Hasil untuk "Motor vehicles. Aeronautics. Astronautics"

Jawad Ali, Giovanna Citti, Alessandro Sarti

This study aims to better understand the functional geometry of the motor cortex, starting from different sources of experimental evidence. Recent studies have proved that cells of the primary motor cortex (M1) are sensitive to short hand trajectories called fragments. Here, we propose a sub-Riemannian higher-dimensional geometry accounting for geometric and kinematic properties. Due to the constraints of the geometry, horizontal curves naturally satisfy a relation between geometric and kinematic properties experimentally observed. In the space of trajectories, we also apply a clustering algorithm based on the Wasserstein distance: we obtain a grouping which nicely fits the observed experimental data much more efficiently than the Sobolev distance.

Zhenming Qu, Feiteng Luo, Yaosong Long et al.

This study proposes a quantitative evaluation framework to assess the performance of boundary layer injection (BLI) technology, establishing standardized metrics for integration into performance analysis of scramjets. We comparatively evaluate inert gas and fuel BLI strategies under typical combustor inflow conditions through systematic numerical investigations employing this evaluation framework. Key findings reveal that fuel injection demonstrates superior skin friction reduction efficacy compared to inert gases, especially hydrogen, achieving skin friction reduction performance up to 600 s at Mach 8+ conditions with an injection equivalence ratio (ER) of 0.1. Hydrogen's advantage arises from its inherently low density, coupled with combustion-induced density reduction in the log-law region. This dual mechanism suppresses turbulent momentum transport and attenuates skin friction through large-scale flow restructuring. However, when benchmarked against reacting mainstream flows without BLI, fuel injection efficacy diminishes significantly (100 s level) — local density reduction effects induced by boundary layer combustion are attenuated by mainstream heat release, limiting further momentum transport suppression and reducing drag reduction performance to inert gas levels. These results underscore the critical influence of ambient combustion conditions on BLI effectiveness, emphasizing that BLI implementation must prioritize non-reacting or weakly reacting flow environments. The proposed standardized metrics address this operational dependency, enabling BLI optimization within full-engine design paradigms to prevent counterproductive “pseudo-optimization.''

Zhe Liu, Guiqing He, Yang Hu

In recent years, detection methods for generic object detection have achieved significant progress. However, due to the large number of small objects in aerial images, mainstream detectors struggle to achieve a satisfactory detection performance. The challenges of small object detection in aerial images are primarily twofold: (1) Insufficient feature representation: The limited visual information for small objects makes it difficult for models to learn discriminative feature representations. (2) Background confusion: Abundant background information introduces more noise and interference, causing the features of small objects to easily be confused with the background. To address these issues, we propose a Multi-Level Contextual and Semantic Information Aggregation Network (MCSA-Net). MCSA-Net includes three key components: a Spatial-Aware Feature Selection Module (SAFM), a Multi-Level Joint Feature Pyramid Network (MJFPN), and an Attention-Enhanced Head (AEHead). The SAFM employs a sequence of dilated convolutions to extract multi-scale local context features and combines a spatial selection mechanism to adaptively merge these features, thereby obtaining the critical local context required for the objects, which enriches the feature representation of small objects. The MJFPN introduces multi-level connections and weighted fusion to fully leverage the spatial detail features of small objects in feature fusion and enhances the fused features further through a feature aggregation network. Finally, the AEHead is constructed by incorporating a sparse attention mechanism into the detection head. The sparse attention mechanism efficiently models long-range dependencies by computing the attention between the most relevant regions in the image while suppressing background interference, thereby enhancing the model’s ability to perceive targets and effectively improving the detection performance. Extensive experiments on four datasets, VisDrone, UAVDT, MS COCO, and DOTA, demonstrate that the proposed MCSA-Net achieves an excellent detection performance, particularly in small object detection, surpassing several state-of-the-art methods.

