Material Science, Engineering and Applications
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Analysis and synthesis of a controllable crank-slider mechanism with parallel springs for frame saws
Full text linkFrame saws suffer from large unbalanced inertia forces, limiting operating speed and requiring heavy construction. This study aims to overcome these limitations by synthesizing a dynamically balanced main drive mechanism using a novel approach based on prescribed motion laws. The methodology involves proposing a crank-slider mechanism featuring a cam-actuated variable-length crank. The mechanism configuration with parallel spring is analyzed allowing for balancing inertia forces, achieved using a prescribed cosine slider motion law. For the considered configuration, the required variable crank length function (cam profile) and associated mechanism parameters (connecting rod length, spring stiffness) are analytically synthesized. The results of the carried-out numerical modeling demonstrate successful synthesis of a near-circular cam profile and very low pressure angles for the case studied. These findings show that synthesizing the saw drive kinematics based on force balancing requirements can theoretically eliminate inertial loads, offering the potential for higher speeds of saw frames and reduced loads. The synthesized near-circular cam profile suggests a pathway towards simpler manufacturing. The implications of successfully implementing such dynamically balanced frame saw mechanisms are potentially transformative for the sawmilling industry. Eliminating the primary inertial forces removes the major obstacle to increasing operating speeds. This could allow frame saws to operate closer to the optimal cutting speeds for wood (e.g., 40-50 m/s), leading to significant gains in productivity
Dynamic performance analysis of 1000 MW double reheat steam turbine foundation
Full text linkIn 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
Effect of Si addition on phase structure and wear resistance of CoCrFeMoNi alloy coatings
Full text linkCoCrFeMoNi high entropy alloy coating was prepared on Q235 substrate by plasma cladding method. The phase structure, morphology characteristics, element distribution, microhardness, and wear resistance for this alloy without and with Si doping were investigated by XRD, OM, SEM, EDS, microhardness tester, and friction-wear tester, respectively. The results show that CoCrFeMoNi alloy is composed of a single FCC phase, while Si-containing alloy is composed of FCC main phase and HCP phase. Both alloys have a typical dendritic structure. There is a layer of isotropic fine-grained region near the fusion line, and a columnar crystal region away from the fusion line. After adding Si element, the enrichment of Mo element in the interdendrite region and Co element in the dendrite region significantly decreased, which is related to the Si-containing alloy can provide a liquid environment with longer duration, lower viscosity, and greater fluidity. The change of Cr element enrichment from interdendrite region to dendrite region is the result of comprehensive competition of mixing enthalpy, atomic radius difference, electronegativity, density, and melt flowability between alloying elements. The friction coefficients of the two alloys show a rapid increase first and then gradually stabilize with the increase of time. After adding Si element, the hardness and wear resistance of the alloy are greatly improved, which is mainly related to the increase of the lattice distortion of FCC phase, the formation of high-strength HCP phase and the reduction of internal defects
Small sample fault diagnosis method based on dual convolutional kernel feature fusion and channel attention weighted temporal convolutional network (DCK-CAM-TCN)
Full text linkIn actual industrial environments, equipment failures often occur sporadically during operation, resulting in insufficient labeled data for training. To address the issues of difficult feature extraction and poor generalization caused by insufficient data in small-sample fault diagnosis, a small sample fault diagnosis method based on dual convolutional kernel feature fusion and channel attention weighted temporal convolutional network (DCK-CAM-TCN) is proposed. Firstly, dual convolution kernels are employed to extract signal features, with the large kernel capturing low-frequency components and the small kernel extracting additional features to enhance the network's expressiveness. Secondly, the channel attention mechanism adaptively adjusts the feature responses of each channel, enabling the network to focus on the most informative and relevant features while suppressing unimportant ones. Finally, the Temporal Convolutional Network (TCN) is utilized to capture dependency features within long time series, further improving the model's ability to process sequential data. Experimental results demonstrate that the DCK-CAM-TCN model significantly outperforms traditional Convolutional Neural Networks (CNNs) and other comparison models in small-sample scenarios. The results indicate the significant advantages of the DCK-CAM-TCN model in small-sample fault diagnosis
