Recent Patents on Mechanical Engineering - Volume 16, Issue 3, 2023

Background: The vortex pump is a type of sewage pump renowned for its non-clogging performance. As the vortex pump has a special structure type, there are many vortex structures in the volute and impeller flow channel, which reduce the efficiency of the vortex pump. Reducing the energy loss and improving the efficiency of the vortex pump has been one of the main research objectives of designers. In this paper, the research progress of vortex pumps is summarized from the two aspects of transporting solid medium and low efficiency, which can provide a reference for future research. Methods: The latest patents and papers on vortex pumps were collected. The solid-liquid flow characteristics from the experimental and numerical perspectives, the influence of geometric parameters on external characteristics, and optimization design methods of the vortex pump were studied. Results: The particles, fibers, and cloth in the vortex pump will become trapped and blocked in the cavity. And the geometric parameters have an obvious effect on the pump. By using the intelligent optimization algorithm to optimize the impeller parameters, the pump efficiency can be increased by 10.25% under large flow conditions and the effective blade shear stress. Conclusion: The concentration and diameter of particles could change the performance of the pump. The retention and plugging of the solid medium in the vortex pump are related to flow structure and backflow. Appropriate geometric parameters should be selected when designing a vortex pump. Too large or too small a structure design will lead to poor performance of the vortex pump. This can be combined with intelligent optimization algorithms for pump design, which is a very effective method.

Background: Obstacle avoidance path planning is an important parameter of robot manipulators. Path planning can directly affect the working efficiency of the manipulator. Objective: This study aims to summarize the optimization design method from a large number of literature and propose a new optimization design method to make the planned obstacle avoidance path of the manipulator shorter and smoother. Methods: The forward and inverse kinematics of the redundant manipulator is solved. Secondly, the obstacle and the robot manipulator envelope are simplified for collision detection. Then, the ant colony algorithm is improved by adding an obstacle environment to the construction of the heuristic function and dynamically adjusting the heuristic function factor to make the shortest path distance planned by the algorithm. Finally, the worst ant colony is added to the pheromone update to avoid the algorithm falling into the local optimal solution. Results: Through experiments and comparative studies, the optimized design process shows that the path planned by the improved ant colony algorithm has obvious advantages of shorter path distance and smoother path distance, which verifies the rationality of the improved algorithm. Conclusion: This method optimizes the convergence speed of the ant colony algorithm and avoids the ant colony algorithm from falling into the local optimal solution, which is of great significance for improving the obstacle avoidance path planning problem of a redundant manipulator with a degree of freedom.

Background: Catenary is a crucial component of an electrified railroad's traction power supply system. There is a considerable incidence of abnormal status and failures due to prolonged outside exposure. Driving safety will be directly impacted if an abnormal status or failure occurs. Currently, catenary detection vehicles are the most often utilized technique for gathering data and identifying faults based on manual experience. However, this technology cannot meet the demands of prompt detection and correction of faults in railways engineering due to its extremely low work efficiency. Objective: Based on the above, an abnormal status detection method of catenary based on the improved gray wolf (IGWO) algorithm optimized the least squares support vector machine (LSSVM) with the t-distributed stochastic neighbor embedding (TSNE) is proposed in this paper. In order to improve the accuracy of catenary abnormal status detection and shorten the detection time. Methods: Firstly, the TSNE dimensionality reduction technology is used to reduce the original catenary data to three-dimensional space. Then, in order to address the issue that the parameters of the LSSVM detection model are hard to determine, the improved GWO algorithm is used to optimize the penalty factor and kernel parameter in the LSSVM and establish the TSNE-IGWO-LSSVM catenary abnormal status detection model. Finally, contrasting experimental results of different detection models. The T-distributed Stochastic Domain Embedding (TSNE) is an improved nonlinear dimensionality reduction method based on the Stochastic Neighbor Embedding (SNE). TSNE no longer adopts the distance invariance in linear dimensionality reduction methods such as ISOMAP. TSNE is much better than the linear dimensionality reduction method in the reduction degree of the original dimension. The GWO algorithm, which is frequently used in engineering research, has the advantages of a simple model, great generalization capability, and good optimization performance. The premature convergence is one of the remaining flaws. By applying a good point set to initialize the gray wolf population and the nonlinear control parameters, the gray wolf algorithm is improved in this research. The IGWO algorithm effectively makes up for the problem of balancing the local exploitation and global search capabilities of GWO. Additionally, this IGWO algorithm performs the Cauchy variation operation on the current generation optimal solution to improve population diversity, enlarge the search space, and increase the likelihood of the algorithm escaping the local optimal solution in order to prevent the algorithm from failing the local optimum. The Least Squares Support Vector Machine (LSSVM) is an improved version of the Support Vector Machine (SVM), which replaces the original inequality constraint with a linear least squares criterion for the loss function. The kernel parameters of the RBF function and the penalty factor, these two parameters directly determine the detection effect of LSSVM. In this paper, the IGWO is utilized to adjust and determine the LSSVM parameters in order to enhance the detection capacity of the LSSVM model. Results: In this paper, in order to minimize the experiment's bias, the training data and the test data are allocated in a ratio of 4:1, the training data are set to 400 groups, and the test data are set to 100 groups. After training the five models, the test data is used to validate and compare the detection capacity of the models. After each of the five detection models was tested ten times, the TSNE-IGWO-LSSVM model is compared with the IGWO-LSSVM model, the TSNE-FA-LSSVM model, the GWO-LSSVM model, and the GWO-ELM model, the results show that the TSNE-IGWO-LSSVM model has the highest average detection accuracy of 97.1% and the shortest running time of 26.9s. For the root mean squared error (RMSE) and the root mean squared error (RMSE), the TSNE-IGWO-LSSVM model is 0.17320 and 2.51% respectively, which is the best among the five models, indicating that it not only has higher detection accuracy but also better convergence of detection accuracy than the other models. Conclusions: With the thousands of miles of catenary and the complexity of the data, it is crucial to shorten the running time in order to improve the efficiency and ease the burden of the processors. The experiments demonstrate that the TSNE-IGWO-LSSVM detection model can detect the abnormal status of catenary more accurately and quickly, providing a new method for the abnormal status detection of catenary, which has certain application value and engineering significance in the era of fully electrified railways.

