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Kolmogorov microscales

The Kolmogorov microscales refer to the smallest characteristic length, time, and velocity scales in a turbulent flow, where viscous forces dominate over inertial forces, leading to the dissipation of turbulent kinetic energy into heat.^[1] These scales were first proposed by Soviet mathematician Andrey Nikolaevich Kolmogorov in his seminal 1941 paper on the local structure of turbulence in incompressible viscous fluids at high Reynolds numbers.^[2] In Kolmogorov's theory, turbulence exhibits a hierarchical energy cascade: energy is injected at large macroscales (typically on the order of the flow domain size), transferred conservatively through an inertial subrange of intermediate eddies via nonlinear interactions, and ultimately dissipated at the microscales.^[3] The three fundamental Kolmogorov microscales are derived dimensionally from the kinematic viscosity \nu (with units m²/s) and the mean energy dissipation rate per unit mass \epsilon (with units m²/s³), assuming local isotropy and homogeneity at small scales.^[1] The length scale, denoted \eta or l_0, is given by \eta = \left( \frac{\nu^3}{\epsilon} \right)^{1/4}, representing the size of the smallest eddies; typical values are on the order of millimeters depending on the flow conditions.^[3] The time scale, \tau_\eta = \left( \frac{\nu}{\epsilon} \right)^{1/2}, quantifies the turnover time of these eddies, often on the order of milliseconds or less.^[2] The velocity scale, u_\eta = \left( \nu \epsilon \right)^{1/4}, describes the typical speed fluctuations at this scale, typically small (e.g., cm/s in atmospheric turbulence).^[4] These microscales mark the boundary between the inertial subrange—where the energy spectrum follows the universal E(k) \propto \epsilon^{2/3} k^{-5/3} law, with k as the wavenumber—and the dissipation range, where viscous diffusion smooths out velocity gradients.^[2] Kolmogorov's framework has profoundly influenced fluid dynamics, enabling predictions of turbulence statistics independent of large-scale forcing, and remains foundational for modeling applications in engineering, atmospheric science, and oceanography, though refinements account for intermittency and anisotropy at very high Reynolds numbers.^[5]

Overview

Definition and significance

Kolmogorov microscales represent the smallest length, time, and velocity scales in turbulent flows, where viscous forces dominate over inertial forces, resulting in the direct conversion of kinetic energy into internal energy through viscous dissipation. These scales, typically denoted as the length scale η, time scale τ, and velocity scale u, characterize the finest structures in turbulence, such as the smallest eddies whose motion is primarily damped by molecular viscosity rather than nonlinear interactions.^[2] At these microscales, the local Reynolds number approaches unity, marking the boundary beyond which turbulent fluctuations cease and laminar-like dissipation prevails.^[6] The significance of Kolmogorov microscales lies in their role as the universal cutoff for the inertial subrange of turbulence, applicable to a wide array of flows at sufficiently high Reynolds numbers. Independent of the specific large-scale forcing mechanisms, these scales depend solely on the fluid's kinematic viscosity and the mean rate of energy dissipation per unit mass, enabling a standardized description of small-scale dynamics across diverse turbulent systems.^[1] This universality facilitates the prediction and modeling of energy dissipation without requiring exhaustive details of the macroscopic flow structure, forming a cornerstone of statistical turbulence theory.^[7] By bridging the energy input at macroscales—such as those driven by external forces or boundaries—to the ultimate dissipation at microscales, the Kolmogorov scales encapsulate the hierarchical energy transfer process inherent to turbulence. In this framework, kinetic energy cascades downward through intermediate scales until viscous effects at the microscales halt the process and convert it to heat, resolving key paradoxes in high-Reynolds-number flows.^[2]

