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Results for “Direct numerical simulation”

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At least 505 records · Page 28

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NASA NTRS2008

Toward Better Modeling of Supercritical Turbulent Mixing

study was done as part of an effort to develop computational models representing turbulent mixing under thermodynamic supercritical (here, high pressure) conditions. The question was whether the large-eddy simulation (LES) approach, developed previously for atmospheric-pressure compressible-perfect-gas and incompressible flows, can be extended to real-gas non-ideal (including supercritical) fluid mixtures. [In LES, the governing equations are approximated such that the flow field is spatially filtered and subgrid-scale (SGS) phenomena are represented by models.] The study included analyses of results from direct numerical simulation (DNS) of several such mixing layers based on the Navier-Stokes, total-energy, and conservation- of-chemical-species governing equations. Comparison of LES and DNS results revealed the need to augment the atmospheric- pressure LES equations with additional SGS momentum and energy terms. These new terms are the direct result of high-density-gradient-magnitude regions found in the DNS and observed experimentally under fully turbulent flow conditions. A model has been derived for the new term in the momentum equation and was found to perform well at small filter size but to deteriorate with increasing filter size. Several alternative models were derived for the new SGS term in the energy equation that would need further investigations to determine if they are too computationally intensive in LES.

Selle, Laurent↗

NASA NTRS1991

Modeling the pressure-dilatation correlation

It is generally accepted that pressure dilatation, which is an additional compressibility term in turbulence transport equations, may be important for high speed flows. Recent direct simulations of homogeneous shear turbulence have given concrete evidence that the pressure dilatation is important insofar that it contributes to the reduced growth of turbulent kinetic energy due to compressibility effects. The problem of modeling pressure dilatation is addressed. A component of the pressure dilatation is isolated which exhibits temporal oscillations and, using direct numerical simulations of homogeneous shear turbulence and isotropic turbulence, show that it has a negligible contribution to the evolution of turbulent kinetic energy. Then, an analysis for the case of homogeneous turbulence is performed to obtain a model for the nonoscillatory pressure dilatation. This model algebraically relates the pressure dilatation to quantities traditionally obtained in incompressible turbulence closures. The model is validated by direct comparison with the pressure dilatation data obtained from the simulations.

Sarkar, S.↗

NASA NTRSDate not supplied

WMLES of K-Type and Bypass Boundary-Layer Transition

A new wall model for transitional flows is applied to the wall-modeled large-eddy simulation of the Klebanoff (fundamental) and bypass transition mechanisms, continuing an effort to improve computational efficiency by mitigating resolution requirements for the disturbances upstream of transition. Comparison with direct-numerical simulation results shows good agreement in the region prior to transition, but agreement during and after transition is not as good as previous wall-modeled large-eddy simulations of the Herbert (subharmonic) transition mechanism using this wall model. This is apparently due to weaknesses in the modelling of the transition process itself.

wall-modeled large-eddy simulation↗

NASA NTRSDate not supplied

WMLES of K-Type and Bypass Boundary-Layer Transition

wall-modeled large-eddy simulation↗

DOE OSTI2024

A Study of the Transition to Turbulence in a Bed of 67 Spherical Pebbles

Packed beds are commonly found in many engineering systems and have been widely studied for decades. A relatively new packed bed system is the Pebble Bed Reactor, a type of generation-IV nuclear reactor. Unlike many of the packed beds encountered in chemical and process engineering applications, Pebble Bed Reactors are larger and operate at significantly higher Reynolds numbers. As a result of these differences, there is a very limited amount of information on the detailed flow physics that exist in these complex geometries. This work seeks to contribute to a growing database of flow data for Pebble Bed Reactor systems by performing Direct Numerical Simulations of the flow in an experimental bed of 67 pebbles for a range of conditions. Simulations are performed at a Prandtl number of 0.66 and Reynolds numbers from 300–600. These Reynolds numbers are chosen to gain additional knowledge on the spatial development of turbulence in these systems. Analysis of the Turbulent Kinetic Energy, turbulence anisotropy, and Turbulent Heat Flux is performed. Results demonstrate significant development of the TKE across the tested range of Reynolds numbers. Examination of both the TKE and THF reveal that development first occurs near the center of the bed and propagates radially as the flow moves further into the bed. Notable regions of negative production of turbulent kinetic energy are observed in regions where flow accelerates around pebble contact points. Furthermore, these regions are found to coincide with regions of 1-component turbulence.Kindly check and confirm, all authors email id is correctly identified.These are correct

