In this work, the aeroelastic stability of an aerial refueling system is investigated. The system is formed by a classical hose and drogue, and the novelty of our work is the inclusion of a grid fin configuration to improve its stability. The unsteady aerodynamic forces on the grid fins are determined using the concept of a unit grid fin (UGF). For each UGF, the unsteady aerodynamic forces are computed using the Doublet-Lattice Method, and the forces on the complete grid fins are calculated using interfering factors obtained from wind tunnel measurements for the steady case. The static equilibrium position of the system influences the linearized perturbed unsteady motion of the ensemble. This effect, together with the phase lag angle introduced to account for the unsteady aerodynamic forces in the hose, makes the flutter computation of the complete system a non-typical one. The results show that, by adding the grid fins, the stability of the refueling system is improved, delaying or annulling flutter occurrence.

Tianshu Liu

Abstract This paper presents a critical evaluation of the physical aspects of lift generation to prove that no lift can be generated in a steady inviscid flow. Hence, the answer to the recurring question in the paper title is negative. In other words, the fluid viscosity is necessary in lift generation. The relevant topics include D’Alembert’s paradox of lift and drag, the Kutta condition, the force expression based on the boundary enstrophy flux (BEF), the vortex lift, and the generation of the vorticity and circulation. The physical meanings of the variational formulations to determine the circulation and lift are discussed. In particular, in the variational formulation based on the continuity equation with the first-order Tikhonov regularization functional, an incompressible flow with the artificial viscosity (the Lagrange multiplier) is simulated, elucidating the role of the artificial viscosity in lift generation. The presented contents are valuable for the pedagogical purposes in aerodynamics and fluid mechanics.

L. Tang, Y. Li, J. Yuan et al.

Active safety systems on vehicles often face problems with false alarms. Most active safety systems predict the driver's trajectory with the assumption that the driver is always in a normal emotion, and then infer risks. However, the driver's trajectory uncertainty increases under abnormal emotions. This paper proposes a new trajectory prediction model: CPSOR-GCN, which predicts vehicle trajectories under abnormal emotions. At the physical level, the interaction features between vehicles are extracted by the physical GCN module. At the cognitive level, SOR cognitive theory is used as prior knowledge to build a Dynamic Bayesian Network (DBN) structure. The conditional probability and state transition probability of nodes from the calibrated SOR-DBN quantify the causal relationship between cognitive factors, which is embedded into the cognitive GCN module to extract the characteristics of the influence mechanism of emotions on driving behavior. The CARLA-SUMO joint driving simulation platform was built to develop dangerous pre-crash scenarios. Methods of recreating traffic scenes were used to naturally induce abnormal emotions. The experiment collected data from 26 participants to verify the proposed model. Compared with the model that only considers physical motion features, the prediction accuracy of the proposed model is increased by 68.70%. Furthermore,considering the SOR-DBN reduces the prediction error of the trajectory by 15.93%. Compared with other advanced trajectory prediction models, the results of CPSOR-GCN also have lower errors. This model can be integrated into active safety systems to better adapt to the driver's emotions, which could effectively reduce false alarms.

Renteng Yuan, Mohamed Abdel-Aty, Qiaojun Xiang et al.

Accurate Vehicle Trajectory Prediction is critical for automated vehicles and advanced driver assistance systems. Vehicle trajectory prediction consists of two essential tasks, i.e., longitudinal position prediction and lateral position prediction. There is a significant correlation between driving intentions and vehicle motion. In existing work, the three tasks are often conducted separately without considering the relationships between the longitudinal position, lateral position, and driving intention. In this paper, we propose a novel Temporal Multi-Gate Mixture-of-Experts (TMMOE) model for simultaneously predicting the vehicle trajectory and driving intention. The proposed model consists of three layers: a shared layer, an expert layer, and a fully connected layer. In the model, the shared layer utilizes Temporal Convolutional Networks (TCN) to extract temporal features. Then the expert layer is built to identify different information according to the three tasks. Moreover, the fully connected layer is used to integrate and export prediction results. To achieve better performance, uncertainty algorithm is used to construct the multi-task loss function. Finally, the publicly available CitySim dataset validates the TMMOE model, demonstrating superior performance compared to the LSTM model, achieving the highest classification and regression results. Keywords: Vehicle trajectory prediction, driving intentions Classification, Multi-task

Robert J. Rovetto

This paper describes a repository for ontologies of astronomy, astronautics, and other space-related topics. It may be called AstroPortal (or SpacePortal), AstroHub (or SpaceHub), etc. The creation of this repository will be applicable to academic, research and other data-intensive sectors. It is relevant for space sciences (including astronomy), Earth science, and astronautics (spaceflight), among other data-intensive disciplines. The repository should provide a centralized platform to search, review and create ontologies for astro-related topics. It thereby can decrease research time, while also providing a user-friendly means to study and compare knowledge organization systems or semantic resources of the target domains. With no apparent repository available on the target domain, this paper also expresses a novel concept.

