Hasil untuk "Motor vehicles. Aeronautics. Astronautics"

Syed Hammad ALI, Yu YAO, Bangfu WU et al.

Andra-Ana-Maria GHEORGHIU, Ionut BUNESCU, Mihai-Vladut HOTHAZIE et al.

This paper presents a detailed analysis of the Blade Element Theory (BET) as applied to the aerodynamic performance of propellers and rotor systems. BET, a low-fidelity but widely used method, models the rotor blade as a series of independent elements along its span, allowing local aerodynamic forces to be calculated based on flow conditions. The approach combines the Blade Element Theory with Momentum Theory, implemented in a MATLAB program for a forward flight helicopter, facilitating the computation of thrust, torque and power coefficients. To account for inflow distribution and induced velocity effects, actuator disk theory is applied at the rotor disk level, providing a consistent estimation of the induced velocity required for accurate flow velocity values. The rotor solidity is used to compare lifting rotor system to an ideal actuator disk. Results demonstrate that the coupled method of BET and Momentum Theory provides accurate and consistent aerodynamic predictions for use in rotor design, optimization and validation against experimental measurements or CFD simulations.

Cheng Chen, Qiangang Zheng, Siyuan Hu et al.

Yago Emanoel Ramos, Raphael Silva do Rosário, Adriana de Faria Gehres et al.

Collective improvisation in dance provides a rich natural laboratory for studying emergent coordination in coupled neuro-motor systems. Here, we investigate how training shapes spontaneous synchronization patterns in both movement and brain signals during collaborative performance. Using a dual-recording protocol integrating 3D motion capture and hyperscanning EEG, participants engaged in free, interaction-driven, and rule-based improvisation before and after a program of generative dance, grounded in cellular-automata. Motor behavior was modeled through a time-resolved α-exponent derived from Movement Element Decomposition scaling between mean velocity and displacement, revealing fluctuations in energetic strategies and degrees of freedom. Synchronization events were quantified using Motif Synchronization (biomechanical data) and multilayer Time-Varying Graphs (neural data), enabling the detection of nontrivial lead-lag dependencies beyond zero-lag entrainment. Results indicate that training produced an intriguing dissociation: inter-brain synchronization increased, particularly within the frontal lobe, while interpersonal motor synchrony decreased. This opposite trend suggests that enhanced participatory sense-making fosters neural alignment while simultaneously expanding individual motor explorations, thereby reducing coupling in movement. Our findings position collaborative improvisation as a complex dynamical regime in which togetherness emerges not from identical motor outputs but from shared neural intentionality distributed across multilayer interaction networks, exemplifying the coupling-decoupling paradox, whereby increasing inter-brain synchrony supports the exploration of broader and mutually divergent motor trajectories. These results highlight the nonlinear nature of social coordination, offering new avenues for modeling creative joint action in human systems.

Wenxue Chen, Yingjing Qian, Changsheng Gao et al.

This work concerns defense guidance problems for 3-player target–defender–attacker engagement scenarios, in which the defender is launched to capture the incoming attacker in advance of the target being hit; a line-of-sight (LOS) triangle-based prescribed-time guidance is derived as an interception strategy for the defender. Integrating both the attacker–target and defender–attacker perspectives into the relative engagement model, the 3-player nonlinear engagement model is deduced considering the second-order lag dynamics of the actuator. Next, to reduce the defender and attacker overload ratio, the LOS triangle-guidance concept is presented to keep the defender on the LOS frame between the attacker and the target, in which the lateral overloads of the defender and attacker cancel each other out. In addition, in order to achieve perfect capture of the attacker in advance, the prescribed-time control can preallocate convergence time such that the defender can be guided and kept at the LOS frame between the attacker and the target in the preset time. What is even more unique is that the proposed guidance law design ignores the maneuvering of the attacker; i.e., the overload of the attacker is unknown, which is treated as a disturbance and estimated by the improved adaptive law. Numerical simulations are executed to validate performance and superiority, which has potential application value.