Xuanyu WU, Hailun XU

To comprehensively investigate the formation of high-enthalpy flow fields in hydrogen-driven shock tunnels, this study conducted an in-depth numerical simulation using an implicit algorithm with second-order spatial accuracy and dual-time stepping. The simulation covered the entire evolution from tunnel initiation to the establishment of a stable flow field at the nozzle exit. Results demonstrate that under initial conditions of 50 MPa hydrogen in the high-pressure section and 100 kPa air in the low-pressure section (both at 300 K), the double diaphragms ruptured at 8.54 ms. By 10.20 ms, the flow pressure at the nozzle inlet reached 29.8 MPa with a temperature of 3500 K, and remained stable for 6.80 ms. The shock wave evolution in the throat region exhibited a characteristic sequence: incident shock; oblique shock; shock train; bow shock; lambda shock. In the downstream nozzle section, a primary shock wave formed fint, followed by a secondary shock. These two shocks propagated cooperatively downstream, ultimately establishing a stable flow field at 13.00 ms, which persisted for 2.00 ms. The stabilized flow field exhibited a core region with a Mach number of about 9.1, static pressure of about 900 kPa, and static temperature of about 260 K, while a uniform flow zone with a diameter of approximately 840 mm was achieved at the nozzle exit. This research enhances the fundamental understanding of high-enthalpy flow dynamics in shock tunnels and provides critical insights for optimizing ground-based hypersonic testing facilities to meet the demands of advanced aerospace vehicle development.

Ting Yue, Xianlong Wang, Bo Wang et al.

Icing on an unmanned aerial vehicle (UAV) can degrade aerodynamic performance, reduce flight capabilities, impair maneuverability and stability, and significantly impact flight safety. At present, most flight control methods for icing-affected aircraft adopt a conservative control strategy, in which small control inputs are used to keep the aircraft’s angle of attack and other state variables within a limited range. However, this approach restricts the flight performance of icing aircraft. To address this issue, this paper innovatively proposes a design method of an ice tolerance flight envelope protection control system for a UAV on the base of icing severity detection using a long short-term memory (LSTM) neural network. First, the icing severity is detected using an LSTM neural network without requiring control surface excitation. It relies solely on the aircraft’s historical flight data to detect the icing severity. Second, by modifying the fuzzy risk level boundaries of the icing aircraft flight parameters, a nonlinear mapping relationship is established between the tracking command risk level, the UAV flight control command magnitude, and the icing severity. This provides a safe range of tracking commands for guiding the aircraft out of the icing region. Finally, the ice tolerance flight envelope protection control law is developed, using a nonlinear dynamic inverse controller (NDIC) as the inner loop and a nonlinear model predictive controller (NMPC) as the outer loop. This approach ensures boundary protection for state variables such as the angle of attack and roll angle while simultaneously enhancing the robustness of the flight control system. The effectiveness and superiority of the method proposed in this paper are verified for the example aircraft through mathematical simulation.

CHEN Xiao, YE Jikun, HUANG Qiaogao et al.

A manta ray is a typical representation of its pectoral fin propulsion mode, which is stable and conducive to wide area cruise, thus being suitable for bionic targets of a bionic vehicle. This paper established a computational model of the two-degree-of-freedom self-propulsion of the manta ray. Its motion equation and fluid dynamic equation were coupled to numerically simulate its self-propulsion process from stationary-state start-up and acceleration to steady-state cruise. The time history changes of the manta ray's swimming speed, hydrodynamic force, pressure distribution and three-dimensional flow field structure were analyzed. The simulation results show that, during the self-propulsion process of the manta ray, its speed, acceleration and displacement in its forward direction are completely determined by the net thrust generated during the flexible deformation of the pectoral fins and the net resistance encountered during its forward swimming. In the acceleration stage, the net thrust is superior and mainly generated near the tips of the pectoral fins. When the balance between the thrust and the resistance is reached, the manta ray is in its steady cruise stage. The flexible deformation of the spanwise and chordwise superposition of the pectoral fins may produce complex three-dimensional vortex structures. The numerical simulation method proposed in this paper and the study of the manta ray's self-propulsion process lay the foundation for further revealing its swimming mechanisms.

HU Weijun, ZHANG Weijie, YIN Wei et al.

