Search Results (3,909)

The fluctuation in the rotational speed of the inner ring can lead to significant instability in the motion of both the inner ring and the cage of rolling bearings. This instability seriously impacts the operational performance and service life of the bearings. In this paper, a nonlinear dynamic model of a fully flexible angular contact ball bearing was established by comprehensively considering various nonlinear factors, including elastic contact relationships, internal collisions, friction, and clearance. The dynamic characteristics of the inner ring and cage under sinusoidal rotational speed fluctuations were studied. The effects of amplitude and frequency of rotational speed fluctuation of the inner ring on the motion stability of the inner ring and cage were analyzed. The results show that a greater the fluctuation amplitude leads to a higher the fluctuation amplitude in the cage’s rotational speed curve, while a higher fluctuation frequency correlates with an increased frequency in the cage’s rotational speed curve. These results indicate that increases in both the amplitude and frequency of rotational speed fluctuations result in more pronounced oscillations of the inner ring. The validity of the model was confirmed by comparing the LS-DYNA results with the analytical results and experimental results. The research findings can provide a theoretical foundation for enhancing motion stability and optimizing design of the bearings. Full article

Precise extrinsic calibration is a fundamental prerequisite for data fusion in multi-LiDAR systems. However, conventional methods are often encumbered by dependencies on initial estimates, auxiliary sensors, or manual feature selection, which renders them complex, time-consuming, and limited in adaptability across diverse environments. To address these limitations, this paper proposes a novel, high-precision extrinsic calibration method for multi-LiDAR systems with a narrow Field of View (FoV), achieved through the synergistic use of circular and planar features. Our approach commences with the automatic segmentation of the calibration target’s point cloud using an improved VoxelNet. Subsequently, a denoising step, combining RANSAC and a Gaussian Mean Intensity Filter (GMIF), is applied to ensure high-quality feature extraction. From the refined point cloud, planar and circular features are robustly extracted via Principal Component Analysis (PCA) and least-squares fitting, respectively. Finally, the extrinsic parameters are optimized by minimizing a nonlinear objective function formulated with joint constraints from both geometric features. Simulation results validate the high precision of our method, with rotational and translational errors contained within 0.08° and 0.8 cm. Furthermore, real-world experiments confirm its effectiveness and superiority, outperforming conventional point-cloud registration techniques. Full article

Rotating machinery is a core component of modern industry, and its operational state directly affects system safety and reliability. In order to achieve intelligent fault diagnosis of bearings under complex working conditions, the health management of bearings has become an important issue. Although deep learning has shown remarkable advantages, its performance still relies on the assumption that the training and testing data share the same distribution, which often deteriorates in real applications due to variations in load and rotational speed. This study focused on the scenario of domain generalization (DG) and proposed a Meta-Learning with Gradient Alignment and Data Augmentation (MGADA) method for cross-domain bearing fault diagnosis. Within the meta-learning framework, Mixup-based data augmentation was performed on the support set in the inner loop to alleviate overfitting under small-sample conditions and enhanced task-level data diversity. In the outer loop optimization stage, an arithmetic gradient alignment constraint was introduced to ensure consistent update directions across different source domains, thereby reducing cross-domain optimization conflicts. Meanwhile, a centroid convergence constraint was incorporated to enforce samples of the same class from different domains to converge to a shared centroid in the feature space, thus enhancing intra-class compactness and semantic consistency. Cross-working-condition experiments conducted on the Case Western Reserve University (CWRU) bearing dataset demonstrate that the proposed method achieves high classification accuracy across different target domains, with an average accuracy of 98.89%. Furthermore, ablation studies confirm the necessity of each module (Mixup, gradient alignment, and centroid convergence), while t-SNE and confusion matrix visualizations further illustrate that the proposed approach effectively achieves cross-domain feature alignment and intra-class aggregation. The proposed method provides an efficient and robust solution for bearing fault diagnosis under complex working conditions and offers new insights and theoretical references for promoting domain generalization in practical industrial applications. Full article

