Penetration modeling of ultra-high performance concrete using multiscale meshfree methods

Terminal ballistics of concrete is of extreme importance to the military and civil communities. Over the past few decades, ultra-high performance concrete (UHPC) has been developed for various applications in the design of protective structures because UHPC has an enhanced ballistic resistance over conventional strength concrete. Developing predictive numerical models of UHPC subjected to penetration is critical in understanding the material's enhanced performance. This study employs the advanced fundamental concrete (AFC) model, and it will run inside the reproducing kernel particle method (RKPM)-based code known as the nonlinear meshfree analysis program (NMAP). NMAP is advantageous for modeling impact and penetration problems that exhibit extreme deformation and material fragmentation. A comprehensive experimental study was conducted to characterize the UHPC. The investigation consisted of fracture toughness testing, the utilization of nondestructive microcomputed tomography analysis, and lastly projectile penetration shots on the UHPC targets. To improve the accuracy of the model, a new scaled damage evolution law (SDEL) is employed within the microcrack informed damage model. During the homogenized macroscopic calculation, the corresponding microscopic cell needs to be dimensionally equivalent to the mesh dimension when the partial differential equation becomes ill posed and strain softening ensues. To ensure arbitrary mesh geometry for which the homogenized stress-strain curves are derived, a size scaling law is incorporated into the homogenized tensile damage evolution law. This ensures energy-bridging equivalence of the microscopic cell to the homogenized medium irrespective of arbitrary mesh geometry. Results of numerical investigations will be compared with results of penetration experiments.     

Model identification and parameter estimation of elastoplastic constitutive model by data assimilation using the particle filter

Data assimilation, using the particle filter and incorporating the soil‐water coupled finite element method, is applied to identify the yield function of the elastoplastic constitutive model and corresponding parameters based on the sequential measurements of hypothetical soil tests and an actual construction sequence. In the proposed framework of the inverse analysis, the unknowns are both the particular parameter within the exponential contractancy model, n_E, which parameterizes various shapes for the yield function of the competing constitutive models, including the original/the modified Cam‐Clay models and in‐between models and the parameters of the corresponding constitutive model. An appropriate set, consisting of the yield function of the constitutive model and the parameters of the constitutive model, can be simultaneously identified by the particle filter to describe the most suitable soil behavior. To examine the validity of the proposed procedure, hypothetical and actual measurements for the displacements of a soil specimen were obtained for consolidated and undrained tests through a synthetic FEM computation and for consolidated and drained tests, respectively. After examining the applicability of the proposed procedure to these test results, the present paper then focuses on the actual measured data, ie, the settlement behavior including the lateral deformation of the Kobe Airport Island constructed on reclaimed land.     

城市形态演化的粒子群智能随机元胞模型与应用——以上海市嘉定区为例

城市形态演化是一个非线性的复杂时空动态过程,认识、理解和模拟此变化过程,有助于探索城市扩展的机理。地理元胞自动机(CA)因其较强的复杂系统模拟和预测能力,越来越多地应用于城市形态的演变研究。CA"自下而上"的结构特性,与粒子群智能(PSO)由底层单元交互而呈现系统全局的自组织性,本质上是一致的。本研究将两者结合,以模拟结果和真实形态的差异最小化为基础,利用粒子群智能,以快速随机搜索的方式,获取CA参数的优化组合和模型结构,从而建立了一种粒子群智能地理元胞自动机模型(PSO-CA)。以上海市嘉定区为案例,通过较长时段的历史数据对PSO-CA模型进行校正,成功模拟了该区域1989-2006年的城市形态演化过程,并进行了2010年发展预测。与传统地理CA模型比较,PSO-CA模型模拟结果的精度更高。     

基于粒子群算法的神经网络在水资源评价中的应用

为了改善传统的人工神经网络,在训练过程中容易陷入局部最小导致应用于水资源评价时存在对训练样本的拟合精度不高的缺点,采用粒子群算法优化人工神经网络的权值和阈值,然后将其应用于中国12个地区的水资源可持续利用系统评价实例中,并和传统的人工神经网络进行了对照。结果表明,基于粒子群算法的人工神经网络和传统的人工神经网络相比,能较好的提高对训练样本的拟合精度,表明基于粒子群算法的人工神经网络,用于水资源可持续利用系统评价是可行的。     

基于粒子群算法的神经网络在水资源评价中的应用

为了改善传统的人工神经网络,在训练过程中容易陷入局部最小导致应用于水资源评价时存在对训练样本的拟合精度不高的缺点,采用粒子群算法优化人工神经网络的权值和阈值,然后将其应用于中国12个地区的水资源可持续利用系统评价实例中,并和传统的人工神经网络进行了对照。结果表明,基于粒子群算法的人工神经网络和传统的人工神经网络相比,能较好的提高对训练样本的拟合精度,表明基于粒子群算法的人工神经网络,用于水资源可持续利用系统评价是可行的。     

