Extended self-similarity in moment-generating-functions in wall-bounded turbulence at high Reynolds number
Abstract: In wall-bounded turbulence, the moment generating functions (MGFs) of the streamwise velocity fluctuations $\left<\exp(qu_z+)\right>$ develop power-law scaling as a function of the wall normal distance $z/\delta$. Here $u$ is the streamwise velocity fluctuation, $+$ indicates normalization in wall units (averaged friction velocity), $z$ is the distance from the wall, $q$ is an independent variable and $\delta$ is the boundary layer thickness. Previous work has shown that this power-law scaling exists in the log-region {\small $3Re_\tau{0.5}\lesssim z+$, $z\lesssim 0.15\delta$}, where $Re_\tau$ is the friction velocity-based Reynolds numbers. Here we present empirical evidence that this self-similar scaling can be extended, including bulk and viscosity-affected regions $30<z+$, $z<\delta$, provided the data are interpreted with the Extended-Self-Similarity (ESS), i.e. self-scaling of the MGFs as a function of one reference value, $q_o$. ESS also improves the scaling properties, leading to more precise measurements of the scaling exponents. The analysis is based on hot-wire measurements from boundary layers at $Re_\tau$ ranging from $2700$ to $13000$ from the Melbourne High-Reynolds-Number-Turbulent-Boundary-Layer-Wind-Tunnel. Furthermore, we investigate the scalings of the filtered, large-scale velocity fluctuations $uL_z$ and of the remaining small-scale component, $uS_z=u_z-uL_z$. The scaling of $uL_z$ falls within the conventionally defined log region and depends on a scale that is proportional to {\small $l+\sim Re_\tau{1/2}$}; the scaling of $u{S}_z$ extends over a much wider range from $z+\approx 30$ to $z\approx 0.5\delta$. Last, we present a theoretical construction of two multiplicative processes for $uL_z$ and $uS_z$ that reproduce the empirical findings concerning the scalings properties as functions of $z+$ and in the ESS sense.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.