WANG Xin, GAO Chen, LIU Lei

The distant retrograde orbits(DROs) form a family of planar periodic orbits that are retrograde around the Moon in the Earth-Moon circular restricted three-body problem(CR3BP). Known for their "low-energy insertion, long-term stability, and global accessibility", DROs are regarded as ideal candidates for future cis-lunar exploration. Sun's gravity and solar radiation pressure(SRP), as primary perturbations in the Earth-Moon system, could significantly alter the orbit dynamics of spacecraft. Based on the above considerations, the effect of the Sun's gravity and SRP on the dynamics and geometry of Sun-resonant DROs within a quasi-bicircular problem(QBCP) is investigated. Each resonant DRO in the CR3BP bifurcates into at least two branches in the QBCP. Furthermore, the Sun's gravity can qualitatively alter the phase-space structure of most resonant DROs, transforming them from stable to unstable, which opens the possibility of low-energy transfer. Regarding SRP, it induces more complex changes in the phase-space structures and leads to more bifurcation types, such as tangent bifurcation, period-doubling bifurcation and second-class Hopf bifurcation, thus deriving richer families of orbits. It is worthily noted that SRP may help stabilize orbits, making it beneficial for station-keeping. Additionally, SRP can significantly alter the geometry of DROs. Especially when the spacecraft which has a relatively large lightness number, is pitched at certain angles, the planar DROs evolve into spacial orbits to make them more suitable for cis-lunar space missions.

S. E. Maksimova

Currently, there is an urgent need to create a high-quality automated risk assessment tool for the use of (unmanned aircraft) UAVs. There is no universal approach to risk management in unmanned civil aviation, and the risk assessment of the operator is largely individual. At the moment, no tool has been developed for plotting optimal routes for UAV flights in airspace, which would avoid piloting in areas with unacceptable risk. The article suggests the use of fully functional geographic information systems (GIS) to assess the risks of performing a flight mission. For a qualitative assessment of the risks of a particular flight assignment, it is proposed to take into account the situational component in the relevant segment of airspace and the ground (surface) situation. The article systematizes the main groups of factors that are important for assessing the risks of using BV. UAV flights are exposed to environmental factors, while posing a danger to surrounding objects. A formula for analyzing the spatial and temporal distribution of risk values in the airspace is derived. The minimum size of the simulation cell is proposed. A universal approach to assessing the risks of a UAV flight by various operators is substantiated, and a methodology for spatiotemporal analysis of the distribution of risk values based on the use of GIS is given. The results of the analysis of spatial and temporal information in the GIS environment make it possible to zone the airspace according to the degree of flight acceptability and build the optimal route outside areas with an increased risk of an aviation incident or accident. The developed spatio-temporal risk-oriented model can be used to support management decision-making in terms of building optimal routes for the movement of UVs.

Cristina Rossi, Rini Varghese, Amy J Bastian

Virtual reality (VR) is increasingly used to enhance the ecological validity of motor control and learning studies by providing immersive, interactive environments with precise motion tracking. However, designing realistic VR-based motor tasks remains complex, requiring advanced programming skills and limiting accessibility in research and clinical settings. MovementVR is an open-source platform designed to address these challenges by enabling the creation of customizable, naturalistic reaching tasks in VR without coding expertise. It integrates physics-based hand-object interactions, real-time hand tracking, and flexible experimental paradigms, including motor adaptation and reinforcement learning. The intuitive graphical user interface (GUI) allows researchers to customize task parameters and paradigm structure. Unlike existing platforms, MovementVR eliminates the need for scripting while supporting extensive customization and preserving ecological validity and realism. In addition to reducing technical barriers, MovementVR lowers financial constraints by being compatible with consumer-grade VR headsets. It is freely available with comprehensive documentation, facilitating broader adoption in movement research and rehabilitation.

Xing Chen, Xiaobin Xu, Junjie Shen et al.

Eleni Charoniti, Gerdien Klunder, Marcel Meeuwissen

The rapid introduction of automated driving functionalities in vehicles, has forced the necessity to systematically organize the methodologies for traffic safety assessment. Their safety impacts depend on many factors: human, vehicle, traffic, size of implementation. Modelling and simulations on the driver up to the societal level, play a vital role in assessment. This paper discusses the need, as well as an approach, to develop a multi-level safety assessment framework, facilitating the mitigation of risks. Via a research-driven format, this paper provides practitioners with a strategy to effectively perform safety assessment, offering practical, stepwise guidelines regarding the relevant models and tools.

Michael Wiesheu, Theodor Komann, Melina Merkel et al.

This work features the optimization of a Permanent Magnet Synchronous Motor using 2D nonlinear simulations in an Isogeometric Analysis framework. The rotor and stator designs are optimized for both geometric parameters and surface shapes via modifications of control points. The scaling laws for magnetism are employed to allow for axial and radial scaling, enabling a thorough optimization of all critical machine parameters for multiple operating points. The process is carried out in a gradient-based fashion with the objectives of lowering motor material cost, torque ripple and losses. It is shown that the optimization can be efficiently conducted for many optimization variables and all objective values can be reduced.

