颗粒增强铝基复合材料冲击疲劳裂纹扩展的行为和机理

本文针对发展新一代步兵战车复合材料履带板所面临的关键问题，结合其实际受载特点，设计制备了冲击疲劳实验加载装置，并着重从实验设计及机理分析上进行细致深入的探索，揭示了Al_2O_3/LC_4复合材料冲击疲劳破坏的微观过程和机理。首先分别对SiC_P/LC_4、Al_2O_(3P)/LC_4 及基体 LC_4 进行了显微组织的观察与定量分析，并对其拉伸、三点弯曲破坏过程进行了在位观察，结合其断裂形貌的观察与分析，揭示出颗粒增强铝基复合材料断裂破坏的根本原因是颗粒的聚集及脆性相在晶界的严重偏聚。针对这一结论，给材料制备单位提出工艺改进意见。对工艺改进后制备的复合材料进行常规力学性能的测试，结果表明，其拉伸性能明显优于改进前制备的相应材料。为了进行冲击疲劳的实验研究，在分析步兵战车履带板实际受载特点的基础上，自行设计制备了冲击疲劳实验的加载装置。主要包括主体框架和测量系统，前者与小型振动系统配合使用可以实现冲击能量为 0.3J、冲击频率为 1Hz、冲击速度为 0.6m/s 的多次冲击实验；后者可以准确记录下任意时刻的冲击载荷波形及冲击疲劳载荷的循环数。为了考察颗粒与加载速率对复合材料疲劳机理的影响，实验研究了 Al_2O_3/LC_4 复合材料和 LC_4 纯基体材料在冲击疲劳和常规疲劳过程中裂纹的扩展过程及扩展速率。综合结果发现：与LC_4纯基体材料相比，Al_2O_3/LC_4复合材料疲劳裂纹扩展得更为迅速。复合材料中，由于颗粒的加入，两种疲劳方式下袭纹都发生严重偏转；裂纹经过颗粒时，多数是绕过，少数是切过颗粒；冲击疲劳裂纹扩展速率明显高于常规疲劳裂纹扩展速率。纯基体材料中，两种加载方式下，裂纹基本都以穿晶的方式扩展，裂纹常常表现为小锯齿状；冲击疲劳裂纹尖端的塑性变形程度比常规疲劳更大；冲击疲劳裂纹比常规疲劳裂纹更曲折，表现出多尺度的锯齿状（Zig-Zag）特征；冲击疲劳裂纹扩展速率高于常规疲劳的裂纹扩展速率。在基本实验的基础上，进一步对断口及裂纹扩展途径进行了微观观察和定量分析，最后综合全文的实验和统计结果，讨论了颗粒增强铝基复合材料的冲击疲劳机理。复合材料疲劳裂纹扩展速率的提高主要与裂纹的偏转有关，裂纹更倾向于沿着颗粒与基体的界面扩展；两种材料的疲劳裂纹扩展速率均随加载速率的增加而增加，呈现加载速率的反作用。加载方式的改变，一方面，由于冲击情况下载荷持续时间降低，使裂纹扩展速率降低；另一方面，加载速率的提高使得断裂韧性值降低，材料变脆，裂纹扩展速率升高。这两个方面相互影响，相互竞争，决定实际的裂纹扩展速率。两种材料中，不同加载速率下的疲劳裂纹扩展的微观机制基本一致，没有明显的本质区别。

Cooperative artificial propagation programs for salmon and steelhead, 1995-1996

Fifteen cooperative fish rearing and planting programs for salmon and steelhead were active from July 1, 1995 through June 30, 1996. For all programs, 134,213 steelhead trout,(Oncorhynchus mykiss), 7,742,577 chinook salmon,(~ tshawytscha),and 25,075 coho salmon(~ kisutch) were planted. (PDF contains 26 pages.)

Cooperative artificial propagation programs for salmon and steelhead, 1996-1997

Fourteen cooperative fish rearing and planting programs for salmon and steelhead were active from July 1, 1996 through June 30, 1997. For all programs, 208,922 steelhead trout, (Oncorhynchus mykiss), 10,334,457 chinook salmon,(O. tshawytscha),and 60,681 coho salmon(O. kisutch) were planted. (PDF contains 24 pages.)

Classification of neocortical interneurons using affinity propagation

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. Neuronal classification has been a difficult problem because it is unclear what a neuronal cell class actually is and what are the best characteristics are to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological or molecular characteristics, when applied to selected datasets, have provided quantitative and unbiased identification of distinct neuronal subtypes. However, better and more robust classification methods are needed for increasingly complex and larger datasets. We explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. In fact, using a combined anatomical/physiological dataset, our algorithm differentiated parvalbumin from somatostatin interneurons in 49 out of 50 cases. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.