Tandem cylinders, widely used in heat exchangers, water storage units, and electronic cooling, require optimized flow and heat transfer to enhance engineering performance. However, the combined effects of various factors in tandem configurations remain insufficiently explored. This study proposes an innovative approach that integrates multiple parameters to systematically investigate the influence of surface pattern characteristics and rotational speed on the fluid dynamics and heat transfer performance of tandem cylinders. Numerical simulations are conducted to evaluate the effects of various pattern dimensions (w/D = 0.12–0.18), surface shapes (square, triangular, and dimpled grooves), rotational speeds (|Ω| ≤ 1), and frequencies (N = 2–10) on fluid flow and heat transfer efficiency at Re = 200. The study aims to establish the relationship between the complexity of the coupling effects of the considered parameters and the heat transfer behavior as well as fluid dynamic variations. The results demonstrate that, under stationary conditions, triangular grooves exhibit larger vortex structures compared to square grooves. When a positive rotation is applied, coupled with increases in w/D and N, square grooves develop a separation vortex at the front. Furthermore, the square and dimpled grooves exhibit significant phase control capabilities in the time evolution of lift and drag forces. Under conditions of w/D = 0.12 and w/D = 0.18, the C_L of the upstream cylinder decreases by 17.2% and 20.8%, respectively, compared to the standard smooth cylinder. Moreover, the drag coefficient C_D of the downstream cylinder is reduced to half of the initial value of the upstream cylinder. As the surface amplitude increases, the C_D of the smooth cylinder surpasses that of the other groove types, with an approximate increase of 8.8%. Notably, at Ω = −1, the downstream square-grooved cylinder’s C_L is approximately 12.9% lower than that of other groove types, with an additional 6.86% reduction in amplitude during counterclockwise rotation. When N increases to 10, the of the upstream square-grooved cylinder at w/D = 0.18 decreases sharply by 20.9%. Conversely, the upstream dimpled-groove cylinder significantly enhances at w/D = 0.14 and N = 4. However, the upstream triangular-groove cylinder achieves optimal stability at w/D ≥ 0.16. Moreover, at w/D = 0.18 and N = 6, square grooves show the most significant enhancement in vortex mixing, with an increase of approximately 42.7%. Simultaneously, the local recirculation zones in dimpled grooves at w/D = 0.14 and N = 6 induce complex and geometry-dependent heat transfer behaviors. Under rotational conditions, triangular and dimpled grooves exhibit superior heat transfer performance at N = 6 and w/D = 0.18, with TPI values exceeding those of square grooves by 33.8% and 28.4%, respectively. A potential underlying mechanism is revealed, where groove geometry enhances vortex effects and heat transfer. Interestingly, this study proposes a correlation that reveals the relationship between the averaged Nusselt number and groove area, rotational speed, and frequency. These findings provide theoretical insights for designing high-efficiency heat exchangers and open up new avenues for optimizing the performance of fluid dynamic systems. Full article

The monitoring technology used during drilling has become a crucial means of gathering information about the underground rock mass. However, the drilling response parameters are affected by the coupling of operating parameters and rock mass properties, which leads to the challenge of lithology inversion based on drilling parameters in complex strata. At present, the precise quantitative response mechanism between operating parameters and drilling parameters is still not clear in the common lithology of mining, which restricts the further improvement of the accuracy of lithology identification while drilling and the optimization of drilling technology. In order to improve the measurement of drilling technology, the relationship between rock parameters and drilling parameters in the process of mining drilling is explored. This paper carried out physical and mechanical experiments; built a small drilling platform (including magnetic suction drilling, a data monitoring system, and a rock confining pressure system); carried out three different specifications, 330 r/min, 360 r/min, and 390 r/min, of the initial speed of the drilling experiment; and added 330 r/min initial-speed-drilling different-strength rock-drilling experiments. The experimental results show that rock drilling is divided into three stages: the initial stage of drilling, the crack propagation stage, and the bit retreating stage. The rotation speed has a great influence on the drilling speed, torque, weight on bit, and drilling time. According to the Pearson fitting relationship of drilling parameters, the correlation between F and PR is −0.783, indicating a strong positive correlation, and the correlation between RPM and PR is 0.827, indicating a strong negative correlation. The power function y = ax^b is used to fit the drilling parameters and rock parameters. The fitting effect is good, and the torque and uniaxial tensile strength R2 is as high as 0.9966. The experimental conclusion provides a theoretical basis for lithology identification in intelligent mining drilling and discusses the feasibility of a dynamic monitoring scheme for the drilling rig. Full article

The conical pick is an essential component of roadheaders used for cutting rock. During the rock-breaking process, these picks interact with the rock, resulting in self-rotation, which enhances the wear uniformity of conical picks, thereby prolonging their service life. Since the phenomenon of self-rotation is generated passively by random contact forces with the rock surface, it is challenging to quantitatively measure the extent of self-rotatory speed. In order to investigate the correlation between the self-rotatory speed of conical picks and wear, this article establishes various self-rotatory speeds for vertical rock-breaking wear experiments involving conical picks. It analyzes the relationship between quantitative parameters, such as the equivalent stress and wear, through simulation. The results of the study indicate that the optimal self-rotatory speed of the conical pick is 16 rpm when it is rotated vertically to break the rock, resulting in minimal wear. When the equivalent stress and Mohr–Coulomb safety factor are optimized, it is essential to consider the changes in normal force and the variation in the area affected by the safety factor. This leads to an increase in wear as the cutting distance increases, indicating that a higher self-rotatory speed does not necessarily improve the wear performance of conical picks. Full article

