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Results for “Reynolds-Averaged Navier-Stokes Equations”

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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

ECAR-7932 Rev 0 Large Eddy Simulation of MARVEL Reactor Core Subchannel to Evaluate Model Uncertainty of Reynolds-Averaged Navier-Stokes Equation Based Computational Fluid Dynamics Analysis

In the previous work (ECAR-7210), the peak cladding temperature of the MARVEL microreactor has been evaluated by steady-state Reynolds-Averaged Navier-Stokes (RANS) based computational fluid dynamics (CFD) simulations. Although numerical uncertainties of RANS-based CFD simulations has been assessed in ECAR-7210, the model uncertainty of RANS turbulence models must be investigated to resolve the issues related to inaccurate prediction of turbulent heat flux and flow pulsation in a tight lattice rod bundle using the steady-state RANS simulations. Consequently, this ECAR conducted a high-fidelity CFD analysis utilizing Large Eddy Simulation (LES) to generate reference data and investigated the model uncertainty of RANS-based CFD simulations.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2022

CAMERA: A method for cost-aware, adaptive, multifidelity, efficient reliability analysis

Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails of probability distributions, and Monte Carlo sampling methods are intractable when expensive high-fidelity simulations have to be queried. Here, we propose a method to use models of multiple fidelities that trade accuracy for computational efficiency. Specifically, we propose the use of multifidelity Gaussian process models to efficiently fuse models at multiple fidelity, thereby offering a cheap surrogate model that emulates the original model at all fidelities. Furthermore, we propose a novel sequential acquisition function based experiment design framework that can automatically select samples from appropriate fidelity models to make predictions about quantities of interest at the highest fidelity. We use our proposed approach in an importance sampling setting and demonstrate our method on the failure level set and probability estimation on synthetic test functions and two real-world applications, namely, the reliability analysis of a gas turbine engine blade using a finite element method and a transonic aerodynamic wing test case using Reynolds-averaged Navier-Stokes equations. We show that our method predicts the failure boundary and probability more accurately and at a fraction of the computational cost compared with using just a single expensive high-fidelity model. Finally, we show that our sequential approach is guaranteed to asymptotically converge to the true failure boundary with high probability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Towards multi-fidelity deep learning of wind turbine wakes

We report engineering wake models that accurately predict wake in a computationally efficient manner are very important for tasks such as layout optimization and control of wind farms. In this paper, we explore an application of deep learning (DL) to learn the wake model from hierarchies of physics-based approaches ranging from analytical models to an approximate form of the Reynolds-averaged Navier-Stokes equations. We first illustrate the application of principal component analysis to obtain a lower-dimensional representation that allows a computationally tractable training and deployment of DL models. Then, the DL model is trained to learn the mapping from input parameter space to the principal components, which are then used to reconstruct the three-dimensional flow field. Additionally, we investigate a composite framework consisting of two neural networks to learn the correlation between low- and high-fidelity data with Gauss and curl models treated as proxies for low- and high-fidelity models, respectively. The prediction from both DL models matches well with the high-fidelity data with a maximum relative percentage error for the kinetic energy flux of <1%. This work opens up possibilities for data-efficient construction of surrogate models for wake prediction that can be used to study the influence of wind speed and yaw angles on wind farm power production.

17 WIND ENERGY↗
DOE OSTI2023

Bayesian Parameter Estimation of the k-ω Shear Stress Transport Model for Accurate Simulations of Impinging-Jet Heat Transfer

Given the lack of fusion-relevant component test facilities, current estimates of the thermo-fluid performance of plasma-facing components are based for the most part on numerical simulations. A major source of uncertainty in these simulations is the semiempirical turbulence (closure) models for the Reynolds stresses appearing in the governing Reynolds-averaged Navier-Stokes equations, which involve a set of constants that depend upon the flow. The objective of this study is to evaluate Bayesian parameter estimation of turbulence closure constants in ANSYS Fluent to model heat transfer in impinging jets. The Bayesian statistical calibration produces a probability distribution for these constants from experimental data; the maximum a posteriori estimates are then taken to be the calibrated constants, or parameters. The turbulence model constants are calibrated using an experimental study of a submerged jet of air impinging on a flat heated surface at Reynolds numbers Re = O(10 4 ) and impingement distance in jet diameters H/d = 2. Numerical predictions using the calibrated model parameters are then compared with those generated using the default constants. Predictions obtained with model parameters calibrated on datasets of two different sizes are compared to evaluate the effect of the number of calibration samples. Lastly, the extrapolative ability of the calibrated model is examined by predictions at a Re beyond the calibration values.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Hydrogen Dispersion Modeling for Development of Smart Distributed Monitoring

