Hilbert-Huang transform is a method that has been introduced recently to decompose nonlinear, nonstationary time series into a sum of different modes, each one having a characteristic frequency. Here we show the first successful application of this a
pproach to homogeneous turbulence time series. We associate each mode to dissipation, inertial range and integral scales. We then generalize this approach in order to characterize the scaling intermittency of turbulence in the inertial range, in an amplitude-frequency space. The new method is first validated using fractional Brownian motion simulations. We then obtain a 2D amplitude-frequency representation of the pdf of turbulent fluctuations with a scaling trend, and we show how multifractal exponents can be retrieved using this approach. We also find that the log-Poisson distribution fits the velocity amplitude pdf better than the lognormal distribution.
In this work, the scaling statistics of the dissipation along Lagrangian trajectories are investigated by using fluid tracer particles obtained from a high resolution direct numerical simulation with $Re_{lambda}=400$. Both the energy dissipation rat
e $epsilon$ and the local time averaged $epsilon_{tau}$ agree rather well with the lognormal distribution hypothesis. Several statistics are then examined. It is found that the autocorrelation function $rho(tau)$ of $ln(epsilon(t))$ and variance $sigma^2(tau)$ of $ln(epsilon_{tau}(t))$ obey a log-law with scaling exponent $beta=beta=0.30$ compatible with the intermittency parameter $mu=0.30$. The $q$th-order moment of $epsilon_{tau}$ has a clear power-law on the inertial range $10<tau/tau_{eta}<100$. The measured scaling exponent $K_L(q)$ agrees remarkably with $q-zeta_L(2q)$ where $zeta_L(2q)$ is the scaling exponent estimated using the Hilbert methodology. All these results suggest that the dissipation along Lagrangian trajectories could be modelled by a multiplicative cascade.
