Hasil untuk "Motor vehicles. Aeronautics. Astronautics"

Nan Luo, Zhihao Yu, Weidong Yang

This paper proposes a data-driven model for predicting rotor fluid-structure interaction (FSI) load with efficient aeroelastic analysis. Unsteady flow-field snapshots obtained from computational fluid dynamics (CFD) simulations are first processed using Proper Orthogonal Decomposition (POD) to reduce the dimensionality of the flow data and extract the dominant modal time coefficients. Based on these reduced-order representations, the Dynamic Mode Decomposition with control (DMDc) method is used to identify a time-domain state-space model of the aerodynamic system. The identified data-driven aerodynamic model is coupled with the structural dynamic equations, which allows time-domain reconstruction and prediction of unsteady aerodynamic forces and structural loads under aeroelastic interactions. Hence, an efficient reduced-order model for aerodynamic load is established. The proposed approach is first validated using a two-dimensional airfoil subjected to different motion inputs, where the reduced-order aerodynamic predictions are compared with high-fidelity CFD results. Then, a three-dimensional sectional reduced-order model for a rotor is developed based on blade element theory, and aeroelastic coupled simulations are conducted for the SA349 rotor. The results demonstrate that the proposed method can accurately capture unsteady aerodynamic loads and aeroelastic responses, while significantly improving computational efficiency compared to high-fidelity simulations.

Haotian Wu, Yan Guo, Juliang Cao et al.

In response to the issues of severe pitch oscillation and unstable roll attitude present in existing reinforcement learning-based aircraft cruise control methods during dynamic maneuvers, this paper proposes a precise control method for aircraft cruising based on proximal policy optimization (PPO) with nonlinear attitude constraints. This method first introduces a combination of long short-term memory (LSTM) and a fully connected layer (FC) to form the policy network of the PPO method, improving the algorithm’s learning efficiency for sequential data while avoiding feature compression. Secondly, it transforms cruise control into tracking target heading, altitude, and speed, achieving a mapping from motion states to optimal control actions within the policy network, and designs nonlinear constraints as the maximum reward intervals for pitch and roll to mitigate abnormal attitudes during maneuvers. Finally, a JSBSim simulation platform is established to train the network parameters, obtaining the optimal strategy for cruise control and achieving precise end-to-end control of the aircraft. Experimental results show that, compared to the cruise control method without dynamic constraints, the improved method reduces heading deviation by approximately 1.6° during ascent and 4.4° during descent, provides smoother pitch control, decreases steady-state altitude error by more than 1.5 m, and achieves higher accuracy in overlapping with the target trajectory during hexagonal trajectory tracking.

WANG Weixin, CHANG Xuelian, OU Shifeng

Conventional adaptive filtering algorithms often exhibit performance degradation when processing multipath interference in raw echoes of spaceborne synthetic aperture radar （SAR） systems due to anomalous outliers， manifesting as insufficient convergence and low estimation accuracy. To address this issue， this study proposes a novel robust adaptive filtering algorithm， namely the M-estimation-based minimum error entropy with affine projection （APMMEE） algorithm. This algorithm inherits the joint multi-data-block update mechanism of the affine projection algorithm， enabling rapid adaptation to the dynamic characteristics of raw echoes and achieving fast convergence. Meanwhile， it incorporates the M-estimation-based minimum error entropy （MMEE） criterion， which weights error samples in raw echoes through M-estimation functions， effectively suppressing outlier interference during the algorithm update. Both the system identification simulations and practical multipath interference suppression experiments using raw echoes demonstrate that the proposed APMMEE algorithm exhibits superior filtering performance.

