Electrostatic demulsification is a widely used technique for treating high-water-cut emulsions (O/W type). However, in high-water-cut emulsions, continuous electric fields often lead to short-circuiting and chain formation. Meanwhile, pure shear fields rely on random collisions, which is inefficient for fine droplets. To address these limitations, this article established a lattice Boltzmann model coupled with electric and shear field. It explored the impact of the combined action of bidirectional pulsed electric field (BPEF) and shear field on the demulsification process. Simulation results reveal that: Under the action of pure shear field, increasing the shear effect will lead oil droplets to move around the flow field center, and forming a stable state eventually. However, after applying bidirectional pulsed electric field on both sides of the flow field, the electric field force breaks this stable state and promotes further aggregation of oil droplets. Analysis of streamline diagrams indicates that: Bidirectional pulsed electric field periodically changes the direction of the electric field force. It leads to induce an unbalanced velocity distribution in the flow field. And this phenomenon can accelerate or decelerate the aggregation of oil droplets. Quantitative analysis indicates that the synergistic coupling of BPEF and shear field significantly outperforms single-field methods. Under the optimal electric potential of V = 300 and shear rate of γ=0.001, the composite morpho-dynamic index (Dr) reached 11.5. This represents a 4.7-fold increase compared to the pure shear field (Dr≈2.0) and a 109% improvement compared to the low-voltage condition (V = 100), indicating a highly aggregated and moderately deformed stable state. These simulation results are significant important for understand the synergistic demulsification effects of electric and shear fields.

This paper establishes a numerical simulation model for the droplet coalescence process in two-dimensional porous media based on the Lattice Boltzmann Method (LBM). The Peng-Robinson equation of state is employed to describe the thermodynamic properties of non-ideal fluids. By introducing external force terms such as fluid-fluid interaction forces, solid-liquid adhesion forces, and gravity, a complete set of multiphase flow governing equations is constructed. 1. Numerical simulations demonstrate the complete evolution process of droplets within porous media, transitioning from an initial dispersed state to a quasi-steady aggregated state. Qualitative analysis reveals that the complex pore topology exerts a significant "geometric confinement effect" on fluid behavior, restricting droplet migration and coalescence. 2. To overcome the limitations of conventional contour plot observations, this study introduces the "compactness ratio (Dr)" (the ratio of droplet area to perimeter) as a core morphological indicator. This metric quantitatively confirms that high porosity enhances aggregation compactness, while strong hydrophilicity, despite improving fluid connectivity, significantly reduces morphological compactness due to excessive droplet spreading. 3. This research comprehensively elucidates the competitive regulatory mechanisms of pore topology, interfacial wettability, and initial distribution conditions on droplet aggregation.

With oily wastewater treatment emerging as a critical global issue, porous media and shear forces have received significant attention as environmentally friendly methods for oil-water separation. This study systematically simulates the dynamics of oil-in-water emulsion demulsification under porous media and shear forces using a color-gradient Lattice Boltzmann model. The morphological evolution and demulsification efficiency of emulsions are governed by porous media and shear forces. The effects of porosity and shear velocity on demulsification are quantitatively analyzed. (1) The presence of porous media enhances the ability of the flow field to trap oil droplets, with lower porosity corresponding to improved demulsification performance. Moreover, a more orderly arrangement of porous media promotes oil droplet coalescence. (2) Higher shear velocity in the flow field facilitates the aggregation of oil droplets. However, oscillatory shear conditions reduce the demulsification efficiency of emulsions. (3) Among the combined effects of shear velocity and porosity, porosity emerges as the dominant factor influencing emulsion demulsification. (4) Higher initial oil concentrations enhance demulsification efficiency. These simulation results provide valuable insights for further research on emulsion demulsification.