Studying hydrogen dispersion is crucial for ensuring the safe and effective deployment of hydrogen as an energy carrier. This study presents a comprehensive CFD modeling framework for simulating hydrogen dispersion at a real-world hydrogen production, storage, and utilization facility. Utilizing the Hydrogen Research Facility under the Advanced Research on Integrated Energy Systems (ARIES) at the National Renewable Energy Laboratory's (NREL) Flatirons campus, controlled hydrogen releases at 27 kg-H2/hr were simulated. The model incorporated site-specific atmospheric conditions, including hourly wind speeds and temperatures recorded between 8 AM and 8 PM from October to December 2023. To reduce computational demands, a statistical reduction technique was applied to condense the dataset to 100 representative scenarios, validated by statistical tests for wind speeds and power law coefficients. Simulations were conducted using the Reynolds-Averaged Navier-Stokes equations. Results demonstrated that wind speed substantially influences hydrogen dispersion, with low wind conditions forming concentrated clouds and higher wind speeds stretching the plume. Additionally, clustering analysis informed optimal sensor placement at various elevations with up to 10 sensor locations on each elevation. This framework offers a robust approach for understanding hydrogen behavior in ambient conditions and informing detection strategies.

08 HYDROGEN↗
DOE OSTI2023

Data-Driven RANS Turbulence Closures for Forced Convection Flow in Reactor Downcomer Geometry

Recent progress in data-driven turbulence modeling has shown its potential to enhance or replace traditional equation-based Reynolds-averaged Navier-Stokes (RANS) turbulence models. Here, this work utilizes invariant neural network (NN) architectures to model Reynolds stresses and turbulent heat fluxes in forced convection flows (when the models can be decoupled). As the considered flow is statistically one dimensional, the invariant NN architecture for the Reynolds stress model reduces to the linear eddy viscosity model. To develop the data-driven models, direct numerical and RANS simulations in vertical planar channel geometry mimicking a part of the reactor downcomer are performed. Different conditions and fluids relevant to advanced reactors (sodium, lead, unitary-Prandtl-number fluid, and molten salt) constitute the training database. The models enabled accurate predictions of velocity and temperature, and compared to the baseline k–τ turbulence model with the simple gradient diffusion hypothesis, do not require tuning of the turbulent Prandtl number. The data-driven framework is implemented in the open-source graphics processing unit–accelerated spectral element solver nekRS and has shown the potential for future developments and consideration of more complex mixed convection flows.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Passive Ultrasonic Deterrents to Reduce Bat Mortality in Wind Farms (Final Report)

The overall objective of this project was to develop active and passive ultrasonic deterrent concepts to mitigate bat mortality at wind turbines. The research was supported by the US Department of Energy under the contract DE-EE0008731. Novel ultrasonic bat deterrents are investigated in this study. The deterrents are based on the idea of aerodynamic whistles wherein flow-acoustic resonance is used to produce high-amplitude tonal sound at desired ultrasonic frequencies. The deterrents can be grouped into active and passive. The active deterrents/whistles are driven by pressurized air supplied by an external source (e.g., an air compressor). The passive deterrents/whistles are designed to be mounted on wind turbine blades and are “powered” by the energy in the air moving past the rotor blades. Multiple active and passive deterrent ideas are proposed and investigated numerically and experimentally. The computational analysis involves solving the compressible unsteady Reynolds-averaged Navier-Stokes (URANS) equations and coupling the near-field aerodynamic solution with an integral method based on the Ffowcs Williams-Hawkings acoustic analogy to predict ultrasound in the farfield. Experiments are performed in the anechoic chamber at ISU (for the active whistles) and in the Stability wind tunnel at Virginia Tech (for the passive whistles).

