  Turbulence is prevalent in nature and industry, from large-scale wave
dynamics to small-scale combustion nozzle sprays. In addition to the
multi-scale nonlinear complexity and both randomness and coherent structures in
its dynamics, practical turbulence is often non-homogeneous and anisotropic,
leading to great modeling challenges. In this letter, an efficient model is
proposed to predict turbulent jet statistics with high accuracy. The model
leverages detailed knowledge of readily available velocity signals from
idealized homogeneous turbulence and transforms them into Lagrangian
trajectories of a turbulent jet. The resulting spatio-temporal statistics are
compared against experimental jet data showing remarkable agreement at all
scales. In particular the intermittency phenomenon is accurately mapped by the
model to this inhomogeneous situation, as observed by higher-order moments and
velocity increment probability density functions. Crucial to the advancement of
turbulence modeling, the transformation is simple to implement, with possible
extensions to other inhomogeneous flows such as wind turbine wakes and canopy
flows, to name a few.