Historical development

The Kolmogorov microscales were first introduced by Andrey Kolmogorov in two seminal papers published in 1941 amid World War II in the Soviet Union.^[8]^[9] In the initial paper, titled "The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers," Kolmogorov proposed the concept of local isotropy at small scales in high-Reynolds-number flows, laying the groundwork for the dissipative microscales.^[8] The follow-up paper, "Dissipation of energy in the locally isotropic turbulence," further developed these ideas by deriving the statistical properties of velocity fluctuations at the smallest scales, where viscous dissipation dominates.^[9] These works built upon earlier foundational contributions to turbulence theory, including Lewis Fry Richardson's 1922 description of the energy cascade across scales and Ludwig Prandtl's mixing-length hypothesis from the 1920s, which emphasized the role of eddies in momentum transfer. Following Kolmogorov's theoretical advancements, experimental validations emerged in the 1950s and 1960s, confirming the predicted scaling behaviors of the microscales in laboratory flows. George Batchelor's 1953 book The Theory of Homogeneous Turbulence provided detailed theoretical support and referenced early wind-tunnel experiments aligning with Kolmogorov's predictions for isotropic turbulence. Similarly, A.A. Townsend's 1956 monograph The Structure of Turbulent Shear Flow incorporated hot-wire anemometry measurements from grid turbulence experiments, demonstrating the existence of the predicted dissipative range and universal small-scale statistics. These efforts solidified the microscales as observable phenomena in controlled settings. In subsequent decades, refinements extended Kolmogorov's framework beyond incompressible, neutral fluids to more complex regimes. Adaptations for compressible flows appeared in the 1950s and 1960s, accounting for density variations and shock structures while retaining analogous dissipative scales. By the 1990s, extensions to magnetohydrodynamics incorporated magnetic field effects, with theories like Goldreich and Sridhar's anisotropic cascade model deriving modified microscales influenced by Alfvén waves. Kolmogorov's 1941 contributions remain foundational to modern turbulence theory, underpinning simulations, modeling, and experiments across engineering and geophysical applications as of 2025.

Theoretical foundations

Turbulence and energy cascade

Turbulence in fluid dynamics refers to a chaotic state of fluid motion characterized by irregular, multi-scale fluctuations in velocity, pressure, and other flow properties, occurring predominantly at high Reynolds numbers where inertial forces dominate over viscous forces.^[10] This regime features a hierarchy of eddies—coherent vortical structures of varying sizes—that interact to produce mixing and transport of momentum, heat, and mass far more efficiently than in laminar flows.^[11] The transition to turbulence typically occurs when the Reynolds number, defined as the ratio of inertial to viscous forces (Re = ρUL/μ, where ρ is density, U is characteristic velocity, L is length scale, and μ is dynamic viscosity), exceeds a critical value, separating flows into inertial-dominated turbulent regimes and viscous-dominated laminar ones.^[12] Central to turbulent flows is the energy cascade, a hierarchical process where kinetic energy is injected at large scales—such as by external forcing from wind, mechanical stirring, or buoyancy—and transferred progressively to smaller scales through nonlinear interactions governed by the convective terms in the Navier-Stokes equations.^[13] In this direct cascade, energy moves from energy-containing eddies at the integral scale (comparable to the flow domain size) to progressively smaller eddies via vortex stretching and straining, with minimal dissipation by viscosity at intermediate scales.^[14] This transfer sustains the turbulent motion until the smallest scales, where viscous effects finally dissipate the energy into heat.^[15] The inertial subrange represents the intermediate scales in this cascade, where the flow is ostensibly inviscid and the energy transfer is local in scale space, independent of both the large-scale forcing and the small-scale dissipation. In this range, the energy spectrum E(k)—the distribution of turbulent kinetic energy per unit wavenumber k—follows the universal scaling

E(k) \sim \varepsilon^{2/3} k^{-5/3},

known as Kolmogorov's -5/3 law, with ε denoting the mean energy dissipation rate per unit mass.^[2] This power-law behavior arises from the assumption of scale-invariant, self-similar turbulence in the inertial regime, providing a cornerstone for understanding how high-Reynolds-number flows maintain statistical stationarity through balanced injection and dissipation.^[16] At the end of the inertial subrange, the cascade reaches the dissipative microscales, where viscosity arrests the energy transfer.^[15]

