The acceleration of fluid particles in fully developed turbulence is far more intermittent than Eulerian dissipation fluctuations, and the standard multifractal prediction built on the dimensional mapping , namely , fails increasingly badly with moment order at high Reynolds number. Buaria and Sreenivasan (Phys. Rev. Lett. 128, 234502, 2022) traced the observed acceleration-variance scaling to an unexplained Reynolds-number growth of the fourth-order Eulerian structure-function prefactor, versus the expected from multifractal and log-normal models, but their piecewise-integration framework does not extend to higher moments, and no mechanism explains why Lagrangian intermittency exceeds Eulerian dissipation intermittency. The open problem is a predictive, Navier–Stokes-faithful theory of Lagrangian acceleration moments: deriving the growth of , predicting higher-order moment exponents, and identifying the roles of transverse velocity gradients, nonlocal pressure effects, and temporal correlations along particle trajectories. Follow-up work through January 2026 has constrained the mechanism (pressure-gradient dominance of the variance, a Lagrangian–transverse-Eulerian spectral correspondence, and shell-model counter-evidence) but closes none of these components.
Background
Turbulence can be described from two complementary viewpoints: the Eulerian frame, in which the velocity field is observed at fixed spatial points, and the Lagrangian frame, which follows fluid particles along trajectories . The Lagrangian acceleration is the instantaneous acceleration of such a fluid particle, ; it is among the most intermittent quantities measured in turbulence, with extremely heavy-tailed probability distributions. Its natural scale follows the Kolmogorov (Heisenberg–Yaglom) dimensional estimate , where is the kinetic-energy dissipation rate per unit mass, is the kinematic viscosity, and angular brackets denote ensemble (volume) averages. The relevant control parameter is the Taylor-scale Reynolds number (based on the root-mean-square velocity and the Taylor microscale ), with well-resolved direct numerical simulations (DNS) of isotropic turbulence now reaching .
Small-scale intermittency denotes the increasingly violent, rare fluctuations of velocity increments and gradients at smaller scales, which defeat mean-field (Kolmogorov-1941, K41) scaling. An -th order structure function is the moment of a velocity increment across a spatial separation ; in the inertial range (scales between the large-scale length and the Kolmogorov dissipation length ) it scales as with anomalous exponents , while in the dissipation range it behaves as with a prefactor that itself grows with . The multifractal formalism models as locally Hölder continuous with exponent , the set of points with exponent having fractal dimension ; a steepest-descent evaluation then predicts the scaling of dissipation moments .
Biferale et al. (Phys. Rev. Lett. 93, 064502, 2004) extended this framework to Lagrangian statistics by combining the mapping with the multifractal dissipation moments, predicting with . This treats Lagrangian intermittency as a naive extension of Eulerian dissipation intermittency. An earlier interpolation formula for the normalized acceleration variance with (Sawford et al., Phys. Fluids 15, 3478, 2003) fit the then-available data, and Hill (J. Fluid Mech. 452, 361, 2002) had proposed by a different route.
Buaria and Sreenivasan (Phys. Rev. Lett. 128, 234502, 2022) tested these predictions with new, very well-resolved DNS at – combined with literature data. The variance data depart from the interpolation formula and follow ; higher moments deviate from the multifractal predictions far more strongly, and replacing dissipation by enstrophy (squared vorticity, the more intermittent transverse-gradient quantity) does not repair the disagreement. As a constructive step they used the exact identity (the viscous contribution being small), with the pressure-gradient variance given by an integral over fourth-order Eulerian longitudinal, transverse, and mixed structure functions, and evaluated it piecewise across the dissipation and inertial ranges. This yields , where the dissipation flatness (), the fourth-order exponent , and the fourth-order structure-function prefactor . The measured substantially exceeds the value predicted by multifractal and log-normal models, and it is this excess that drives the stronger variance scaling; with one would recover essentially the old (wrong) exponent. The authors state that this framework 'does not seem amenable for generalization to higher-order moments' and that 'the intermittency of Lagrangian quantities remains an open problem, even more compellingly than before.'
Subsequent work has sharpened where a solution must act. The acceleration variance is dominated by the irrotational pressure-gradient contribution (scaling as ), the convective and local Eulerian parts individually scale as and nearly cancel (consistent with the random-sweeping idea that small scales are advected past a probe by large-scale motion), and Lagrangian inertial-range exponents saturate at for moment orders , matching the transverse — not longitudinal — Eulerian multifractal spectrum (via the frozen-flow identification of temporal and spatial increments). Meanwhile, shell models of turbulence, which lack coherent vortical structures, are accurately described by the standard multifractal acceleration prediction. A theory must therefore be tensorial (distinguishing longitudinal from transverse gradients), account for the nonlocal pressure (a Poisson solve over all scales), and address temporal correlations along trajectories – none of which the scalar multifractal mapping captures.
Problem Statement
Construct a predictive, physically grounded theory of the moments of the Lagrangian fluid-particle acceleration in Navier–Stokes turbulence as functions of the moment order and the Taylor-scale Reynolds number — one that does not rest on the naive dissipation-based mapping whose moment consequences fail quantitatively. A complete answer should accomplish three jointly supporting objectives: (i) derive, rather than fit, the Reynolds-number growth of the fourth-order Eulerian structure-function prefactor — the measured versus from multifractal and log-normal models — which is the main driver of the acceleration-variance scaling for ; (ii) predict the higher-order Lagrangian acceleration moment exponents, which the piecewise-integration treatment of the variance does not reach and which deviate from increasingly strongly with ; and (iii) identify the mechanism by which Lagrangian intermittency is stronger than Eulerian dissipation intermittency — in particular the respective roles of transverse velocity gradients, nonlocal pressure effects, and temporal correlations along particle trajectories. These three objectives are jointly required for a complete answer: in (i) must be deduced from quantities the theory itself predicts, not fitted to the measured acceleration or structure-function data; the three roles named in (iii) must each be addressed; and a result that establishes only one of the objectives is a partial solution rather than a resolution. The theory's predictions must be falsifiable against existing DNS and experimental data (up to in DNS, with experiments now reaching ) and must state at least one nontrivial prediction testable beyond the data used to construct it.
References
- Dhawal Buaria and Katepalli R. Sreenivasan, 'Scaling of acceleration statistics in high Reynolds number turbulence', Physical Review Letters 128, 234502 (2022). DOI: 10.1103/PhysRevLett.128.234502, https://doi.org/10.1103/PhysRevLett.128.234502; arXiv:2202.09682, https://arxiv.org/abs/2202.09682
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