کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4633152 | 1340663 | 2008 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
SOM-Based particle matching algorithm for 3D particle tracking velocimetry
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
ریاضیات کاربردی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The self-organizing maps (SOM) neural network is applied to the particle matching algorithm of the 3D particle tracking velocimetry (PTV). In the particle tracking velocimetry, the matching result of particles between two time-differential image frames is directly related to the velocity of particles, i.e., the velocity of the fluid flow in which the particles are suspended. The new particle matching method is basically based on the SOM model by Labonté [G. Labonté, A new neural network for particle tracking velocimetry, Experiments in Fluids 26-4 (1999) 340-346] but has been improved in many aspects for more reliable matching at larger numbers of distributed particles, larger dynamic range of velocity and more robustness against loss-of-pair particles between two image frames. In addition the new method is now applied to 3D particle flows for the use in 3D particle tracking velocimetry. In the present study, the new method is tested with 2D and 3D synthetic particle images as well as with 2D experimental images with a large number of particles.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Mathematics and Computation - Volume 205, Issue 2, 15 November 2008, Pages 890-898
Journal: Applied Mathematics and Computation - Volume 205, Issue 2, 15 November 2008, Pages 890-898
نویسندگان
Kazuo Ohmi,