Jonas Eschmann, Dario Albani, Giuseppe Loianno

Recently non-linear control methods like Model Predictive Control (MPC) and Reinforcement Learning (RL) have attracted increased interest in the quadrotor control community. In contrast to classic control methods like cascaded PID controllers, MPC and RL heavily rely on an accurate model of the system dynamics. The process of quadrotor system identification is notoriously tedious and is often pursued with additional equipment like a thrust stand. Furthermore, low-level details like motor delays which are crucial for accurate end-to-end control are often neglected. In this work, we introduce a data-driven method to identify a quadrotor's inertia parameters, thrust curves, torque coefficients, and first-order motor delay purely based on proprioceptive data. The estimation of the motor delay is particularly challenging as usually, the RPMs can not be measured. We derive a Maximum A Posteriori (MAP)-based method to estimate the latent time constant. Our approach only requires about a minute of flying data that can be collected without any additional equipment and usually consists of three simple maneuvers. Experimental results demonstrate the ability of our method to accurately recover the parameters of multiple quadrotors. It also facilitates the deployment of RL-based, end-to-end quadrotor control of a large quadrotor under harsh, outdoor conditions.

Jae-Hyuk Lee, Eun-Sung Lee, Hyung-Seok Han et al.

Vitiation air heater (VAH) combustion characteristics for a direct-connect scramjet combustor (DCSC) were experimentally studied. The VAH consists of a head, modular chamber, and circular-to-rectangular shape transition (CRST) nozzle. The CRST nozzle transforms the circular cross-sectioned rocket-type VAH into a rectangular cross-sectioned scramjet combustor. The CRST nozzle exit Mach numbers at the top, middle, and bottom were measured using a tungsten wedge. The oblique shock formed by the wedge was captured using Schlieren visualization and recorded with a high-speed camera. The θ-β-M relation showed that the exit Mach number was 2.04 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>±</mo></mrow></semantics></math></inline-formula> 0.04 with a chamber pressure of 1.685 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>±</mo></mrow></semantics></math></inline-formula> 0.07 MPa. With the VAH design point verified, preliminary scramjet combustor ignition tests were conducted. As the fuel was not auto-ignited by the vitiated air, the forced ignition method, in which VAH ignition flame ignites the scramjet fuel, was used. The Schlieren images showed that a cavity shear layer combustion mode was formed and also showed that the forced ignition method could be used as a reference model for the ignitor-ignition method.

Xiandong Wang, Fengqin Yao, Ankun Li et al.

Recently, the research on drone-view object detection (DOD) has predominantly centered on efficiently identifying objects through cropping high-resolution images. However, it has overlooked the distinctive challenges posed by scale imbalance and a higher prevalence of small objects in drone images. In this paper, to address the challenges associated with the detection of drones (DODs), we introduce a specialized detector called DroneNet. Firstly, we propose a feature information enhancement module (FIEM) that effectively preserves object information and can be seamlessly integrated as a plug-and-play module into the backbone network. Then, we propose a split-concat feature pyramid network (SCFPN) that not only fuses feature information from different scales but also enables more comprehensive exploration of feature layers with many small objects. Finally, we develop a coarse to refine label assign (CRLA) strategy for small objects, which assigns labels from coarse- to fine-grained levels and ensures adequate training of small objects during the training process. In addition, to further promote the development of DOD, we introduce a new dataset named OUC-UAV-DET. Extensive experiments on VisDrone2021, UAVDT, and OUC-UAV-DET demonstrate that our proposed detector, DroneNet, exhibits significant improvements in handling challenging targets, outperforming state-of-the-art detectors.

Matteo Priorelli, Federico Maggiore, Antonella Maselli et al.

The way the brain selects and controls actions is still widely debated. Mainstream approaches based on Optimal Control focus on stimulus-response mappings that optimize cost functions. Ideomotor theory and cybernetics propose a different perspective: they suggest that actions are selected and controlled by activating action effects and by continuously matching internal predictions with sensations. Active Inference offers a modern formulation of these ideas, in terms of inferential mechanisms and prediction-error-based control, which can be linked to neural mechanisms of living organisms. This article provides a technical illustration of Active Inference models in continuous time and a brief survey of Active Inference models that solve four kinds of control problems; namely, the control of goal-directed reaching movements, active sensing, the resolution of multisensory conflict during movement and the integration of decision-making and motor control. Crucially, in Active Inference, all these different facets of motor control emerge from the same optimization process - namely, the minimization of Free Energy - and do not require designing separate cost functions. Therefore, Active Inference provides a unitary perspective on various aspects of motor control that can inform both the study of biological control mechanisms and the design of artificial and robotic systems.

