A novel simulation method for the multivariate non-stationary non-Gaussian wind speed based on KL expansion and translation process theory

Accurate simulation of multivariate non-stationary non-Gaussian wind speed is the premise of evaluating the response of nonlinear structures. The methods based on Karhunen-Loève (KL) expansion and translation process method are extensively applied to predict non-stationary non-Gaussian simulation because it is easy for use and has relatively satisfactory simulation efficiency. However, these methods perform poorly in simulating the non-stationary strongly non-Gaussian process, especially the wind speed processes with highly skewed or bimodal distributions. This study comprehensively utilizes the KL expansion, the maximum entropy methods (MEM), and piecewise Hermite polynomial model (PHPM) to formulate a novel approach for simulating multivariate non-stationary non-Gaussian wind speed. In this method, the KL expansion is firstly used to generate the non-stationary Gaussian process. Then, a new strategy, the MEM is used to approximate the probability density function (PDF) of the target process which is then used to establish PHPM, is proposed to achieve the accurate and efficient simulation of non-stationary non-Gaussian process. The numerical results show that the proposed method has better simulation accuracy than traditional KL-based methods for non-stationary strongly non-Gaussian wind speed processes, especially the processes with highly skewed or bimodal distributions. Note that the proposed method can also be applied to simulate other non-Gaussian non-stationary excitations such as the wind pressure processes influenced by complex effects such as interference effect.