Gabriel de Sousa Meira, João Victor Ferreira Guedes, Edilson de Souza Bias

The use of geotechnologies in the field of diagnostic engineering has become ever more present in the identification of pathological manifestations in buildings. The implementation of Unmanned Aerial Vehicles (UAVs) and embedded sensors has stimulated the search for new data processing and validation methods, considering the magnitude of the data collected during fieldwork and the absence of specific methodologies for each type of sensor. Regarding data processing, the use of deep learning techniques has become widespread, especially for the automation of processes that involve a great amount of data. However, just as with the increasing use of embedded sensors, deep learning necessitates the development of studies, particularly those focusing on neural networks that better represent the data to be analyzed. It also requires the enhancement of practices to be used in fieldwork, especially regarding data processing. In this context, the objective of this study is to review the existing literature on the use of embedded technologies in UAVs and deep learning for the identification and characterization of pathological manifestations present in building façades in order to develop a robust knowledge base that is capable of contributing to new investigations in this field of research.

Yaqiong Zhang, Fubin Wang, Jinchao Liu et al.

The rotating blade is a key component of an aero-engine, and its vibration characteristics have an important impact on the performance of the engine and are vital for condition monitoring. This paper reviews the research progress of blade dynamics, including three main aspects: modeling of blades, solution methods, and vibration characteristics. Firstly, three popular structural dynamics models for blades are reviewed, namely lumped-mass model, finite element model, and semi-analytical model. Then, the solution methods for the blade dynamics are comprehensively described. The advantages and limitations of these methods are summarized. In the third part, this review summarizes the properties of the modal and vibration responses of aero-engine blades and discusses the typical forms and mechanisms of blade vibration. Finally, the deficiencies and limitations in the current research on blade modeling and vibration analysis are summarized, and the directions for future efforts are pointed out. The purpose of this review is to provide meaningful insights to researchers and engineers in the field of aero-engine blade modeling and dynamic characteristics analysis.

Darío Domingo, Cristina Gómez, Francisco Mauro et al.

Pine species are a key social and economic component in Mediterranean ecosystems, where insect defoliations can have far-reaching consequences. This study aims to quantify the impact of pine processionary moth (PPM) on canopy structures, examining its evolution over time at the individual tree level using high-density drone LiDAR-derived point clouds. Focusing on 33 individuals of black pine (<i>Pinus nigra)</i>—a species highly susceptible to PPM defoliation in the Mediterranean environment—bitemporal LiDAR scans were conducted to capture the onset and end of the major PPM feeding period in winter. Canopy crown delineation performed manually was compared with LiDAR-based methods. Canopy metrics from point clouds were computed for trees exhibiting contrasting levels of defoliation. The structural differences between non-defoliated and defoliated trees were assessed by employing parametric statistical comparisons, including analysis of variance along with post hoc tests. Our analysis aimed to distinguish structural changes resulting from PPM defoliation during the winter feeding period. Outcomes revealed substantive alterations in canopy cover, with an average reduction of 22.92% in the leaf area index for defoliated trees, accompanied by a significant increase in the number of returns in lower tree crown branches. Evident variations in canopy density were observed throughout the feeding period, enabling the identification of two to three change classes using LiDAR-derived canopy density metrics. Manual and LiDAR-based crown delineations exhibited minimal differences in computed canopy LiDAR metrics, showcasing the potential of LiDAR delineations for broader applications. PPM infestations induced noteworthy modifications in canopy morphology, affecting key structural parameters. Drone LiDAR data emerged as a comprehensive tool for quantifying these transformations. This study underscores the significance of remote sensing approaches in monitoring insect disturbances and their impacts on forest ecosystems.

Shangzhe Sun, Chi Chen, Bisheng Yang et al.

