Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “turbulence modeling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3

Turbulence modelling in neutron star merger simulations

Observations of neutron star mergers have the potential to unveil detailed physics of matter and gravity in regimes inaccessible by other experiments. Quantitative comparisons to theory and parameter estimation require nonlinear numerical simulations. However, the detailed physics of energy and momentum transfer between different scales, and the formation and interaction of small scale structures, which can be probed by detectors, are not captured by current simulations. This is where turbulence enters neutron star modelling. This review will outline the theory and current status of turbulence modelling for relativistic neutron star merger simulations.

79 ASTRONOMY AND ASTROPHYSICS↗

Turbulence modeling to aid tidal energy resource characterization in the Western Passage, Maine, USA

Numerical models combined with field measurements are regularly used to characterize tidal energy resources at potential energetic sites. However, most existing works only focus on the tidal hydrodynamic characteristics, and turbulence parameters are often not reported because of the lack of high-quality turbulence measurements and the limitations of numerical models in resolving turbulent eddies. In this study, we used FVCOM - a hydrostatic primitive equation (HPE) model - to characterize the tidal energy resource in the Western Passage, Maine, USA, by taking care of the essential macro-scale turbulence properties. We observed an excellent model performance using the Mellor-Yamada Level 2.5 Turbulence Model; estimating the spatial and vertical distribution of the turbulent kinetic energy and intensity added a new perspective to the site ranking for tidal energy converter (TEC) deployments. In addition, we also examined the role of channel geometry and bathymetry, such as headlands and underwater sills, in enhancing turbulent eddies around potential TEC siting locations. Ultimately, the detailed analysis of the turbulent flow characteristics has changed the site-ranking results and demonstrated that the regional-scale HPE models could be used for the relative understanding of more or less turbulent sites for a refined resource assessment.

16 TIDAL AND WAVE POWER↗

Modeling Turbulence in Galactic Centers

Turbulence is a prevalent phenomenon in the interstellar medium, and in particular, the environment at the centers of galaxies. For example, detailed observations of the Milky Way’s Central Molecular Zone (CMZ) revealed that it has a complex and turbulent structure. Turbulence on galactic scales is often modeled using star formation and feedback. However, these effects do not appear to be sufficient for explaining the high-velocity dispersion observed in the CMZ, indicating that additional gas-stirring processes are likely to be operating. Here we introduce a proof-of-concept method to drive turbulence in gas that orbits under the influence of a galactic potential. Instead of relying on a particular physical mechanism, we have adopted a Fourier forcing module and have applied it using a smoothed particle hydrodynamics code. To test our method, we performed simulations of a simplistic model of the CMZ. Our turbulence injection method is capable of balancing the self-gravity of the gas, which allows us to run the simulations for long timescales and thereby follow the evolution of the CMZ. Our results show that turbulence induces a flocculent spiral pattern in our model, analogous to that found in galactic-scale simulations. Furthermore, we find that our turbulence injection method induces inward migration of gas, a result consistent with previous numerical simulations. We submit that this injection method is a promising new tool to simulate turbulence in galactic centers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Physics-informed Machine Learning for Modeling Turbulence in Supernovae

Abstract Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSNe), but current simulations must rely on subgrid models, since direct numerical simulation is too expensive. Unfortunately, existing subgrid models are not sufficiently accurate. Recently, machine learning (ML) has shown an impressive predictive capability for calculating turbulence closure. We have developed a physics-informed convolutional neural network to preserve the realizability condition of the Reynolds stress that is necessary for accurate turbulent pressure prediction. The applicability of the ML subgrid model is tested here for magnetohydrodynamic turbulence in both the stationary and dynamic regimes. Our future goal is to utilize this ML methodology (available on GitHub) in the CCSN framework to investigate the effects of accurately modeled turbulence on the explosion of these stars.

79 ASTRONOMY AND ASTROPHYSICS↗

Global gyro-kinetic ion temperature gradient and trapped electron mode turbulence modelling in X -point geometry in negative and positive triangularity

Comparative modelling of ion temperature gradient (ITG)/trapped electron mode (TEM) turbulence in negative (NT) and positive (PT) triangularity plasma shapes was done using the nonlinear global full-f gyrokinetic particle code JOREK-GK in the realistic X-point tokamak geometry including the Scrape Off Layer (SOL) for TCV and DIII-D parameters. A comparison of JOREK-GK code with the gyrokinetic codes GS2 and GENE was done using NT/PT triangularity TCV L-modes parameters showing good agreement between codes in linear growth rates and clear beneficial effect of NT as compared to PT. Global non-linear modelling of the ITG/TEM saturated turbulence for realistic DIII-D NT pulses was done and compared with numerically constructed PT equilibrium with the same plasma profiles. Existence of longer correlation length of density fluctuations in PT compared to NT was demonstrated. Stronger and more sheared zonal flows are generated via Reynolds stress in NT compared to PT. These factors are stabilizing for TEM/ITG turbulence in NT and lead to smaller heat fluxes and heat conductivities in NT compared to PT configuration. Weak dependence of plasma confinement on collisionality and plasma rotation was found in modelling of DIII-D NT shots similar to the experiment. The confinement scaling with normalized ion gyro-radius ρ* was estimated both for NT and PT. Bohm-like scaling was obtained in both configurations, however with better confinement for NT compared to PT which could be favourable factor for reactor size machines.