Introduction: The current digital twin systems usually have the drawback of high cost and complex technology, and it is necessary to develop a simple solution to reduce the cost and cycle for the development of digital twin systems, especially for small projects or systems with simple structures. Objective: A low-cost patent technology of digital twin system was proposed by taking the motion control and state monitoring system (MCSMS) of pneumatic muscle as an example. Methods: The MCSMS is developed based on the browser/server architecture. The software of 3ds Max and SolidWorks are used to make the virtual model, Three.js and JavaScript are applied to build the browser side. Data of the physical world is collected and processed on the server side firstly, and then is sent to the browser side through HTTP communication protocol to realize data interchange between the browser and server. Results: In the roaming experiment and the experiment of motion control and state monitoring of pneumatic muscle, the MCSMS can work smoothly without obvious delay and has good real-time performance, which can realize the 3D visual monitoring of the pneumatic muscle very well. Conclusion: The experimental results indicate that the proposed method possesses the ability of good feasibility and effectiveness.

Background: The traction power supply system (TPSS) of railway mainly focuses on power quality analysis. In the study of harmonic and negative order currents, about 80% of the literature analysis are not specific enough there is a lack of completeness in the simulation system. Objective: Analyze the influence of harmonic and negative current sequences of TPSS on the system circuit, and realize intelligent recognition for different working conditions. Methods: The converter is designed based on the transient direct current control technology and the harmonic model of grid-side regenerative braking is established. According to the parameters of CRH2 (CRH380AL) locomotive, the EMU model is built and run in the TPSS for joint simulation. The availability of the model is verified by combining the harmonic content and voltage level. Then, the distribution of negative sequence current under the no-load, traction and regenerative braking conditions of the system is analyzed in detail, and the negative sequence characteristic waveform under various conditions is obtained, so as to obtain the variation law of negative sequence current under different conditions. Results: Under the regenerative braking condition, the current harmonic distortion is much higher than that under the traction condition. From the analysis of voltage and current phase, the power factor of regenerative braking is also small. In the negative sequence analysis, the tip negative sequence current impact phenomenon occurs mostly during the traction operation of the train, while the current impact effect is weakened during regenerative braking, but the amplitude of the negative sequence fluctuation shows an increasing trend. Conclusion: The energy generated by regenerative braking will be utilized by the locomotive under traction, and these bad electric energy forms will have extremely adverse effects on the process of high-speed train receiving and changing current. These negative sequence analysis results can be used to identify and classify different working conditions and divide and conquer energy compensation actions to achieve energy saving and consumption reduction.

Background: The stacker-reclaimer is a device for transporting bulk materials in ironmaking raw material yards. An excellent scheduling plan can provide a good raw material supply basis for steel enterprises. It is of great significance to improve the efficiency of steel production, reduce unnecessary operating waste and management costs, and realize scientific management of steel production. Objective: This patent aims to optimize the total material transportation time and equipment utilization balance within a single operation plan of the stacker-reclaimer involved in the raw material yard. Methods: A multi-objective optimization model for the stacker reclaimer is established, and the Reverse learning and Population Competitive-NSGA II (RPC-NSGA II) algorithm is introduced for solving. This algorithm uses reverse learning and population competition mechanism to improve the convergence and diversity of the algorithm. Results: The proposed method was experimentally verified in a raw material yard with a 360m² sintering machine and a bulk material port. The method converges well and obtains a Pareto front with a uniform distribution. Compared with the actual scheduling plan, the scheduling plan under the optimal compromise solution reduces the maximum completion time by 11.23 minutes and increases the equipment utilization balance rate by 11.70%. Conclusion: The proposed method can consider the material transportation time and equipment utilization balance, which is of great significance for the optimized use of the stacker reclaimer in steel enterprises and the quality assurance of raw material supply.