Lei Yan

The wind fields in extreme wind areas have highly nonstationary characteristics, so the accurate simulation of nonstationary wind fields is a fundamental concern in the buffeting analysis of large-span bridges in these areas. Due to the time-varying coherence, classical spectral representation method (SRM) needs for extensive Cholesky decomposition and harmonic superposition. To improve efficiency, interpolation schemes are introduced into the decomposition, and time–frequency decoupling methods are employed to apply the fast Fourier transform. However, the frequency and time interpolation node distribution schemes are complicated and lack adaptive ability. Meanwhile, the traditional two-dimensional (2D) time–frequency interpolation method is less efficient. Therefore, new adaptive frequency and time interpolation-enhanced schemes and an improved 2D Hermite interpolation method are proposed in this study, aimed to improve the accuracy and efficiency of simulating nonstationary wind fields. The frequency interpolation scheme takes the wind spectrum derivative as an index and sets the interpolation node distribution adaptively. In addition, the time interpolation scheme sets time interpolation nodes based on the peak and trough locations in time-varying mean wind speed. Then, the improved 2D Hermite interpolation method is conducted to decrease the Cholesky decomposition and decouple the time–frequency spectrum, and time-domain basis functions in the same frequency-domain region are summed, to further reduce the computational effort of this interpolation method. Finally, the interpolation scheme and the proposed method are validated through parametric analysis and numerical examples to assess their accuracy and efficiency.

In this paper, the pressure distributions, aerodynamic force coefficients, buffeting force spectrum, spanwise correlation, and aerodynamic admittance functions for the middle vehicle of a Chinese Railway High-speed (CRH) train (named CRH2) and a China Railway (CR) train (named CR400) on a streamlined box girder in turbulence flow, respectively, at yaw angles (β) ranging from 30° to 90°, with a 15° interval, were analyzed using pressure measurement. The locations of the peaks of the mean and root mean square pressure coefficients at different wind yaw angles are relatively constant, mainly occurring at the windward roof and bottom corners of the trains. It is clearly observed that the CR400 train exhibits higher negative pressure near the separation points at the roof corner of windward surface. The absolute values of side force coefficients and moment coefficients of the stationary trains increase with β values. It has been proved that the results of side force coefficients and moment coefficients obtained based on the wind speed component vertical to the train axis under skew winds mainly depend on the normal wind speed component, but the wind speed component parallel to the train axis still has some effect on them. Furthermore, the side force spectra and span-wise correlation results of CRH2 and CR400 trains are basically similar. The aerodynamic admittance functions of the side force decreases with increasing wind yaw angle, and the result is slightly larger due to the larger cross-sectional height of the CR400 train compared to the CRH2 train.

Understanding and quantifying the predictability of various meteorological variables remains a significant challenge in atmospheric sciences, particularly in regions with complex climates. This study aims to assess the predictability limit of wind speed, pressure, temperature, and relative humidity using permutation entropy (PermEn) analysis. By analyzing the statistical properties of PermEn values associated with each variable, our results revealed distinctive patterns of complexity and predictability. It shows clear evidence that pressure generally exhibits the lowest PermEn value, while wind speed tends to have the largest PermEn. However, the variation of PermEn values remains largely stable across different terrain conditions, which is primarily attribute to the use of daily observational data. The study also discusses the impact of window length on PermEn calculations, highlighting that longer window lengths are more likely to capture a broader range of dynamics, including more intricate and diverse temporal structures. This expanded pattern diversity leads to increased complexity of the time series, resulting in higher PermEn values. Additionally, the seasonal variation patterns of permutation entropy were found to differ among the meteorological variables considered. These findings provide valuable insight into the relative predictability of various meteorological variables, which can have important implications for atmospheric modeling, weather forecasting, and risk management.

Previous studies of two-dimensional (2D) aerodynamic admittance function (AAF) identification methods have basically ignored the effects of different turbulence components, which may lead to unpredictable errors for practical engineering applications. This paper presents pressure measurement experiments on thin sections with a geometric ratio of 1:50 in various turbulent fields. Based on the three-dimensional (3D) two-wavenumber theoretical analytical framework, the two-wavenumber coherence function is obtained by fitting the measured spanwise root-coherence function at the effective spacing with an empirical model. A new method is proposed, assuming that the ratio relationship between the lift AAFs due to longitudinal and vertical turbulence components of the measured model section satisfies its corresponding theoretical solution ratio relationship. The 2D lift AAFs of the airfoil section induced by different turbulence components are identified following the proposed identification framework. Consistent with the previous research, force correlation is significantly larger than turbulence correlation, and the traditional 3D one-wavenumber AAFs obtained are different in different turbulent fields. The separated 2D lift AAFs demonstrate that the contributions of u- and w- turbulence to the buffeting lift force are significantly different, and are more precise, according to the ratio between the Horlock function and Sears function. Then, the method is successfully extended to estimate the AAFs with respect to the u- and w-turbulence components of a streamlined box girder section, further validating the applicability of the identification method to the streamlined box girder. Furthermore, comparing the separated 2D lift AAFs under different turbulence fields reveals their independence from turbulence characteristics.