In order to improve the energy capture efficiency of an oscillating buoy wave energy converter (WEC), a buoy-shape optimization design method based on the mean annual power prediction model is proposed. According to the statistical data of long-term wave characteristics in the Chinese sea area, the optimal design space is determined. Sixty-three sample points were randomly selected in the optimized space. Based on simulation, the mean annual power corresponding to each sample point is calculated to quantitatively describe the energy capture ability. The response surface method (RSM), radial basis function neural network (RBFNN), and elliptical basis functions neural network (EBFNN) are used to establish the mean annual power prediction models, respectively. By combining the prediction model with the multi-island genetic algorithm (MIGA), the optimal solution in the design space is easily obtained. The reliability of the optimal solution is further proved by quantitative analysis about the influence of optimization parameters on the mean annual captured power. Compared with the common RSM and RBFNN methods, the prediction model established by the EBFNN method has a higher prediction accuracy. In the optimization process, the simulation calculation is replaced by a prediction model, which can effectively solve the problem of high simulation calculation cost.

The problem of H∞-control based on the output feedback for fuzzy singular system was studied. With an equivalent system constructed, the analysis of H∞-control for fuzzy singular system is converted to the analysis of H∞-control for common singular system. When the premise variables is unmeasurable, A new method for nonlinear H∞-controller design of fuzzy singular system is given which avoid analysing the subsystems of fuzzy singular system.

High heterogeneity and nonuniformly distributed multiscale pore systems are two characteristics of the unconventional reservoirs, which lead to very complex transport mechanisms. Limited by inadequate computational capability and imaging field of view, flow simulation cannot be directly performed on complex pore structures. The traditional methods usually coarsen the grid to reduce the computational load but will lead to the missing microstructure information and inaccurate simulation results. To develop a better understanding of flow properties in unconventional reservoirs, this study proposed a new upscaling method integrated gray lattice Boltzmann method (GLBM) and pore network model (PNM), accounting for the fluid flow in heterogeneous porous media. This method can reasonably reduce the computational loads while preserving certain micropore characteristics. Verifications are conducted by comparing the simulation and experimental results on tight sandstones, and good agreements are achieved. The proposed method is proven to be capable of estimating bulk properties in highly heterogenous unconventional reservoirs. This method could contribute to the development of multiscale pore structure characterizations and enhance the understandings of fluid flow mechanisms in unconventional reservoirs.