It is difficult for conventional methods like the diagram theory in a complex environment to carry out modeling and calculation so as to make large-scale cluster behavior decisions. Hence, this paper studies small fixed wings and establishes the decentralized behavior decision-making model for a UAV cluster that has communication limitations and scale ceiling effects. The idea of swarm intelligence is combined with the decoupling multi-agent deep deterministic strategy gradient (DE-MADDPG) for the constructed model to do adaptive learning. Finally, the optimal behavior decision of the UAV cluster is made. Simulations are carried out to verify the model. The consistent movement of the UAV cluster and the maneuvering obstacle avoidance behavior in complex environments are realized. Compared with the MADDPG, the DE-MADDPG exhibits superior precision and real-time capability.

Liping Wang, Kun Yang, Fang Yu et al.

Fan blade icing can affect efficiency and aerodynamic stability, and the shed ice may be sucked into the core of the engine, causing adverse effects or even damage to the compressor components. Ice accretion and shedding are among the key issues in engine design and tests. But they have not been clearly understood. In this work, ice shedding from rotating aero-engine fan blades during continuous icing is experimentally investigated under the relevant airworthiness requirements. The phenomena of icing and ice shedding under different ambient temperatures and engine speeds are recorded to obtain the ice-shedding time and the characteristic length of the residual ice. Force analysis is used to understand the corresponding behavior. The degree of ice-shedding balance <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>D</mi><mi>b</mi></msub></semantics></math></inline-formula> is defined to explore the symmetry of ice shedding. The results show that the shedding time is significantly affected by the rotational speed, and the characteristic length will first shorten and then grow as the ambient temperature decreases. When the ice shedding is completed instantaneously, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>D</mi><mi>b</mi></msub></semantics></math></inline-formula> will show a violent shock. There is a critical ambient temperature, below which the ice accretion will worsen significantly as temperature decreases. For aero-engine fan blade icing tests during ground idle, the critical ambient temperature ranges from −<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow><mn>5</mn><mo> </mo></mrow><mo>∘</mo></msup></semantics></math></inline-formula>C to −<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow><mn>9</mn><mo> </mo></mrow><mo>∘</mo></msup></semantics></math></inline-formula>C. In order for the ice to shed faster, the engine speed has to reach a threshold. This study can shed light on the preliminary characteristics of ice shedding from rotating components and provide guidance and a data basis for the numerical simulation of fan blade icing and the design of an aero-engine.

Federico Nardi, Jinpei Han, Shlomi Haar et al.

Graph Neural Networks (GNN) can capture the geometric properties of neural representations in EEG data. Here we utilise those to study how reinforcement-based motor learning affects neural activity patterns during motor planning, leveraging the inherent graph structure of EEG channels to capture the spatial relationships in brain activity. By exploiting task-specific symmetries, we define different pretraining strategies that not only improve model performance across all participant groups but also validate the robustness of the geometric representations. Explainability analysis based on the graph structures reveals consistent group-specific neural signatures that persist across pretraining conditions, suggesting stable geometric structures in the neural representations associated with motor learning and feedback processing. These geometric patterns exhibit partial invariance to certain task space transformations, indicating symmetries that enable generalisation across conditions while maintaining specificity to individual learning strategies. This work demonstrates how GNNs can uncover the effects of previous outcomes on motor planning, in a complex real-world task, providing insights into the geometric principles governing neural representations. Our experimental design bridges the gap between controlled experiments and ecologically valid scenarios, offering new insights into the organisation of neural representations during naturalistic motor learning, which may open avenues for exploring fundamental principles governing brain activity in complex tasks.