Direct numberical simulation↗

DOE OSTI2023

Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges

Here, we examine the application of neural network-based methods to improve the accuracy of large eddy simulations of incompressible turbulent flows. The networks are trained to learn a mapping between flow features and the subgrid scales, and applied locally and instantaneously—in the same way as traditional physics-based subgrid closures. Models that use only the local resolved strain rate are poorly correlated with the actual subgrid forces obtained from filtering direct numerical simulation data. We see that highly accurate models in a priori testing are inaccurate in forward calculations, owing to the preponderance of numerical errors in implicitly filtered large eddy simulations. A network that accounts for the discretization errors is trained and found to be unstable in a posteriori testing. We identify a number of challenges that the approach faces, including a distribution shift that affects networks that fail to account for numerical errors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS2016

Stationary Crossflow Breakdown due to Mixed Mode Spectra of Secondary Instabilities

Numerical simulations are used to study laminar breakdown characteristics associated with stationary crossflow instability in the boundary-layer flow over a subsonic swept-wing configuration. Previous work involving the linear and nonlinear development of individual, fundamental modes of secondary instability waves is extended by considering the role of more complex, yet controlled, spectra of the secondary instability modes. Direct numerical simulations target a mixed mode transition scenario involving the simultaneous presence of Y and Z modes of secondary instability. For the initial amplitudes investigated in this paper, the Y modes are found to play an insignificant role during the onset of transition, in spite of achieving rather large, O(5%), amplitudes of RMS velocity fluctuation prior to transition. Analysis of the numerical simulations shows that this rather surprising finding can be attributed to the fact that the Y modes are concentrated near the top of the crossflow vortex and exert relatively small influence on the Z modes that reside closer to the surface and can lead to transition via nonlinear spreading that does not involve interactions with the Y mode. Finally, secondary instability calculations reveal that subharmonic modes of secondary instability have substantially lower growth rates than those of the fundamental modes, and hence, are less likely to play an important role during the breakdown process involving complex initial spectra.

Li, Fei↗

DOE OSTI2023

A constitutive model for sheared dense fiber suspensions

Here we propose a constitutive model to predict the viscosity of fiber suspensions, which undergoes shear thinning, at various volume fractions, aspect ratios, and shear stresses/rates. We calibrate the model using the data from direct numerical simulation and prove the accuracy by predicting experimental measurements from the literature. We use a friction coefficient decreasing with the normal load between the fibers to quantitatively reproduce the experimentally observed shear thinning in fiber suspensions. In this model, the effective normal contact force, which is directly proportional to the bulk shear stress, determines the effective friction coefficient. A rise in the shear stress reduces the effective friction coefficient in the suspension. As a result, the jamming volume fraction increases with the shear stress, resulting in a shear thinning in the suspension viscosity. Moreover, we extend the model to quantify the effects of fiber volume fraction and aspect ratio in the suspension. We calibrate this model using the data from numerical simulations for the rate-controlled shear flow. Once calibrated, we show that the model can be used to predict the relative viscosity for different volume fractions, shear stresses, and aspect ratios. The model predictions are in excellent agreement with the available experimental measurements from the literature. The findings of this study can potentially be used to tune the fiber size and volume fraction for designing the suspension rheology in various applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1988

Turbulence modeling based on direct simulation of the Navier-Stokes equations

The underlying physics of the transport equation for turbulence dissipation is analyzed. The study is developed based on the dimensional analysis of turbulence scales, higher-order rapid distortion assumptions, and direct numerical simulations. A preliminary turbulence model for the dissipation rate of turbulent kinetic energy is proposed; allowance is made for the effects of mean rotation rates on the production and removal of dissipation rate. It is shown that the production of dissipation due to mean-velocity gradients is proportional to the production of turbulent kinetic energy.

Bardina, Jorge↗

NASA NTRS1994

A method for obtaining a statistically stationary turbulent free shear flow

The long-term goal of the current research is the study of Large-Eddy Simulation (LES) as a tool for aeroacoustics. New algorithms and developments in computer hardware are making possible a new generation of tools for aeroacoustic predictions, which rely on the physics of the flow rather than empirical knowledge. LES, in conjunction with an acoustic analogy, holds the promise of predicting the statistics of noise radiated to the far-field of a turbulent flow. LES's predictive ability will be tested through extensive comparison of acoustic predictions based on a Direct Numerical Simulation (DNS) and LES of the same flow, as well as a priori testing of DNS results. The method presented here is aimed at allowing simulation of a turbulent flow field that is both simple and amenable to acoustic predictions. A free shear flow is homogeneous in both the streamwise and spanwise directions and which is statistically stationary will be simulated using equations based on the Navier-Stokes equations with a small number of added terms. Studying a free shear flow eliminates the need to consider flow-surface interactions as an acoustic source. The homogeneous directions and the flow's statistically stationary nature greatly simplify the application of an acoustic analogy.

