多层吸能元件在冲击波作用下大变形问题的模态解

通过实验研究了多层吸能元件在冲击波作用下的大变形模式，基于虚速度原理建立了问题的模态近似解．实例计算表明理论值与实验结果吻合很好，分析了材料硬化、动态应变率以及元件静态承载力、层数和惯性对结果的影响．

爆炸载荷作用下钢管拼装结构大变形模态解

钢管拼装吸能元件在爆炸载荷作用下的大变形问题,对于抗爆结构的吸能装置设计具有重要意义。本文在对单个钢管动力压扁过程研究的基础上,根据实验分析了拼装钢管在径向受爆炸载荷作用的塑性行为,并且基于虚速度原理建立了八塑性铰机构模态解方程,描述了拼装钢管受爆炸载荷作用的位移速度场,最后进行了实验验证。

排式矩形薄壁钢管横向冲击全屈曲模态解

研究钢管在横向冲击载荷下的屈曲大变形行为对抗爆及抗冲击吸能装置设计具有重要意义。根据实验结果,对钢管屈曲过程进行了简要分析,分阶段设定了钢管动力屈曲变形模态,并据此建立相应的位移、速度场,基于虚速度原理推导出了模态解控制方程,将问题转化为单一变量方程,便于下一步求解。结果表明,模态解方法是分析结构动态响应的有效近似方法,应变率效应是不可忽略的因素。

简支梁中部受撞击的完全解与模态解比较研究

以简支梁中部受到飞射物横向撞击的问题为例,求其刚塑性动力响应的完全解及模态解并加以比较,考察了质量比这一关键性的参数以及截面弱化因素对模态解偏离完全解程度的影响,比较发现,对于具有简支边界的梁,模态近似解的误差较小,特别是在考虑了截面弱化的情况下,模态解更接近完全解,用模态解代替完全解可以满足精度上的要求。

矩形槽介质光栅的矢量模态解

本文采用严格的矢量模态理论研究矩形槽介质光栅的衍射效率问题。当入射光为线偏振光时,其结果与已发表的文献[4]结果相同。

基于Kanai-Tajimi谱线性结构随机响应的实模态统一解析法

针对当前计算Kanai-Tajimi谱下结构响应表达式复杂的问题,提出了计算单自由度结构相对位移、相对速度和绝对加速度的解析法.首先,利用三角函数关系,获得单自由度结构相对位移、相对速度和绝对加速度响应的统一杜哈梅积分表达式;其次,基于Kanai-Tajimi随机激励的协方差特点,给出了Kanai-Tajimi随机激励的协方差简明表达式,利用随机振动理论,结合三角函数关系获得单自由线性结构相对位移、相对速度和绝对加速度的统一简明的实模态解析表达式.通过算例,验证了该方法的正确性及计算高效性,并为多自由线性结构随机响应、结构动力可靠度分析提供了新途径.

一类强非线性二阶微分方程的多模态近似解析解研究

利用自治力学系统的哈密顿函数为守恒量的性质,提出一种求非线性二阶微分方程多模态近似解析解的方法,称为哈密顿函数法.首先,介绍哈密顿函数法求多模态近似解的基本理论.其次,以质点在旋转的抛物线上运动为模型建立强非线性二阶微分方程.最后,用哈密顿函数法求得在给定初始条件和参数下强非线性二阶微分方程的三模态近似解析解表达式,作出三模态近似解析解的解曲线,并与直接用Mathematica软件作出的解曲线进行比较,讨论三模态近似解析解的精确性.结果表明:用哈密顿函数法求得的三模态近似解析解的解曲线与直接用Mathematica软件作出的解曲线十分吻合.

排式矩形薄壁钢管径向冲击屈曲模态分析

钢管在径向冲击下的屈曲大变形问题对抗爆及抗冲击吸能装置设计具有重要意义。根据实验结果,设定了排式矩形钢管动力屈曲的变形模态,建立了相应的运动场,基于虚速度原理推导出了模态解方程,将问题转化为只含单一变量的方程。计算结果与实验数据对比发现,对于排式矩形钢管径向冲击屈曲问题,模态解方法可以较准确的反映实验现象;当偏转角度较小时,理论值与实验值几乎一致,但当偏转角较大时,理论值明显偏大。分析得出,导致这一偏差的主要原因是应变率效应,因此应变率效应对钢管的变形过程影响显著。

Slope displacement prediction based on multisource domain transfer learning for insufficient sample data

Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.

