A time integral formulation and algorithm for structural dynamics with nonlinear stiffness

A newly-developed numerical algorithm, which is called the new Generalized-α (G-α) method, is presented for solving structural dynamics problems with nonlinear stiffness. The traditional G-α method has undesired overshoot properties as for a class of α-method. In the present work, seven independent parameters are introduced into the single-step three-stage algorithmic formulations and the nonlinear internal force at every time interval is approximated by means of the generalized trapezoidal rule, and then the algorithm is implemented based on the finite difference theory. An analysis on the stability, accuracy, energy and overshoot properties of the proposed scheme is performed in the nonlinear regime. The values or the ranges of values of the seven independent parameters are determined in the analysis process. The computational results obtained by the new algorithm show that the displacement accuracy is of order two, and the acceleration can also be improved to a second order accuracy by a suitable choice of parameters. Obviously, the present algorithm is zero-stable, and the energy conservation or energy decay can be realized in the high-frequency range, which can be regarded as stable in an energy sense. The algorithmic overshoot can be completely avoided by using the new algorithm without any constraints with respect to the damping force and initial conditions.The English text was polished by Keren Wang.     

考虑竖向地震作用的振动控制及其优化研究

目前,MRD结构的分析通常只考虑水平地震动而不考虑竖向地震动的影响,地面的水平运动和竖向运动具有相关性,从而影响控制效果．因此对双向地震耦合作用下MRD结构的理论进行研究,建立动力分析模型并得出运动微分方程．以8层MRD结构为例进行地震反应分析,研究表明,考虑和不考虑竖向地震作用,MRD对结构均有良好的控制作用,但是竖向地震作用的存在,使结构各层的层间位移峰值有不同程度的增加,其增量随着竖向地震作用的增加而增加,因此建议在高烈度地区的建筑物考虑竖向地震作用对结构的影响．建立MRD结构优化设计模型,采用IHGA程序对结构进行优化设计．结果表明, MRD结构在各种工况下的各项地震反应均得到更好的控制．     

随机振动数值仿真中基础激励的自动反馈识别

利用ANSYS的APDL(ANSYS Parametric Design Language)语言的强大功能,结合工程实际问题,实现了随机振动试验数值仿真计算中基础输入功率谱的自动反馈识别．反馈过程中使用多个表格来存储基础输入谱,突破了基础输入谱只能用50个以内的数据点来描述的限制,故可以一次性完成基础输入谱的反馈识别,大大提高计算效率与精度．     

汽车车轮流场的数值模拟

首先以独立车轮为研究对象,以商用计算流体力学软件STAR—CD为工具,利用移动边界条件,并依据k-ε双方程高雷诺数湍流模型对三维定常不可压缩流N-S方程进行数值模拟计算．然后在合适尺寸的计算域下对3种不同宽度的静止和滚动车轮分别进行数值模拟计算,研究车轮宽度和来流速度对阻力系数的影响,将计算结果与实验结果进行了对比分析．计算结果表明,车轮宽度的减小以及来流速度的减小均明显降低汽车的气动阻力系数．     

影响石英单模光纤数值孔径测量的因素及分析

本文采用远场光斑法和光强法测量了石英单模光纤的数值孔径.实验结果表明,由光纤耦合条件不同导致的光斑形状变化,对数值孔径的测量影响不大,单模光纤的纤芯直径小,导致出射光存在较强的衍射现象,对数值孔径的测量造成较大的影响,无论采用光斑法还是光强法,需以衍射第1次极大为计算标准,尽量选择光斑中心为亮斑时测量其数值孔径.此外,...     

Application of scaled boundary finite element method in static and dynamic fracture problems

The scaled boundary finite element method (SBFEM) is a recently developed numerical method combining advantages of both finite element methods (FEM) and boundary element methods (BEM) and with its own special features as well. One of the most prominent advantages is its capability of calculating stress intensity factors (SIFs) directly from the stress solutions whose singularities at crack tips are analytically represented. This advantage is taken in this study to model static and dynamic fracture problems. For static problems, a remeshing algorithm as simple as used in the BEM is developed while retaining the generality and flexibility of the FEM. Fully-automatic modelling of the mixed-mode crack propagation is then realised by combining the remeshing algorithm with a propagation criterion. For dynamic fracture problems, a newly developed series-increasing solution to the SBFEM governing equations in the frequency domain is applied to calculate dynamic SIFs. Three plane problems are modelled. The numerical results show that the SBFEM can accurately predict static and dynamic SIFs, cracking paths and load-displacement curves, using only a fraction of degrees of freedom generally needed by the traditional finite element methods.The project supported by the National Natural Science Foundation of China (50579081) and the Australian Research Council (DP0452681)The English text was polished by Keren Wang.     

叶片与蜗舌相对位置对离心泵性能数值模拟精度影响的研究

本文选择RANS方程和RNG k-ε湍流模型,采用有限体积法,在假定流动定常的前提下对离心泵中叶片与蜗舌不同相对位置时的流场进行数值模拟,总结分析了各性能参数的计算误差在不同的相对位置时的变化规律.通过叶片与蜗舌不同相对位置时对各性能参数计算误差的均方差进行比较,认为存在一个合适的相对位置使得对各性能参数的总体预测精度保持最高,为数值方法分析离心泵的性能提供了指导.     

一种新型均匀扩散器流场的数值模拟

异性纤维检出设备中扩散器内部流场的情况,直接影响着落棉率和整个清除系统的工作效率.本文用计算流体力学软件FLUENT对新型均匀扩散器的流场进行数值模拟,通过改进扩散器的结构参数,实现流场的均匀和稳定,进而提高整个系统纤维检出设备的清除效率.之后对计算结果进行分析,提出了两种经过结构优化的改进模型,并分析了其内部流场.     

钝头机体用FADS-α的求解算法及精度研究

针对钝头机体用嵌入式大气数据传感(flush air data sensing,FADS)系统的4类攻角求解算法及算法的求解精度进行研究.针对典型的15°钝头体外形,在Mach数Ma=2.04,3.02,5.01,攻角α=-5°～30°,侧滑角β=0°的条件下,首先基于势流理论及修正的Newton流理论建立了钝头机体用...     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.