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Numerical simulation of surrounding rock damage induced by different explosive casings in slotted charge blasting
Get PDFTo investigate the influence of slotted charge casings made of different materials on surrounding rock damage, single-hole and double-hole numerical models were established using finite element software. The effects of five casing materials – Cuprum (Cu), Aluminum (Al), Polyvinyl Chloride (PVC), Acrylonitrile Butadiene Styrene (ABS), and Polymethyl Methacrylate (PMMA) – on rock damage were compared, with particular attention to the evolution of stress distribution, crack propagation, and directional energy transfer in the surrounding rock. The results show that in the single-hole model, Cu casings exhibit pronounced fracture directionality and strong crack connectivity along the slotting direction, whereas in the double-hole model, the interaction between boreholes further enhances fracture penetration. PVC demonstrates stable main-fracture orientation, while PMMA casings provide moderate energy transfer and effective control of damage in both single-hole and double-hole cases. These findings offer a theoretical reference for the optimized design of slotted explosive charges and material selection, and provide technical support for achieving efficient, low-damage rock blasting in engineering applications
Research of the stress-strain state and durability of freight wagon trolley springs by the method of finite element modeling in ANSYS
Get PDFThis paper presents a study of the stress-strain state and the prediction of the cyclic durability of a set of trolley springs for freight wagons using the finite element modeling (FEM) method in the ANSYS software package. The aim of the study is to evaluate the influence of materials (steel 55Si2 and 60Si2CrVA) on the performance characteristics of springs under static and dynamic loads corresponding to loaded and empty conditions of the wagon. To achieve this goal, parametric 3D models of springs have been created, finite element models have been developed, and strength and fatigue calculations have been performed. The distributions of equivalent stresses and deformations are analyzed, fatigue durability is predicted, and safety margin coefficients are determined. The results obtained make it possible to evaluate the reliability and durability of springs made of various materials in real-world operating conditions, as well as identify critical stress concentration zones
Improving the technical and economic performance of diesel engines for shunting locomotives
Get PDFAn experimental verification of the effectiveness of applying a compromise fuel injection advance angle (FIAA) was conducted on a test stand for a PD1M type diesel generator unit. The study included an analysis of injection pressure changes at various FIAA values, as well as tests of the installation with the ESUVT.01 electronic fuel injection control system under load. Additionally, modeling of the diesel engine’s working process was performed using the “Diesel-RK” software package, followed by processing of the obtained data. Comparison of test results in locomotive characteristic modes at fuel injection advance angles of 14° and 29° crankshaft rotation showed that using a compromise FIAA value ensures a reduction in the locomotive’s average operational fuel consumption by 7-10 %, depending on operating conditions. Furthermore, decreasing the advance angle positively affects the reduction of maximum cylinder pressure and exhaust gas temperature, indicating an increase in the overall effectiveness of this approach
Dynamic performance analysis of 1000 MW double reheat steam turbine foundation
Get PDFIn recent years, power equipment has been developing towards low-carbon, high-efficiency, and green environmental protection. The double reheat unit has been increasingly employed in power plants due to its advantages of low energy consumption and less pollution. As a core component of power plants, the dynamic performance analysis of the steam turbine foundation is essential for ensuring the overall safety of double reheat unit. For this reason, the dynamic performance of a steam turbine foundation is investigated based on the engineering background of frame-type reinforced concrete foundations of 1000 MW double reheat steam turbine set in a power plant. The solid finite element model of the steam turbine foundation is first established by using ANSYS software, along with a detailed description of foundation information and modelling methodology. Subsequently, the dynamic characteristic and response analyses of the steam turbine foundation are performed to evaluate its dynamic performance, respectively. The results indicate that the 1000 MW steam turbine foundation demonstrates satisfactory dynamic performance. Within the operating speed range, the transverse, longitudinal, and vertical vibration displacements of the foundation bearings and columns remain below 20 μm, while the vibration velocity does not exceed 3.8 mm/s, both of which comply with relevant specifications. Moreover, enhancing the stiffness of the fifth and sixth beams, along with increasing the cross-sectional area of columns C3 and C4 on the steam turbine foundation, should be considered to mitigate its vibration responses and thus improve its dynamic performance. The research findings can serve as a reference for the type selection and optimization design of 1000 MW double reheat steam turbine foundations
PSO-PPO-based reinforcement learning control strategy for active suspension systems under multiple operating conditions
Get PDFTo address the poor generalization capability and extended training duration of reinforcement learning (RL)-based active suspension control systems, this study proposes a PSO-PPO algorithm for multiple operating condition suspension control. The methodology initiates with establishing a 4-DOF suspension dynamic model under three characteristic driving conditions: constant-speed operation, vehicle launch, and emergency braking, which is subsequently converted into state-space representation. The novel PSO-PPO framework synergizes particle swarm optimization with proximal policy optimization to train condition-specific agents. Based on the trained optimal agents, the entropy weight method is applied to adjust the reward function weight coefficients to develop a generalized multi-condition controller. Finally, the control effectiveness of the PSO-PPO algorithm is validated through constant-speed, launch, emergency braking, and multi-condition concatenated scenarios. Simulation results demonstrate that the PSO-PPO algorithm achieves shorter training times while maintaining balanced performance in ride comfort, handling stability, and safety across all conditions
Experimental results of reducing harmful vibrodynamic effects caused by the interaction between rolling stock and track through the use of elastic under-sleeper pads in the rail joint zone
