Browse Topic: Power and Propulsion
The track irregularity has an important influence on the comfort, noise, and safety of the vehicle. At present, track unevenness is still measured using a track inspection trolley and other equipment, which is less efficient. In order to measure the track roughness on the existing operation lines, an evaluation method of track roughness based on inertial reference is proposed, and the method of calculating track roughness through the acceleration signal of the existing vehicle axle box is studied. The track roughness is obtained by integrating the acceleration signal twice in the time and frequency domains, respectively, and the simulation signal is established for theoretical verification, which is verified by experiments using the acceleration signal of an intelligent vehicle axle box. The analog signal is used for theoretical verification, and the axle box acceleration signal of the intelligent locomotive and rolling stock is used for experimental verification. The experimental results show that the axle-box acceleration signal of the rail vehicle can accurately reflect the characteristics of track unevenness, and at the same time, after removing the zero drift of the sensor and the trend term generated in the integration process, the calculation results of the two methods are highly consistent, which is of great significance for the state assessment of the rail line.
For rail infrastructure, its service life and operational safety are significantly affected by the rail and wheel wear on sharp curves. To figure out the inherent mechanisms behind the phenomenon and develop corresponding strategies, this study establishes a vehicle-track dynamic simulation framework for heavy-haul freight railways coupled with a 25-ton axle load using UM multibody dynamics software. Based on the Archard wear model for simulation, this paper investigates the wear on both inner and outer rails across different cant conditions to determine the correlations among the side friction coefficient, dynamic wheel-rail interactions, and wear evolution. The results illustrate that necessary maintenance interventions should be prioritized to slow down the wear process when rail wear intensifies markedly at a cumulative traffic tonnage of approximately 90 million tonnes. Moreover, an under-superelevation ranging from 10% to 20% alleviates side wear on curved tracks. Rail-specific wear patterns are observed, where side wear dominates on the outer rail and vertical wear prevails on the inner rail, with peak wear zones located in the horizontal coordinate range of [20, 36] mm and [10, 16] mm, respectively. The side friction coefficient significantly affects side wear on the outer rail in sharp curves. When the value of this coefficient rises from 0.15 to 0.35, the side wear depth increases by 179% accordingly. In contrast, the vertical wear change of the inner rail is negligible. Therefore, it is recommended to keep the side friction coefficient rather low by using friction management techniques, thereby reducing outer rail wear and extending rail service life.
To achieve the key control objectives of thrust regulation and inlet protection for a rocket-based combined cycle (RBCC) engine under wide flight envelope, high-speed operating conditions, this study is based on a nonlinear component-level RBCC engine model developed in MATLAB/Simulink. A state space model is established using a least squares system identification method. By incorporating actuator dynamics, an augmented system model is formulated, and a Differential Evolution (DE) optimized Linear Quadratic Regulator (LQR) multivariable closed-loop control strategy is proposed. Simulation results show that the proposed strategy significantly improves both response speed and disturbance-rejection capability. Compared with the conventional LQR controller, the maximum oscillation of the inlet stability margin is reduced from 11% to 4.4%, and the thrust settling time is shortened by 83%. These results verify the feasibility of the proposed control method under complex operating conditions.
This paper focuses on recursive estimation for stochastic systems under the round-robin (RR) protocol. In recursive estimation, it is assumed that the state evolution process of the signal is unknown, and the relevant estimation research is only carried out relying on the mean and covariance functions of the process. To effectively avoid the occurrence of transmission channel conflicts and ensure the fairness of data allocation during transmission, this paper chooses to adopt the RR protocol to schedule and manage data communication. Specifically, this scheduling rule stipulates that only one sensor is allowed to acquire the network communication opportunity within a fixed time period, and the entire scheduling process is executed cyclically in a preset fixed order. Based on the observable information available in the system, this paper further designs and proposes a recursive estimation algorithm by means of the innovation method. Finally, a numerical simulation experiment is given to verify the effectiveness and feasibility of the proposed estimation algorithm.
Asynchronous federated learning (AFL) is essential for low-altitude networks due to its benefits in reducing communication bandwidth, lowering transmission delays, and preserving data privacy. However, the highly dynamic mobility of drones, combined with inherent heterogeneities in AFL, including variations in data volume, data distribution, and device capability, introduces severe challenges, such as inconsistent model update frequencies, biased local training, and inefficient global aggregation. Consequently, these issues lead to significant accuracy degradation and hinder practical deployment. In this paper, we propose a novel AFL framework tailored for low-altitude networks to address these challenges, which combines a multi-dimensional clustering mechanism with a multi-weight aggregation mechanism (MDC-MWA). To mitigate local model bias induced by data heterogeneity, the multi-dimensional clustering allows the cloud server to compute a comprehensive score for each drone based on multiple dimensions, including the label distribution, the gradient direction, and the local data proportion. It then employs K-Means clustering to partition drones into highly homogeneous subgroups, each managed by a dedicated roadside unit (RSU). Within each subgroup, after performing local updates, each drone synchronously uploads parameters to its RSU. Between these subgroups, to address inconsistent update frequencies caused by imbalanced computing capabilities, RSUs perform asynchronous training via the multi-weight aggregation mechanism, which updates the global model considering staleness weight and direction consistency weight. Experimental results demonstrate that the proposed method outperforms baseline approaches in highly heterogeneous scenarios, achieving a 4.74% absolute improvement in training accuracy on the CIFAR-10 dataset. This work provides a practical framework for distributed intelligent applications in dynamic and resource-constrained low-altitude networks.
To address the complexity of the influence of expansion-section geometry on the thrust performance of solid rocket motor nozzles and the low efficiency of numerical-simulation-based optimization, this study proposes a deep-learning-based method for rapid nozzle thrust prediction. With a representative 70-lb BATES nozzle as the research object, the expansion-section contour is parameterized using cubic spline functions. Combined with Latin hypercube sampling and two-dimensional inviscid numerical simulations, a nozzle geometry– thrust dataset covering a wide range of contour characteristics is constructed. On this basis, surrogate models based on a fully connected neural network, a one-dimensional convolutional neural network, and a two-dimensional convolutional neural network are developed, and the applicability of different network architectures to the nozzle thrust prediction task is systematically compared. The results demonstrate that convolutional neural networks outperform fully connected networks in terms of geometric feature representation capability and prediction stability. In particular, by explicitly modeling the spatial structural features of the nozzle contour, the two-dimensional convolutional model achieves a coefficient of determination close to unity on the test set, with an average relative error of 0.03%. Further hyperparameter sensitivity analysis clarifies the effects of batch size, number of training epochs, and learning rate on model convergence behavior and generalization performance. This work provides a feasible technical support for rapid thrust evaluation and efficient optimization-oriented nozzle design.
In recent years, global automotive manufacturers have been steadfastly engaged in the pursuit of advanced technical solutions to address the increasingly stringent noise pollution standards mandated by national and international regulatory authorities for their domestic and export markets respectively. Aligned with this overarching objective, the Engine Design and Development team at Maruti Suzuki India Limited (MSIL) has undertaken focused efforts to reduce powertrain-induced noise and vibration (NV). Recognizing that the engine unit’s principal static subsystems, namely the intake and exhaust assemblies and critical structural components such as the cylinder head (CH) and cylinder block (CB), serve as primary sources of acoustic emissions, special emphasis was placed on their refinement. Additionally, particular attention was directed toward the engine’s rotating and reciprocating components, especially the camshaft and crankshaft assemblies, to minimize overall measured sound pressure levels.
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