纵横载荷作用下功能梯度梁的弯曲和过屈曲

基于一阶非线性梁理论和物理中面概念,导出了纵横向载荷作用下功能梯度材料(FGM)梁非线性弯曲和过屈曲问题的控制方程,并获得了该问题的精确解;据此解研究了梯度材料性质、外载荷、横向剪切变形以及边界条件等因素对功能梯度材料梁非线性力学行为的影响,分析中假设功能梯度材料性质只沿梁厚度方向,并按成分含量的幂指数函数形式变化。结果表明:纵横载荷共同作用下,功能梯度梁的弯曲构形将有无限多个;随着梯度指数的增大,梁的变形减小,临界载荷升高;随着长高比的增大,横向剪切变形的影响减小。     

稳定分层湍流中逆梯度输运的关联分析研究

本文采用关联分析方法研究了稳定温度分层湍流中的结构特性、输运特性，以及热量、动量逆梯度输运现象的尺度效应及其参数演化．首先采用大涡模拟方法对稳定分层湍流中的结构特性和输运特性进行了分析，将逆梯度输运发生的时间尺度作为已知条件，结合关联量分析方法在波数空间中的解析解，对逆梯度输运现象的尺度效应进行了分析研究．结果发现，稳定分层强度较大的流动中发生垂向热量及动量逆梯度输运现象，发生的结构尺度与关联分析所发现垂向热量、动量逆梯度输运的波数形成了呼应．随着分层强度增加，热量、动量的输运强度均受抑制，与逆梯度输运关联的流场结构尺度减小，同样的效应也发生在流场结构向下游演化的过程中．     

染料敏化太阳能电池载流子传输的数值模拟

由于在染料敏化太阳能电池(dye-sensitized solar cell, DSSC)中存在染料弛豫、半导体薄膜中电子与氧化态染料分子发生反应和电子在电解质中与氧化态离子复合等不利反应,利用一个更完善的DSSC载流子传输模型对电池的光电性能进行模拟就显得非常重要。为此,本文基于由多重俘获理论建立的DSSC中的包括电子、染料阳离子、碘化物和三碘化物在内的载流子传输模型,数值模拟得到了不同TiO₂薄膜厚度、不同入射光强度与不同染料分子吸收系数下DSSC的J-V曲线。结果表明,随着TiO₂薄膜厚度的增加,太阳能电池的短路电流密度增大,开路电压减小,光电转换效率先增大后减小。当DSSC的TiO₂薄膜厚度为20 μm时,光电转换效率达到最大值7.41%,同时光电转换效率随入射光强度与染料分子吸收系数的增大均有一定程度提高,其中在吸收系数为4 500 cm^-1时,光电转换效率为6.73%。以上结果可以为改进DSSC的光电性能提供理论指导。     

一类分数阶Langevin方程block-by-block算法的数值分析

分数阶Langevin方程有重要的科学意义和工程应用价值，基于经典block-by-block算法，求解了一类含有Caputo导数的分数阶Langevin方程的数值解.Block-by-block算法通过引入二次Lagrange基函数插值，构造出逐块收敛的非线性方程组，通过在每一块耦合求得分数阶Langevin方程的数值解.在0<α<1条件下，应用随机Taylor展开证明block-by-block算法是3+α阶收敛的，数值试验表明在不同α和时间步长h取值下，block-by-block算法具有稳定性和收敛性，克服了现有方法求解分数阶Langevin方程速度慢精度低的缺点，表明block-by-block算法求解分数阶Langevin方程是高效的.     

计算代数方程组孤立奇异解的符号数值方法

求解代数方程组是计算代数几何的最基本问题之一,孤立奇异解的计算则是其中最具挑战性的课题之一,在科学与工程计算中有着广泛的应用,如机器人、计算机视觉、机器学习、人工智能、运筹学、密码学和控制论等.本文结合作者的研究成果,综述了符号数值方法在计算代数系统孤立奇异解、特别是近似奇异解精化与验证方面的研究进展,并对未来的研究方向提出了展望.     

一道高考题的理论计算与数值模拟

2021年高考题河北卷第10题涉及有限电荷系统的场强与电势分布，其中选项C为考查难点，要求比较两个点的电势高低，笔者用理论分析和数值模拟突破这一难点.     

Application of DEM modified with enlarged particle model to simulation of bead motion in a bead mill

We applied the discrete element method （DEM） of simulation modified by an enlarged particle model to simulate bead motion in a large bead mill. The stainless-steel bead mill has inner diameter of 102 mm and mill length of 198 mm. The bead diameter and filling ratio were fixed respectively at 0.5 mm and 85%. The agitator rotational speed was changed from 1863 to 3261 rpm. The bead motion was monitored experimentally using a high-speed video camera through a transparent mill body. For the simulation, enlarged particle sizes were set as 3-6 mm in diameter. With the DEM modified by the enlarged particle model, the motion of enlarged particles in a mill was simulated.The velocity data of the simulated enlarged particles were compared with those obtained in the experiment. The simulated velocity of the enlarged particles depends on the virtual frictional coefficient in the DEM model. The optimized value of the virtual frictional coefficient can be determined by considering the accumulated mean value. Results show that the velocity of the enlarged particles simulated increases with an increase in the optimum virtual frictional coefficient, but the simulated velocity agrees well with that determined experimentally by optimizing the virtual frictional coefficient in the simulation. The computing time in the simulation decreases with increased particle size.     