A multidimensional streamline-based method to simulate reactive solute transport in heterogeneous porous media

We present a new streamline-based numerical method for simulating reactive solute transport in porous media. The key innovation of the method is that both longitudinal and transverse dispersion are incorporated accurately without numerical dispersion. Dispersion is approximated in a flow-oriented grid using a combination of a one-dimensional finite difference scheme and a meshless approximation. In contrast to previous hybrid alternatives to incorporate dispersion in streamline-based simulations, the proposed scheme does not require a grid and, hence, it does not introduce numerical dispersion. In addition, the proposed scheme eliminates numerical oscillations and negative concentration values even when the dispersion tensor includes the off-diagonal coefficients and the flow field is non-uniform. We demonstrate that for a set of two- and three-dimensional benchmark problems, the new proposed streamline-based formulation compares favorably to two state of the art finite volume and hybrid Eulerian–Lagrangian solvers.     

颗粒阻尼技术及其在土木工程中的应用展望

介绍了国内外颗粒阻尼发展现状,比较了非阻塞性颗粒阻尼、柔性颗粒阻尼、带活塞振动颗粒阻尼的异同,按照颗粒阻尼的类型,系统地阐述了颗粒阻尼内部作用机理、常用计算模型、耗能影响因素等问题,指出其在应用中的诸多优点,及优于粘滞与机械阻尼的性能,验证了颗粒阻尼良好的工程应用前景与发展潜力。同时指出了现今理论模型、实际运用及设计中存在的一些问题与不足,对颗粒阻尼的研究与发展方向提出了建议。     

TBM滚刀破岩过程影响因素数值模拟研究

全断面岩石掘进机（TBM）的破岩效率主要受刀盘设计和岩体特征的影响。采用颗粒流方法建立了岩石试件与滚刀的数值模型,对TBM滚刀破岩过程的影响因素进行了分析。研究表明,单刃滚刀交替作用下强度较低的岩石中易形成规则的张拉裂纹而生成较大岩碴;强度较高的岩石中滚刀的侧向挤压促使形成块度较小的片状岩碴;双刃滚刀作用下岩石表面受到强烈挤压后出现较大的拉应力,使岩石更易破碎,且仅在强度较高的岩石中才易形成透镜状岩碴。滚刀破岩过程中存在能耗最小的最佳间距,该最佳间距随着岩石强度的增大而减小。滚刀破岩过程中,结构面对裂纹扩展具有显著的控制性作用,并阻隔损伤向结构面下的岩石中渗透;随着结构面与滚刀侵入方向夹角的减小,结构面将引导裂纹向岩石深部扩展,而当夹角较大时,结构面则会引导裂纹横向扩展,易导致大块岩碴的形成     

考虑不均质度的岩石声发射数值模拟研究

利用离散元颗粒流软件模拟了岩石单轴压缩破坏过程,采用内嵌FISH语言,通过设置随机缺陷来建立不同均质度的岩石数值试样对岩石的声发射规律进行了研究。结果表明,不均质度对于岩石声发射特性有着重要影响,随不均质度的增加,试样的峰值强度下降,声发射总数大幅增加,空间分布也更为分散,声发射出现时间更早,持续时间更长,峰值后的声发射保持更高的强度;单轴应力状态下声发射最大强度出现时间相比峰值破坏时间有不同程度滞后,不均质度越高,滞后效应越明显,显示岩石破坏要早于声发射峰值出现时间。     

深埋隧洞开挖损伤区的检测及特征分析

隧洞开挖以后围岩会出现损伤,并在洞周形成开挖损伤区,在损伤区范围内声波波速会出现衰减。针对锦屏二级水电站3#引水隧洞在隧道掘进机（TBM）开挖过程中可能形成的损伤区,首先利用数值方法探讨了损伤区的分布特征,通过监测特征点在开挖过程中的应力路径,确定了特征点的应力状态和可能出现声波波速衰减的部位。在此基础上确定了对声波钻孔的布置方案,并利用单孔声波检测方法确定了损伤区深度。为了实现颗粒流程序PFC对损伤区的模拟,通过三轴压缩试验确定了PFC的细观参数。最后利用PFC描述了损伤区的分布,分析了损伤局部化特征和损伤程度的变化。结果表明PFC的模拟结果和检测结果具有较好的一致性,能够实现对损伤区特征的准确描述。研究成果对损伤区的检测和支护优化设计具有指导意义。