Reconstruction of velocity data, using optimization
2003 (English)In: Computational Fluid and Solid Mechanics 2003 / [ed] K.J. Bathe, 2003, 2324-2327 p.Conference paper (Other academic)
From a given velocity field u*, a flow field that satisfies a given differential equation and minimize some norm is determined. The gradient for the optimization is updated using adjoint technique. The numerical solution of the non-linear partial differential equation is done using a multigrid scheme. The test case shows promising results. The method handles missing data as well as disturbances.
This chapter discusses reconstruction of velocity data, using optimization. There is a growing interest in obtaining velocity data on a higher temporal and/or spatial resolution than is currently possible to measure. The problem originates from a vast array of topics—such as meteorology, hydrology, wind tunnel, or water tunnel experiments—and from noninvasive medical measurement devices, such as 3D time-resolved-phase-contrast magnetic resonance imaging. The rapid development in computer performance gave birth to new methods, based on optimization and simultaneous numerical solution of partial differential equations, well-suited for the task of up-sampling. The data may be of several kinds—low spatial and/or temporal resolution with or without areas of missing and/or uncertain data. It determines a flow field that satisfies a given differential equation and minimize some norm from a given velocity field. The gradient for the optimization can be updated through adjoint technique. The numerical solution of the nonlinear partial differential equation can be done through a multigrid scheme. The method handles missing data as well as disturbances.
Place, publisher, year, edition, pages
2003. 2324-2327 p.
IdentifiersURN: urn:nbn:se:liu:diva-14648DOI: 10.1016/B978-008044046-0.50571-6OAI: oai:DiVA.org:liu-14648DiVA: diva2:24090
Proceedings Second MIT Conference on Compurational Fluid and Solid Mechanics June 17–20, 2003