![]() Fluid experimental flow estimation based on an optical-flow scheme.Abstract of research paper on Materials engineering, author of scientific article - J. Exp Fluids, 2005, 38: 21–32Ĭorpetti T, Heitz D, Arroyo G, et al. Variational optical flow estimation for particle image velocimetry. Ruhnau P, Kohlberger T, Schnörr C, et al. In: Proceedings of the International Symposium on Applications of Laser Techniques to Fluid Mechanics. Adaptive PIV with variable interrogation window size and shape. ![]() Theory of non-isotropic spatial resolution in PIV Exp Fluids, 2003, 35: 268–277 Standard images for particle-image velocimetry. A circulant-matrix-based hybrid optical flow method for PIV measurement with large displacement. IEEE Trans Pattern Anal Mach Intell 2015, 37: 583–596 High-speed tracking with kernelized correlation filters. Henriques J F, Caseiro R, Martins P, et al. Time-resolved flow reconstruction with indirect measurements using regression models and Kalman-filtered POD ROM. Enhancing particle image tracking performance with a sequential Monte Carlo method: The bootstrap filter. A Kalman tracker for super-resolution PIV. Adaptive control of the dynamics of a fully turbulent bimodal wake using real-time PIV. Simultaneous micro-PIV measurements and real-time control trapping in a cross-slot channel. J Robot Mechatron, 2013, 25: 586–595Īkbaridoust F, Philip J, Hill D R A, et al. A real-time microscopic PIV system using frame straddling high-frame-rate vision. Toward real-time particle tracking using an event-based dynamic vision sensor. Exp Fluids, 2010, 48: 105–110ĭrazen D, Lichtsteiner P, Häfliger P, et al. Real-time image processing for particle tracking velocimetry. Experimental study of the wake characteristics of a two-blade horizontal axis wind turbine by time-resolved PIV. CFD investigation and PIV validation of flow field in a compact return diffuser under strong part-load conditions. Velocity refinement of PIV using global optical flow. Spatially adaptive PIV interrogation based on data ensemble. Theunissen R, Scarano F, Riethmuller M L. Adaptive incremental stippling for sample distribution in spatially adaptive PIV image analysis. Sci China-Phys Mech Astron, 2015, 58: 104704Įdwards M, Theunissen R. Evaluating the accuracy performance of Lucas-Kanade algorithm in the circumstance of PIV application. Globally optimized cross-correlation for particle image velocimetry. Spatial pyramidal cross correlation for particle image velocimetry. Hybrid optical-flow-cross-correlation method for particle image velocimetry. Outlier detection for particle image velocimetry data using a locally estimated noise variance. A particle image velocimetry study of vibrating ionic polymer metal composites in aqueous environments. Particle Image Velocimetry: A Practical Guide. Improvements in the accuracy of wavelet-based optical flow velocimetry (wOFV) using an efficient and physically based implementation of velocity regularization. To fully analyze the performance of the RTA-PIV method, we conducted a series of numerical experiments on ground-truth image pairs and on real-world image sequences. To adaptively adjust the interval time for capturing two particle images, a new high-speed frame-straddling vision system is developed for the proposed RTA-PIV method. Then, a Kalman predictor model is established to predict the velocity of the next time instant and a suitable interval time can be determined. In the proposed closed-loop RTA-PIV method, a new correlation-filter-based PIV measurement algorithm is introduced to calculate the velocity field in real time. ![]() In this study, a novel real-time adaptive particle image velocity (RTA-PIV) method is proposed to accurately measure the instantaneous velocity field of an unsteady flow field. Almost all conventional open-loop particle image velocimetry (PIV) methods employ fixed-interval-time optical imaging technology and the time-consuming cross-correlation-based PIV measurement algorithm to calculate the velocity field. ![]()
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