Timson, Stephen F.↗

NASA NTRS1993

Large-Eddy Simulation of Boundary Layer Transition on Swept Wings

The large-eddy simulation of the spatial evolution of a stationary crossflow vortex packet in a three-dimensional boundary layer was performed. Although a coarse grid was used (compared to that required by a direct numerical simulation) the essential features of the disturbance evolution, such as the spanwise disturbance spreading and the vortex rollover, were captured accurately. The eddy viscosity became significant only in the late nonlinear stages of the simulation.

Huai, Xiaoli↗

NASA NTRS1989

An algebraic model for the turbulent flux of a passive scalar

Direct numerical simulations of the unsteady incompressible Navier-Stokes equations have been performed in order to investigate the behavior of passive-scalar fields resulting from mean scalar gradients in each of three orthogonal directions in homogeneous turbulent shear flow. For all orientations of the mean scalar gradient, the sum of the pressure-scalar gradient and velocity gradient-scalar gradient terms in the turbulent scalar flux balance equation are found to be approximately aligned with the scalar flux vector itself. The simulation results are used to obtain dimensionless model coefficients as a function of the turbulence Reynolds and Peclet numbers.

Rogers, Michael M.↗

NASA NTRS2002

An Evaluation of Linear Instability Waves as Sources of Sound in a Supersonic Turbulent Jet

Mach wave radiation from supersonic jets is revisited to better quantify the extent to which linearized equations represent the details of the actual mechanism. To this end, we solve the linearized Navier-Stokes equations (LNS) with precisely the same mean flow and inflow disturbances as a previous direct numerical simulation (DNS) of a perfectly expanded turbulent M = 1.92 jet. We restrict our attention to the first two azimuthal modes, n = 0 and n = 1, which constitute most of the acoustic field. The direction of peak radiation and the peak Strouhal number matches the DNS reasonably well, which is in accord with previous experimental justification of the linear theory. However, it is found that the sound pressure level predicted by LNS is significantly lower than that from DNS. In order to investigate the discrepancy, individual frequency components of the solution are examined. These confirm that near the peak Strouhal number, particularly for the first helical mode n = 1, the amplification of disturbances in the LNS closely matches the DNS. However, away from the peak frequency (and generally for the azimuthal mode n = 0), modes in the LNS are damped while those in the DNS grow at rates comparable to those at the peak Strouhal number.

Mohseni, Kamran↗

DOE OSTI2023

Deep learning closure models for large-eddy simulation of flows around bluff bodies

Near-wall flow simulation remains a central challenge in aerodynamics modelling: Reynolds-averaged Navier–Stokes predictions of separated flows are often inaccurate, and large-eddy simulation (LES) can require prohibitively small near-wall mesh sizes. A deep learning (DL) closure model for LES is developed by introducing untrained neural networks into the governing equations and training in situ for incompressible flows around rectangular prisms at moderate Reynolds numbers. The DL-LES models are trained using adjoint partial differential equation (PDE) optimization methods to match, as closely as possible, direct numerical simulation (DNS) data. They are then evaluated out-of-sample – for aspect ratios, Reynolds numbers and bluff-body geometries not included in the training data – and compared with standard LES models. The DL-LES models outperform these models and are able to achieve accurate LES predictions on a relatively coarse mesh (downsampled from the DNS mesh by factors of four or eight in each Cartesian direction). We study the accuracy of the DL-LES model for predicting the drag coefficient, near-wall and far-field mean flow, and resolved Reynolds stress. A crucial challenge is that the LES quantities of interest are the steady-state flow statistics; for example, a time-averaged velocity component $\langle {u}_i\rangle (x) = \lim _{t \rightarrow \infty } ({1}/{t}) \int _0^t u_i(s,x)\, {\rm d}s$ . Calculating the steady-state flow statistics therefore requires simulating the DL-LES equations over a large number of flow times through the domain. It is a non-trivial question whether an unsteady PDE model with a functional form defined by a deep neural network can remain stable and accurate on $t \in [0, \infty )$ , especially when trained over comparatively short time intervals. Our results demonstrate that the DL-LES models are accurate and stable over long time horizons, which enables the estimation of the steady-state mean velocity, fluctuations and drag coefficient of turbulent flows around bluff bodies relevant to aerodynamics applications.

