Hasil untuk "Motor vehicles. Aeronautics. Astronautics"

Guangtong Zhu, Zixuan Zheng, Yufei Guo et al.

Y. V. Trofimenko, V. A. Ginzburg, A. N. Yakubovich et al.

A methodology is proposed to obtain consistent and coherent estimates of greenhouse gas and precursor gas emissions from road transport and off-road vehicles and to quantify their accuracy and uncertainty. Accuracy increase of the estimates is achieved by detailing the initial data, reflecting the specific features of the national vehicle fleet structure and transport activity indicators, for which a wide range of statistical, probabilistic and expert methods are used. Using the methodology implemented in the “Transport Model” software product, it is possible to reconstruct time series of emissions for previous periods with an assessment of their accuracy and uncertainties, as well as to perform scenario modelling in order to forecast future emissions. The methodology was used to obtain quantitative estimates of three types of greenhouse gases emissions and three precursor gases from road transport and off-road vehicles for the period of 2010 to 2022, to compare the results with National Inventory data, to produce emission estimates for four categories of off-road vehicles for the first time, and to estimate fuel consumption and changes in the number of vehicles of all categories for the period of 1990 to 2022. An information array containing official statistical data on indirect indicators of transport activity by vehicle category for the period of 1990 to 2022 was compiled, the results of the emissions calculations were compared with the indirect indicators using multiple regression methods, and the accuracy and uncertainties of these results were quantitatively assessed. The estimated greenhouse gas emissions were considered as random values for which a confidence interval was calculated as an indicator of uncertainty and a median correction as an indicator of accuracy. The high quality of the results of the calculations based on the methodology considered was confirmed.

Jianlin Chen, Lei Liu, Yang Xu et al.

This paper studies spacecraft control under communication congestion, multi-source uncertainties, and input constraints. To reduce communication load, a static event-triggered mechanism is used so that transmissions occur only when necessary. Unknown nonlinearities are estimated online by a radial basis function neural network (RBFNN). To address sensor outliers and external disturbances, an event-triggered stubborn composite disturbance observer (ESCDO) is proposed, and sufficient conditions are derived to ensure its globally uniformly bounded stability. Based on this, an MPC-based composite anti-disturbance controller is designed to satisfy input constraints, and conditions are provided to guarantee the uniform bounded stability of the closed loop. Numerical simulations are conducted to demonstrate the effectiveness of the proposed approach.

Mara-Florina NEGOITA, Alina BOGOI, Ionut BUNESCU et al.

While aerodynamic optimization of wing geometry in cruise flight has been widely investigated, such modifications inevitably alter the aerodynamic characteristics of high-lift devices, which remain essential during take-off and landing regimes. In this context, the present study addresses the influence of flap slot geometry on the aerodynamic characteristics of single-slotted flap configurations. For this reason, a parametrization method is introduced, combining cubic Bézier curves for flap definition with local curvature parameters for the cove region of the airfoil. This approach ensures geometric continuity in the retracted configuration while enabling rigorous control of the deployed flap geometry. The parametrization was integrated into an automated CFD and gradient-based optimization framework, enabling the efficient exploration of six geometric parameters across multiple configurations. The analysis revealed that lip length and the curvature of the flap’s upper surface have the most significant impact on aerodynamic performance, influencing lift generation, flow attachment, and drag reduction. Optimized configurations achieved up to a 7% increase in maximum lift coefficient and a 5% reduction in drag relative to the baseline geometry. These results highlight the potential of precise geometric control of flap slots to enhance aerodynamic efficiency, particularly in low-speed regimes relevant to take-off and landing. The proposed methodology establishes a foundation for advanced parametrization and optimization strategies for multi-element airfoil configurations and next-generation high-lift systems.

Yang Wang, Yongxin Feng, Fan Zhou et al.