Kolmogorov's hypotheses

In 1941, Andrey Kolmogorov formulated two fundamental hypotheses that underpin the theory of small-scale turbulence in high-Reynolds-number flows. The first hypothesis posits that, at sufficiently small scales, turbulence becomes locally isotropic and its statistical properties are universal, independent of the large-scale forcing and the specific geometry of the flow, provided the Reynolds number is very large.^[5] This assumption of local isotropy implies that the detailed structure of velocity fluctuations at microscales is determined solely by the local kinematic viscosity \nu and the mean rate of energy dissipation per unit mass \varepsilon, leading to a standardized description of turbulence statistics in this regime.^[2] The second hypothesis addresses the inertial subrange, where the separation r between points satisfies the condition that r is much smaller than the integral scale but much larger than the Kolmogorov length scale \eta. In this range, the differences in velocity components are governed exclusively by \varepsilon, rendering them independent of \nu and the large-scale motions.^[5] This leads to self-similar structure functions, where higher-order moments of velocity increments scale with powers of r and \varepsilon, establishing the universality of the energy spectrum in the inertial range.^[17] These hypotheses collectively define a dissipation range at scales below the inertial subrange, where viscous effects dominate and energy is ultimately dissipated into heat, completing the cascade from large to small scales.^[5] However, the assumptions are most valid for homogeneous and isotropic turbulence at high Reynolds numbers; in anisotropic flows, such as those influenced by rotation or shear, or in low-Reynolds-number regimes, deviations from local isotropy occur, limiting the applicability of the universal scaling.

Mathematical formulation

Length scale

The Kolmogorov length scale, denoted as \eta, represents the characteristic size of the smallest eddies in a turbulent flow, where viscous forces balance inertial forces and mark the transition to the dissipative range.^[2] This scale defines the cutoff for the turbulent energy cascade, below which the flow behaves in a laminar-like manner due to the dominance of molecular viscosity.^[18] The formula for the Kolmogorov length scale is given by

\eta = \left( \frac{\nu^3}{\epsilon} \right)^{1/4},

where \nu is the kinematic viscosity of the fluid and \epsilon is the mean rate of turbulent kinetic energy dissipation per unit mass.^[2] This expression arises from dimensional analysis under the assumption of local isotropy at small scales in high-Reynolds-number flows.^[1] Physically, \eta corresponds to the typical size of eddies at which turbulent kinetic energy is converted into internal energy through viscous dissipation, effectively smoothing out smaller fluctuations.^[19] The scale decreases with increasing Reynolds number (Re), as \eta / L \sim Re^{-3/4}, where L is a large-scale length; for instance, in atmospheric turbulence such as cloud flows, \eta is on the order of 1 mm, while in laboratory flows, it can reach values around 200 \mum.^[1]^[20]^[21]

Time and velocity scales

The Kolmogorov time scale, denoted as \tau, is defined as \tau = (\nu / \epsilon)^{1/2}, where \nu is the kinematic viscosity and \epsilon is the mean rate of energy dissipation per unit mass.^[2] This scale characterizes the turnover time of the smallest eddies in turbulent flow, where viscous effects become dominant and kinetic energy is converted to heat. The Kolmogorov velocity scale, denoted as u, is given by u = (\nu \epsilon)^{1/4}.^[2] It represents the root-mean-square velocity fluctuation associated with these dissipative eddies, providing a measure of the intensity of small-scale motions in locally isotropic turbulence. These scales are dynamically linked through the relation u = \eta / \tau, where \eta is the Kolmogorov length scale, ensuring consistency across spatial, temporal, and velocity dimensions at the dissipative range. Physically, \tau establishes the characteristic frequency of dissipation events in the smallest eddies, while u quantifies the vigor of these localized, high-strain-rate structures.^[2]