ITG↗

Deep Learning for Subgrid‐Scale Turbulence Modeling in Large‐Eddy Simulations of the Convective Atmospheric Boundary Layer

Abstract In large‐eddy simulations, subgrid‐scale (SGS) processes are parameterized as a function of filtered grid‐scale variables. First‐order, algebraic SGS models are based on the eddy‐viscosity assumption, which does not always hold for turbulence. Here we apply supervised deep neural networks (DNNs) to learn SGS stresses from a set of neighboring coarse‐grained velocity from direct numerical simulations of the convective boundary layer at friction Reynolds numbers Re τ up to 1243 without invoking the eddy‐viscosity assumption. The DNN model was found to produce higher correlation between SGS stresses compared to the Smagorinsky model and the Smagorinsky‐Bardina mixed model in the surface and mixed layers and can be applied to different grid resolutions and various stability conditions ranging from near neutral to very unstable. The DNN model can capture key statistics of turbulence in a posteriori (online) tests when applied to large‐eddy simulations of the atmospheric boundary layer.

54 ENVIRONMENTAL SCIENCES↗

Evaluating turbulence models at high energy densities

The RESHOCK campaign at Lawrence Livermore National Laboratory has been working on increasing our understanding of the evolution of turbulent, unstable plasma interfaces which are applicable in various HED applications including ICF implosions. A common approach to model such interfaces involves the use of Renolds-Averaged-Navier-Stokes (RANS) models whose parameters have been constrained by theory and experiments. These parameter sets are not unique and the applicability of one set of tuned model parameters to systems with differing shock strengths or material densities has not been well tested. Our new NIF experiments, along with related studies of turbulent shear flows, provide data and model validation for plasma interfaces in the HED regime. Our experiments measure mixing-layer width at an unstable interface in the plasma regime and are specifically designed to challenge mix models of the Reynolds-Averaged-Navier-Stokes (RANS) type where turbulence is assumed to be fully developed, i.e. the flow exhibits a broad spectrum of length scales without memory of the initial condition.4 We utilize precise control of the initial interface conditions (densities on both sides of the interface, an initial rippled perturbation pattern consisting of equally weighted wavelengths between 10 and 20 microns, and initial material compositions) along with a repeatable drive history

36 MATERIALS SCIENCE↗

Embedded training of neural-network subgrid-scale turbulence models

We report that the weights of a deep neural-network model are optimized in conjunction with the governing flow equations to provide a model for subgrid-scale stresses in a temporally developing plane turbulent jet at Reynolds number Re 0 = 6000 . The objective function for training is first based on the instantaneous filtered velocity fields from a corresponding direct numerical simulation, and the training is by a stochastic gradient descent method, which uses the adjoint Navier-Stokes equations to provide the end-to-end sensitivities of the model weights to the velocity fields. In-sample and out-of-sample testing on multiple dual-jet configurations show that its required mesh density in each coordinate direction for prediction of mean flow, Reynolds stresses, and spectra is half that needed by the dynamic Smagorinsky model for comparable accuracy. The same neural-network model trained directly to match filtered subgrid-scale stresses, without the constraint of being embedded within the flow equations during the training, fails to provide a qualitatively correct prediction. The coupled formulation is generalized to train based only on mean-flow and Reynolds stresses, which are more readily available in experiments. The mean-flow training provides a robust model, which is important, though a somewhat less accurate prediction for the same coarse meshes, as might be anticipated due to the reduced information available for training in this case. The anticipated advantage of the formulation is that the inclusion of resolved physics in the training increases its capacity to extrapolate. This is assessed for the case of passive scalar transport, for which it outperforms established models due to improved mixing predictions.

42 ENGINEERING↗

Dynamic Subgrid Turbulence Modeling for Shallow Cumulus Convection Simulations beyond LES Resolutions