17 WIND ENERGY↗
DOE OSTI2022

Comparison of Some RANS Solvers

We will take a look at solving the Reynolds-averaged Navier-Stokes (RANS) equations that are encountered in the context of wind farm performance simulations and optimizations. We will compare some of the more popular ways to solve these equations with a focus on using iterative solvers for the linear solve. We will compare their performance and reliability to a direct solve as we scale the problem both by adding more and parallel resources and by increasing the size of the domain, both in two and three dimensions. There are many strategies that can be applied to solving the RANS equations, some are very efficient, while others are very insensitive to, for example, the Reynolds number. The first contender we will consider is the Pressure-Convection-Diffusion (PCD) preconditioner. Early results suggest that PCD is indeed a very efficient solver, in particular in two dimensions, as long as the Reynold's number remains small. Next we will try to reorder our degrees of freedom such that we can use GMRES with ILU for our linear solve. Another popular choice we will consider for solving the RANS equations is SIMPLE (and its derivatives). For all of our implementations we make use of either FEniCS or Firedrake, basing our work on both existing implementations of some of these solvers while also writing new extensions for others.

CFD↗
DOE OSTI2022

Large-eddy simulation and Reynolds-averaged Navier-Stokes modeling of three Rayleigh-Taylor mixing configurations with gravity reversal

In this work, high-fidelity large-eddy simulation (LES) is performed of Rayleigh-Taylor (RT) mixing in three different configurations involving gravity reversal. In each configuration, LES results are compared with one-dimensional Reynolds-averaged Navier-Stokes (RANS) results, and a deficiency in a commonly used transport equation for the mass-flux velocity, $a_j$, is identified. In the first configuration, a classical two-component RT mixing layer is allowed to develop before it is subjected to rapid acceleration reversal. In the second configuration, a three-component RT mixing layer with an intermediate density layer is allowed to develop before being subjected to rapid acceleration reversal. Finally, in the third configuration, a light layer is interposed between two heavy layers; in this configuration, only one interface is RT-unstable at a time as it undergoes rapid acceleration reversal. In all cases, a commonly used buoyancy production closure in the $a_j$ transport equation is shown to lead to significant over-prediction of mixing layer growth after gravity reversal. An alternative formulation for this closure is then presented which is shown to more accurately capture the stabilization effect of gravity reversal.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Internal energy balance and aerodynamic heating predictions for hypersonic turbulent boundary layers

The elemental equation governing heat transfer in aerodynamic flows is the internal energy equation. For a boundary layer flow, a double integration of the Reynolds-averaged form of this equation provides an expression of the wall heat flux in terms of the integrated effects, over the boundary layer, of various physical processes: turbulent dissipation, mean dissipation, turbulent heat flux, etc. Recently available direct numerical simulation data for a Mach 11 cold-wall turbulent boundary layer allows a comparison of the exact contributions of these terms in the energy equation to the wall heat flux with their counterparts modeled in the Reynolds-averaged Navier-Stokes (RANS) framework. Various approximations involved in RANS, both closure models as well as approximations involved in adapting incompressible RANS models to a compressible form, are assessed through examination of the internal energy balance. There are a number of potentially problematic assumptions and terms identified through this analysis. Here, the effect of compressibility corrections of the dilatational dissipation type is explored, as is the role of the modeled turbulent dissipation, in the context of wall heat flux predictions. The results indicate several potential avenues for RANS model improvement for hypersonic cold-wall boundary-layer flows.

42 ENGINEERING↗
DOE OSTI2025

Applying a Compact Porous Media Model to Numerically Derive Resistance Coefficients for Lattice Structures

Additive Manufacturing allows for exploring various geometries to achieve specific engineering criteria. Lattices are one geometry with unique properties, including being periodically repeating structures which allow flow through them to be represented as a porous media according to Darcy-Forchheimer equations. These equation’s coefficients are generally experimentally derived, but this work demonstrates the ability to numerically derive them with CFD. Simulations were performed using three-dimensional stead state Reynolds-averaged Navier-Stokes with a k-ω Shear Stress Transport turbulence model using Ansys Fluent. Three lattice geometries were investigated and drag coefficients were derived. The method was validated against externally published data for similar geometries demonstrating strong agreement, and grid convergence for all simulations was calculated with a Grid Convergence Index method. Wall roughness is demonstrated to have a non-negligible impact on results and roughness values are considered for the primary focus Octahedral geometry where both smooth wall and rough wall coefficients were derived. The porosity coefficients for the Octahedral geometry at 1.0 [m/s] were found to be 2.89×10 6 and 2.90×10 6 [1/(Pa*m*s)] for the permeability coefficients, 6.37×10 1 and 5.44×10 1 [m 2 /kg] for the inertial resistance coefficients, and with a max pressure drop of 5116.7 [Pa] and 4429.5 [Pa] for the smooth walls and rough walls, respectively. The derived numerical method enables rapid exploration and optimization of new lattice designs for diverse engineering applications.