Derivations and relations

Dimensional analysis

The Kolmogorov microscales emerge from dimensional analysis applied to the hypotheses of local homogeneity and isotropy, which restrict the small-scale turbulence statistics to dependence on the kinematic viscosity \nu (dimensions [L^2 T^{-1}]) and the mean energy dissipation rate per unit mass \epsilon (dimensions [L^2 T^{-3}]).^[2] Under the Buckingham \pi theorem, the dissipative length scale \eta forms a dimensionless group with \nu and \epsilon. Assuming the power-law form \eta \sim \nu^a \epsilon^b, dimensional homogeneity requires [L] = [L^2 T^{-1}]^a [L^2 T^{-3}]^b, yielding the system of equations $2a + 2b = 1 for length and -a - 3b = 0 for time; solving gives a = 3/4, b = -1/4, so \eta = \left( \frac{\nu^3}{\epsilon} \right)^{1/4}.^[2] The same approach applies to the other scales. For the time scale \tau (dimensions [T]), \tau \sim \nu^{1/2} \epsilon^{-1/2} or equivalently \tau = \left( \frac{\nu}{\epsilon} \right)^{1/2}; for the velocity scale u (dimensions [L T^{-1}]), u \sim \nu^{1/4} \epsilon^{1/4} or u = (\nu \epsilon)^{1/4}.^[2] These derivations rely on the homogeneity and isotropy assumptions, which exclude influences from large-scale parameters like the integral length or mean flow velocity, confining the microscale behavior to \nu and \epsilon alone.^[2]

Energy dissipation connections

The dissipation of turbulent kinetic energy occurs primarily at scales on the order of the Kolmogorov length scale η, where viscous forces dominate over inertial ones, converting mechanical energy into internal energy through molecular diffusion. At these microscales, the fluctuating strain rate tensor components lead to velocity gradients of order ∂u/∂x ≈ u_η / η, where u_η is the characteristic velocity fluctuation. The mean dissipation rate per unit mass ε is then given by ε = 2ν ⟨s_{ij} s_{ij}⟩, where s_{ij} = (1/2)(∂u_i/∂x_j + ∂u_j/∂x_i) is the fluctuating strain-rate tensor and ν is the kinematic viscosity; approximating the magnitude of the strain rate S ≈ 1/τ_η (with τ_η the Kolmogorov time scale), this yields ε ≈ ν S^2 ≈ ν / τ_η^2 ≈ u_η^2 / τ_η, highlighting the direct physical link between microscale dynamics and energy loss.^[22] This mechanism integrates seamlessly with the broader energy cascade, where the rate at which energy arrives at the dissipation range from larger scales must balance the viscous dissipation. Dimensionally, the turnover of eddies at the microscales implies ε ≈ u_η^3 / η, a relation that confirms the consistency of the velocity and length scales derived from ε and ν alone. This equivalence—ε ≈ u_η^3 / η ≈ u_η^2 / τ_η—demonstrates how the cascade flux, constant across the inertial range, is ultimately set by the microscale dissipation, ensuring statistical equilibrium in stationary, homogeneous turbulence at high Reynolds numbers.^[2] The universality of the Kolmogorov microscales stems from ε serving as the pivotal parameter that, together with ν, defines η, u_η, and τ_η independently of large-scale flow details, provided the local Reynolds number based on η is order unity. In experimental and engineering contexts, ε is typically not measured directly at microscales but inferred from macroscopic quantities, such as the mechanical power input per unit mass in stirred tanks or the pressure drop in pipe flows, often via ε ≈ P / ρ (where P is power and ρ is density) or from the decay law in grid turbulence.^[2]^[22] In non-stationary flows, such as decaying turbulence or those with intermittency, ε exhibits spatial and temporal variations, leading to local deviations in the microscales and challenging the assumption of uniform dissipation. These fluctuations arise because the balance between energy production and dissipation is not instantaneous, particularly at moderate Reynolds numbers where the cascade may not fully equilibrate.^[23]