Zhanyue Zhao, Charles Bales, Gregory Fischer

Precise surgical procedures may benefit from intra-operative image guidance using magnetic resonance imaging (MRI). However, the MRI's strong magnetic fields, fast switching gradients, and constrained space pose the need for an MR-guided robotic system to assist the surgeon. Piezoelectric actuators can be used in an MRI environment by utilizing the inverse piezoelectric effect for different application purposes. Piezoelectric ultrasonic motor (USM) is one type of MRI-compatible actuator that can actuate these robots with fast response times, compactness, and simple configuration. Although the piezoelectric motors are mostly made of nonferromagnetic material, the generation of eddy currents due to the MRI's gradient fields can lead to magnetic field distortions causing image artifacts. Motor vibrations due to interactions between the MRI's magnetic fields and those generated by the eddy currents can further degrade image quality by causing image artifacts. In this work, a plastic piezoelectric ultrasonic (USM) motor with more degree of MRI compatibility was developed and induced with preliminary optimization. Multiple parameters, namely teeth number, notch size, edge bevel or straight, and surface finish level parameters were used versus the prepressure for the experiment, and the results suggested that using 48 teeth, thin teeth notch with 0.39mm, beveled edge and a surface finish using grit number of approximate 1000 sandpaper performed a better output both in rotary speed and torque. Under this combination, the highest speed reached up to 436.6665rpm when the prepressure was low, and the highest torque reached up to 0.0348Nm when the prepressure was approximately 500g.

Tyler Ward

Since 2014, the California Department of Motor Vehicles (CDMV) has compiled information from manufacturers of autonomous vehicles (AVs) regarding factors that lead to the disengagement from autonomous driving mode in these vehicles. These disengagement reports (DRs) contain information detailing whether the AV disengaged from autonomous mode due to technology failure, manual override, or other factors during driving tests. This paper presents a machine learning (ML) based analysis of the information from the 2023 DRs. We use a natural language processing (NLP) approach to extract important information from the description of a disengagement, and use the k-Means clustering algorithm to group report entries together. The cluster frequency is then analyzed, and each cluster is manually categorized based on the factors leading to disengagement. We discuss findings from previous years' DRs, and provide our own analysis to identify areas of improvement for AVs.

E. V. Kudashov, I. A. Grachev, M. A. Bolotov

The reasons for the occurrence of increased vibration of the engine rotor due to aerodynamic imbalance of the fan of the first stage of the impeller are determined. A method for estimating the aerodynamic imbalance of the gas turbine engine fan is proposed, taking into account the influence of the following factors: geometric errors in the manufacture of blade airfoils and their positioning in the disk; deformation of the blade airfoil during the assembly of the impeller, as well as the factors of the working process occurring in the impeller. The use of the technique makes it possible to evaluate the aerodynamic imbalance of the impeller at the stage of its balancing and significantly reduce the amount of aerodynamic imbalance by determining the parameters for removing a layer of material or adding corrective masses. The influence of geometric errors of the blades on the value of the aerodynamic imbalance of the impeller was analyzed. Based on the results of the research, the type of dependence of unbalanced gas forces on the influence of technological and operational factors of the impeller under consideration was determined.

Moses C. Nah, Johannes Lachner, Neville Hogan

Motor primitives are fundamental building blocks of a controller which enable dynamic robot behavior with minimal high-level intervention. By treating motor primitives as basic "modules," different modules can be sequenced or superimposed to generate a rich repertoire of motor behavior. In robotics, two distinct approaches have been proposed: Dynamic Movement Primitives (DMPs) and Elementary Dynamic Actions (EDAs). While both approaches instantiate similar ideas, significant differences also exist. This paper attempts to clarify the distinction and provide a unifying view by delineating the similarities and differences between DMPs and EDAs. We provide eight robot control examples, including sequencing or superimposing movements, managing kinematic redundancy and singularity, obstacle avoidance, and managing physical interaction. We show that the two approaches clearly diverge in their implementation. We also discuss how DMPs and EDAs might be combined to get the best of both approaches. With this detailed comparison, we enable researchers to make informed decisions to select the most suitable approach for specific robot tasks and applications.

Andrea De Martin, G. Jacazio, Vincenzo Parisi et al.

