The hydrodynamics of the waterjet-propelled ship is a challenging issue due to the sophistication of waterjet system geometry as well as waterjet–hull interaction. In current study, three different propulsion models, namely, multiple reference frame (MRF) model, sliding mesh model and body-force model, are utilized to simulate the self-propulsion of a waterjet-propelled ship model. A body-force model based on a User-Defined Function (UDF) on the ANSYS Fluent platform is proposed. The computational cost, wave pattern, jet stream surface, and self-propelled hull resistance of the MRF model and body-force model are compared. Comprehensive comparisons of the internal ingested flow field of the control volume are made, especially at the capture area and nozzle section, with the two different models. In addition, the resistance increment fraction and jet system thrust deduction fraction with the two different models are investigated. Lastly, the flow field with the steady MRF model, the body-force model (RANS) and the transient sliding mesh model (URANS) are compared.

In this paper, the circulating current is suppressed by improved particle swarm optimization (IPSO) is proposed. First of all, closed-loop control model of MMC with proportional-resonance (PR) controller is established. Next, the PSO algorithm is improved, and the IPSO algorithm is adopted to optimize the PR parameters. Finally, the simulations in MATLAB/Simulink environment are launched, by comparing IPSO with ant colony optimization and PSO, it is shown that the amplitude of internal circulation can be prominently reduced under fault, and the internal circulation of MMC can be effectively suppressed.

With the popularity of electric vehicles, the charging load of electric vehicles has gradually become a challenge to the power grid’s stability. Therefore, the load forecasting of the electric vehicle is the premise of analyzing the influence of the electric vehicle to the power grid, and it is also the basis of the power grid operation and dispatch. Traditional prediction methods need the support of large data and the prediction results are weak in pertinence. Therefore, a method is proposed to further optimize the results by combining Bureau of Public Road path resistance function(BPR) and the extreme learning machine, so as to make the prediction results effective. Finally, the projected gradient algorithm is used to optimize the dispatch plan of the power grid containing wind power based on the predicted power demand of the electric vehicles, so as to achieve the most economical operation of the power grid.

The large-scale access of distributed generation (DG) makes microgrids gradually become a key part of smart grids. Geographically adjacent microgrids in the region are connected to each other to form a regional multi-microgrid (MMG) system. However, how to achieve the communication between the sub-microgrids and the energy management within the system becomes very important. Therefore, this paper focuses on the optimal dispatch problem in the operation of regional multi-microgrid. Firstly, the typical structure and internal composition of regional multi-microgrid system are introduced. Secondly, combined with the relevant theory of topology identification, a dynamic networking strategy of regional multi-microgrid based on graph theory is designed. Finally, based on the dynamic networking strategy of regional multi-microgrid, a dynamic networking strategy of regional multi-microgrid considering economy and active loss is proposed. In the operation process of regional multi-microgrid, the price-based demand response (PBDR) strategy is introduced to optimize the operation process of regional multi-microgrid with the goal of optimal economic operation and lowest total active power loss, and the results after solution are analyzed by TOPSIS method. The simulation results show that through dynamic networking, the system operating cost of the regional multi-microgrid is reduced by about 39.4% and the active power loss is reduced by about 10.6% under normal operation, which reduces the active power loss of the system while ensuring economic operation, which verifies the effectiveness and feasibility of the model.

This paper presents a nonlinear trajectory controller with improved performances for a general model of the waverider based on feedback linearization theory and composite nonlinear feedback (CNF) technique. First, a nonlinear controller is presented using the dynamic inversion and CNF technique for the MIMO Model, and the robust stability of the proposed controller is proved. Then, the nonlinear model is established on the basis of hypersonic aerodynamic principle, and the dynamic characteristics are analyzed accordingly, and the periodic trajectory is designed and optimized in combination with a fuel optimization problem. Furthermore, the nonlinear controller is applied to the trajectory tracking of the waverider model, and the general design steps are provided the flight controller using this nonlinear control method. Finally, an illustrative example is given to verify the effectiveness of the nonlinear controller of the waverider, and the flight performances are improved accordingly, including system stability, robustness, and tracking ability.