Applications and validation

Engineering and experiments

In engineering applications, Kolmogorov microscales are utilized to estimate energy dissipation rates in systems such as chemical reactors, combustors, and atmospheric dispersion models, where the length scale η informs predictions of mixing efficiency and scalar transport. For instance, in turbulent chemical reactors, the Kolmogorov length scale helps characterize micro-mixing processes by relating the smallest eddy sizes to the local dissipation rate ε, enabling optimization of impeller designs to enhance reaction uniformity without excessive energy input.^[24] In combustors, these scales predict flame extinction limits under intense small-scale turbulence, as demonstrated in studies of premixed flames. Similarly, in atmospheric dispersion engineering, η guides modeling of pollutant spread by quantifying the viscous dissipation that limits fine-scale mixing in turbulent boundary layers. Experimental validations of Kolmogorov microscales have relied on grid-generated turbulence setups since the mid-20th century, confirming the predicted scaling of η with dissipation. Pioneering grid turbulence experiments by Batchelor and collaborators in the 1940s and 1950s demonstrated the decay of isotropic turbulence downstream of grids, where measurements aligned with Kolmogorov's η ~ (ν³/ε)^{1/4} through hot-wire anemometry of velocity fluctuations, verifying isotropy and energy cascade at small scales. Modern validations employ particle image velocimetry (PIV) and laser Doppler anemometry (LDA) in wind tunnels to resolve microscale isotropy; for example, PIV studies of oscillating-grid turbulence show velocity statistics conforming to Kolmogorov predictions within η, with spatial resolutions achieving 5-10 Kolmogorov lengths.^[25] These techniques have confirmed the universality of small-scale behavior in controlled flows, such as decaying isotropic turbulence, where PIV/LDA data reveal the expected roll-off in energy spectra at wavenumbers corresponding to the dissipation range.^[26] Challenges in applying Kolmogorov microscales arise primarily from the difficulty in directly measuring the dissipation rate ε, often addressed via hot-wire anemometry but limited by spatial resolution and noise at sub-Kolmogorov scales. Hot-wire probes, with wire diameters of 1-5 μm, estimate ε from high-frequency velocity derivatives, yet require corrections for finite wire length effects that can underestimate dissipation in grid turbulence.^[27] In oceanographic contexts, such as breaking waves, these challenges are amplified by multiphase interactions; measurements indicate Kolmogorov lengths η on the order of 0.1 to 1 millimeters, with ε ranging from 10^{-4} to 10^{-2} m²/s³, complicating in-situ probes due to wave-induced accelerations.^[28] Laboratory simulations using PIV have mitigated some issues by quantifying dissipation in controlled breaking waves, but field validations remain sparse owing to sensor durability in harsh environments.^[29] Contemporary applications extend Kolmogorov microscales to multiphase flows, where adaptations account for particle entrainment and relative velocities at η-sized eddies. In sediment-laden turbulent flows, the scales predict particle settling modulation, with Kolmogorov-time particles showing enhanced clustering and entrainment rates in direct numerical simulations validated against experiments.^[30] For biofluids, such as aqueous microbial environments, η estimates (tens to hundreds of microns) inform models of nutrient mixing and early-life coherent structures below the viscous subrange, highlighting turbulence's role in biological dispersion.^[31] In droplet-laden jets, these scales describe evaporation and entrainment by linking particle inertia to local dissipation, aiding designs in spray combustion and cloud physics.^[32]