The global rise in electricity demand necessitates extensive transmission infrastructure, where insulators play a critical role in ensuring the safe operation of power transmission systems. However, insulators are susceptible to burst defects, which can compromise system safety. To address this issue, we propose an insulator defect detection framework, ID-Det, which comprises two main components, i.e., the Insulator Segmentation Network (ISNet) and the Insulator Burst Detector (IBD). (1) ISNet incorporates a novel Insulator Clipping Module (ICM), enhancing insulator segmentation performance. (2) IBD leverages corner extraction methods and the periodic distribution characteristics of corners, facilitating the extraction of key corners on the insulator mask and accurate localization of burst defects. Additionally, we construct an Insulator Defect Dataset (ID Dataset) consisting of 1614 insulator images. Experiments on this dataset demonstrate that ID-Det achieves an accuracy of 97.38%, a precision of 97.38%, and a recall rate of 94.56%, outperforming general defect detection methods with a 4.33% increase in accuracy, a 5.26% increase in precision, and a 2.364% increase in recall. ISNet also shows a 27.2% improvement in Average Precision (AP) compared to the baseline. These results indicate that ID-Det has significant potential for practical application in power inspection.

Hong Zhu, Jie Qin, Qinghua Zhu et al.

A high-precision attitude control satellite uses an angular momentum exchange device such as a flywheel or a control moment gyro as the actuator for attitude stability control. Once the accumulation of angular momentum exceeds the upper limit of the angular momentum exchange device, the satellite will lose its attitude control ability. Therefore, it is necessary to unload the angular momentum exchange device to ensure the attitude control ability of the satellite platform. The angular momentum accumulation of GEO(Geosynchronous Orbit, GEO) satellites with asymmetric structure can reach 40 Nms per day, and the accumulation speed is more than 20 times that of GEO satellites with symmetrical structure. Therefore, it is necessary to carry out angular momentum unloading for GEO satellites with asymmetric structure every day. The previous method of angular momentum unloading using electric propulsion has weak unloading capacity, which is not suitable for angular momentum unloading of asymmetric satellites. This paper presents a method of angular momentum unloading using a four-joint mechanical arm plus an electric thruster. Large angular momentum unloading with near-zero burn-up can be achieved through the thrust generated by station keeping. In addition, the problem of attitude and orbit coupling control can be solved by controlling the thrust direction of the electric thruster with a mechanical arm.

Dustin Holley, Jovin Dsa, Hossein Nourkhiz Mahjoub et al.

Enhancing simulation environments to replicate real-world driver behavior is essential for developing Autonomous Vehicle technology. While some previous works have studied the yielding reaction of lag vehicles in response to a merging car at highway on-ramps, the possible lane-change reaction of the lag car has not been widely studied. In this work we aim to improve the simulation of the highway merge scenario by including the lane-change reaction in addition to yielding behavior of main-lane lag vehicles, and we evaluate two different models for their ability to capture this reactive lane-change behavior. To tune the payoff functions of these models, a novel naturalistic dataset was collected on U.S. highways that provided several hours of merge-specific data to learn the lane change behavior of U.S. drivers. To make sure that we are collecting a representative set of different U.S. highway geometries in our data, we surveyed 50,000 U.S. highway on-ramps and then selected eight representative sites. The data were collected using roadside-mounted lidar sensors to capture various merge driver interactions. The models were demonstrated to be configurable for both keep-straight and lane-change behavior. The models were finally integrated into a high-fidelity simulation environment and confirmed to have adequate computation time efficiency for use in large-scale simulations to support autonomous vehicle development.

Dinesh Cyril Selvaraj, Christian Vitale, Tania Panayiotou et al.

Intersection crossing represents one of the most dangerous sections of the road infrastructure and Connected Vehicles (CVs) can serve as a revolutionary solution to the problem. In this work, we present a novel framework that detects preemptively collisions at urban crossroads, exploiting the Multi-access Edge Computing (MEC) platform of 5G networks. At the MEC, an Intersection Manager (IM) collects information from both vehicles and the road infrastructure to create a holistic view of the area of interest. Based on the historical data collected, the IM leverages the capabilities of an encoder-decoder recurrent neural network to predict, with high accuracy, the future vehicles' trajectories. As, however, accuracy is not a sufficient measure of how much we can trust a model, trajectory predictions are additionally associated with a measure of uncertainty towards confident collision forecasting and avoidance. Hence, contrary to any other approach in the state of the art, an uncertainty-aware collision prediction framework is developed that is shown to detect well in advance (and with high reliability) if two vehicles are on a collision course. Subsequently, collision detection triggers a number of alarms that signal the colliding vehicles to brake. Under real-world settings, thanks to the preemptive capabilities of the proposed approach, all the simulated imminent dangers are averted.