In this work, the phase separation behavior and pattern formation in binary fluids with chemical reactions controlled by ultrasonic radiation were systematically investigated. We incorporated the density-dependent Arrhenius equation into a novel and modified model for phase separation. The coupling effects of the pre-exponential factor K, density, and frequency on the phase separation under the condition of ultrasonic field-regulated chemical reactions were evaluated. 1) The rate of chemical reaction can be slowed down and even blocked by controlling the frequency of the ultrasonic field. 2)We have established a criterion for evaluating the competition between chemical reactions and the ultrasonic fields. When the value of pre-exponential factor K is greater than or equal to 10-4, phase separation is primarily regulated by the chemical reaction; otherwise, the ultrasonic field dominates the phase separation. 3) By analyzing the average structure factor, it was quantitatively proven that an increase in the frequency can significantly shorten the phase preservation period of the chemical reaction and ultrasonic radiation force and accelerate the merging of the separated phases into a larger phase. 4) We have successfully simulated the morphological evolution of phase separation regulated by traveling waves in the ultrasonic field.

In this study, the separation and coalescence of oil-in-water emulsions are explored in an ultrasonic field using the lattice Boltzmann method. By simulating the propagation of ultrasonic waves, this study focuses on examining the effects of acoustic wave frequency, the ratio of oil to water components, and the aspect ratio of the boundary on the emulsification and separation processes of oil-water mixtures. The following conclusions are drawn.（1） Frequency affects the speed of oil droplet separation, leading to an increase in droplet size over time. Larger droplets are found near the source, while smaller droplets are distributed throughout the wave web.（2） As the boundary aspect ratio increases, the emulsification efficiency of the droplets weakens, and the system takes longer to stabilize.（3） Emulsions with a higher component of oil can better resist acoustic waves.（4） At the same acoustic frequency, longer wavelength ultrasonic fields promote the formation of uniformly distributed, smaller oil droplets, which is beneficial to the storage of emulsions. These numerical simulation results offer insights for optimizing conditions for oil-in-water separation and serve as a numerical reference for the study of oil-in-water emulsion separation in ultrasonic environments.

The discrete element method (DEM) coupled with computational dynamics (CFD) has been considered one of the most sensitive ways of studying fluidized beds. This paper presents gas-solid fluidized bed simulations by use of CFD-DEM. The two-phase interaction is calculated in a way of decoupling. Within the effective neighborhood of the particles, the kernel approximation method is used to calculate the local porosity, thereby calculating the drag force on the particles. At the grid scale, the momentum exchange coefficient is calculated using a two fluid model based method, thereby calculating the source term of the phase interaction in the fluid control equation. The simulated form, size and motion process of the big bubble are all consistent with experimental observations. The simulated solid volume fraction and relative pressure averaged in a horizontal plane at 45 mm above the bottom are in reasonable agreement with the experimental data. The fluctuation of the simulated bed height over time is similar to the experimental measurement results. The reproduction of these qualitative and quantitative results indicates that the proposed method effectively improves simulation performance and accuracy.

processing and integrated forecasting strategy has always been a major obstacle to the development of wind power forecasting system. In view of this, a novel decomposition method with SSA and CEEMDAN is constructed to decompose the original data, and also a sample entropy parsimonious integration model is applied to achieve ultra-short term wind speed prediction. Considering the respective data characteristics of each subsequence, we divide the decomposed multiple subsequences into three parts: high complexity group, low complexity group and residual group. PSO-ELM, IHOA-LSSVR, and IHOA-LSTM are applied to predict them respectively. Compared with other models with high accuracy in this paper, our model has higher prediction accuracy.

This study investigate the morphology of oil-in-water at high density ratio controlled by electric field. We incorporated the electric field into the Lattice Boltzmann method (LBM). The focus is on the modified lattice Boltzmann color gradient model simulate the evolution of the oil-in-water and analyze the relation between morphologies and electric field parameters. The results show that the stretching, merging and even breaking can be regulated by electric field strength, conductivity, dielectric constant, oil-water density ratio and droplet radius. Simulation results showed that the larger dielectric constant resulted in the smaller deformation, and the larger conductivity related to the greater deformation. Meanwhile, the larger radius droplet is easier to deform and break, and the higher density droplet is less likely to break. And this paper also gives the morphology of the stretching and destabilization of the droplets at each stage. These results are in good agreement with the relevant theoretical and experimental results.

