Abstract
To identify the field of velocities of a fluid, the postprocessing stage in the analysis of fluids using PIV images associates tracers in two consecutive images. Statistical methods have been used to perform this task and investigations have reported models of artificial neural networks, as well. The Self-Organized Map (SOM) model stands out for its simplicity and effectiveness, additionally to presenting areas of opportunity for exploring. The SOM model is efficient in the correlation of tracers detected in consecutive PIV images; however, the necessary operations are computationally expensive. This paper discusses the implementation of these operations on GPU to reduce the time complexity. Furthermore, the function calculating the learning factor of the original network model is too simple, and it is advisable to use one that can better adapt to the characteristics of the fluid’s motion. Thus, a proposed 3PL learning factor function modifies the original model for good, because of its greater flexibility due to the presence of three parameters. The results show that this 3PL modification overcomes the efficiency of the original model and one of its variants, in addition to decreasing the computational cost.
This research project has been supported by the Mexican Public Education Bureau (PRODEP).
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Hernández-Pérez, R., Gabbasov, R., Suárez-Cansino, J., López-Morales, V., Franco-Árcega, A. (2018). Efficiency Analysis of Particle Tracking with Synthetic PIV Using SOM. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Soft Computing. MICAI 2018. Lecture Notes in Computer Science(), vol 11288. Springer, Cham. https://doi.org/10.1007/978-3-030-04491-6_11
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