Cheng Chang, Jiawei Zhang, Jingwei Ge et al.

Intelligent vehicles and autonomous driving systems rely on scenario engineering for intelligence and index (I&I), calibration and certification (C&C), and verification and validation (V&V). To extract and index scenarios, various vehicle interactions are worthy of much attention, and deserve refined descriptions and labels. However, existing methods cannot cope well with the problem of scenario classification and labeling with vehicle interactions as the core. In this paper, we propose VistaScenario framework to conduct interaction scenario engineering for vehicles with intelligent systems for transport automation. Based on the summarized basic types of vehicle interactions, we slice scenario data stream into a series of segments via spatiotemporal scenario evolution tree. We also propose the scenario metric Graph-DTW based on Graph Computation Tree and Dynamic Time Warping to conduct refined scenario comparison and labeling. The extreme interaction scenarios and corner cases can be efficiently filtered and extracted. Moreover, with naturalistic scenario datasets, testing examples on trajectory prediction model demonstrate the effectiveness and advantages of our framework. VistaScenario can provide solid support for the usage and indexing of scenario data, further promote the development of intelligent vehicles and transport automation.

Tao Zhang, Xinyu Song, Xingping Kai et al.

In order to understand the breakup characteristics of a transverse liquid jet flow in an actual combustion chamber, a numerical study was conducted using the Volume of Fluid (VOF) method combined with grid adaptation technology. The study focused on the primary breakup characteristics of liquid jets under the conditions of a steady and oscillating air crossflow. The simulated mediums were set to water and air. The research findings revealed that fluctuations in the incoming gas velocity can influence the development speed of surface waves and the mode of jet breakup during the initial stage of jet development as compared to the steady condition. In both conditions, the surface waves were initially observed to appear within 1/4 T–2/4 T. The surface wave of the jet develops faster under steady conditions because the average velocity of the steady flow is higher than that of the oscillation flow during this stage. As a result, the fragmentation of the jet is primarily influenced by the surface wave. Under an oscillating flow, the rear of the jet begins to break up earlier due to the slower development of surface waves. The velocity of the oscillating air inflow increases over time, and the speed of surface wave development also increases, gradually leading to the dominance of surface-wave-induced jet breakup. In the second stage of air inflow oscillation, an “up and down slapping” phenomenon occurs at the tail of the jet. Additionally, increasing the air inflow velocity leads to a longer jet breakup length and a higher number of droplets near the jet column. Surface waves are observed on both the windward and leeward sides of the jet. The penetration depth of the jet fluctuates with changes in the crossflow velocity, and the response of the jet penetration depth to the velocity fluctuations in the transverse air is delayed by half a period.

Aravind SEENI, Bernd SCHÄFER

The design process of a Mars rover is driven by multiple design constraints, namely overall mass, power consumption and volume (dimensions). Various systems, such as mobility, manipulation, handling, power, thermal, communication, navigation, avionics and science instruments, together make a complete rover vehicle and they should function collectively to perform a given task. Each of the subsystems can be thought of as modular building blocks that are integrated together to form a fully functional rover vehicle. When approaching the design of such a vehicle, the designer should take into account of cross design dependencies existent between different subsystems and technology limitations. Performing any particular task, would lead to many design possibilities. Choosing the final design from many feasible solutions is arguably a daunting task. In order to make this process simple and convenient, as well as to understand the design non-linearity existing in this solution space, the authors have employed a systems engineering approach to develop a tool comprising subsystem models. The subsystem models comprise parametric and physics-based models. For designing suitable user-defined objectives, these models when integrated with Genetic Algorithm forms an effective tool to support design trade-offs during the conceptual design process. This integrated modeling and optimization approach is thought to be efficient in identifying rover system concepts.