In this paper, the two-dimensional Kelvin–Helmholtz (KH) instability occurring in the shear flow of polymer fluids is modeled by the dissipative particle dynamics (DPD) method at the coarse-grained molecular level. A revised FENE model is proposed to properly describe the polymer chains. In this revised model, the elastic repulsion and tension are both considered between the adjacent beads, the bond length of which is set as one segment’s equilibrium length. The entanglements between polymer chains are described with a bead repulsive potential. The characteristics of such a KH instability in polymer fluid shear flow can be successfully captured in the simulations by the use of the modified FENE model. The numerical results show that the waves and vortexes grow more slowly in the shear flow of the polymer fluids than in the Newtonian fluid case, these vortexes become flat, and the polymer impedes the mixing of fluids and inhibits the generation of turbulence. The effects of the polymer concentration, chain length, and extensibility are also investigated regarding the evolution of KH instability. It is shown that the mixing of two polymer fluids reduces, and the KH instability becomes more suppressed as the polymer concentration increases. The vortexes become much longer with the evolution of the elongated interface as the chain length turns longer. As the extensibility increases, the vortexes become more flattened. Moreover, the roll-up process is significantly suppressed if the polymer has sufficiently high extensibility. These observations show that the polymer and its properties significantly influence the formation and evolution of the coherent structures such as the waves and vortexes in the KH instability progress.

The Bingham model can effectively describe the flow behavior of viscoplastic fluid. It is important to study the flow characteristics of Bingham fluid to understand the dynamic mechanism of viscous debris flow. In this study, the Bingham fluid flow on a slope is numerically researched using a corrected smooth particle hydrodynamics (CSPH) method based on periodic density re-initialization and artificial stress. First, the accuracy and stability of the improved SPH method are verified by the benchmark problem impacting droplets. Then, the flow characteristics of the Bingham fluid on the slope and the influence of the slope inclination angle on the Bingham fluid movement process are studied with the improved SPH method. The numerical results show that the improved SPH numerical scheme has higher accuracy and better stability and can deal with the complex flow behavior of the unsteady Bingham fluid.

We consider state estimation for networked systems (NSs), where measurements from sensor nodes are contaminated by outliers. A new hierarchical measurement model is formulated for outlier detection by integrating an outlier-free measurement model with a binary indicator variable for each sensor. The binary indicator variable, which is assigned a beta-Bernoulli prior, is utilized to characterize if the sensor’s measurement is nominal or an outlier. Based on the proposed outlier-detection measurement model, both centralized and decentralized information fusion filters are developed. Specifically, in the centralized approach, all measurements are sent to a fusion center where the state and outlier indicators are jointly estimated by employing the mean-field variational Bayesian (VB) inference in an iterative manner. In the decentralized approach, however, every node shares its information, including the prior and likelihood, only with its neighbors based on a hybrid consensus strategy. Then each node independently performs the estimation task based on its own and shared information. In addition, a distributed solution with an approximation is proposed to reduce the local computational complexity and communication overhead. Simulation results reveal that the proposed algorithms are effective in dealing with outliers compared with several recent robust solutions.

The chaotic nature of wind speed will damage power system seriously, and cause economic losses. Therefore, timely wind prediction is crucial for the safety of power system. However, the traditional prediction method is hard to fully learn the characteristic of wind speed. This paper proposes an optimal component IGSCV-SVR ensemble model to predict ultra-short-term wind speed. It changes the traditional single parameter optimization method of time series prediction. Firstly, the VMD based component correlation is applied to decomposing the original wind speed dataset to obtain multiple subsequences. Our model can find the dissimilarity of each subsequence, and then the model fully learns the feature of each subsequence. It can help improve the overall efficiency of ultra-short-term wind speed prediction accuracy. Finally, estimates are obtained by summing the prediction of all components. The case study proves the feasibility of our method through the comparative experiments with some previous prediction models in MSE, MAE, MAPE and running time in the experimental part of this paper.

This study investigates phase separation behavior and pattern formation in a binary fluid with chemical reaction controlled by thermal diffusion. By incorporating the Arrhenius equation into the lattice Boltzmann method (LBM), the coupling effects of the pre-exponential factor K, viscosity $$\eta $$ , and thermal diffusion D on phase separation were successfully evaluated. The effect of the competition between thermal diffusion and concentration on the phase separation morphology and dynamics of binary mixtures under a chemically reacting controlled by slow cooling is assessed based on the extended LBM. The calculations indicated that increases in viscosity and thermal diffusion can obtain interconnected structures (ISs) and lamellar structures (LSs) for cases with small K. However, concentric phase-separated structures (CSs) were observed in cases with large K. The increase in the degree and efficiency of phase separation were significantly greater in cases with decreased viscosity and increased thermal diffusion.