Zhilei Ge, Yanling Li, Shaoxiong Ma

This paper proposes an approach to suppressing the elastic vibration and propellant sloshing in attitude control of a high slenderness ratio rocket. The main method is to combine a variable-gain robust observer with a variable structure controller for the purpose of attitude stability and elastic vibration suppression. A variable-gain robust observer is designed to reconstruct the attitude variable and complex multi-order elastic state. In this way, each order elastic vibration can be transformed into an additional attitude with the attitude characteristics of the rocket, which is easy to control. The reconstructed rocket body with an additional attitude is treated as the input of the designed variable structure controller to output the control signal. Under the simultaneous action between the variable-gain robust observer and variable structure controller, attitude stability is achieved for the rocket considering multi-order elastic vibration, and the propellant sloshing in the launch vehicle storage tank can be suppressed simultaneously. According to the simulation results, the proposed method produces a satisfactory stabilization outcome on each order of elastic vibration (especially low-order elastic vibration) and is better than a single variable structure controller.

LIU Qian, LIU Hanru, LI Jiahui et al.

The highly-efficient and unsteady aerodynamic simulation of turbomachinery is urgently required. The panel-vortex particle method is coupled with a far free field sound model established with the Lowson method and aims to fast predict aerodynamic and acoustic properties. The aerodynamic results show that, compared with the aerodynamic results acquired with the finite volume method, the use of the panel-vortex particle method may obtain appropriate pressure distribution and velocity distribution in the downstream region of a propeller and that the overall thrust prediction is accurate enough. The vortex distribution features show that the panel-vortex particle method has less numerical diffusion. Therefore, the velocity gradient is more accurate near the wake vortex. Compared with the sound pressure level acquired with the finite volume method, the sound pressure level simulated with the panel-vortex particle method has the same directivity pattern. The relative error of sound pressure in the 60° forward direction is 5% under 1BPF(blade passing frequency), which satisfies acoustic analysis requirements. As for time consumption, the use of the panel-vortex particle method consumes 10% of time when the finite volume method is used, proving that the panel-vortex particle method coupled with the Lowson method can satisfy the design and application needs of unsteady aerodynamic and aeroacoustic noise of a distributed electric propulsion system.

Yefeng Yang, Tao Huang, Xinxin Wang et al.

This paper proposes a multi-stage dueling deep Q-network (MS-DDQN) algorithm to address the high-speed aerial vehicle evasion problem. High-speed aerial vehicle pursuit and evasion are an ongoing game attracting significant research attention in the field of autonomous aerial vehicle decision making. However, traditional maneuvering methods are usually not applicable in high-speed scenarios. Independent of the aerial vehicle model, the implemented MS-DDQN-based method searches for an approximate optimal maneuvering policy by iteratively interacting with the environment. Furthermore, the multi-stage learning mechanism was introduced to improve the training data quality. Simulation experiments were conducted to compare the proposed method with several typical evasion maneuvering policies and to reveal the effectiveness and robustness of the proposed MS-DDQN algorithm.

Xiao Yihan, Li Dongnian, Yu Xiangzhen, Song Ke

In order to solve the problem of low accuracy of radar emitter individual identification in complex electromagnetic environment, a radar emitter individual identification technology based on parameter optimization VMD and LightGBM is proposed. Firstly, the unintentional features of the radar emitter are analyzed, and the added phase noise is taken as the fingerprint feature of radar emitter in the simulation. Secondly, sparrow search algorithm (SSA) is used to automatically optimize the decomposition parameters of variational modal decomposition (VMD), and the optimal decomposition parameter combination is accurately and quickly obtained as [2, 2 950]. Then, based on the optimal VMD decomposition parameters, the energy entropy and sample entropy of the emitter signal are extracted as feature vector. Finally, the feature vector is sent to the LightGBM classifier to complete the emitter individual identification. Through the verification of measured data, the recognition rate can reach more than 85% when the signal-to-noise ratio is 25 dB, which has ideal recognition results.