Improved AVOA based on LSSVM for wind power prediction

Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM).Firstly,the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs)using variational modal decomposition(VMD).Secondly,random numbers in population initialization were replaced by Tent chaotic mapping,multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator,and then each component was predicted.Finally,Tent multi-objective AVOA-LSSVM(TMOALSSVM)method was used to sum each component to obtain the final prediction result.The simulation results show that the improved AVOA based on Tent chaotic mapping,the improved non-dominated sorting algorithm with elite strategy,and the improved crowding operator are the optimal models for single-objective and multi-objective prediction.Among them,TMOALSSVM model has the smallest average error of stroke power values in four seasons,which are 0.0694,0.0545 and 0.0211,respectively.The average value of DS statistics in the four seasons is 0.9902,and the statistical value is the largest.The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision,and faster and more accurately finds the optimal solution on the current solution space sets,which proves that the method has a certain scientific significance in the development of wind power prediction technology.

横向爆炸载荷下薄壁圆管的动态响应

采用实验研究、理论分析和有限元模拟相结合的方法,研究了横向爆炸载荷作用下薄壁圆管的动态响应。利用弹道冲击摆锤系统,对圆管在爆炸载荷下的动力响应进行了实验研究,分析了薄壁圆管的变形模式;基于地基梁模型,建立了横向爆炸载荷作用下圆管跨中挠度的理论模型,并进行了无量纲化;通过有限元模拟,分析了圆管的几何参数对其变形模式和跨中挠度的影响,并与理论结果进行了对比。研究结果表明:随着TNT药量增加圆管的变形区域和跨中挠度增大;圆管的长径比、厚度及爆炸载荷参数对圆管的变形模式有较大影响;理论预测、有限元模拟结果与实验结果吻合较好。

The filtering characteristics of HHT and its application in acoustic log waveform signal processing

Array acoustic logging plays an important role in formation evaluation. Its data is a non-linear and non-stationary signal and array acoustic logging signals have time-varying spectrum characteristics. Traditional filtering methods are inadequate. We introduce a Hilbert- Huang transform （HHT） which makes full preservation of the non-linear and non-stationary characteristics and has great advantages in the acoustic signal filtering. Using the empirical mode decomposition （EMD） method, the acoustic log waveforms can be decomposed into a finite and often small number of intrinsic mode functions （IMF）. The results of applying HHT to real array acoustic logging signal filtering and de-noising are presented to illustrate the efficiency and power of this new method.

Random noise attenuation by f–x spatial projection-based complex empirical mode decomposition predictive filtering

The frequency–space（f–x） empirical mode decomposition（EMD） denoising method has two limitations when applied to nonstationary seismic data. First, subtracting the first intrinsic mode function（IMF） results in signal damage and limited denoising. Second, decomposing the real and imaginary parts of complex data may lead to inconsistent decomposition numbers. Thus, we propose a new method named f–x spatial projection-based complex empirical mode decomposition（CEMD） prediction filtering. The proposed approach directly decomposes complex seismic data into a series of complex IMFs（CIMFs） using the spatial projection-based CEMD algorithm and then applies f–x predictive filtering to the stationary CIMFs to improve the signal-to-noise ratio. Synthetic and real data examples were used to demonstrate the performance of the new method in random noise attenuation and seismic signal preservation.

Seismic attribute extraction based on HHT and its application in a marine carbonate area

The Hilbert-Huang transform（HHT） is a new analysis method suitable for nonlinear and non-stationary signals.It is very appropriate to seismic signals because they show both non-stationary and nonlinear characteristics.We first introduce the realization of HHT empirical mode decomposition（EMD） and then comparatively analyze three instantaneous frequency algorithms based on intrinsic mode functions（IMF） resulting from EMD,of which one uses the average instantaneous frequency of two sample intervals having higher resolution which can determine that the signal frequency components change with time.The method is used with 3-D poststack migrated seismic data of marine carbonate strata in southern China to effectively extract the three instantaneous attributes.The instantaneous phase attributes of the second intrinsic mode functions（IMF2） better describe the reef facies of the platform margin and the IMF2 instantaneous frequency attribute has better zoning.Combining analysis of the three IMF2 instantaneous seismic attributes and drilling data can identify the distribution of sedimentary facies well.

