By Ki-Han Kim, Georges Chahine, Jean-Pierre Franc, Ayat Karimi
This ebook offers a finished therapy of the cavitation erosion phenomenon and state of the art learn within the box. it's divided into elements. half 1 contains seven chapters, providing a variety of computational and experimental ways to cavitation erosion. It incorporates a basic advent to cavitation and cavitation erosion an in depth description of amenities and dimension strategies typical in cavitation erosion stories, an intensive presentation of varied phases of cavitation harm (including incubation and mass loss) and insights into the contribution of computational how to the research of either fluid and fabric habit. The proposed process is predicated on a close description of influence so much generated by way of collapsing cavitation bubbles and a actual research of the cloth reaction to those a lot. half 2 is dedicated to a range of 9 papers awarded on the overseas Workshop on complex Experimental and Numerical options for Cavitation Erosion Prediction (Grenoble, France, 1-2 March 2011) representing the vanguard of study on cavitation erosion. cutting edge numerical and experimental investigations illustrate the main complex breakthroughs in cavitation erosion examine.
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Additional resources for Advanced Experimental and Numerical Techniques for Cavitation Erosion Prediction
7 Typical pressure signal in a cavitation flow . 6 mm, transducer sensitivity 137 N/V, sampling frequency 2 M samples/s, flow velocity 89 m/s) 8 4 0 -40 -20 0 Time (µs) 20 40 2 1 0 -20 -10 0 10 20 Time (µs) Fig. 8 Typical examples of cavitation erosion impulsive loads measured using PVDF pressure sensors. a. Example of an impulsive load measured in a cavitating jet apparatus. The PVDF film was 110 lm thick and protected against cavitation by polyamide tapes. Sensitivity estimated from both the pencil lead breaking technique and the ball dropping technique is of the order of 10 N/V (adapted from , with permission from ASME) b.
3 Typical example of cumulative histograms of pitting rates versus pit size. The count for the cumulative number of pits is started from the largest pit. Histograms are approximated by straight lines in semi-logarithmic scales. 5 lm. 2 Histograms of Pit Size A pitted surface exhibits a large variety of pits with various equivalent diameters (based on the contour areas) and maximum depths. The statistical distribution of pits with diameter can be characterized by histograms of pit size. 3 shows typical examples of cumulative histograms which count the number of pits whose diameter exceeds a specified value plotted along the horizontal axis.
The interruption of the stacking sequences stores a certain amount of energy called stacking fault energy (SFE). This energy influences the nature and arrangement of lattice defects and thereby affects mechanisms of the plastic deformation. When the SFE is low, the mobility of dislocations is reduced making the occurrence of cross-slip and climb more difficult. In contrast at high SFE, materials deform mainly by dislocation glide, which favours deviation of dislocations from one plane to another, and results in the formation of 3D structures.