Variational Estimation of Experimental Fluid Flows with Physics-Based Spatio-Temporal Regularization
نویسندگان
چکیده
We present a variational approach to motion estimation of instationary experimental fluid flows from image sequences. Our approach extends prior work along two directions: (i) The full incompressible Navier-Stokes equation is employed in order to obtain a physically consistent regularization which does not suppress turbulent variations of flow estimates. (ii) Regularization along the time-axis is employed as well, but formulated in a receding horizon manner contrary to previous approaches to spatiotemporal regularization. This allows for a recursive on-line (non-batch) implementation of our variational estimation framework. Ground-truth evaluations for simulated turbulent flows demonstrate that due to imposing both physical consistency and temporal coherency, the accuracy of flow estimation compares favourably even with advanced cross-correlation approaches and optical flow approaches based on higher-order div-curl regularization. Variational Estimation of Experimental Fluid Flows 2
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