Mechanics↗

DOE OSTI2022

Multi-fidelity modeling to predict the rheological properties of a suspension of fibers using neural networks and Gaussian processes

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications. There are multiple factors, such as fiber aspect ratio and volume fraction, that play a significant role in altering the rheological behavior of suspensions. Three-dimensional (3D) numerical simulations of coupled differential equations of the suspension of fibers are computationally expensive and time-consuming. Machine learning algorithms can be trained on the available data and make predictions for the cases where no numerical data are available. However, some widely used machine learning surrogates, such as neural networks, require a relatively large training dataset to produce accurate predictions. Multi-fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified constitutive equations, can pave the way for more accurate predictions. Here, we focus on neural networks and the Gaussian processes with two levels of fidelity, i.e., high and low fidelity networks, to predict the steady-state rheological properties, and compare them to the single-fidelity network. High-fidelity data are obtained from direct numerical simulations based on an immersed boundary method to couple the fluid and solid motion. The low-fidelity data are produced by using constitutive equations. Multiple neural networks and the Gaussian process structures are used for the hyperparameter tuning purpose. Results indicate that with the best choice of hyperparameters, both the multi-fidelity Gaussian processes and neural networks are capable of making predictions with a high level of accuracy with neural networks demonstrating marginally better performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Numerical simulation of frost formation and heat transfer on fin-and-tube heat exchangers in turbulent cross-flow

Frost formation in fin-and-tube heat exchangers in turbulent cross-flow presents significant challenges in industrial refrigeration applications, affecting heat transfer efficiency and operational reliability. The purpose of this work is to investigate frost deposition and growth on a staggered bank of a fin-and-tube freezer coil under turbulent forced convection conditions. The focus here is on investigating conditions that closely replicate real-world scenarios in large walk-in industrial freezers. Using a direct numerical simulation approach, we examine the flow dynamics and thermal behaviour in the presence of frost, considering turbulent regimes characterized by a Reynolds number in the range 1050 ≤ R e D , avg ≤ 4800 , with the characteristic length being the outer diameter of the tube and the velocity being the bulk fluid velocity between the plates (fins). Computational fluid dynamics simulations are employed to resolve the interactions between turbulent airflow and the frost layer. Our approach incorporates a modified immersed boundary method and a slow-time acceleration technique to address the complex dynamic interface between the continuously evolving frost layer and the flowing air stream. Our findings indicate that frost forms more on the sides of the finned surfaces (plates) and less on the tubes themselves. This article is part of the theme issue ‘Heat and mass transfer in frost and ice’.

Science & Technology - Other Topics↗

NASA NTRS1996

Multiple Scale Simulation for Transitional and Turbulent Flow

A new concept, Multiple Scale Simulation (MSS), is presented in this paper. The basic idea is that the flow is decomposed into several component groups according to spatial and temporal length scales. Each group has its own subdomain, governing system, mesh size, and discretization method. The simulation is then performed groupwise. This approach has been successfully applied in combination with the intergrid dissipation technique for simulation of transitional and turbulent flow in 3-D boundary layers, and it is feasible for 3-D airfoils and other more complex configurations. MSS should prove to ameliorate the scale problems associated with conventional direct numerical simulation.

Liu, Chaoqun↗

DOE OSTI2022

Dendrite-resolved, full-melt-pool phase-field simulations to reveal non-steady-state effects and to test an approximate model

In this report we study the epitaxial, columnar growth of (multiply oriented) dendrites/cells for a spot melt in a polycrystalline Al–Cu substrate using two-dimensional, phase-field, direct numerical simulations (DNS) at the full-melt-pool scale. Our main objective is to compare the expensive DNS model to a much cheaper but approximate “line” model in which a single-crystal phase-field simulation is confined to a narrow rectangular geometry. To perform this comparison, we develop algorithms that automatically extract quantities of interest (QoIs) from both DNS and line models. These QoIs allow us to quantitatively assess the assumptions in the line model and help us analyze its discrepancy with the DNS model. We consider four sets of heat source parameters, mimicking welding and additive manufacturing conditions, that create a combination shallow and deep melt pools. Our largest DNS simulation used 16K x 14K grid points in space. Our main findings can be summarized as follows. Under AM conditions, the QoIs of line models are in excellent agreement with the full DNS results for both shallow and deep melt pools. Under welding conditions, the primary spacing of the DNS model is smaller than the prediction of line model. We identify a geometric crowding effect that accounts for the discrepancies between the DNS and line models. We propose two potential mechanisms that determine the response of the microstructure to geometric crowding.

36 MATERIALS SCIENCE↗