As drone technology develops rapidly and many users emerge in airspace networks, various forms of interference have caused the wireless spectrum to exhibit a dense, diverse, and dynamic trend. This increases the probability of spectrum conflicts among users and seriously impacts the quality and transmission rate of communication. How to effectively improve the detection accuracy of each frequency component in the electromagnetic space mixed signals and avoid spectrum conflicts will become one of the crucial issues currently faced by unmanned aerial vehicle (UAV) communication technologies. However, the existing methods overlook the mutual interference among the component signals as well as the noise during the frequency detection process, which affects their detection performance. In this paper, we propose a mixed-signal frequency detection method based on the reconstruction of sparse feature signals. Without information such as frequency range, bandwidth, and the number of components, it can utilize the autoencoder network to learn the sparse features of each component signal in the high-dimensional frequency domain space and construct a nonlinear reconstruction function to reconstruct each component signal in the mixed signal, thereby realizing the separation of signals. On this basis, complex dilated convolution and deconvolution are used successively to perform feature extraction on the separated signals, which enhances the receptive field and frequency resolution ability of the network for signals, reduces the interference between noise and different component signals, and realizes the accurate estimation of the number of components and carrier frequencies. The simulation results show that when <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>S</mi><mi>N</mi><mi>R</mi><mo>≥</mo></mrow></semantics></math></inline-formula> 6 dB, the detection accuracy of the number of component signals is greater than 96.3%. The detection error and detection accuracy of component frequencies are less than 3.19% and greater than 90.7%, respectively.

Barouch Giechaskiel, Christian Ferrarese, Theodoros Grigoratos et al.

Tyre wear is a major contributor to global microplastic pollution, affecting air, soil, water, and wildlife as well as human health. In the European Union (EU), the latest Euro 7 regulation foresees the introduction of tyre abrasion limits covering all tyre categories, referring to two testing methods (convoy on road or laboratory drum) developed by the United Nations (UN) Economic Commission for Europe (UNECE) World Forum for Harmonization of Vehicle Regulations (WP.29). In this study, we applied the convoy method adopted by the UNECE Working Group on Noise and Tyres (GRBP) as part of the UN Regulation 117 on tyre performance parameters. The method has been developed by the Task Force on Tyre Abrasion (TFTA) of the UNECE and involves vehicles driving on public roads for about 8000 km. Candidate and reference tyres are fitted in a convoy of up to four vehicles, and an abrasion index for each candidate tyre is determined as a ratio of the abrasion of the candidate and reference tyres. In our tests, in addition to the abrasion rate, we measured the tread depth reduction and defined a service life index (i.e., total mileage potential) without the need of a different methodology. The results from six summer and nine winter C1 class passenger car tyres of various sizes showed a wide range of abrasion rates and service life values. We also compared our results with values reported in the literature and on websites. The conclusions of this study are expected to support the ongoing discussion on limit setting for C1 tyres and the definition of a service life index.

Ke Wang, Chibing Shen, Bo Fan

To investigate the influence of injector recess depth on the combustion characteristics of air heaters, high-speed shadowgraph imaging technology combined with numerical simulation was employed. Targeting a tripropellant coaxial direct-flow single injector, three test cases with recess depths of 0 mm, 5 mm, and 10 mm were designed to systematically study the ignition process, flame propagation characteristics, quasi-steady combustion, and flow field evolution mechanisms. Experimental results indicate that the recessed structure can expand the liquid mist distribution range before ignition: the dimensionless spray width ratios of the 5 mm and 10 mm recess cases are increased by 57.5% and 64.9% respectively compared to the non-recessed case, with an obvious “saturation effect” observed. Injectors with recess exhibit the characteristic of “jet head priority ignition”, which shortens the ignition time and improves ignition efficiency. The 5 mm shallow recess case achieves the optimal combustion stability with the smallest chamber pressure fluctuation (±0.1 MPa). Although the 10 mm deep recess enhances near-field mixing and combustion intensity, it tends to induce flame oscillation and combustion instability. Simulation results verify the experimental observations: the recess depth regulates droplet atomization, component mixing, and combustion heat release processes by altering the recirculation zone range, velocity gradient, and gas–liquid momentum exchange efficiency. This research provides experimental and theoretical support for the structural optimization of injectors in combustion-type air heaters.

HUANG Xiqiao, ZHOU Taiyuan, LIU Luping et al.

