The differentially weighted operator-splitting Monte Carlo (DWOSMC) method is further developed to describe the droplet aerosol dynamic behaviors, including coagulation, deposition, condensation, and evaporation processes. It is first proposed that the droplet aerosols will experience firstly condensation and then evaporation, and this phenomenon is first implemented into the Monte Carlo method and sectional method with considering coagulation, deposition, and condensation/evaporation processes in both single-component and two-component aerosol particle systems. It is found that the calculated results of the DWOSMC method agree well with both the analytical solutions and the sectional method. The further developed DWOSMC method can predict the variation of particle number density, total particle volume, mean particle diameter, particle size distributions, and the component-related particle volume densities in both single component and two-component droplet aerosol systems considering coagulation, deposition, and condensation/evaporation processes.

This paper studies the dynamic filtration behavior of coal particles in metal fiber felt, by developing a three-dimensional model based on microstructure and arrangement mode of metal fiber felt identified by scanning electron microscope. Discrete element method (DEM) is coupled with Computational fluid dynamics (CFD) to simulate filtration of coal particles in metal fiber felt. The simulation mainly studies the moving trail and deposition characteristics of spherical coal particles. The results demonstrated that coal particles can bypass the metal fibers and enter the inside of the metal fiber felt through mesh channels. The moving trail of coal particles is similar to a broken line. It is also shown that most coal particles are trapped due to the deep filtration of metal fiber felt and the distribution pattern of them is inhomogeneous. The number of particles trapped by metal fiber felt is reduced in the direction of thickness. It is found that coal particles can be trapped not only by randomly arranged metal fibers, but also by coal particles deposited previously. Finally, the kinetic energy loss of coal particles mainly occurs in the initial stage when particles pass through metal fiber felt. And the velocity of coal particles inside metal fiber felt is maintained at 0.15–0.25 m/s. Highlights

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.

In this study, a combined experimental and finite element modeling methodology (FEM) for a nanoindentation study is presented to quantitatively investigate the influence of hydrogen on the mechanical properties of ferrite and austenite in 2205 duplex stainless steel. The experimental results showed that, during hydrogen charging, the nano-hardness of ferrite and austenite gradually increased with time, showing a hydrogen-induced hardening phenomenon. After 3 h of hydrogen charging, the nano-hardness of both ferrite and austenite reached a saturation state, and the values of the nano-hardness of ferrite and austenite increased by 17.5% and 46.1%, respectively. FEM is employed by using a dual-phase microstructure-based model to reproduce nanoindentation load–displacement curves. To minimize the indentation size effect, an analytical correction model considering geometrically necessary dislocations (GNDs) was proposed. By considering GNDs, the errors between numerical predictions and experimental data reduced from about 50% to less than 5%.