The evolution towards “more electric” aircrafts has seen a decisive push in the last decade, due to the growing environmental concerns and the development of new market segments (flying taxis). Such push interested both the propulsion components and the aircraft systems, with the latter seeing a progressive trend in replacing the traditional solutions based on hydraulic power with electrical or electromechanical devices. Although more attention is usually devised towards the flight control actuation, an interesting and fast-developing application field for electro-mechanical systems is that of the aeronautical brakes. Electro-mechanical brakes, or E-Brakes hereby onwards, would present several advantages over their hydraulic counterparts, mainly related to the avoidance of leakage issues and the simplification of the system architecture. The more difficult heat dissipation, associated with the thermal issues that usually constitute one of the most significant sizing constraints for electromechanical actuators, limits so far, their application (or proposal of application) to light-weight vehicles. Within this context, the development of PHM solutions would align with the need for an on-line monitoring of a relatively unproven component. This paper deals with the preliminary stages of the development of such PHM system for an E-Brake to be employed on a future executive class aircraft, where the brake is actuated through four electro-mechanical actuators. Since literature on fault diagnosis and prognosis for electrical motors is fairly extensive, we focused this preliminary analysis on the development of PHM techniques suitable to monitor and prognose the evolution of the brake pads wear instead. The paper opens detailing the system architecture and continues presenting the high-fidelity dynamic model used to build synthetic data-sets representative of the possible operating conditions faced by the E-Brake within realistic operative scenarios. Such data are then used to foster a preliminary feature selection process, where physics-based indexes are compared and evaluated. Simulated degradation histories are then used to test the application of data-driven fault detection algorithm and the possible application of particle-filtering routines for prognosis.

Peijian Lv, Mengmeng Zhang, Fei Cao et al.

This paper aims to establish an approach of power bookkeeping in a numerical study. To study the process of power conversion coursed in the flow field, the methodology employs a power-based analysis to quantify power terms. This approach is examined in a simulation of jet flow and then applied to the cases of an isolated actuator disc and an isolated flat plate. Eventually, a numerical simulation is carried out for the boundary layer ingestion (BLI) case that integrates the flat plate and a wake-filling actuator disc. This study quantitatively discusses the mechanisms of BLI under the conditions of laminar and turbulent flow. The proposed power-based analysis might offer insights for the aircraft aerodynamic design using favorable airframe propulsion integration.

Guang Zhan, Zheng Gong, Quanhui Lv et al.

This paper reports on the formation and transformation of multiple fixed-wing unmanned aerial vehicles (UAVs) in three-dimensional space. A cooperative guidance law based on the classic missile-type parallel-approach method is designed for the multi-UAV formation control problem. Additionally, formation transformation strategies for multi-UAV autonomous assembly, disbandment, and special circumstances are formed, effective for managing and controlling the formation. When formulating the management strategy for formation establishment, its process is divided into three steps: (i) selecting and allocating target points, (ii) forming loose formations, and (iii) forming short-range formations. The management of disbanding the formation is formulated through reverse thinking: the assembly process is split and recombined in reverse, and a formation disbanding strategy that can achieve a smooth transition from close to lose formation is proposed. Additionally, a strategy is given for adjusting the formation transformation in special cases, and the formation adjustment is completed using the adjacency matrix. Finally, a hardware-in-the-loop simulation and measured flight verification using a simulator show the practicality of the guidance law in meeting the control requirements of UAV formation flight for specific flight tasks.

Jia Song, Xiaowei Xu, Xindi Tong et al.

Time cooperation guidance is a key technology which can greatly increase the success rate of flight missions. However, it is difficult to satisfy all the strict constraints when designing the guidance system for multiple hypersonic vehicles. To solve these problems, a time cooperation framework is proposed. In this paper, the longitudinal predictor–corrector guidance law is firstly applied to meet the terminal and path constraints simultaneously. To settle the inaccurate estimation problem of residual flight time, a long short-term memory network (LSTM network) is trained and adopted in a time decision module, whose inputs are selected as six-dimensional feature vectors combined with the features of the sequential ballistics. In the time control module, the traditional artificial potential field method is modified to handle the no-fly zone constraints problem. Furthermore, the time potential field as a new type of potential field is added to indirectly control the flight time of hypersonic vehicles. The final simulation results show that the novel time potential field is compatible with the traditional potential field, which can satisfy the no-fly zone and flight time constraints at the same time. Meanwhile, compared with other time cooperative guidance, the algorithm proposed in this paper performs better in terms of time adjustable range.