It is difficult to measure the combustion efficiency in the unsteady process of detonation combustion. Therefore, isoctane was used as gasoline single-component alternative fuel and its simplification mechanism was established to numerically simulate the flame acceleration and the DDT process of gaseous gasoline. The gas analysis method was used to study the detonation combustion efficiency under different sampling conditions. The results show that the simplification mechanism model is in good agreement with the combustion characteristics of isooctane within the study range. The numerical study finds that in the detonation combustion process, the concentration of gas components in different regions after detonation wave is quite different and that the combustion near the flame front is not sufficient, thus there being a lot of intermediate products. After detonation wave passes by, the combustion continues. At the same time, the reverse wave may promote the further reaction of combustion and make the combustion more complete. When the equivalent ratio is 1, the average concentration of all gases after detonation wave was taken to calculate the combustion efficiency, which is 82.6%.

Jingjing Huang, Yirong Yang, Bilian Peng et al.

According to the characteristics of the rotor system in an aero-engine and the vibrational test requirements of the aero-engine ground test, suitable vibration measurement sensors and test positions were selected. The vibration signals at the casings for the compressor and turbine of a twin-spool turbo-jet engine were collected under the states of maximum power and afterburning respectively, and the power spectrum analysis was carried out to determine the positions and causes of vibration. Furthermore, methods and preventive measures for eliminating vibration have been proposed. The results indicated that the main rotor vibration excited by mass imbalance in the twin-spool turbo-jet engine was significant. Rotor spindle misalignment or rotor radial stiffness unevenness also induced the vibration. The aerodynamic pulse vibration formed by the rotor blades of the first stage of the low pressure compressor was large, and rub induced vibration fault may occur at the turbine rotor seals. Based on the power spectrum analysis technology, the rotor system faults information including the type, position, and the degree can be quickly identified, and useful attempts and explorations have been made to reduce the vibration faults of the twin-spool turbo-jet engine.

Wanneng Wu, Ao Liu, Jianwen Hu et al.

Crafting an edge-based real-time object detector for unmanned aerial vehicle (UAV) aerial images is challenging because of the limited computational resources and the small size of detected objects. Existing lightweight object detectors often prioritize speed over detecting extremely small targets. To better balance this trade-off, this paper proposes an efficient and low-complexity object detector for edge computing platforms deployed on UAVs, termed EUAVDet (Edge-based UAV Object Detector). Specifically, an efficient feature downsampling module and a novel multi-kernel aggregation block are first introduced into the backbone network to retain more feature details and capture richer spatial information. Subsequently, an improved feature pyramid network with a faster ghost module is incorporated into the neck network to fuse multi-scale features with fewer parameters. Experimental evaluations on the VisDrone, SeaDronesSeeV2, and UAVDT datasets demonstrate the effectiveness and plug-and-play capability of our proposed modules. Compared with the state-of-the-art YOLOv8 detector, the proposed EUAVDet achieves better performance in nearly all the metrics, including parameters, FLOPs, mAP, and FPS. The smallest version of EUAVDet (EUAVDet-n) contains only 1.34 M parameters and achieves over 20 fps on the Jetson Nano. Our algorithm strikes a better balance between detection accuracy and inference speed, making it suitable for edge-based UAV applications.

Heitor Lifsitch, Gabriel Rocha, Hendrio Bragança et al.

To enhance the field of continuous motor health monitoring, we present FAN-COIL-I, an extensive vibration sensor dataset derived from a Fan Coil motor. This dataset is uniquely positioned to facilitate the detection and prediction of motor health issues, enabling a more efficient maintenance scheduling process that can potentially obviate the need for regular checks. Unlike existing datasets, often created under controlled conditions or through simulations, FAN-COIL-I is compiled from real-world operational data, providing an invaluable resource for authentic motor diagnosis and predictive maintenance research. Gathered using a high-resolution 32KHz sampling rate, the dataset encompasses comprehensive vibration readings from both the forward and rear sides of the Fan Coil motor over a continuous two-week period, offering a rare glimpse into the dynamic operational patterns of these systems in a corporate setting. FAN-COIL-I stands out not only for its real-world applicability but also for its potential to serve as a reliable benchmark for researchers and practitioners seeking to validate their models against genuine engine conditions.