For the problem of attitude control of a quad tilt rotor aircraft with unknown external disturbances, a class of control methods based on a new exponential fast nonsingular terminal sliding surface, a new fast reaching law, and a super twisting sliding mode disturbance observer is investigated. First, the new exponential nonsingular terminal sliding surface is designed by using the advantages of nonsingular terminal sliding mode finite time convergence and strong robustness. Second, to solve the problem of a long convergence time and the serious shaking of the traditional reaching law, a new fast reaching law model with characteristics of the second-order sliding mode is put forward. Third, considering the existence of complex disturbances, the super twisting sliding mode disturbance observer is used to estimate and compensate the composite disturbances online. Finally, compared with the traditional nonsingular fast sliding mode control, simulation results show that the proposed control scheme achieves a good control performance.

We consider the robust smoothing problem for a state-space model with outliers in measurements. A unified framework for robust smoothing based on M-estimation is developed, in which the robust smoothing problem is formulated by replacing the quadratic loss for measurement fitting in the conventional Kalman smoother by a robust cost function from robust statistics. The majorization-minimization method is employed to iteratively solve the formulated robust smoothing problem. In each iteration, a surrogate function is constructed for the robust cost, which enables the states update procedure to be implemented in a similar way as that in a conventional Kalman smoother with a reweighted measurement covariance. Numerical experiments show that the proposed robust approach outperforms the traditional Kalman smoother and several robust filtering methods. (C) 2018 Elsevier B.V. All rights reserved.

We consider the problem of aerodynamic parameter estimation for aircraft dynamics modeled by a state space model where the statistic information of both the process and measurement noises are missing. To deal with the missing statistics, we propose in this work a new approach in which an augmented sigma point Rauch–Tung–Striebel (RTS) Kalman smoother is integrated with the expectation maximization (EM) algorithm. We define a new state vector by combining the original states and the unknown aerodynamic parameters. In addition, we impose a Gaussian random walk model for the unknown aerodynamic parameters and then build the extended state space model for the augmented RTS Kalman smoother. The expectation terms in the EM algorithm are approximated by the sigma point rule which is also applied in the augmented RTS Kalman smoother. Moreover, the non-convex optimization problem involved in the EM is solved in analytical forms rather than in numerical approaches. A comparative study of identifying the aerodynamic parameters of the flight test platform HFB-320 shows that the proposed approach achieves a substantial performance improvement over the existing ones, especially in terms of the convergence rate.

We consider the robust filtering problem for a nonlinear state-space modelwith outliers in measurements. To improve the robustness of the traditionalKalman filtering algorithm, we propose in this work two robust filters based onmixture correntropy, especially the double-Gaussian mixture correntropy andLaplace-Gaussian mixture correntropy. We have formulated the robust filteringproblem by adopting the mixture correntropy induced cost to replace thequadratic one in the conventional Kalman filter for measurement fitting errors.In addition, a tradeoff weight coefficient is introduced to make sure theproposed approaches can provide reasonable state estimates in scenarios wheremeasurement fitting errors are small. The formulated robust filtering problemsare iteratively solved by utilizing the cubature Kalman filtering frameworkwith a reweighted measurement covariance. Numerical results show that theproposed methods can achieve a performance improvement over existing robustsolutions.

We consider robust smoothing for nonlinear state space models in which both the process and measurement noises have heavy tails. To improve the robustness of Kalman smoothing, we formulate the robust smoothing problem by replacing the quadratic loss in the conventional Gaussian Kalman smoother by Huber's cost function. However, the Huber based smoother cannot be directly implemented within the Kalman smoothing framework, which has well recognized benefits in computational efficiency and stability. To address this issue, we introduce an auxiliary parameter to construct a surrogate function for the Huber cost function, leading to a reformulation of the robust Kalman smoothing problem. The reformulated robust smoothing is solved by an alternating minimization method, which iterates between a simple auxiliary parameter update step and a modified conventional Kalman smoothing step. Simulation results show that the proposed method achieves a performance improvement over several conventional and existing robust smoothers with a slight increase of computational time. (C) 2019 Elsevier B.V. All rights reserved.