Enhancing loess deformation resistance using waste tire rubber particles
Full text linkLoess, characterized by its large pore structure and vertical joints, is prone to collapsible deformation upon moisture infiltration and significant settlement under load, threatening the stability of buildings and infrastructure. This study systematically investigates the effects of rubber particle size (10, 20, 40, and 100 mesh), content (0 %, 5 %, 10 %, 15 %, and 20 % by volume), moisture content, and freeze-thaw cycles on the deformation properties of loess. This systematic investigation distinguishes itself by using waste tire rubber particles as the sole amendment to elucidate both the individual and coupled effects of these factors. Results demonstrate that incorporating rubber particles significantly reduces the compression coefficient of loess, with optimal compressibility achieved at a 5 % rubber particle content and 40 mesh particle size. The collapsibility coefficient is minimized at a 20 mesh particle size with the same 5 % content. Moisture content significantly influences deformation behavior, with both high and low levels increasing the compression and collapsibility coefficients. The study also reveals that rubber particle-loess mixtures exhibit superior freeze-thaw resistance, with smaller increases in deformation coefficients after multiple freeze-thaw cycles compared to remolded loess. The particle size and content of rubber particles are identified as the most important factors influencing the compressibility and collapsibility of loess. This research provides specific guidelines for optimizing rubber particle size and content, controlling moisture levels, and evaluating freeze-thaw impacts to enhance the engineering performance of loess. The findings offer a scientific basis for sustainable waste tire management and advance the application of rubber particles in geotechnical engineering
Application of unsupervised identification of dissolved gases in transformer oil based on spin coating film making process
Full text linkAddressing the issues of low efficiency and uneven collection of dissolved gases in transformer oil leading to overfitting and poor performance of identification models, we propose a novel film-making process that integrates Gaussian process and unsupervised pre-classification to enhance the recognition efficiency of dissolved gases in transformer oil. This method not only forms a thinner and more uniform separation layer, significantly improving degassing performance and collection efficiency, but also addresses the problems of insufficient data labeling and sample imbalance by introducing the K-means++ clustering algorithm and pseudo-random integration technology, thereby enhancing model robustness and generalization ability. Moreover, the designed Gaussian Process Multi-Classification (GPMC) method employs probabilistic interpretation for result presentation, which increases the accuracy of fault identification. Experimental results show that under consistent starting conditions, the RCC and ARI indicators of our pre-classification method are close to 0.8, with the test set’s recognition rate exceeding 80 %, while the GPMC method misclassified only 2.4 % of the cases in the 1800-case dataset. These improvements make our method particularly effective for handling uncertainties and imbalances in dissolved gas cases in transformer oil, showcasing its potential for practical applications
Use of fragility curves to assess the seismic vulnerability of soft rock tunnels: a review
Full text linkDue to their distinct geotechnical and structural features, soft rock tunnels pose serious issues because of their seismic sensitivity. These tunnels, often constructed in formations with lower shear strength and higher deformability, are particularly susceptible to damage during earthquakes. Fragility curves, which graphically represent the probability that a structure may sustain damage up to or beyond a particular threshold as a function of seismic intensity, are essential tools for evaluating the seismic resilience of these infrastructures. This research looks closely at the use of fragility curves to assess the seismic vulnerability of soft rock tunnels. Exploring the fundamental concepts and methodologies involved in constructing fragility curves, including seismic hazard analysis, structural modeling, damage state definition, data collection and statistical analysis is looked at first. The review highlighted the