Yogesh Sapkota, Suman Devkota, Vamsi Borra et al.

As the demands for electric vehicles, electric aircrafts, unmanned aircraft systems, and other motor-driven systems increase, high-performance motor drives employing variable frequency control with higher efficiency and reliability are becoming increasingly important parts of the ever-changing technological landscape. This study proposes a Field Programmable Gate Array (FPGA)-based variable frequency soft-starting motor drive for a three-phase induction motor. The inverter output voltage and the load currents are analyzed for the harmonic contents using MATLAB. In the experimental realization, a four-pole squirrel cage delta-connected induction motor is utilized with a switching frequency of 4 kHz. The current and voltage characteristics of the induction motor are studied under different operating conditions to study harmonic contents and the effect of changing soft-start duration. The findings demonstrate a low-cost, flexible control of the induction motor with improved harmonic performance.

Pierre Guetschel, Michael Tangermann

We present a simple deep learning-based framework commonly used in computer vision and demonstrate its effectiveness for cross-dataset transfer learning in mental imagery decoding tasks that are common in the field of Brain-Computer Interfaces (BCI). We investigate, on a large selection of 12 motor-imagery datasets, which ones are well suited for transfer, both as donors and as receivers. Challenges. Deep learning models typically require long training times and are data-hungry, which impedes their use for BCI systems that have to minimize the recording time for (training) examples and are subject to constraints induced by experiments involving human subjects. A solution to both issues is transfer learning, but it comes with its own challenge, i.e., substantial data distribution shifts between datasets, subjects and even between subsequent sessions of the same subject. Approach. For every pair of pre-training (donor) and test (receiver) dataset, we first train a model on the donor before training merely an additional new linear classification layer based on a few receiver trials. Performance of this transfer approach is then tested on other trials of the receiver dataset. Significance. First, we lower the threshold to use transfer learning between motor imagery datasets: the overall framework is extremely simple and nevertheless obtains decent classification scores. Second, we demonstrate that deep learning models are a good option for motor imagery cross-dataset transfer both for the reasons outlined in the first point and because the framework presented is viable in online scenarios. Finally, analysing which datasets are best suited for transfer learning can be used as a reference for future researchers to determine which to use for pre-training or benchmarking.

Vishnu Pandi Chellapandi, Liangqi Yuan, Christopher G. Brinton et al.

Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data for model training presents significant challenges related to in-vehicle user privacy and communication overhead generated by massive data volumes. Federated learning (FL) is a decentralized ML approach that enables multiple vehicles to collaboratively develop models, broadening learning from various driving environments, enhancing overall performance, and simultaneously securing local vehicle data privacy and security. This survey paper presents a review of the advancements made in the application of FL for CAV (FL4CAV). First, centralized and decentralized frameworks of FL are analyzed, highlighting their key characteristics and methodologies. Second, diverse data sources, models, and data security techniques relevant to FL in CAVs are reviewed, emphasizing their significance in ensuring privacy and confidentiality. Third, specific applications of FL are explored, providing insight into the base models and datasets employed for each application. Finally, existing challenges for FL4CAV are listed and potential directions for future investigation to further enhance the effectiveness and efficiency of FL in the context of CAV are discussed.

Zheng-Lian Su, Xun-Lin Jiang, Ning Li et al.

