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Register a new userNon-Kolmogorov cascade of helicity driven turbulence
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We solve the Navier-Stokes equations with two simultaneous forcings. One forcing is applied at a given large-scale and it injects energy. The other forcing is applied at all scales belonging to the inertial range and it injects helicity. In this way we can vary the degree of turbulence helicity from non helical to maximally helical. We find that increasing the rate of helicity injection does not change the energy flux. On the other hand the level of total energy is strongly increased and the energy spectrum gets steeper. The energy spectrum spans from a Kolmogorov scaling law $k^{-5/3}$ for a non-helical turbulence, to a non-Kolmogorov scaling law $k^{-7/3}$ for a maximally helical turbulence. In the later case we find that the characteristic time of the turbulence is not the turnover time but a time based on the helicity injection rate. We also analyse the results in terms of helical modes decomposition. For a maximally helical turbulence one type of helical mode is found to be much more energetic than the other one, by several orders of magnitude. The energy cascade of the most energetic type of helical mode results from the sum of two fluxes. One flux is negative and can be understood in terms of a decimated model. This negative flux is however not sufficient to lead an inverse energy cascade. Indeed the other flux involving the least energetic type of helical mode is positive and the largest. The least energetic type of helical mode is then essential and cannot be neglected.
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We investigate non-equilibrium turbulence where the non-dimensionalised dissipation coefficient $C_{varepsilon}$ scales as $C_{varepsilon} sim Re_{M}^{m}/Re_{ell}^{n}$ with $mapprox 1 approx n$ ($Re_M$ and $Re_{ell}$ are global/inlet and local Reynolds numbers respectively) by measuring the downstream evolution of the scale-by-scale energy transfer, dissipation, advection, production and transport in the lee of a square-mesh grid and compare with a region of equilibrium turbulence (i.e. where $C_{varepsilon}approx mathrm{constant}$). These are the main terms of the inhomogeneous, anisotropic version of the von K{a}rm{a}n-Howarth-Monin equation. It is shown in the grid-generated turbulence studied here that, even in the presence of non-negligible turbulence production and transport, production and transport are large-scale phenomena that do not contribute to the scale-by-scale balance for scales smaller than about a third of the integral-length scale, $ell$, and therefore do not affect the energy transfer to the small-scales. In both the non-equilibrium and the equilibrium decay regions, the peak of the scale-by-scale energy transfer scales as $(overline{u^2})^{3/2}/ell$ ($overline{u^2}$ is the variance of the longitudinal fluctuating velocity). In the non-equilibrium case this scaling implies an imbalance between the energy transfer to the small scales and the dissipation. This imbalance is reflected on the small-scale advection which becomes larger in proportion to the maximum energy transfer as the turbulence decays whereas it stays proportionally constant in the further downstream equilibrium region where $C_{varepsilon} approx mathrm{constant}$ even though $Re_{ell}$ is lower.
Helicity, as one of only two inviscid invariants in three-dimensional turbulence, plays an important role in the generation and evolution of turbulence. From the traditional viewpoint, there exists only one channel of helicity cascade similar to that of kinetic energy cascade. Through theoretical analysis, we find that there are two channels in helicity cascade process. The first channel mainly originates from vortex twisting process, and the second channel mainly originates from vortex stretching process. By analysing the data of direct numerical simulations of typical turbulent flows, we find that these two channels behave differently. The ensemble averages of helicity flux in different channels are equal in homogeneous and isotropic turbulence, while they are different in other type of turbulent flows. The second channel is more intermittent and acts more like a scalar, especially on small scales. Besides, we find a novel mechanism of hindered even inverse energy cascade, which could be attributed to the second-channel helicity flux with large amplitude.
Advanced nuclear reactors often exhibit complex thermal-fluid phenomena during transients. To accurately capture such phenomena, a coarse-mesh three-dimensional (3-D) modeling capability is desired for modern nuclear-system code. In the coarse-mesh 3-D modeling of advanced-reactor transients that involve flow and heat transfer, accurately predicting the turbulent viscosity is a challenging task that requires an accurate and computationally efficient model to capture the unresolved fine-scale turbulence. In this paper, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor. The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data to ensure accuracy. A novel neural network architecture, combining a densely connected convolutional network and a long-short-term-memory network, is developed that can efficiently learn from the spatial-temporal CFD transient simulation results. The neural network model was trained and optimized on a loss-of-flow transient and demonstrated high accuracy in predicting the turbulent viscosity field during the whole transient. The trained models generalization capability was also investigated on two other transients with different inlet conditions. The study demonstrates the potential of applying the proposed data-driven approach to support the coarse-mesh multi-dimensional modeling of advanced reactors.
Recent numerical simulations showed that the mean flow is generated in inhomogeneous turbulence of an incompressible fluid accompanied with helicity and system rotation. In order to investigate the mechanism of this phenomenon, we carry out a numerical simulation of inhomogeneous turbulence in a rotating system. In the simulation, an external force is applied to inject inhomogeneous turbulent helicity and the rotation axis is taken to be perpendicular to the inhomogeneous direction. No mean velocity is set in the initial condition of the simulation. The simulation results show that only in the case with both the helical forcing and the system rotation, the mean flow directed to the rotation axis is generated and sustained. We investigate the physical origin of this flow-generation phenomenon by considering the budget of the Reynolds-stress transport equation. It is found that the pressure diffusion term has a large contribution in the Reynolds stress equation and supports the generated mean flow. It is shown that a model expression for the pressure diffusion can be expressed by the turbulent helicity gradient coupled with the angular velocity of the system rotation. This implies that inhomogeneous helicity can play a significant role for the generation of the large-scale velocity distribution in incompressible turbulent flows.
Generalizability of machine-learning (ML) based turbulence closures to accurately predict unseen practical flows remains an important challenge. It is well recognized that the ML neural network architecture and training protocol profoundly influence the generalizability characteristics. The objective of this work is to identify the unique challenges in finding the ML closure network hyperparameters that arise due to the inherent complexity of turbulence. Three proxy-physics turbulence surrogates of different degrees of complexity (yet significantly simpler than turbulence physics) are employed. The proxy-physics models mimic some of the key features of turbulence and provide training/testing data at low computational expense. The focus is on the following turbulence features: high dimensionality of flow physics parameter space, non-linearity effects and bifurcations in emergent behavior. A standard fully-connected neural network is used to reproduce the data of simplified proxy-physics turbulence surrogates. Lacking a rigorous procedure to find globally optimal ML neural network hyperparameters, a brute-force parameter-space sweep is performed to examine the existence of locally optimal solution. Even for this simple case, it is demonstrated that the choice of the optimal hyperparameters for a fully-connected neural network is not straightforward when it is trained with the partially available data in parameter space. Overall, specific issues to be addressed are identified, and the findings provide a realistic perspective on the utility of ML turbulence closures for practical applications.
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