Numerical simulations

Direct numerical simulations (DNS) of turbulent flows necessitate resolving the full range of spatial and temporal scales, including the Kolmogorov length scale η, to accurately capture viscous dissipation without subgrid modeling. The grid spacing Δx must be on the order of or smaller than η (typically Δx ≈ η or finer) to prevent artificial numerical dissipation from contaminating the physical processes at small scales. This resolution requirement stems from the need to integrate the Navier-Stokes equations directly, ensuring that the smallest eddies contribute properly to the energy cascade. Seminal works, such as the DNS of homogeneous isotropic turbulence, established that failing to resolve η leads to underprediction of dissipation rates and distortion of the inertial subrange.^[33]^[34] The computational demands of DNS are formidable, with the total number of grid points N scaling approximately as Re^{9/4} in three dimensions, where Re is the Reynolds number based on the integral scale. This arises because the number of degrees of freedom grows with the inverse cube of η (since η ~ Re^{-3/4}), and the time step must resolve the smallest time scale τ_η ~ Re^{-1/2}, leading to overall cost proportional to approximately Re^{11/4} (or higher, often ~Re^3 in practice) for fixed physical time simulation. Consequently, DNS has historically been limited to moderate Re (e.g., Re_τ < 1000 in wall-bounded flows until the 2010s), but it provides benchmark data for validating turbulence theories, including the universality of Kolmogorov scales.^[35]^[36] In contrast, large eddy simulations (LES) and Reynolds-averaged Navier-Stokes (RANS) methods do not resolve down to η, instead parameterizing the effects of subgrid scales using models informed by the energy dissipation rate ε. The Smagorinsky model, a foundational eddy-viscosity approach from 1963, computes subgrid stress as proportional to the filtered strain rate tensor, with the model coefficient calibrated to match the equilibrium between subgrid production and dissipation in the inertial range, aligning with Kolmogorov's second hypothesis. This parameterization allows LES to simulate flows at higher Re by filtering out small scales while capturing the large-scale dynamics, though it requires dynamic adjustments (e.g., Germano-Lilly procedure) to handle varying local conditions near η. RANS extends this further by averaging all fluctuations, relying on similar ε-based closures like k-ε models.^[37]^[38]^[27] DNS validations often focus on reproducing the Kolmogorov -5/3 energy spectrum E(k) ~ ε^{2/3} k^{-5/3} in the inertial subrange, extending toward the dissipation wavenumber k_d ~ 1/η. Benchmark turbulent channel flow simulations, such as those by Kim, Moin, and Moser (1987) at low Re_τ ≈ 180, confirmed this spectral slope through premultiplied spectra, demonstrating the cascade's universality even in wall-bounded geometries. Higher-Re extensions, like Hoyas and Jiménez (2006) up to Re_τ = 2000, further showed the -5/3 range persisting across decades in k, with dissipation peaking near η, providing quantitative tests of microscale isotropy. By 2025, GPU-accelerated frameworks have pushed DNS to unprecedented Re, enabling rigorous tests of Kolmogorov microscale universality in complex flows. For example, multi-GPU solvers for channel flow have achieved Re_τ > 5000, revealing subtle deviations from perfect -5/3 scaling near η due to finite-Re effects, while confirming overall cascade invariance. These advances underscore the microscales' role in bridging theory and computation, with costs mitigated by parallel efficiency gains of orders of magnitude.^[39]

Taylor microscale

The Taylor microscale, denoted \lambda, serves as an intermediate length scale in turbulent flows, bridging the energy-containing eddies and the dissipative smallest scales. It was introduced by G. I. Taylor to characterize the spatial variation of velocity fluctuations based on two-point correlations in isotropic turbulence.^[40] Specifically, for the longitudinal component, \lambda = \sqrt{ \frac{ \langle u'^2 \rangle }{ \langle (\partial u' / \partial x)^2 \rangle } }, where u' is the root-mean-square fluctuating velocity in the streamwise direction x, and the angle brackets denote statistical averaging. This formulation quantifies the scale over which velocity gradients are prominent, reflecting the curvature of the autocorrelation function at zero separation.^[41] In high-Reynolds-number turbulence, the Taylor microscale significantly exceeds the Kolmogorov microscale \eta, the smallest dissipative scale, with \lambda \gg \eta. The ratio scales as \lambda / \eta \sim \mathrm{Re}_\lambda^{1/2}, or equivalently \lambda^2 / \eta^2 \sim \mathrm{Re}_\lambda, where \mathrm{Re}_\lambda = u' \lambda / \nu is the Taylor-Reynolds number based on kinematic viscosity \nu. This separation highlights \lambda as residing within the inertial range's upper end, where viscous effects begin to influence but do not dominate, contrasting with the universal dissipative nature of \eta. Unlike the Kolmogorov scale, which exhibits universality independent of large-scale forcing due to local isotropy in the dissipation range, the Taylor microscale is less universal and influenced by large-scale anisotropy; however, in homogeneous isotropic turbulence, it assumes an isotropic average value. Experimentally, \lambda is more accessible than \eta, as it can be estimated directly from velocity gradient statistics or correlation functions using techniques like hot-wire anemometry, without requiring precise measurements of the energy dissipation rate \varepsilon. Additionally, in isotropic turbulence, \lambda connects to enstrophy \Omega = \langle \omega^2 \rangle / 2, the intensity of vorticity fluctuations, via \langle \omega^2 \rangle = 15 u'^2 / \lambda^2, linking gradient scales to rotational dissipation.