Get PDFIn the current era of independent development and market relations, the importance of railways continues to grow steadily. This, in turn, places great responsibility on the system of measures aimed at ensuring railway reliability. However, despite the advantages and advancements of the railway industry, it still faces technical complexities that can lead to track deterioration. In heavily loaded and high-speed railway sections, the interaction between the rolling stock and the track causes various issues in the rail joint zones – such as the development of defects and irregularities, deterioration of track geometry, reduction of track stability, as well as problems related to noise and vibration that must be mitigated. To address these challenges, scientific studies and experimental investigations have been conducted on the installation of elastic under-sleeper pads in the rail joint zones. These studies aim to modify the vertical stiffness transferred from the wheelsets of the rolling stock to the track structure, reduce harmful vibrations and oscillations, and thereby ensure uniform stability along the entire track. The conducted research, testing, and their results are presented in this article
Analysis of the structural performance of reinforced concrete under fire loading
Get PDFThis study examined the behavior of reinforced concrete structures when exposed to high temperatures resulting from fire. Deterioration in material strength due to fire exposure alters a reinforced concrete structure’s load-bearing capacity and overall behavior. Elevated temperatures negatively affect key material properties of reinforced concrete, including density, coefficient of thermal expansion, thermal conductivity, and elastic modulus. As a result, if a structure experiences fire either concurrently with or prior to an earthquake, these changes in material properties will significantly influence its dynamic performance. For the numerical simulation, the selected structure was designed with a formwork plan and load-bearing system in accordance with earthquake-resistant design principles. Based on this design, fixed and variable loads acting on the beams were assigned. By promoting resilient infrastructure capable of withstanding severe environmental conditions such as earthquakes and fires, this study contributes to the achievement of sustainable development goals. It underscores the necessity of integrating fire resistance into earthquake-resistant design to foster disaster-resilient urban development. The findings may encourage more flexible and sustainable construction practices aligned with SDGs 9 (Industry, Innovation and Infrastructure), 11 (Sustainable Cities and Communities), and 13 (Climate Action)
Fatigue performance analysis and reinforcement measures for foundation connection components of wind turbine towers
Get PDFIn recent years, frequent tower collapses have been mostly related to fatigue damage. Therefore, this paper systematically studies the fatigue resistance performance and reinforcement methods of tower foundation connection components through on-site tests and finite element analysis. The test analyzed the lifespan, stress-strain characteristics, crack development and mechanical properties of the connection components under fatigue loads; numerical simulation compared the fatigue life and safety of ordinary components, reinforced with steel mesh, C100 high-strength concrete components, and C40 and C100 composite components, etc., providing key basis for engineering reinforcement
Complex fault diagnosis in wind turbine bearings: a hybrid approach combining the improved feature mode decomposition and convolutional neural networks
Get PDFThe complex noise interference and diverse fault-induced signals in vibration data from wind turbine equipment pose significant challenges for bearing fault diagnosis, including cumbersome methodologies, prolonged processing times, and compromised accuracy. To address these limitations, this study proposes a novel composite fault diagnosis framework that integrates Feature Mode Decomposition (FMD), Fast Spectral Kurtosis (FSK), and Convolutional Neural Network (CNN). While conventional Empirical Mode Decomposition (EMD) exhibits limited noise robustness and struggles to extract subtle fault signatures in composite failure scenarios, our approach employs FMD to decompose fault-related intrinsic mode functions (IMFs)and further filters the IMF components using fast spectral cliffs with enhanced feature separability. Subsequently, the Short-Time Fourier Transform (STFT) is applied to derive time-frequency representations, followed by Fast Spectral Kurtosis analysis to identify optimal demodulation bands for non-stationary signals. The energy spectrum of denoised signals is converted into grayscale images, serving as input to a tailored CNN architecture for hierarchical feature learning. Experimental validation demonstrates that this hybrid methodology achieves a fault recognition accuracy of 98 % under compound fault conditions, outperforming conventional EMD-based approaches in terms of noise immunity and diagnostic precision. Comparative analysis reveals an 8 % improvement in detection reliability over standalone deep learning models, particularly in low signal-to-noise ratio (SNR) environments. The proposed framework offers a robust solution for multi-fault identification in industrial Bearing machinery, demonstrating superior generalization capability across varying operational conditions
Fault diagnosis method for wind turbine rolling bearings based on adaptive deep learning
Get PDFIn response to the problem of difficulty in extracting fault features of rolling bearings in wind turbine transmission systems under complex working conditions, which limits the accuracy of fault diagnosis. This article proposes an Adaptive Deep Learning based Rolling Bearing Fault Diagnosis Method (ADLM). Introducing dynamic convolution into Convolutional Neural Networks (CNNs) can adaptively capture data features; At the same time, the fishing optimization algorithm (CFOA) was used to optimize the hyperparameters of the bidirectional long short-term memory network (BiLSTM), and the CFOA-BiLSTM network was constructed to fully leverage its advantages in time series analysis. The specific implementation steps are as follows: first, preprocess the collected vibration signals and divide the processed dataset into a training set and a testing set; Then, parallel adaptive convolutional neural networks (ACNN) are used to process the training set and extract spatial domain local features from the vibration signal; Then, the features extracted from the two branches are weighted and fused through a dynamic weight adjustment mechanism, and the fused features are input into the CFOA-BiLSTM network to further capture the time-dependent features of the signal; Finally, the extracted features are input into the classifier to complete model training, and the model performance is evaluated using a test set. Experimental verification shows that on the dataset of Southeast University, the diagnostic accuracy of the ADLM model reached 98.52 %, demonstrating good reliability, robustness, and superiority in the diagnosis of rolling bearing faults