A scale-dependent dynamic Smagorinsky model is implemented in the Met Office/NERC Cloud (MONC) model using two averaging flavors, along Lagrangian pathlines and local moving averages. The dynamic approaches were compared against the conventional Smagorinsky–Lilly scheme in simulating the diurnal cycle of shallow cumulus convection. The simulations spanned from the LES to the near-gray-zone and gray-zone resolutions and revealed the adaptability of the dynamic model across the scales and different stability regimes. The dynamic model can produce a scale- and stability-dependent profile of the subfilter turbulence length scale across the chosen resolution range. At gray-zone resolutions the adaptive length scales can better represent the early precloud boundary layer leading to temperature and moisture profiles closer to the LES compared to the standard Smagorinsky. As a result, the initialization and general representation of the cloud field in the dynamic model is in good agreement with the LES. In contrast, the standard Smagorinsky produces a less well-mixed boundary layer, which fails to ventilate moisture from the boundary layer, resulting in the delayed spinup of the cloud layer. Moreover, strong downgradient diffusion controls the turbulent transport of scalars in the cloud layer. However, the dynamic approaches rely on the resolved field to account for nonlocal transports, leading to overenergetic structures when the boundary layer is fully developed and the Lagrangian model is used. Introducing the local averaging version of the model or adopting a new Lagrangian time scale provides stronger dissipation without significantly affecting model behavior.

54 ENVIRONMENTAL SCIENCES↗

Density wavenumber spectrum measurements, synthetic diagnostic development, and tests of quasilinear turbulence modeling in the core of electron-heated DIII-D H-mode plasmas

Abstract Measurements of the turbulent density wavenumber spectrum, δ n ˆ e ( k ⊥ ) , using the Doppler Back-Scattering (DBS) diagnostic are reported from DIII-D H-mode plasmas with electron cyclotron heating as the only auxiliary heating method. These electron-heated plasmas have low collisionality, ν e ∗ < 1 , T e / T i > 1 , and zero injected torque—a regime expected to be relevant for future fusion devices. We probe density fluctuations in the core ( ρ ≈ 0.7) over a broad wavenumber range, 0.5 ⩽ k ⊥ ⩽ 16 cm −1 ( 0.1 ⩽ k ⊥ ρ s ⩽ 5 ), to characterize plasma instabilities and compare with theoretical predictions. We present a novel synthetic DBS diagnostic to relate the back-scattered power spectrum, P s ( k ⊥ ) —which is directly measured by DBS—to the underlying electron density fluctuation spectrum, δ n ˆ e ( k ⊥ ) . The synthetic DBS P s ( k ⊥ ) spectrum is calculated by combining the SCOTTY beam-tracing code with a model δ n ˆ e ( k ⊥ ) predicted either analytically or numerically. In this work we use the quasi-linear code Trapped Gyro-Landau Fluid (TGLF) to approximate the δ n ˆ e ( k ⊥ ) spectrum. We find that TGLF, using the experimental profiles, is capable of closely reproducing the DBS measurements. Both the DBS measurements and the TGLF-DBS synthetic diagnostic show a wavenumber spectrum with variable decay. The measurements show weak decay ( k −0.6 ) for k < 3.5 cm −1 , with k −2.6 at intermediate- k ( 3.5 ⩽ k ⩽ 8.5 cm −1 ), and rapid decay ( k −9.4 ) for k > 8.5 cm −1 . Scans of physics parameters using TGLF suggest that the normalized ∇ T e scale-length, R / L T e , is an important factor for distinguishing microturbulence regimes in these plasmas. A combination of DBS observations and TGLF simulations indicate that fluctuations remain peaked at ITG-scales (low k ) while R / L T e -driven TEM/ETG-type modes (intermediate/high k ) are marginally sub-dominant.

synthetic diagnostics↗

Using Taylor Green Vortex to Assess Modeling Turbulence Transition

The Taylor Green Vortex problem is an interesting problem to study the transition from laminar flow to fully developed turbulence. The problem consists initially of large-scale vortices that interact and eventually form smaller vortices that create a cascade of energy from larger scales to smaller scales. This document describes a) when fully developed turbulence occurs, and b) the scale separation in the inertial range. In addition, the resolution needed to capture perturbations in transitional flow is also proposed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Using Particle Image Velocimetry to Determine Turbulence Model Parameters

The primary parameter of a standard k-ϵ model, C μ , was calculated from stereoscopic particle image velocimetry (PIV) data for a supersonic jet exhausting into a transonic crossflow. This required the determination of turbulent kinetic energy, turbulent eddy viscosity, and turbulent energy dissipation rate. Image interrogation was optimized, with different procedures used for mean strain rates and Reynolds stresses, to produce useful turbulent eddy viscosity fields. The eddy viscosity was calculated by a least-squares fit to all components of the three-dimensional strain-rate tensor that were available from the PIV data. This eliminated artifacts and noise observed when using a single strain component. Local dissipation rates were determined via Kolmogorov’s similarity hypotheses and the second-order structure function. The eddy viscosity and dissipation rates were then combined to determine C μ . Considerable spatial variation was observed in C μ , with the highest values found in regions where turbulent kinetic energy was relatively low but where turbulent mixing was important, e.g., along the high-strain jet edges and in the wake region. This suggests that use of a constant C μ in modeling may lead to poor Reynolds stress predictions at mixing interfaces. A data-driven modeling approach that can predict this spatial variation of C μ based on known state variables may lead to improved simulation results without the need for calibration.

42 ENGINEERING↗