Optical in-situ analysis of the cavitation damage on technical alloys under repeated single bubbles

重复单气泡作用下技术合金空化损伤的光学原位分析

基本信息

项目摘要

With time, cavitation – that is, the formation and collapse of gas bubbles in liquids – can damage the surface even of high-strength materials. The resistance of materials to cavitation is typically investigated using ultrasound sonotrodes. Due to the large number and stochastic nature of the acoustically generated bubbles, in general the resulting damage cannot be assigned to individual bubble events. However, by means of focused laser pulses it is possible to generate individual bubbles that are precisely reproducible in location and time. Besides many studies on the fluid dynamics of the collapsing bubble, some works examine the effect of such individual bubbles on a deformable solid surface. With few exceptions, the sample is from a soft material, such that the deformation resulting from a single bubble can be correlated with its dynamics. However, it is unclear how these results can be transferred to higher-strength materials.The proposed project aims at this knowledge gap between microscopically uncontrolled damage to technical alloys by acoustic cavitation on one hand and precisely reproducible loading on soft materials by individual bubbles on the other. Series of single bubbles will provide defined, repeated stress on surfaces of technical alloys. The dynamics of each bubble are recorded by imaging, then after each bubble collapse the incremental increase in damage is recorded in situ by optical microscopy, and this is complemented by high-resolution ex-situ techniques to yield a detailed picture of the damage evolution. This enables examining the development of damage during the incubation and erosion phases in unprecedented spatio-temporal resolution, as well as correlating the details of the collapse processes with material changes. In parallel, standard tests with a sonotrode are performed to put this damage into context with what is generally known about the materials’ cavitation resistance.First, microscopy optics are integrated into an existing single-bubble experiment and synchronized with high-speed visualization of the bubbles. Using samples from a soft material, this in-situ imaging is validated by ex-situ confocal microscopy. In a second step, series of tests are carried out on NiAl bronze and 316L steel in order to develop semi-automated methods to efficiently evaluate the very large data sets and to correlate in-situ and ex-situ microscopy. Finally, these methods are used in experiments on both materials, in which the number of bubbles, their distance from the sample surface, and the bubble diameter are varied. Ex-situ, the damage mechanisms in the technical alloys are analyzed in detail by high-resolution electron microscopy and compared with samples from experiments with ultrasonic cavitation according to ASTM G32. The extensive data generated in this project will be made publicly accessible, in particular for modeling work outside of the project.
随着时间的流逝,液体中的气体气泡的格式和崩溃损害了高强度的材料的表面。无法通过焦点脉冲将损坏分配给单个气泡事件。 OM材料,单个气泡产生的变形可以与动态相关。由单个气泡上的单个BBLE定义的,每种气泡的动力学都会通过成像记录。 - 在空前的时空分辨率中侵蚀损害的态度,并将崩溃过程的细节与材料中的崩溃过程相关联。材料的空化。首先,显微镜光学元件被整合到单式实验中,并与气泡的高速可视化一起使用。评估大数据集An-situ和-situ显微镜,其中气泡的数量,它们与样品表面的距离和气泡直径是多样的,而技术中的损坏机制则可以详细介绍电子显微镜和带有超声气腔的样品M与ASTM G32进行了比较。

项目成果

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Dr.-Ing. Stefanie Hanke其他文献

Dr.-Ing. Stefanie Hanke的其他文献

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{{ truncateString('Dr.-Ing. Stefanie Hanke', 18)}}的其他基金

Influence of Mg and Si Content in Aluminium Alloys on Severe Plastic Deformation Behaviour during Solid-State Coating Deposition using Friction Surfacing
铝合金中 Mg 和 Si 含量对摩擦堆焊固态涂层沉积过程中严重塑性变形行为的影响
  • 批准号:
    323162991
  • 财政年份:
    2016
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Deformation and failure mechanisms in austenitic steel under coupled compressive and torsional loading
压缩和扭转耦合载荷下奥氏体钢的变形和失效机制
  • 批准号:
    441180620
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Classifying wear characteristics in lubricated sliding wear based on time series sensor signals using artificial intelligence
使用人工智能根据时间序列传感器信号对润滑滑动磨损的磨损特征进行分类
  • 批准号:
    525173005
  • 财政年份:
  • 资助金额:
    --
  • 项目类别:
    Research Grants

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