The field of autonomous driving has seen continuous advances, yet achieving higher levels of automation in real-world applications remains challenging. A critical requirement for autonomous navigation is accurate map construction, particularly in novel and unstructured environments. In recent years, Simultaneous Localization and Mapping (SLAM) has evolved to support diverse sensor modalities, with some implementations incorporating machine learning to improve performance. However, these approaches often demand substantial computational resources. The key challenge lies in achieving efficiency within resource-constrained environments while minimizing errors that could degrade downstream tasks. This paper presents an enhanced ORB-SLAM2 (Oriented FAST and Rotated BRIEF Simultaneous Localization and Mapping, version 2) algorithm implemented on a Raspberry Pi 3 (ARM A53 CPU) to improve mapping performance under limited computational resources. ORB-SLAM2 comprises four main stages: Tracking, Local Mapping, Loop Closing, and Full Bundle Adjustment (BA). The proposed improvements include employing a more efficient feature descriptor to increase stereo feature-matching rates and optimizing loop-closing parameters to reduce accumulated errors. Experimental results demonstrate that the proposed system achieves notable improvements on the Raspberry Pi 3 platform. For monocular SLAM, RMSE is reduced by 18.11%, mean error by 22.97%, median error by 29.41%, and maximum error by 17.18%. For stereo SLAM, RMSE decreases by 0.30% and mean error by 0.38%. Furthermore, the ROS topic frequency stabilizes at 10 Hz, with quad-core CPU utilization averaging approximately 90%. These results indicate that the system satisfies real-time requirements while maintaining a balanced trade-off between accuracy and computational efficiency under resource constraints. Full article

The current study examines the influence of tool rotational speed (TRS) and reinforcement volume fraction (%vol.) of SiC on particle distribution in the stir zone (SZ) of AZ91 Mg alloy. Two parameter sets were analyzed: S1 (500 rpm TRS, 13% vol.) and S2 (1500 rpm TRS, 10% vol.), with a constant tool traverse speed (TTS) of 60 mm/min. SPH simulations revealed that in S1, lower TRS resulted in limited SiC displacement, leading to significant agglomeration zones, particularly along the advancing side (AS) and beneath the tool pin. Cross-sectional observations at 15 mm and 20 mm from the plunging phase indicated the formation of reinforcement clusters along the tool path, with inadequate SiC transference to the retreating side (RS). The reduced stirring force in S1 caused poor reinforcement dispersion, with most SiC nodes settling at the SZ bottom due to insufficient upward movement. In contrast, S2 demonstrated enhanced particle mobility due to higher TRS, improving reinforcement homogeneity. Intense stirring facilitated lateral and upward SiC movement, forming an interconnected reinforcement network. SPH nodes exhibited improved dispersion, with particles across the SZ and more evenly deposited on the RS. A comparative assessment of experimental and simulated reinforcement distributions confirmed a strong correlation. Results highlight the pivotal role of TRS in reinforcement movement and agglomeration control. Higher TRS enhances stirring and promotes uniform SiC dispersion, whereas an excessive reinforcement fraction increases matrix viscosity and restricts particle mobility. Thus, optimizing TRS and reinforcement content through numerical analysis using SPH is essential for producing a homogeneous, well-reinforced composite layer with improved surface properties. The findings of this study have significant practical applications, particularly in industrial material selection, improving manufacturing processes, and developing more efficient surface composites, thereby enhancing the overall performance and reliability of Mg alloys in engineering applications. Full article

Block-based hybrid video coders typically use inter-prediction and bidirectionally coded (B) frames to improve compression efficiency. For this purpose, they employ look-ahead buffers, perform out-of-sequence frame coding, and implement similarity search-based general-purpose algorithms for motion estimation. While effective, these methods increase computational complexity and may not suit delay-sensitive practical applications such as real-time drone video transmission. If future motion can be predicted from external metadata, encoding can be optimized with lower complexity. In this study, a mathematical model for predicting motion vectors in drone video using only flight parameters is proposed. A remote-controlled drone with a fixed downward-facing camera recorded 4K video at 50 fps during autonomous flights over a marked terrain. Four flight parameters were varied independently, altitude, horizontal speed, vertical speed, and rotational rate. OpenCV was used to detect ground markers and compute motion vectors for temporal distances of 5 and 25 frames. Polynomial surface fitting was applied to derive motion models for translational, rotational, and elevational motion, which were later combined. The model was validated using complex motion scenarios (e.g., circular, ramp, helix), yielding worst-case prediction errors of approximately −1 ± 3 and −6 ± 14 pixels at 5 and 25 frames, respectively. The results suggest that flight-aware modeling enables accurate and low-complexity motion vector prediction for drone video coding. Full article