Yan Liu, Zhenzhen Liu, Hongfu Zuo et al.

Remaining useful life prediction is one of the essential processes for machine system prognostics and health management. Although there are many new approaches based on deep learning for remaining useful life prediction emerging in recent years, these methods still have the following weaknesses: (1) The correlation between the information collected by each sensor and the remaining useful life of the machinery is not sufficiently considered. (2) The accuracy of deep learning algorithms for remaining useful life prediction is low due to the high noise, over-dimensionality, and non-linear signals generated during the operation of complex systems. To overcome the above weaknesses, a general deep long short memory network-based approach for mechanical remaining useful life prediction is proposed in this paper. Firstly, a two-step maximum information coefficient method was built to calculate the correlation between the sensor data and the remaining useful life. Secondly, the kernel principal component analysis with a simple moving average method was designed to eliminate noise, reduce dimensionality, and extract nonlinear features. Finally, a deep long short memory network-based deep learning method is presented to predict remaining useful life. The efficiency of the proposed method for remaining useful life prediction of a nonlinear degradation process is demonstrated by a test case of NASA’s commercial modular aero-propulsion system simulation data. The experimental results also show that the proposed method has better prediction accuracy than other state-of-the-art methods.

Wenbin He, Xi Yang, Ding Luo et al.

To prevent the possible accident of a large passenger plane due to rapid decompression, transient load analysis is of vital importance in the assessment of structure strength and also an important clause of airworthiness standard. A 0-D isentropic model and a 1-D model based on the characteristic line are developed to simulate the rapid decompression process of the cockpit-cabin model due to a cracked windshield. The accuracy of these models is presented by comparing them with experiments and 3-D CFD simulations. Then, the 1-D model is applied to study the influence of cabin and cockpit volume, windshield and decompression panel area, compartments, and environment pressure on the decompression load. The non-dimensional decompression time and the non-dimensional decompression load are developed to evaluate the decompression characteristics, and the correlation equations are established. The relative deviation between the results of the correlation equation fit and the results of the one-dimensional simulation is less than 3%. This work provides a new engineering method for structure strength design and decompression load analysis with high accuracy and low resource consumption.

Guohong Zhao, Zeyu Kang, Yixin Huang et al.

In this paper, for an Low-Earth Orbit (LEO) satellite network with inter-satellite links, a routing optimization method is developed in the case of stochastic link failure. First, a discrete-time strategy is used for the satellite network to acquire several static topological graphs during a cycle. Based on the static topological graphs regarding stochastic link failure, a constraint model is established that constructs the task revenue, switching times and routing cost as indicators. Then, an improved Genetic Algorithm based on A* is proposed to optimize the topology under the constraint model. In particular, to reduce the cost of computation, a new generation strategy for the initial solution is presented which combines the roulette wheel operator and the A* algorithm. Finally, the effectiveness of the proposed method is illustrated by a group of numerical simulations for the network with stochastic link failure.

Karine Klippel, Elisa Valentim Goulart, Gilberto Fisch et al.

The Alcantara Launch Center (ALC) is the main Brazilian access to space. It is positioned over a complex terrain, and it has some important buildings for assembling, integration and launching activities, such as the Mobile Integration Tower. Being in a region of prevalent trade winds, the flow interaction between the complex terrain and the buildings can affect the safety of operations on the platform, and the dispersion of toxic gases emitted during the launching. The main objective of this work was to study the influence of topography and buildings on the atmospheric flow of ALC using computational fluid dynamics (CFD) techniques. Three geometries were considered: simplified terrain (case 1), smooth complex terrain (case 2), and roughness complex terrain (case 3). The flow conditions over ALC were simulated using the ANSYS Fluent 19.0 CFD commercial code. The numerical simulations used a realizable κ-ε to model turbulence effects and the results presented a good agreement with the in-situ field measurements for the most complex geometry (case 3). The topography clearly influences the flow pattern at ALC, with the cliff influence over the wind being the major cause for establishing the flow patterns.