Zhanyue Zhao, Yang Wang, Charles Bales et al.

Piezoelectric ultrasonic motors perform the advantages of compact design, faster reaction time, and simpler setup compared to other motion units such as pneumatic and hydraulic motors, especially its non-ferromagnetic property makes it a perfect match in MRI-compatible robotics systems compared to traditional DC motors. Hollow shaft motors address the advantages of being lightweight and comparable to solid shafts of the same diameter, low rotational inertia, high tolerance to rotational imbalance due to low weight, and tolerance to high temperature due to low specific mass. This article presents a prototype of a hollow cylindrical ultrasonic motor (HCM) to perform direct drive, eliminate mechanical non-linearity, and reduce the size and complexity of the actuator or end effector assembly. Two equivalent HCMs are presented in this work, and under 50g prepressure on the rotor, it performed 383.3333rpm rotation speed and 57.3504mNm torque output when applying 282$V_{pp}$ driving voltage.

Kavana Venkatesh, Neethi M

Induction motors are one of the most crucial electrical equipment and are extensively used in industries in a wide range of applications. This paper presents a machine learning model for the fault detection and classification of induction motor faults by using three phase voltages and currents as inputs. The aim of this work is to protect vital electrical components and to prevent abnormal event progression through early detection and diagnosis. This work presents a fast forward artificial neural network model to detect some of the commonly occurring electrical faults like overvoltage, under voltage, single phasing, unbalanced voltage, overload, ground fault. A separate model free monitoring system wherein the motor itself acts like a sensor is presented and the only monitored signals are the input given to the motor. Limits for current and voltage values are set for the faulty and healthy conditions, which is done by a classifier. Real time data from a 0.33 HP induction motor is used to train and test the neural network. The model so developed analyses the voltage and current values given at a particular instant and classifies the data into no fault or the specific fault. The model is then interfaced with a real motor to accurately detect and classify the faults so that further necessary action can be taken.

Muhammad Samiullah, Hasan Ali, Shehryar Zahoor et al.

The detection and identification of induction motor faults using machine learning and signal processing is a valuable approach to avoiding plant disturbances and shutdowns in the context of Industry 4.0. In this work, we present a study on the detection and identification of induction motor faults using machine learning and signal processing with MATLAB Simulink. We developed a model of a three-phase induction motor in MATLAB Simulink to generate healthy and faulty motor data. The data collected included stator currents, rotor currents, input power, slip, rotor speed, and efficiency. We generated four faults in the induction motor: open circuit fault, short circuit fault, overload, and broken rotor bars. We collected a total of 150,000 data points with a 60-40% ratio of healthy to faulty motor data. We applied Fast Fourier Transform (FFT) to detect and identify healthy and unhealthy conditions and added a distinctive feature in our data. The generated dataset was trained different machine learning models. On comparing the accuracy of the models on the test set, we concluded that the Decision Tree algorithm performed the best with an accuracy of about 92%. Our study contributes to the literature by providing a valuable approach to fault detection and classification with machine learning models for industrial applications.

Riley J. Preston, Daniel S. Kosov

We propose a model for a molecular motor in a molecular electronic junction driven by a natural manifestation of Landauer's blowtorch effect. The effect emerges via the interplay of the electronic friction and diffusion coefficients, each calculated quantum mechanically using nonequilibrium Green's functions, within a semi-classical Langevin description of the rotational dynamics. The motor functionality is analysed through numerical simulations where the rotations exhibit a directional preference according to the intrinsic geometry of the molecular configuration. The proposed mechanism for motor function is expected to be ubiquitous for a range of molecular geometries beyond the one examined here.

Ethan Steinberg, Jason Fries, Yizhe Xu et al.