Farhad Samadzadegan, Farzaneh Dadrass Javan, Farnaz Ashtari Mahini et al.

Drones are becoming increasingly popular not only for recreational purposes but also in a variety of applications in engineering, disaster management, logistics, securing airports, and others. In addition to their useful applications, an alarming concern regarding physical infrastructure security, safety, and surveillance at airports has arisen due to the potential of their use in malicious activities. In recent years, there have been many reports of the unauthorized use of various types of drones at airports and the disruption of airline operations. To address this problem, this study proposes a novel deep learning-based method for the efficient detection and recognition of two types of drones and birds. Evaluation of the proposed approach with the prepared image dataset demonstrates better efficiency compared to existing detection systems in the literature. Furthermore, drones are often confused with birds because of their physical and behavioral similarity. The proposed method is not only able to detect the presence or absence of drones in an area but also to recognize and distinguish between two types of drones, as well as distinguish them from birds. The dataset used in this work to train the network consists of 10,000 visible images containing two types of drones as multirotors, helicopters, and also birds. The proposed deep learning method can directly detect and recognize two types of drones and distinguish them from birds with an accuracy of 83%, mAP of 84%, and IoU of 81%. The values of average recall, average accuracy, and average F1-score were also reported as 84%, 83%, and 83%, respectively, in three classes.

Gregory EP Pearcey, W Zev Rymer

Over the past two decades, there has been a radical transformation in our ability to extract useful biological signals from the surface electromyogram (EMG). Advances in EMG electrode design and signal processing techniques have resulted in an extraordinary capacity to identify motor unit spike trains from the surface of a muscle. These EMG grid, or high-density surface EMG (HDsEMG), recordings now provide accurate depictions of as many as 20-30 motor unit spike trains simultaneously during isometric contractions, even at high forces. Such multi-unit recordings often display an unexpected feature known as onion skin behavior, in which multiple motor unit spike trains show essentially parallel and organized increases in discharge rate with increases in voluntary force, such that the earliest recruited units reach the highest discharge rates, while higher threshold units display more modest rate increases. This sequence results in an orderly pattern of discharge resembling the layers of an onion, in which discharge rate trajectories stay largely parallel and rarely cross. Our objective in this review is to explain why this pattern of discharge rates is unexpected, why it does not accurately reflect our current understanding of motoneuron electrophysiology, and why it may potentially lead to unpredicted disruption in muscle force generation. This review is aimed at the practicing clinician, or the clinician scientist. More advanced descriptions of potential electrophysiological mechanisms associated with onion skin characteristics targeting the research scientist will be provided as reference material.

Shashwat Kumar, Robert Gutierez, Debajyoti Datta et al.

Neuromuscular disorders, such as Spinal Muscular Atrophy (SMA) and Duchenne Muscular Dystrophy (DMD), cause progressive muscular degeneration and loss of motor function for 1 in 6,000 children. Traditional upper limb motor function assessments do not quantitatively measure patient-performed motions, which makes it difficult to track progress for incremental changes. Assessing motor function in children with neuromuscular disorders is particularly challenging because they can be nervous or excited during experiments, or simply be too young to follow precise instructions. These challenges translate to confounding factors such as performing different parts of the arm curl slower or faster (phase variability) which affects the assessed motion quality. This paper uses curve registration and shape analysis to temporally align trajectories while simultaneously extracting a mean reference shape. Distances from this mean shape are used to assess the quality of motion. The proposed metric is invariant to confounding factors, such as phase variability, while suggesting several clinically relevant insights. First, there are statistically significant differences between functional scores for the control and patient populations (p$=$0.0213$\le$0.05). Next, several patients in the patient cohort are able to perform motion on par with the healthy cohort and vice versa. Our metric, which is computed based on wearables, is related to the Brooke's score ((p$=$0.00063$\le$0.05)), as well as motor function assessments based on dynamometry ((p$=$0.0006$\le$0.05)). These results show promise towards ubiquitous motion quality assessment in daily life.