电场下离子型聚合物复合囊泡结构变化的分子动力学模拟

利用粗粒化分子动力学模拟研究了电场作用下离子型聚合物复合囊泡形变与破裂的过程.定量分析了囊泡破裂过程中的结构变化,包括囊泡的形变程度、破裂速度、组分分布以及破裂后的结构.研究表明,电场强度较弱时,囊泡表面所吸附的聚电解质首先脱落,囊泡由球形结构转变为椭球结构.随着电场强度增大,离聚物的离子侧基发生重新排布,囊泡表面电荷的有序结构被破坏,导致囊泡的结构无法维持而破裂,囊泡塌缩,分裂形成离聚物团簇,并进一步破裂为小尺寸的离聚物聚集体,均匀分散于溶液中.本文利用分子动力学模拟明确了电场中离子型高分子复合囊泡破裂过程的分子机理,为药物释放技术的优化及发展提供了理论支持.     

碱性电解液中K3[Fe(CN)6]在锌阳极上的自发还原和吸附延长锌镍电池的循环寿命

In view of the continuously worsening environmental problems, fossil fuels will not be able to support the development of human life in the future. Hence, it is of great importance to work on the efficient utilization of cleaner energy resources. In this case, cheap, reliable, and eco-friendly grid-scale energy storage systems can play a key role in optimizing our energy usage. When compared with lithium-ion and lead-acid batteries, the excellent safety, environmental benignity, and low toxicity of aqueous Zn-based batteries make them competitive in the context of large-scale energy storage. Among the various Zn-based batteries, due to a high open-circuit voltage and excellent rate performance, Zn-Ni batteries have great potential in practical applications. Nevertheless, the intrinsic obstacles associated with the use of Zn anodes in alkaline electrolytes, such as dendrite, shape change, passivation, and corrosion, limit their commercial application. Hence, we have focused our current efforts on inhibiting the corrosion and dissolution of Zn species. Based on a previous study from our research group, the failure of the Zn-Ni battery was caused by the shape change of the Zn anode, which stemmed from the dissolution of Zn and uneven current distribution on the anode. Therefore, for the current study, we selected K₃[Fe(CN)₆] as an electrolyte additive that would help minimize the corrosion and dissolution of the Zn anode. In the alkaline electrolyte, [Fe(CN)₆]^3– was reduced to [Fe(CN)₆]^4– by the metallic Zn present in the Zn-Ni battery. Owing to its low solubility in the electrolyte, K₄[Fe(CN)₆] adhered to the active Zn anode, thereby inhibiting the aggregation and corrosion of Zn. Ultimately, the shape change of the anode was effectively eliminated, which improved the cycling life of the Zn-Ni battery by more than three times (i.e., from 124 cycles to more than 423 cycles). As for capacity retention, the Zn-Ni battery with the pristine electrolyte only exhibited 40% capacity retention after 85 cycles, while the Zn-Ni battery with the modified electrolyte (i.e., containing K₃[Fe(CN)₆]) showed 72% capacity retention. Moreover, unlike conventional organic additives that increase electrode polarization, the addition of K₃[Fe(CN)₆] not only significantly reduced the charge-transfer resistance in a simplified three-electrode system, but also improved the discharge capacity and rate performance of the Zn-Ni battery. Importantly, considering that this strategy was easy to achieve and minimized additional costs, K₃[Fe(CN)₆], as an electrolyte additive with almost no negative effect, has tremendous potential in commercial Zn-Ni batteries.     

机器学习方法在高分子玻璃化研究中的应用

高分子玻璃的物理性质与其结构和动力学密切相关.揭示高分子玻璃化的微观物理图像对高分子玻璃材料的结构调控和分子设计至关重要.然而,高分子的长链结构和复杂单体结构特征致使目前仍然缺乏普适的理论或者模型来定量解释高分子玻璃化的物理机制.因此,亟需发展更为先进的研究方法从而更深入地理解高分子玻璃化.近年来,国内外学者利用基于数据驱动的信息学方法(例如机器学习)对高分子玻璃化开展了研究,并取得了丰富成果.本综述首先介绍了常用的高分子信息学数据库和机器学习算法.之后,从高分子玻璃化转变温度的预测、新型高分子玻璃材料的研发、过冷液体的结构-动力学关系和玻璃体系相变的确定四个方面总结和评述了机器学习应用在玻璃化研究中的代表性进展.最后,探讨了机器学习方法在高分子玻璃化研究中面临的主要挑战,并对玻璃信息学这一领域的发展进行了展望.