Reservoir detection based on EMD and correlation dimension

In hydrocarbon reservoirs, seismic waveforms become complex and the correlation dimension becomes smaller. Seismic waves are signals with a definite frequency bandwidth and the waveform is affected by all the frequency components in the band. The results will not define the reservoir well if we calculate correlation dimension directly. In this paper, we present a method that integrates empirical mode decomposition （EMD） and correlation dimension. EMD is used to decompose the seismic waves and calculate the correlation dimension of every intrinsic mode function （IMF） component of the decomposed wave. Comparing the results with reservoirs identified by known wells, the most effective IMF is chosen and used to predict the reservoir. The method is applied in the Triassic Zhongyou group in the XX area of the Tahe oil field with quite good results.

Seismic data denoising based on mixed time-frequency methods

Deconvolution denoising in the f-x domain has some defects when facing situations like complicated geology structure, coherent noise of steep dip angles, and uneven spatial sampling. To solve these problems, a new filtering method is proposed, which uses the generalized S transform which has good time-frequency concentration criterion to transform seismic data from the time-space to time-frequency-space domain （t-f-x）. Then in the t-f-x domain apply Empirical Mode Decomposition （EMD） on each frequency slice and clear the Intrinsic Mode Functions （IMFs） that noise dominates to suppress coherent and random noise. The model study shows that the high frequency component in the first IMF represents mainly noise, so clearing the first IMF can suppress noise. The EMD filtering method in the t-f-x domain after generalized S transform is equivalent to self-adaptive f-k filtering that depends on position, frequency, and truncation characteristics of high wave numbers. This filtering method takes local data time-frequency characteristic into consideration and is easy to perform. Compared with AR predictive filtering, the component that this method filters is highly localized and contains relatively fewer low wave numbers and the filter result does not show over-smoothing effects. Real data processing proves that the EMD filtering method in the t-f-x domain after generalized S transform can effectively suppress random and coherent noise of steep dips.

横向爆炸载荷下泡沫铝填充管的动态响应

采用数值模拟与理论分析相结合的方法,研究横向爆炸载荷下泡沫铝填充管的动态响应。利用有限元软件ABAQUS/EXPLICIT对横向爆炸载荷下泡沫铝填充管的塑性变形进行了数值模拟研究,分析了泡沫铝的相对密度、外管的直径与壁厚等因素对结构动态响应的影响。基于理想刚塑性地基梁模型,结合模态分析法,建立了预测横向爆炸载荷下泡沫铝填充管跨中挠度的理论分析模型,并进行了无量纲分析,给出了跨中无量纲挠度随无量纲冲量的变化规律。泡沫铝填充管跨中挠度的理论预测与数值模拟结果的误差在20%以内,表明所建立的理论分析模型合理可行。泡沫铝相对密度对横向爆炸载荷下填充管的跨中挠度有较大的影响,随着泡沫铝相对密度的增大,填充管跨中挠度减小。随着外管直径与壁厚的增大,跨中挠度减小。理论分析中,假设的两种模态函数对填充管跨中挠度的影响较小。

强迫范德波振荡的全局分岔的过渡区

本文研究强迫范德波振范在一指定参数平面内的分岔结构，参数平面内特别地包含中等参数域；单个亚谐解１／（２Ｋ＋１）的主模态锁定区顺序排列，并且由地渡区相间隔是分岔的主要结构；提示了有两类过渡区，（１）简单的，牲介转动数为１／（２Ｋ＋１）的两个模态锁定解共存；（２）复杂的，其中模态锁定觚发子区的解的转 是介于１／（２Ｋ＋１）和１／（２Ｋ－１）之间的分数以及准周期、混沌解的存在，并发生鞍结分岔。

Improved random noise attenuation using f-x empirical mode decomposition and local similarity

Conventional f-x empirical mode decomposition（EMD） is an effective random noise attenuation method for use with seismic profiles mainly containing horizontal events.However,when a seismic event is not horizontal,the use of f-x EMD is harmful to most useful signals.Based on the framework of f-x EMD,this study proposes an improved denoising approach that retrieves lost useful signals by detecting effective signal points in a noise section using local similarity and then designing a weighting operator for retrieving signals.Compared with conventional f-x EMD,f-x predictive filtering,and f-x empirical mode decomposition predictive filtering,the new approach can preserve more useful signals and obtain a relatively cleaner denoised image.Synthetic and field data examples are shown as test performances of the proposed approach,thereby verifying the effectiveness of this method.