Stochastic dynamic stiffness of a surface footing for offshore wind turbines: Implementing a subset simulation method to estimate rare events

The purpose of this study, which concerns the stochastic dynamic stiffness of foundations for large offshore wind turbines, is to quantify uncertainties related to the first natural frequency of a turbine supported by a surface footing and to estimate the low event probabilities. Herein, a simple model of a wind turbine structure with equivalent coupled springs at the base is calibrated with the mean soil property values. A semianalytical solution, based on the Green׳s function for a layered half-space is utilized for estimation of foundation responses. Soil elastic modulus and layer depth are considered as random variables with lognormal distributions. The uncertainties are quantified, and the estimation of rare events of the first natural frequency is discussed through an advanced reliability approach based on subset simulation. This analysis represents a first step in the estimation of the safety with respect to the failure of a turbine in the fatigue limit state.     

中强地震随机有限断层模型应力降参数的确定方法

随机有限断层模型是模拟地震动加速度时程的一个重要工具.但将其应用于中强地震时,由于震源信息的准确性较差从而使模型参数具有较大的不确定性.尤其针对其中最为关键的应力降参数,目前相关研究较为缺乏且尚未形成系统的确定方法.本文基于美国Little Skull Mountain Mw5.6级地震2个近场台站记录的地震动模拟,详细研究了采用随机有限断层法拟合中强地震地震动伪加速度反应谱（PSA）来确定应力降参数值的方法,并在计算应力降时引入了其它震源参数的不确定性,随后对此方法的可行性进行了验证.研究表明：采用不同频段反应谱残差和计算得到的应力降值差别较大,确定中强地震应力降较为合适的反应谱频段是中高频,采用该频段确定的应力降参数值模拟的反应谱和峰值加速度与实际记录较为符合;脉冲子断层百分比、断层长宽、倾角和深度等震源参数按截断的正态分布或均匀分布随机抽样,拟合得到的应力降参数值与通过实际震源模型参数得到的值相近.以上研究结果对确定一个区域中强地震应力降或中强地震近场强震动模拟研究提供了更进一步的研究方法和研究方向.     

Robustness of the holistic seismic risk evaluation in urban centers using the USRi

The Urban Seismic Risk index (USRi) published in a previous article (Carreño et al., Nat Hazards 40:137–172, 2007) is a composite indicator that measures risk from an integrated perspective and guides decision-making for identifying the main interdisciplinary factors of vulnerability to be reduced or intervened. The first step of the method is the evaluation of the potential physical damage (hard approach) as a result of the convolution of the seismic hazard with the physical vulnerability of buildings and infrastructure. Subsequently, a set of social context conditions that aggravate the physical effects is also considered (soft approach). According to this procedure, the physical risk index is evaluated for each unit of analysis from existing loss scenarios, whereas the total risk index is obtained by multiplying the former index by an impact factor using an aggravating coefficient, based on variables associated with the socio-economic conditions of each unit of analysis. The USRi has been developed using the underlying holistic and multi-hazard approach of the Urban Risk Index framework proposed for the evaluation of disaster risk in different megacities worldwide. This article presents the sensitivity analysis of the index to different parameters such as input data, weights and transformation functions used for the scaling or normalization of variables. This analysis has been performed using the Monte Carlo simulation to validate the robustness of this composite indicator, understanding as robustness how the cities maintain the ranking as well as predefined risk level ranges, when compared with the deterministic results of risk. Results are shown for different cities of the world.     

Scaling law of the reduced magnetic helicity in fast streams

The reduced magnetic helicity is a quantity related to the handedness of the magnetic field fluctuations. In the present paper we study the scaling law of the reduced magnetic helicity in fast streams in the solar wind by using high-resolution magnetic field data by the Ulysses spacecraft. We show that at high frequencies both the left-hand and the right-hand helicity survives, implying that there is no predominance of a single sign. In addition, the scaling law of the magnetic helicity exhibits a strong dependence on the data set analyzed and we do not observe any universal behavior.     

测井、井间地震与地面地震数据联合约束下的地质统计学随机建模方法研究

利用能够整合测井信息与井间地震信息的地质统计学随机模拟方法,结合传统的地质统计学反演思路,得到了一种能够同时整合测井、井间地震与地面地震三种先验信息的地质统计学反演与储层建模方法.由于井间射线信息、测井信息与地面地震数据在随机反演与建模过程当中都得到了尊重,因此与传统地质统计学反演仅利用了测井与地面地震数据相比,本文的地质统计学反演与建模方法更充分地利用了先验信息,有效提高了反演的精度,降低了随机建模中的多解性.基于理论数据的测试证实了上述观点.     