Unmanned aerial vehicles (UAVs) have been widely used for target detection in modern battlefields. From the viewpoint of the opponents, false target jamming is an effective approach to decrease the UAV detection ability or probability, but currently there are few research efforts devoted to this adversarial problem. This paper formulates an optimization problem of false target jamming based on a counterpart problem of UAV detection, where each false target jamming solution is evaluated according to its adversarial effects on a set of possible UAV detection solutions. To efficiently solve the problem, we propose an evolutionary framework, which is implemented with four popular evolutionary algorithms by designing/adapting their evolutionary operators for false target jamming solutions. Experimental results on 12 test instances with different search regions and numbers of UAVs and false targets demonstrate that the proposed approach can significantly reduce the UAV detection probability, and the water wave optimization (WWO) metaheuristic exhibits the best overall performance among the four evolutionary algorithms. To our knowledge, this is the first study on the optimization of false target jamming against UAV detection, and the proposed framework can be extended to more countermeasures against UAV operations.

Shurui Huang, Yueneng Yang, Ye Yan et al.

The stratospheric satellite is regarded as an ideal stratosphere flight platform and is able to accomplish various missions such as surveillance, earth observation, and remote sensing, which requires a robust and effective trajectory tracking control method to support these tasks. A novel observer-based robust finite-time control scheme is proposed to address the trajectory tracking control problem dedicated to a stratospheric satellite in the presence of external disturbance and actuator saturation. Firstly, an extended state observer (ESO) is adopted to observe the unavailable velocity states and unknown disturbances simultaneously, and the estimated data are utilized in the robust control law design. Then, an auxiliary system based on anti-windup compensator is developed to directly compensate for the actuator saturation difference. After that, a backstepping nonsingular fast terminal sliding mode control (BNFTSMC) strategy is designed to track the desired trajectory with high accuracy, fast convergence rate, and finite-time convergence. Then, a stability analysis using Lyapunov-based theory is performed, in which the stabilization of the stratospheric satellite system and finite-time convergence are proven. Furthermore, a number of simulations are conducted further to verify the excellent performance of the designed control strategy.

Jinlong Li, Runsheng Xu, Xinyu Liu et al.

Deep learning has been widely used in the perception (e.g., 3D object detection) of intelligent vehicle driving. Due to the beneficial Vehicle-to-Vehicle (V2V) communication, the deep learning based features from other agents can be shared to the ego vehicle so as to improve the perception of the ego vehicle. It is named as Cooperative Perception in the V2V research, whose algorithms have been dramatically advanced recently. However, all the existing cooperative perception algorithms assume the ideal V2V communication without considering the possible lossy shared features because of the Lossy Communication (LC) which is common in the complex real-world driving scenarios. In this paper, we first study the side effect (e.g., detection performance drop) by the lossy communication in the V2V Cooperative Perception, and then we propose a novel intermediate LC-aware feature fusion method to relieve the side effect of lossy communication by a LC-aware Repair Network (LCRN) and enhance the interaction between the ego vehicle and other vehicles by a specially designed V2V Attention Module (V2VAM) including intra-vehicle attention of ego vehicle and uncertainty-aware inter-vehicle attention. The extensive experiment on the public cooperative perception dataset OPV2V (based on digital-twin CARLA simulator) demonstrates that the proposed method is quite effective for the cooperative point cloud based 3D object detection under lossy V2V communication.

F. Chandler

The Cal Poly Pomona (CPP) Liquid Rocket Lab project teams in the 2020-2021 academic year further developed the planning of the engine injector water flow testing, oxygen compatibility and cleaning procedures and further advanced the Mobile Rocket Engine Test Stand piping and structural design. Failure Modes and Effects Analysis were also performed on the system elements to be able to target the critical components for failure mitigation design and procedure development. The CPP revised system for FMEAs is shown. These items were needed to be accomplished in order to perform an engine hot firing for the Bronco 1 Launch Vehicle. This paper describes briefing some the status of the CPP FAR-Mars competition progress and the vehicle systems manufacturing and assembly modifications related to safety developed during our program activity. Some of our testing objectives were postponed due to the COVID19 activity constraints. © 2021, American Institute of Aeronautics and Astronautics Inc.. All rights reserved.