SearcharxivSearch

arXiv · nlin/0102015

Experimental assessment of a new form of scaling law for near-wall turbulence

Abstract

Scaling laws and intermittency in the wall region of a turbulent flow are addressed by analyzing moderate Reynolds number data obtained by single component hot wire anemometry in the boundary layer of a flat plate. The paper aims in particular at the experimental validation of a new form of refined similarity recently proposed for the shear dominated range of turbulence, where the classical Kolmogorov-Oboukhov inertial range theory is inappropriate. An approach inspired to the extended self-similarity allows for the extraction of the different power laws for the longitudinal structure functions at several wall normal distances. A double scaling regime is found in the logarithmic region, confirming previous experimental results. Approaching the wall, the scaling range corresponding to the classical cascade-dominated range tends to disappear and, in the buffer layer, a single power law is found to describe the available range of scales. The double scaling is shown to be associated with two different forms of refined similarity. The classical form holds below the shear scale L s . The other, originally introduced on the basis of DNS data for a turbulent channel, is experimentally confirmed to set up above L s . Given the experimental diffulties in the evaluation of the instantaneous dissipation rate, some care is devoted to check that its one-dimensional surrogate does not bias the results. The increased intermittency as the wall is approached is experimentally found entirely consistent with the failure of the refined Kolmogorov-Oboukhov similarity and the establishment of its new form near the wall.

Explore related subjects

Keep this discovery

BibTeXRIS

B. Jacob, A. Olivieri, C. M. Casciola. 2001-02-12. Experimental assessment of a new form of scaling law for near-wall turbulence. https://doi.org/10.1063/1.1425837

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Linear Response Predicts Cusp-Pair Births in Networks with a Localized Cubic

Linear response is cheap to measure; the bistability boundaries it organizes are not. For a passive network with one localized cubic, the driving-point receptance $G$ fixes the period-one cusp set at fundamental-harmonic order: cusps lie on a fixed phase contour of $G$, a tangency of that contour under parameter variation creates a pair, and its curvature separates a gap opening from an isolated loop. For a two-mode absorber the linear prediction locates a benchmark birth coupling to $0.3\%$, and to $0.03\%$ once a third-harmonic correction of scale $|G(3\Omega)/G(\Omega)|$ is included.

nlin.CD

Dynamics Creation through Neural Dynamical Transfer Learning

Data-driven machine learning has established a robust foundation for reconstructing nonlinear dynamical systems from observations, primarily for the purposes of forecasting and control. However, most existing efforts focus on recovering specific observed dynamics rather than the generative synthesis of new ones. Inspired by image fusion and style transfer, we introduce a neural network framework termed Neural Dynamical Transfer Learning (NDTL) to create new systems with prescribed dynamics from pairs of parent nonlinear dynamical systems. By computing fundamental dynamical signatures, including the intrinsic dimension, the Kaplan-Yorke dimension, the invariant measure statistics, and the Lyapunov spectrum, we demonstrate that NDTL preserves key features inherited from the parent models while simultaneously generating novel dynamics. Beyond these validation examples, NDTL induces a criterion for dynamics classification, creates stable oscillatory coexistence in the Hastings-Powell food chain model, produces interpretable epidemiological models, and provides a chaotic source for image encryption.

nlin.CD

The Spectral Skeleton of Chaos: Koopman Wave Packets on Poincar\'e Sections

A Poincar\'e section replaces a flow by a return map, but for a chaotic system this map is usually known only from sampled crossings. We show that coarse transport can be read directly from Koopman spectral data, without fitting the map. Measure-preserving EDMD retains the isometric structure; riggedDMD then approximates spectral measures and constructs finite regularized wave packets. Packet phase supplies a finite-resolution transport coordinate; low modulus marks a singular skeleton where the phase becomes ill-conditioned. We demonstrate the idea on the R\"ossler system, a 32-mode Kuramoto--Sivashinsky Galerkin system, and the forced Duffing oscillator. The packets yield coarse symbolic models on sections ranging from an almost one-dimensional curve to a visibly thick set. Their graphs organize observed low-period orbits and guide targeted searches for others. In Duffing Regime~II, a seven-region rule accounts for $91\%$--$94\%$ of filtered one-step transitions, while failures in the lowest retained modulus decile occur at $5.08$--$5.20$ times the overall rate. The packets are not Koopman eigenfunctions, nor are the regions exact Markov partitions. Together these computations show how spectral information beyond isolated eigenpairs can expose chaotic transport directly from trajectories.

nlin.CD