考虑双轴水平力耦合效应的RPC箱型桥墩恢复力模型试验研究

通过3个施加常轴力的RPC箱型桥墩试件的水平反复荷载试验,考虑水平荷载作用方向对RPC箱型墩抗震性能的影响,分析各试件滞回特性和骨架曲线的特点,对影响RPC箱型桥墩恢复力模型的主要因素进行数值回归分析,建立了计入双轴水平力耦合效应的RPC箱型桥墩恢复力模型。利用基于平截面假定的纤维模型法编制考虑轴力二阶效应的双轴压弯构件非线性分析程序,并对RPC箱型桥墩试件的骨架曲线和滞回曲线进行数值模拟。通过与试验结果进行分析对比表明:所提出的恢复力模型具有较好的精确性,能够较好地模拟和反映RPC箱型桥墩的抗震性能。     

Nonlinear stochastic seismic analysis of buried pipeline systems

In this paper, a nonlinear stochastic seismic analysis program for buried pipeline systems is developed on the basis of a probability density evolution method (PDEM). A finite element model of buried pipeline systems subjected to seismic wave propagation is established. The pipelines in this model are simulated by 2D beam elements. The soil surrounding the pipelines is simulated by nonlinear distributed springs and linear distributed springs along the axial and horizontal directions, respectively. The joints between the segmented pipes are simulated by nonlinear concentrated springs. Thereafter, by considering the basic random variables of ground motion and soil, the PDEM is employed to capture the stochastic seismic responses of pipeline systems. Meanwhile, a physically based method is employed to simulate the random ground motion field for the area where the pipeline systems are located. Finally, a numerical example is investigated to validate the proposed program.     

Stochastic modelling of unresolved eddy fluxes

A subgrid-scale parameterization scheme motivated by statistical closure theory, but employing statistics obtained from high-resolution direct numerical simulations, is applied to large eddy simulations of two-level quasigeostrophic turbulence on the sphere. It is shown that these parameterizations are consistent with the phenomenology of quasigeostrophic turbulence. The parameterizations consist of 2 × 2 dissipation and stochastic forcing covariance matrices at each wavenumber, with the off-diagonal elements of the matrices representing vertical mixing. Two flow regimes, characterized by their deformation scales, are considered, namely atmospheric and oceanic. In the former, the deformation scale is fully resolved, and the truncation scale is within the enstrophy cascading interial range. In the latter, the deformation scale is not fully resolved, and the truncation scale is within the energy cascading inertial range. It is demonstrated through numerical experiments that both stochastic and deterministic variants of the scheme give comparable results for the energy spectra in the atmospheric regime. In the oceanic regime, the stochastic variant again gives excellent results, but the deterministic variant is found to be numerically unstable.     

锈蚀钢筋混凝土压弯构件恢复力模型研究

根据目前已有试验资料和理论分析,结合钢筋锈蚀引起结构破坏形态的改变,综合考虑结构各种耐久性损伤因素,并对箍筋锈蚀进行修正,提出了锈蚀钢筋混凝土压弯构件基于地震损伤的恢复力模型的确定方法.通过与现有试验进行对比分析,表明模型描绘的骨架曲线与试验结果总体吻合较好,模型计算的滞回曲线所描述的现象与试验一致,该恢复力模型可在损伤钢筋混凝土结构地震反应分析中采用.     

A modified stochastic neighbor embedding for multi-feature dimension reduction of remote sensing images

In automated remote sensing based image analysis, it is important to consider the multiple features of a certain pixel, such as the spectral signature, morphological property, and shape feature, in both the spatial and spectral domains, to improve the classification accuracy. Therefore, it is essential to consider the complementary properties of the different features and combine them in order to obtain an accurate classification rate. In this paper, we introduce a modified stochastic neighbor embedding (MSNE) algorithm for multiple features dimension reduction (DR) under a probability preserving projection framework. For each feature, a probability distribution is constructed based on t-distributed stochastic neighbor embedding (t-SNE), and we then alternately solve t-SNE and learn the optimal combination coefficients for different features in the proposed multiple features DR optimization. Compared with conventional remote sensing image DR strategies, the suggested algorithm utilizes both the spatial and spectral features of a pixel to achieve a physically meaningful low-dimensional feature representation for the subsequent classification, by automatically learning a combination coefficient for each feature. The classification results using hyperspectral remote sensing images (HSI) show that MSNE can effectively improve RS image classification performance.