A macro-element strategy based upon spectral finite elements and mortar elements for transient wave propagation modeling. Application to ultrasonic testing of laminate composite materials

Proposing efficient numerical modeling tools for high-frequency wave propagation in realistic configurations, such as the one appearing in ultrasonic testing experiments, is a major challenge, especially in the perspective of inversion loops or parametric studies. We propose a numerical methodology addressing this challenge and based upon the combination of the spectral finite element method and the mortar element method. From a prior decomposition of the scene of interest into “macro-elements,” we show how one can improve the performances of the standard finite element procedures in terms of memory footprint and computational load. Additionally, using this decomposition, we are able to efficiently reconstruct important modeling features on-the-fly, such as orientations of anisotropic materials or splitting directions of perfectly matched layers formulations, altogether in a robust and efficient manner. We believe that this strategy is particularly suitable for parametric studies and sensitivity analysis. We illustrate our strategy by simulating the propagation of an ultrasonic wave into an immersed and curved anisotropic laminate 3D specimen flawed with an internal circular delamination of varying size, thus showing the efficiency and the robustness of our approach.     

ATSP:时态网络可视化的自适应时间片划分方法

现有的时态网络可视化方法大多采用等量时间片来可视化网络的演变，不利于时态模式的快速挖掘和发现。为此，根据时态网络固有的特征提出自适应时间片划分方法（Adaptive Time Slice Partition method，ATSP）。在时态网络的两种表示方式（基于事件的表示方式和基于快照的表示方式）的基础上，构建了ATSP的基础模型，同时提出了一种改进模型用来描述事件间隔时间服从长尾分布的时态网络。为了实现时间片的不等量划分，针对探索任务的不同提出了基于时态模式的ATSP规则和基于中心节点的ATSP规则，并提出了实现算法--层次划分算法（Hierarchical Partition algorithm，HP）和增量划分算法（Incremental Partition algorithm，IP）。实验结果表明，ATSP方法比传统的时间片划分方法更能准确地表示网络的时态特征，且该方法应用于可视化时，能有效归纳并展示网络的特征，明显提高了视觉分析的效率。     

A simple explicit single step time integration algorithm for structural dynamics

In this article, a new single-step explicit time integration method is developed based on the Newmark approximations for the analysis of various dynamic problems. The newly proposed method is second-order accurate and able to control numerical dissipation through the parameters of the Newmark approximations. Explicitness and order of accuracy of the proposed method are not affected in velocity-dependent problems. Illustrative linear and nonlinear examples are used to verify performances of the proposed method.     

多层介质膜脉宽压缩光栅清洗方法研究

采用SPM (Sulfuric-Peroxide Mixtures, 98wt%H₂SO₄+30wt%H₂O₂)+兆声(方法1)和氧等离子体+HPM (Hydrochloric/Peroxide Mixture, 37wt%HCl+30wt%H₂O₂+DIH₂O)+兆声(方法2)两种清洗方法对多层介质膜脉宽压缩光栅进行清洗, 并对清洗前后样品的表面元素含量、衍射效率、表面粗糙度、表面温升以及激光损伤阈值等参数进行测量以评估两种清洗方法清洗效果。在入射角70°, 脉宽12 ns, s偏振, 波长1064 nm的激光辐照下, 经过清洗方法1清洗后的光栅样品单脉冲激光损伤阈值为7.55 J/cm ², 而方法2清洗后的样品损伤阈值为5.32 J/cm ²。另外, 虽然经过方法2清洗后样品表面杂质含量更低, 但是在衍射效率、表面粗糙度和表面温升都劣于经方法1清洗后的样品, 进一步分析发现方法2中氧等离子体清洗过程引入的Fe元素影响了其样品损伤性能和温升性能。因此, SPM清洗方法可以作为多层介质膜脉宽压缩光栅提升抗激光损伤性能的优化清洗方案。     

Biohydrogen production,sludge granulation,and microbial community in an anaerobic inner cycle biohydrogen production (AICHP) reactor at different hydraulic retention times

