Ke Li’s research while affiliated with Fuzhou University and other places

Wind energy is currently one of the most promising alternative energy sources. The optimization of the wind farm layout and the cable layout are two important elements in the design of wind farms. Since increasing the distance between turbines can reduce wake loss but increase cable cost, these two optimizations are coupled and jointly affect the revenue of wind farms. In this paper, we propose a novel nonlinear mathematical programming model based on the 3D Gaussian wake model and use a mathematical programming approach to optimize the layout of the wind farm and the cable layout together, considering both power generation and cable cost. In this method, some of the constraints were linearized to facilitate the solution process. The optimization results show that profit increased by 9.07% when using annual economic efficiency as the objective function, compared with using energy production as the objective function.

Multi-level memristor implemented by cheap precursors with definite mechanisms will be significant for development of high-density memory in the coming Big-data era. Sulfur is a cheap element refined out of...

Nonlinear self-excited forces pose a significant role in wind-induced aeroelasticity of long-span bridges, predominantly characterized by the flutter derivatives. As in other works already in the literature, the flutter derivatives are extended here to the nonlinear case by introducing amplitude dependence. At that point, accurately describing the nonlinearity of aerodynamic damping as a function of amplitude, etc., is crucial for the precise identification of flutter derivatives, while the nonlinearity of amplitude-dependent structural damping should also be considered. Therefore, this study aims to develop a time-domain method, that simultaneously accounts for the structural and aerodynamic nonlinearities in calculating the wind-induce responses of a double-deck truss girder. First, wind tunnel tests were performed to measure the time histories of displacement responses for the section model accounting for various damping ratio levels, from which the corresponding structural and aerodynamic damping was extracted. Next, the generalized Van der Pol oscillator (GVPO) model was employed to characterize the nonlinear structural and aerodynamic damping, and the accuracy was validated by comparing the computed displacement histories by the GVPO model with the experimental results. Subsequently, the nonlinear flutter derivatives at lower damping level are determined, with the nonlinear characteristics captured through the GVPO model. Finally, both the heaving and torsional responses at higher damping levels are predicted using the nonlinear flutter derivatives identified from the responses measured at lower damping levels, and the predicted results align with the experimental results. The time-domain method developed in this study incorporates both the aeroelastic and structural nonlinearities.

A deep learning neural network-assisted design strategy for programmable piezoelectric phononic crystal (PnC) beams with shunt circuits is proposed. The feasibility of integrating deep learning into the design of tunable PnCs to achieve real-time vibration isolation is demonstrated through numerical examples. The influence of shunt circuits (capacitance) on bandgaps of piezoelectric PnCs is studied by finite element (FE) simulations. The results show that the bandgap frequency and range vary with the capacitance and electrode length. Moreover, incorporating supercell structures introduces an additional bandgap, significantly expanding the tunable range of the bandgap and demonstrating that shunt circuit modifications can tailor the frequency and width of the bandgap. A suite of deep learning neural network (NN) algorithms is developed for predicting bandgaps and inversely designing PnC parameters, greatly accelerating the bandgap calculation and enabling faster inverse design than existing models. The accuracy of the NN algorithms is verified by comparing their predictions with those from FE simulations. The combination of designed PnC beams and deep learning NNs enables real-time vibration reduction and isolation. This design strategy is successfully validated in a practical scenario involving real-time vibration isolation of train rails.

Currently, most studies on the optimization of wind farm layouts on flat terrain employ a discrete grid-based arrangement method and result in irregular layouts that may damage the visual appeal of wind farms. To meet the practical requirements of wind farms, a two-step optimization method called “grid–coordinate” based on a genetic algorithm is proposed in this paper. The core idea is to initially determine the number of wind turbines and their initial positions using a grid-based approach, followed by a fine-tuning of the wind farm layout by moving the turbines in a row/column manner. This two-step process not only achieves an aesthetically pleasing arrangement but also maximizes power generation. This algorithm is conducted to optimize a 2 km × 2 km wind farm under three classic wind conditions, one improved wind condition, and a real wind condition employing both the Jensen and Gaussian wake models. To validate the effectiveness of the proposed method, the optimization of configurations based on different wake models was conducted, yielding results including the efficiency, total power output, number of wind turbines, and unit cost of electricity generation. These results were compared and analyzed against the classical literature. The findings indicate that the unit cost of electricity generation using the two-step optimization approach with the Gaussian wake model is higher than that of the initial grid optimization method. Additionally, varying the number of wind turbines can lead to instances of high power generation coupled with low efficiency. This phenomenon should be carefully considered in the wind farm layout optimization process.

This work conducted wind tunnel experiments and high-fidelity numerical simulations to study the flow phenomenology around and aerodynamic characteristics of a transversely inclined cantilever prism—a highly probable but unattended configuration when inclined civil structures are subjected to changing angle of attack. The velocity field, stream and spanwise vorticity, Q-criterion vortex structures, aerodynamic force and spectra, surface pressures, and shear layer morphology have been comprehensively analyzed. Results showed significant deviations from the vertical prism, deeming many classical observations on the prism wake untransferable to the inclined cases. The underlying flow phenomenology was subsequently clarified. Transverse inclination induces a fundamentally different, asymmetric wake morphology, especially near the fix- and free-end, suppressing the horseshoe vortices and intensifying near-wake turbulence. The prism base also experiences a remarkable intensification of wind load and vortical dynamics. The suppression of vortical activities on the near-wind side of the fix-end also gives rise to a new wake morphology – the tripole mode – unique to transverse inclination. Moreover, transversely inclined prisms experience different crosswind loads when angle of attack reverse in sign, which is attributed to changes in shear layer curvature. This work's conclusions partially fill the gap in the fluid mechanics of inclined prisms and, more importantly, highlight the extra cautions needed for engineering applications involving inclined structures.