Integral length scale

The integral length scale, denoted as L, represents the characteristic size of the largest eddies in a turbulent flow, where energy is primarily input into the system. It is defined as L = \int_0^\infty R(r) \, dr, with R(r) = \langle u'(x) u'(x+r) \rangle / \langle u'^2 \rangle being the normalized longitudinal autocorrelation function of the velocity fluctuations u'. This scale typically corresponds to the overall dimensions of the flow domain, such as the container size or duct width in bounded flows. In the context of Kolmogorov microscales, the integral length scale L is significantly larger than the dissipative Kolmogorov length scale \eta, satisfying L \gg \eta. The ratio follows \eta / L \sim \mathrm{Re}_L^{-3/4}, where \mathrm{Re}_L = U L / \nu is the Reynolds number based on the integral scale, with U the characteristic large-scale velocity and \nu the kinematic viscosity; this ensures \mathrm{Re}_L \gg 1 for fully developed turbulence. This separation of scales underpins the energy cascade, with energy injected at L and dissipated at \eta.^[19] The integral length scale is flow-specific and often anisotropic, varying by direction in non-isotropic flows such as those near walls or in shear layers, thereby setting the outer scale for the turbulent cascade. It determines the rate of total energy input, as the dissipation rate \varepsilon \sim U^3 / L, and in the energy spectrum E(k), the peak occurs at wavenumbers k \sim 1/L, reflecting the dominance of large eddies in containing turbulent kinetic energy.

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Direct numerical simulation of incompressible turbulent boundary ...
Nov 15, 2019 · A direct numerical simulation (DNS) initialized with an implicit large eddy simulation (ILES) is performed for temporally evolving planar jets and turbulent ...
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Comparison of low-cost two-equation turbulence models for ...
Direct Numerical Simulation (DNS) [4] simulates and calculates all the scales. With DNS, however, the number of mesh nodes must be of the order of Re9/4.
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GENERAL CIRCULATION EXPERIMENTS WITH THE PRIMITIVE ...
GENERAL CIRCULATION EXPERIMENTS WITH THE PRIMITIVE EQUATIONS. I. THE BASIC EXPERIMENT. J. SMAGORINSKY.Missing: original | Show results with:original
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Smagorinsky Model - an overview | ScienceDirect Topics
The widely used Smagorinsky model is based on the assumption of the balance between the energy production and dissipation effects in the equation for SGS ...
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High-Reynolds-Number Turbulence Database: AeroFlowData - Nature
Aug 27, 2025 · The supersonic/hypersonic turbulence dataset includes DNS and experimental data for representative configurations. Cases encompass flat plate, ...
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A pencil-distributed finite-difference solver for extreme-scale ...
Aug 8, 2025 · We report the revised solver's performance and scalability for large-scale DNS of turbulent channel flow at high Reynolds number. Moreover ...Computational Physics · 2. Methodology · 3. Results
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Statistical theory of turbulenc - Mathematical and Physical Sciences
The theory, as originally put forward, provided a method for defining the scale of turbulence when the motion is defined in the Lagrangian manner.
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[PDF] Turbulent Flows: General Properties - ucla.edu
and between regions of strong strain rate and pattern deformation and cascade to smaller scales. ... Pope, S.B., 2000: Turbulent Flows, Cambridge. Prigogine, I., ...