This study proposes a lightweight double-encryption cryptosystem for 3D medical images such as Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) scans, and Computed Tomography scans (CT). The first encryption process uses chaotic pseudo-random numbers produced by a Lorenz chaotic system while the second applies Cipher Block Chaining (CBC) mode using outputs from a Pseudo-Random Number Generator (PRNG). To enhance diffusion and confusion, additional voxel shuffling and bit rotation operations are incorporated. Various sets of optimized parameters for the Lorenz system are calculated using either a genetic algorithm or a random walk. The master key of the cryptosystem is 672 bits long and consists of two components. The first component is the SHA-512 hash of the input image while the second component consists of the initial conditions of the Lorenz chaotic system and is 160 bits long. The master key is processed by a function that generates fourteen subkeys, which are then used in different stages of the algorithm. The cryptosystem exhibits excellent performance in terms of entropy, NPCR, UACI, key sensitivity, security, and speed, ensuring the confidentiality of personal medical data and resilience against advanced computational threats, and making it a good candidate for real-time 3D medical image encryption in healthcare systems. Full article

Horizontal annular flow channels are widely applied in various fields, including thermal engineering, drilling engineering, and food engineering. Investigating their internal flow patterns is crucial for optimizing pipeline design, selecting appropriate equipment, and understanding the sedimentation and migration modes of multiphase flows within annular geometries. In practical engineering applications, the operational conditions of annular flow channels during gas drilling are the most complex, involving parameters such as eccentricity, rotation, surface roughness, and multiphase flow interactions. This study focuses on the flow characteristics of horizontal annular channels under real-world engineering conditions, examining variations in operational parameters. The pressure drop in annular pipelines is influenced by factors such as flow velocity, eccentricity, and rotational speed, exhibiting complex variation patterns. However, previous studies have not fully considered the impact of rough wellbore walls and the interactions among various factors. Employing experimental methods, this research analyzes the pressure drop characteristics within annular geometries. The results reveal that surface roughness significantly affects pressure drop, with the inner pipe’s roughness having a greater impact when the outer pipe surface is rough compared to when it is smooth. An increase in eccentricity substantially reduces pressure drop, with both positive and negative eccentricities demonstrating symmetric pressure drop patterns. Moreover, a significant positive correlation exists between the total rough area of the annular channel and pressure drop. Furthermore, this study establishes a predictive model through dimensional analysis. Unlike existing models, this new model incorporates the influences of both roughness and eccentricity, achieving a prediction accuracy of over 99%. This research confirms the critical role of roughness in annular flow systems and provides practical implications for selecting more reliable pump power equipment in engineering fields. Full article

In order to enhance the wind-resistance capability and achieve a lightweight design of high-altitude unmanned airships, this study proposes a rapid optimization method for a heterogeneous propeller propulsion system. This system integrates contra-rotating and ducted propellers to exploit their respective aerodynamic advantages. First, surrogate models of the contra-rotating propeller, contra-rotating motor, ducted propeller, and ducted motor were constructed using an optimal Latin hypercube sampling method based on the max–min criterion. Then, within the optimization framework, propeller–motor matching principles and energy balance constraints were incorporated to minimize the total weight of the propulsion and energy systems. A case study on a conventional high-altitude unmanned airship demonstrates that, under the same wind-resistance capability, the adoption of the heterogeneous propeller electric propulsion system reduces the total propulsion-and-energy system weight by 24.94%. This method integrates the advantages of contra-rotating and ducted propellers, thereby overcoming the limitations of conventional propulsion architectures. It provides a new approach for designing lightweight, efficient, and long-endurance propulsion systems for near-space high-altitude platforms. Full article