Nianfei Gan, Miaomiao Zhang, Bing Zhou et al.

Purpose – The purpose of this paper is to develop a real-time trajectory planner with optimal maneuver for autonomous vehicles to deal with dynamic obstacles during parallel parking. Design/methodology/approach – To deal with dynamic obstacles for autonomous vehicles during parking, a long- and short-term mixed trajectory planning algorithm is proposed in this paper. In long term, considering obstacle behavior, A-star algorithm was improved by RS curve and potential function via spatio-temporal map to obtain a safe and efficient initial trajectory. In short term, this paper proposes a nonlinear model predictive control trajectory optimizer to smooth and adjust the trajectory online based on the vehicle kinematic model. Moreover, the proposed method is simulated and verified in four common dynamic parking scenarios by ACADO Toolkit and QPOASE solver. Findings – Compared with the spline optimization method, the results show that the proposed method can generate efficient obstacle avoidance strategies, safe parking trajectories and control parameters such as the front wheel angle and velocity in high-efficient central processing units. Originality/value – It is aimed at improving the robustness of automatic parking system and providing a reference for decision-making in a dynamic environment.

S. A. Zommer, A. P. Kravchunovsky

The paper presents the results of computational study of sections of spatial truss structures. They can form large supporting structures on spacecraft to place onboard equipment. The calculations were carried out by the finite element method in FEMAP with Nastran. The chosen section, the object of study, is a set of straight rods rigidly connected at the nodes in such a way that the cross section of the truss structure is a triangle. Calculation models, procedures for calculation and results analysis are presented. The purpose of the calculation is to determine how the relative position of the rods in the structural scheme affects the mechanical properties of the structure. The main criterion for strength estimating was the magnitude of the stresses derived by load. Stiffness was determined by the value of the first natural frequency. The sequential addition of rods and varying their connection allow modifying the structural schemes of truss structures. Next, the mechanical properties of the structure which effected by made modifications were evaluated again. Thus, six structural schemes of sections of the truss structure, obtained from the results of the study, have been developed. At the same time, the mass of the section, its shape and dimensions, the material and shape of the rods section as well as the initial and boundary conditions, remained unchanged. Based on the results of the analysis, someone can notice that each structure has unique mechanical characteristics. Thus, the paper gives recommendations for choosing a specific structural scheme of the section, depending on the required operating conditions and acceptable manufacturing technology. So, the criteria for choosing one of the above various sections of a truss structure or the principle of its building can be the complexity of manufacturing, maximum stiffness and strength, or minimum displacement. Structural schemes of sections with the highest strength and capability have been selected for use as part of spatial truss rods of spacecraft.

LI Xiaoxuan, FU Qiangang, HU Dou

Oxidation sensitivity is a critical obstacle to the rapid development of carbon/carbon(C/C) composites as the thermal structural materials in aerospace applications. Currently, surface coating technology is the most effective method to achieve the long-term stable service of C/C composites in high temperature oxygen-containing environments. Among them, plasma spraying technology, which has been widely used in the preparation of thermal barrier coatings for aero-engines, has attracted much attention. In this paper, starting from the plasma sprayed high-temperature oxidation resistant coatings for C/C composites, domestic and international research progress of boride, silicide and oxide based oxidation resistant coatings have been reviewed, the protective properties based on different spraying technologies, composition/structure designs and service conditions have been compared and summarized, and the prospect future for subsequent research in this field has been proposed.

Haoxin Wang, Jiang Xie, Muhana Magboul Ali Muslam

The emerging vehicular connected applications, such as cooperative automated driving and intersection collision warning, show great potentials to improve the driving safety, where vehicles can share the data collected by a variety of on-board sensors with surrounding vehicles and roadside infrastructures. Transmitting and processing this huge amount of sensory data introduces new challenges for automotive edge computing with traditional wireless communication networks. In this work, we address the problem of traditional asymmetrical network resource allocation for uplink and downlink connections that can significantly degrade the performance of vehicular connected applications. An end-to-end automotive edge networking system, FAIR, is proposed to provide fast, scalable, and impartial connected services for intelligent vehicles with edge computing, which can be applied to any traffic scenes and road topology. The core of FAIR is our proposed symmetrical network resource allocation algorithm deployed at edge servers and service adaptation algorithm equipped on intelligent vehicles. Extensive simulations are conducted to validate our proposed FAIR by leveraging real-world traffic dataset. Simulation results demonstrate that FAIR outperforms existing solutions in a variety of traffic scenes and road topology.