integration of soft rock characteristics such as strength and deformation properties into the fragility assessment process. Key developments in the topic are covered such as how machine learning and Bayesian inference might improve the precision and usefulness of fragility curves. The paper identified key findings such as the high sensitivity of fragility curves to geotechnical properties and seismic intensity levels and emphasized the importance of accurate data collection and model calibration. Important gaps in seismic risk evaluations are filled by integrating cutting-edge methodologies, such as Bayesian inference and real-time machine learning models that clarify the seismic behaviour of soft rock tunnels in the real world. For the purpose of strengthening earthquake-resistant infrastructure in earthquake-prone areas, engineers, scholars and policymakers are given practical insights
MIMO radar river flow measurement based on space-velocity-time algorithm and adaptive correction model
Full text linkAccurate measurement of river hydrological characteristics is critical for assessing the impacts of flooding caused by meteorological and geomorphological factors. Flow velocity are key indicators in hydrological monitoring. Traditional measurement approaches, such as continuous-wave Doppler radar and pulsed radar systems, are typically mounted on bridges or fixed supports and offer only single-point measurements. These methods often suffer from limited detection range, low accuracy, and poor resistance to environmental interference. To address these limitations, this study proposes a three-dimensional flow detection framework based on multi-input multi-output (MIMO) radar sensors. By leveraging the high reliability and interference resistance of MIMO radar, along with a Space-Velocity-Time (SVT) algorithm that incorporates spatiotemporal information (two-dimensional surface velocity and time), the proposed method enables robust 3D river flow monitoring. In this study, comparative experiments were conducted on four rivers in China with different flow conditions, geomorphic features and weather environments. Results demonstrate that the proposed method achieves a measurement error of less than 5 % compared to acoustic Doppler current profilers (ADCP) and other conventional mechanical approaches, while also offering improved safety and real-time performance. Moreover, an adaptive flow correction algorithm is presented, which uses three optimized prediction models to compute the correction factor and reduces the mean streamflow measurement error to 0.79 % after correction, providing an effective solution for river gauging, flood control and flood resilience
The effect of the quantity and length of fibers on the mechanical properties of fiber-reinforced concrete based on polypropylene fibers
Full text linkIn these studies, the effect of polypropylene fibers on the mechanical properties of concrete was studied, and special attention was paid to determining their optimal amount and acceptable length. The fibers were added to the concrete composition in amounts of 0.1-0.5 % and lengths of 10, 20, 30, 40, 50 mm and tested. According to the results of the study, the highest results were recorded at a fiber content of 0.2-0.3 % and lengths of 20-30 mm, and the compressive strength of concrete increased by up to 15.9 % compared to ordinary concrete. When adding fibers in excess (≥ 0.4 %) or with a length of 50 mm, a decrease in strength was observed. The results obtained showed that it is possible to increase the quality and improve the strength of concrete by selecting the optimal parameters of polypropylene fibers
Research on bearing equipment fault diagnoses via SAWOA-LSTM
Full text linkTo address the current low fault diagnosis accuracy problem for bearing equipment, and improve the detection methods, in this paper a sine-adapted whale optimization algorithm (SAWOA)-based optimization of a long short-term memory (LSTM) network is proposed as the equipment fault diagnosis method (SAWOA-LSTM). First, an optimization strategy based on sinusoidal population initialization and adaptive optimization is proposed for the whale optimization algorithm, which has the two drawbacks of slow convergence and easily falling into a local optimum. Second, to improve the accuracy and efficiency of fault diagnoses, the SAWOA is used to optimize the number of hidden units and the learning rate parameter of the LSTM. Compared with ACO-, PSO-, and WOA-based LSTM models, the proposed method improves diagnostic accuracy by 14.17 %, 15.03 %, and 4.32 %, respectively. In tests on 50 bearing samples, SAWOA-LSTM further improves accuracy for RBD, IRA, and ORD by 1.08 %, 1.62 %, and 1.10 %, respectively. Our algorithm provides an innovative solution for the health management of complex industrial bearing equipment