Under extreme operating conditions, the internal lubricating flow field of high-speed gear transmission systems exhibits a transient oil–gas multiphase flow, predominantly governed by cavitation-induced phase transitions and turbulent shear. This phenomenon involves complex mechanisms of nonlinear multi-physical coupling and energy dissipation. Traditional lubrication theories and single-phase flow simplified models show significant limitations in capturing microsecond-scale flow features, dynamic interface evolution, and turbulence modulation mechanisms. To address these challenges, this study developed a cross-scale coupled numerical framework based on the Lattice Boltzmann method and large eddy simulation (LBM-LES). By incorporating an adaptive time relaxation algorithm, the framework effectively enhances the computational accuracy and stability for high-speed rotational flow fields, enabling the precise characterization of lubricant splashing, distribution, and its interaction with air. The research systematically reveals the spatiotemporal evolution characteristics of the internal flow field within the gearbox and focuses on analyzing the nonlinear regulatory effect of baffle geometric parameters on the system’s energy transport and dissipation characteristics. Numerical results indicate that the baffle structure significantly influences the spatial distribution of the vorticity field and turbulence intensity by reconstructing the shear layer topology. Low-profile baffles optimize the energy transfer pathway, effectively reducing the flow enthalpy, whereas excessively tall baffles induce strong secondary recirculation flows, exacerbating vortex-induced energy losses. Simultaneously, appropriately increasing the spacing between double baffles helps enhance global lubricant transport efficiency and suppresses unsteady dissipation caused by localized momentum accumulation. Furthermore, the geometrically optimized double-baffle configuration can achieve synergistic improvements in lubrication performance, oil film stability, and system energy efficiency by guiding the main shear flow and mitigating localized high-momentum impacts. This study provides crucial theoretical foundations and design guidelines for developing the next generation of theory-driven, energy-efficient lubrication design strategies for gear transmissions. Full article

Agricultural land management is a major determinant of terrestrial carbon (C) fluxes and has substantial implications for greenhouse gas (GHG) mitigation strategies. This study evaluated the net ecosystem carbon balance (NECB) of an agricultural field in an organic integrated crop–livestock system (ICLS) with a ley-arable rotation in northern Germany over two years (2021–2023). Carbon dioxide (CO₂) fluxes were measured using the eddy covariance (EC) method to derive net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (RECO). This approach facilitated an assessment of the temporal dynamics of CO₂ exchange, alongside detailed monitoring of field-based C imports, exports, and management activities, of a crop sequence including grass-clover (GC) ley, spring wheat (SW), and a cover crop (CC). The GC ley acted as a consistent C sink (NECB: −1386 kg C ha⁻¹), driven by prolonged photosynthetic activity and moderate biomass removal. In contrast, the SW, despite high GPP, became a net source of C (NECB: 120 kg C ha⁻¹) due to substantial export via harvest. The CC contributed to C uptake during the winter period. However, cumulatively, it acted as a net CO₂ source, likely due to drought conditions following soil cultivation and CC sowing. Soil cultivation events contributed to short-term CO₂ pulses, with their magnitude modulated by soil water content (SWC) and soil temperature (TS). Overall, the site functioned as a net C sink, with an average NECB of −702 kg C ha⁻¹ yr⁻¹. This underscores the climate mitigation potential of management practices such as GC ley systems under moderate grazing, spring soil cultivation, and the application of organic fertilizers. To optimize CC benefits, their use should be combined with reduced soil disturbance during sowing or establishment as an understory. Additionally, C exports via harvests could be offset by retaining greater amounts of harvest residues onsite. Full article

This study experimentally investigates single-phase forced convection heat transfer and flow characteristics of Al₂O₃-water nanofluids under rotating hypergravity conditions ranging from 1 g to 5.1 g. While nanofluids offer enhanced thermal properties for advanced cooling applications in aerospace and rotating machinery, their performance under hypergravity remains poorly understood. Experiments employed a custom centrifugal test rig with a horizontal test section (D = 2 mm, L = 200 mm) operating at constant heat flux. Alumina nanoparticles (20–30 nm) were dispersed in deionized water at mass fractions of 0.02–0.5 wt%, with stability validated through transmittance measurements over 72 h. Heat transfer coefficients (HTC), Nusselt numbers (Nu), friction factors (f), and pressure drops were measured across Reynolds numbers from 500 to 30,000. Results demonstrate that hypergravity significantly enhances heat transfer, with HTC increasing by up to 40% at 5.1 g compared to 1 g, most pronounced at the transition from 1 g to 1.41 g. This enhancement is attributed to intensified buoyancy-driven secondary flows quantified by increased Grashof numbers and modified particle distribution. Friction factors increased moderately (15–25%) due to Coriolis effects and enhanced viscous dissipation. Optimal performance occurred at 0.5 wt% concentration, effectively balancing thermal enhancement against pumping penalties. Random forest (RF) and eXtreme gradient boosting (XGBoost) achieved R² = 0.9486 and 0.9625 in predicting HTC, respectively, outperforming traditional correlations (Gnielinski: R² = 0.9124). These findings provide crucial design guidelines for thermal management systems in hypergravity environments, particularly for aerospace propulsion and centrifugal heat exchangers, where gravitational variations significantly impact cooling performance. Full article