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Modelling the vortex structures and then translating them into thecorresponding velocity fields are two essential aspects for the vortex-basedmodelling works in wall-bounded turbulence. This work develops a datadrivenmethod, which allows an effective reconstruction for the velocity field basedon a given vortex field. The vortex field is defined as a vector field bycombining the swirl strength and the real eigenvector of the velocity gradienttensor. The distinctive properties for the vortex field are investigated, withthe relationship between the vortex magnitude and orientation revealed by thedifferential geometry. The vortex-to-velocity reconstruction methodincorporates the vortex-vortex and vortex-velocity correlation information andderives the inducing model functions under the framework of the linearstochastic estimation. Fast Fourier transformation is employed to improve thecomputation efficiency in implementation. The reconstruction accuracy isaccessed and compared with the widely-used Biot-Savart law. Results show thatthe method can effectively recover the turbulent motions in a large scalerange, which is very promising for the turbulence modelling. The method is alsoemployed to investigate the inducing effects of vortices at different heights,and some revealing results are discussed and linked to the hot research topicsin wall-bounded turbulence.

Vortex-to-velocity reconstruction for wall-bounded turbulence via a data-driven model