We present a self-supervised, time-to-event (TTE) foundation model called MOTOR (Many Outcome Time Oriented Representations) which is pretrained on timestamped sequences of events in electronic health records (EHR) and health insurance claims. TTE models are used for estimating the probability distribution of the time until a specific event occurs, which is an important task in medical settings. TTE models provide many advantages over classification using fixed time horizons, including naturally handling censored observations, but are challenging to train with limited labeled data. MOTOR addresses this challenge by pretraining on up to 55M patient records (9B clinical events). We evaluate MOTOR's transfer learning performance on 19 tasks, across 3 patient databases (a private EHR system, MIMIC-IV, and Merative claims data). Task-specific models adapted from MOTOR improve time-dependent C statistics by 4.6% over state-of-the-art, improve label efficiency by up to 95% ,and are more robust to temporal distributional shifts. We further evaluate cross-site portability by adapting our MOTOR foundation model for six prediction tasks on the MIMIC-IV dataset, where it outperforms all baselines. MOTOR is the first foundation model for medical TTE predictions and we release a 143M parameter pretrained model for research use at [redacted URL].

YAO Weixing, ZHANG Chao, HUANG Yuxiang et al.

As composite materials are playing more important role in advanced aircraft structures,the change of mechanical properties of composites during service is of significant importance for the overall safety of the aircraft.In order to achieve the goal of fatigue evaluation and life prediction of composite components of aircraft based on guided wave in-situ detection,firstly,the fatigue evolution law of composite materials is studied from the perspectives of macroscopic phenomenology and microscopic physics.Then,the potential of guided wave phase velocity and mode conversion phenomenon for fatigue characterization is discussed through analyzing the guided wave field.At the same time,a deep learning framework is constructed to extract fatigue evolution features from the guided wave field in a data-driven manner.Finally,a fatigue evolution model based on the Bayesian model averaging method is proposed to predict the residual fatigue life of the composite specimen.The results show that,by extracting and analyzing the guided wave propagating features,the fatigue state of composite materials can be accurately characterized.Combining the Bayesian model averaging method and the confidence interval criterion,the goal of residual life prediction before specimen fatigue failure is achieved.

He Song, Shaolin Hu, Wenqiang Jiang et al.

To simultaneously achieve space formation flight and target tracking of multiple unmanned aerial vehicles (UAVs) and solve the rotation buffeting problem of the UAV, a robust formation control and target tracking algorithm is proposed. The artificial potential function consisting of formation control term and target tracking term is established, and its convergence is proved. The sliding mode control method with the saturation function is established, and a sufficient condition for sliding mode to occur is analysed. Finally, the numerical simulation is conducted for the proposed algorithm, and the simulation result is analysed. The results show that the proposed algorithm can quickly achieve the formation flight and target tracking of multi-UAVs and improve the tracking performance; meanwhile, it can effectively weaken the rotation buffeting and improve the robustness.

Sreenadh Chevula, Sankeerth Chillamcharal, Satya Prasad Maddula

This paper discusses about the critical situations faced by the Defence operations with drones in the area of Siachen Glacier in the Himalayas. The reasons for the structural failures in drone’s rotor blades and the low-performance efficiency of the drones at low-temperature conditions are highlighted. A possible solution to the above-mentioned problems has been addressed by introducing a new boundary design in the rotor blades and composite materials. The results which are shown in this paper are obtained by the computational analysis facility located at the Department of Aerospace Engineering, School of Technology, GITAM (Deemed to be University), Hyderabad. By mimicking the Siachen Glacier atmosphere conditions, the proposed rotor blade design has been analysed in CFD.

He Xiaoxiao, Wang Binghan

With the rapid growth of aviation joint combat training and aviation equipment combat test requirements, LVC simulation technology presents the trend of rapid technological progress and frequent demonstration and verification. Military and industrial departments at home and abroad continue to study LVC related technologies. By taking advantage of LVC technology in improving scene fidelity, cost-effectiveness ratio and security, LVC can play a greater role in the military field. In terms of analyzing the requirements and development characteristics of LVC technology, the paper comprehensively combs the development status of joint training and related combat tests of aviation soldiers based on LVC technology. Combined with the research basis and deployment in foreign related fields, the development characteristics and development trend of LVC technology in US are summarized.