In this paper, the collapse behavior of a single cavitation bubble at different distances near a solid particle of typical scales is numerically simulated and researched with the volume of fluid (VOF) method. Based on the key parameters analysis of the pressure field, velocity vector, collapse time the tendency of cavitation bubble collapse characteristics at different distances during the change of particle size is studied with the variable of the distance and relative size between the particle and the cavitation bubble. The dimensionless distance parameter ‘γ’ is specifically presented in the simulation process, the cavitation bubble collapse impact is largely directed to the particle when 3>γ > 2, while the wall hardly affects the interaction between the cavitation bubble and the solid particle as γ > 3. The results illustrate that as the solid particle and wall exist, the distance and particle size affect both the peak collapse pressure and the collapse jet velocity of the cavitation bubble, and the influence of solid wall on the cavitation bubble at the same distance is much greater than that of solid particles. When the particle size increases, the particle gradually affects the cavitation bubble in a way similar to the wall. While as the distance decreases or the particle size increases, the influence of particle and wall on the evolution process of the cavitation bubble expands, meanwhile, the collapse pressure and collapse jet velocity of the cavitation bubble are promoted with the optimized distance and particle size, which brings marvelous cavitation effect. The numerical methods and conclusions of this paper provide a valuable reference for cavitation applications of sand-containing fluids.

As a key component of a mechanical system, the extraction and accurate identification of rolling bearing fault feature information are of great importance to guarantee the normal operation of the mechanical system. Aiming at that the extraction of rolling bearing fault features and traditional support vector machine parameters affects the overall accuracy of pattern classification, the improved CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) time‐domain energy entropy‐based model for fault pattern recognition is proposed. The ICEEMDAN method is developed to decompose the signal to obtain the IMF component series. Then, the particular IMF components are selected according to the trend of correlation coefficient and variance contribution rate; meanwhile, the information entropy (power spectral entropy, singular spectral entropy, and time‐domain energy entropy) of the screened IMF components is calculated to construct the feature vector sets, respectively. Finally, the feature vector sets are input into the PSO‐SVM (particle swarm optimization‐support vector machine) based model for training and pattern recognition. The effectiveness of the proposed method of the improved CEEMDAN time‐domain energy entropy and PSO‐SVM model is testified through numerical simulation and experiments on rolling bearing datasets. The comparison proceeded with the other mainstream intelligent recognition techniques indicates the superiority of the method with the diagnostic accuracy of 100% as for the final validation.

As the crucial part of a transmission assembly, the monitoring of the status of the crankshaft is essential for the normal working of a reciprocating machinery system. In consideration of the interaction between crankshaft system components, the fault vibration feature is typically non-stationary and nonlinear, and the single-scale feature extraction method cannot adequately assess the fault features, therefore a novel impact feature extraction method based on genetic algorithms to optimize multi-scale permutation entropy is proposed. Compared with other traditional feature extraction methods, the proposed method illustrates good robustness and high adaptability in the signal processing of crankshaft vibrations. Firstly, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method is developed on the signal to obtain several intrinsic mode function (IMF) components, and the IMF components with a large kurtosis are selected for array reorganization. Then, the parameters of multi-scale permutation entropy (MPE) are optimized based on genetic algorithm (GA), the multi-scale permutation entropy is calculated and the feature vector set is constructed. The feature vector set is input into the support vector machine (SVM) and optimized by a particle swarm optimization (PSO) model for training and final pattern recognition, where the Variational Mode Decomposition(VMD)-GA-MPE with a PSO-SVM recognition model and the ICEEMDAN-MPE with PSO-SVM recognition model without GA optimization are constructed for a comparison with the proposed method. The research result illustrates that the proposed method, which inputs the genetic algorithm optimized multi-scale permutation entropy extracted from the ICEEMDAN decomposition into the PSO-SVM, performs well in impact feature extraction and the pattern recognition of crankshaft vibrations.

The method of cavitation is increasingly applied in water environmental protection. Based on the numerical simulation method, a study on the structural parameters of the shear-type hydrodynamic cavitation generators for wastewater treatment is proceeded. The internal flow field is described by employing the mixed multiphase flow model and the Zwart cavitation model. Experiments were conducted by applying the wastewater from a dyeing factory as the medium. The degradation rate of COD in water characterizes the cavitation performance of the hydrodynamic cavitation generator, and the rationality of the numerical simulation approach is validated. The findings indicate that different structural parameters have a great influence on the cavitation performance. The appropriate number of tooth rows creates a flow field with a greater vapor and velocity than the other parameters. The number of teeth in a single row, the outer diameter of the hydrodynamic cavitation generator and the tooth bevel angle also affect the cavitation performance to some extent, and there is an optimal value. The study provides a reference for the applicability of a numerical simulation of the flow field inside the hydrodynamic cavitation generator and the structural optimization of the rotary hydrodynamic cavitation generator.

As for the fault diagnosis process of a reciprocating compressor, vibration signals are often non-stationary, nonlinear, and multi-coupled, which makes it difficult to conduct effective fault information extraction. In this paper, a method based on optimized resonance-based sparse signal decomposition (RSSD) and refined composite multiscale dispersion entropy (RCMDE) is proposed. The quality factors in RSSD are optimized by atom search optimization (ASO) primarily, then the optimal quality factors are applied to the RSSD of reciprocating compressor fault signals. The noise interference in the original vibration signal can be effectively distinguished from the low resonance component after decomposition. The genetic algorithm (GA) is employed to optimize the core parameters of RCMDE. Finally, the RCMDE of the low-resonance component is extracted as the eigenvalue for pattern recognition. The experimental study illustrates that the spring failure, valve wear, and normal valve conditions of reciprocating compressors can be effectively distinguished by the proposed method.

As a key component of mechanical system, the extraction and accurate identification of fault characteristic information of rolling bearing is very important to ensure its normal operation. The diagnosis accuracy is occasionally low due to the limitation of information collected by a single type of data source. In this paper, the bearing vibration signal and acoustic emission signal are employed as analysis sources, a novel method based on ICCEMDAN (improved complete ensemble empirical mode decomposition with adaptive noise) with optimized SVM (support vector machine) is presented for the fault information fusion, feature extraction, and fault pattern recognition of rolling bearing. Firstly, ICEEMDAN algorithm is developed to decompose the rolling bearing vibration signal and acoustic emission signal for a series of IMF (intrinsic mode function) components. Secondly, the valuable components that can characterize the original signal status are selected based on the correlation coefficient-variance contribution criterion. Thirdly, the singular spectral entropy of the reconstructed component is calculated as the eigenvalue and the two signal eigenvectors are fused as a new eigenvector set. Finally, the feature vector set is input into the optimized SVM classifier model based on PSO optimization for training and pattern recognition, in which the accuracy and efficiency of the classifier model and SVM classifier model are compared. Study of model simulation and fault simulation experiments show that the presented model based on the singular value entropy fusion of ICEEMDAN and PSO-SVM can effectively extract the fault characteristics of rolling bearing signals and has a desired performance in the accurate pattern recognition.