能勢 和夫

Novel 3D image analysis and particle matching techniques for the use in the volumetric particle tracking velocimetry have been developed and tested by using synthetic images and experimental images of unsteady 3D flows. A tomography based particle reconstruction scheme along with the subsequent process of individual particle detection and validation was used. The detected particles in the two time differential samples are matched by using Self Organising Map (SOM) neural network scheme. SOM neural network tracking algorithm is highly adaptive to time differential tracking even with loss-of-pair particles. The particle location and velocity results of the present new approach turned out accurate, reliable and robust in comparison to the conventional 3D PTV approaches.

Novel 3D image analysis and particle matching techniques for the use in the volumetric particle tracking velocimetry have been developed and tested by using synthetic images and experimental images of unsteady 3D flows. A tomography based particle reconstruction scheme along with the subsequent process of individual particle detection and validation was used. The detected particles in the two time differential samples are matched by using Self Organising Map (SOM) neural network scheme. SOM neural network tracking algorithm is highly adaptive to time differential tracking even with loss-of-pair particles. The particle location and velocity results of the present new approach turned out accurate, reliable and robust in comparison to the conventional 3D PTV approaches.

New algorithms of 3D particle tracking velocimetry (3D PTV) based on a tomographic reconstruction approach have been developed and tested by using synthetic images of unsteady 3D flows. The new algorithms are considered not only in the tomographic reconstruction process of the fluid volume with particles but also in the subsequent process of individual particle detection and validation. In particular, the tomographic reconstruction accuracy is boosted up by using a new recursive validation scheme through which many of ghost particles can be removed effectively. The particle detection process includes the particle mask correlation operator and the dynamic threshold scheme to extract individual particle centroids from the reconstructed intensity clusters of the fluid volume. The overall reconstruction accuracy is checked by the synthetic image data sets with different particle density and different volume thickness.