This study investigated the continuous biohydrogen production in an anaerobic inner cycle biohydrogen production (AICHP) reactor fed with synthetic molasses wastewater as the model substrate under mesophilic conditions (37 ± 1 °C). The hydraulic retention times (HRTs) were set as 6.12, 4.90, 4.08, 3.50, and 3.06 h. Both maximum hydrogen production rate (HPR) (8.08 ± 0.48 L/L/d) and maximum granule formation were achieved at the HRT of 3.50 h. Acetic acid and butyric acid were the dominant metabolites in all tested HRTs throughout the experiment. Microbial community analysis showed that shortening the HRT promoted hydrogen production. This was mainly achieved by enhancing the growth of acetogenic bacteria in the AICHP reactor, rather than the growth of hydrogen-producing bacteria.     

一种作业弹性与截止时间感知的作业调度算法

针对采用MapReduce模型的大数据分析作业的调度问题进行深入研究，并分析现有任务调度算法的缺陷，现有算法没有考虑资源分配对于作业截止时间的影响，也未考虑不同类型作业截止时间的敏感性问题。因作业的完成时间随着分配资源的不同而改变，故称之为弹性作业，截止时间敏感性是指不同类型作业对截止时间要求的严格程度不同。针对以上问题，提出一种截止时间感知的弹性作业调度算法（DA）。该算法将作业依据截止时间敏感程度进行分类，在基于作业整体执行时间预测的基础上，通过调控不同的资源分配策略来改变作业完成时间，同时结合用户对于截止时间的需求及作业预执行的收益来提前规划作业的资源分配及调度次序使得整体收益最大化。将算法在仿真拥有210个物理节点的集群中进行实验，实验表明该算法满足了截止时间的限制并使得作业整体收益值平均提高了2.37倍。     

考虑决策者时序偏好的时域证据融合方法

针对时域不确定信息的融合难题，为充分体现时域信息融合的动态性特点和时间因素对融合的影响，在证据理论的基础上，提出一种考虑决策者时序偏好的时域证据融合方法。首先将决策者对时序的偏好融入时域证据融合，通过分析时域证据序列的特点，在定义时序记忆因子的基础上，对决策者的时序偏好进行度量；然后通过构建优化模型求解时序权重，再结合证据信任度的概念，对证据源进行修正；最后利用Dempster组合规则对修正后的证据进行融合。数值算例表明，与没有考虑时间因素的融合方法相比，考虑决策者时序偏好的证据融合方法可以有效处理时域信息序列中的冲突信息，得到合理的融合结果；同时，所提方法充分考虑了时域证据序列的信任度和决策者的主观偏好，可以反映决策者主观因素对时域证据融合的影响，较好地体现了时域证据融合的动态性特点。     

Sub-band coding of hexagonal images

According to the circle-packing theorem, the packing efficiency of a hexagonal lattice is higher than an equivalent square tessellation. Consequently, in several contexts, hexagonally sampled images compared to their Cartesian counterparts are better at preserving information content. In this paper, novel mapping techniques alongside the wavelet compression scheme are presented for hexagonal images. Specifically, we introduce two tree-based coding schemes, referred to as SBHex (spirally-mapped branch-coding for hexagonal images) and BBHex (breadth-first block-coding for hexagonal images). Both of these coding schemes respect the geometry of the hexagonal lattice and yield better compression results. Our empirical results show that the proposed algorithms for hexagonal images produce better reconstruction quality at low bits per pixel representations compared to the tree-based coding counterparts for the Cartesian grid.     

混菌矿化增强再生粗骨料的物理力学性能

为克服单一微生物培养成本高且矿化鲁棒性不足的缺陷，提出了一种混菌矿化增强再生粗骨料物理力学性能的方法.通过筛选矿化效率较高的好氧嗜碱混菌，考察了混菌矿化对再生粗骨料物理力学性能和混凝土抗压强度的影响.结果表明：相同增强时间下，混菌比纯菌呈现出更优异的矿化增强效果；随着混菌矿化增强时间的延长，再生粗骨料吸水率和压碎指标呈现出先减小后增大的趋势，最优增强时间为15 d；采用矿化增强再生粗骨料制备的再生混凝土抗压强度提高幅度达到22.1%.     

基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。