42 ENGINEERING↗
DOE OSTI2026

Recent progress on coarse graining simulations

We focus on coarse graining simulations based on the primary conservation equations, effectively codesigned physics and algorithms, and low-Mach-number corrected (LMC) hydrodynamics. Simulation methods involve LANL’s x-Radiation-Adaptive-Grid-Eulerian Large-Eddy Simulation, Besnard-Harlow-Rauenzahn (BHR) Reynolds-Averaged Navier-Stokes (RANS) approach, and Dynamic BHR – a paradigm bridging RANS and LES. A relevant question addressed relates to whether 3D RANS and RANS/LES hybrids – the industry standards for aerospace and automotive research, are presently relevant for practical variable-density applications involving shocked and accelerated interface instabilities. Furthermore, recent simulations of the GaTECH inclined mixing-layer shock-tube and NIF ICF-capsule experiments are used to demonstrate issues, challenges, and potential for 3D coarse grained LMC simulation strategies for robustly simulating complex transitional and coupled hydrodynamics-multiphysics with coarser resolution. Present LES readiness to provide accurate predictions at scale is demonstrated – whereas 3D RANS and RANS/LES bridging do not appear impactful in this context.

42 ENGINEERING↗
DOE OSTI2023

Mass and momentum transport in the Tilted Rocket Rig experiment

Here, high-fidelity large eddy simulations of the inclined Rayleigh-Taylor instability in the Tilted Rocket Rig experimental configuration [Smeeton and Youngs, AWE Report No. O 35/87 (1987)] are performed using a tenth-order compact finite difference code. These simulations are analyzed for spatial distributions of turbulent kinetic energy, turbulent mass flux velocity, species mass fraction flux, species mass fraction variance, and Favre-averaged Reynolds stresses at two time instances, t = 37 ms and t = 52 ms. Additionally, the vertical distribution of the components of the unclosed budget equations over the center of the domain for these quantities are examined. The dominant terms of these budget equations are further decomposed to examine the principal contributions to these terms in each axis. Notably, the principal contribution to the horizontal turbulent mass flux velocity budget is found to be from a term which is commonly neglected in many Reynolds-averaged Navier-Stokes models.

42 ENGINEERING↗
DOE OSTI2022

Simulation and Reynolds-averaged Navier-Stokes modeling of a three-component Rayleigh-Taylor mixing problem with thermonuclear burn

Rayleigh-Taylor mixing in the presence of a third component with intermediate density is investigated through three-dimensional large-eddy simulation (LES) with a high-order compact finite-difference code. Two configurations are considered: (1) a symmetric configuration in which the Atwood number between the heavy and intermediate components matches the Atwood number between the intermediate and light components and (2) an asymmetric configuration in which the Atwood number between the heavy and intermediate components is an order of magnitude greater than the Atwood number between the intermediate and light components. Mass fraction covariances are extracted, and proposed Reynolds-averaged Navier-Stokes (RANS) closures for density-specific-volume and density-mass-fraction covariances are evaluated in an a priori fashion. Additionally, a multicomponent extension of the k - Φ - L - a - V RANS model [Morgan, Phys. Rev. E 104, 015107 (2021)] is presented which includes model equations for the upper-triangular elements of the mass fraction covariance matrix. This model, referred to as the k - Φ - L - a - C model, is compared against results from LES and against other RANS models. Profiles of average mass fraction, mass-fraction covariance, and density-specific-volume covariance obtained with the k - Φ - L - a - C model are found to agree well with LES data. Finally, the impact of three-component turbulent mixing on average reaction rate is investigated in both premixed and nonpremixed cases by heating the mixing layer and allowing it to undergo thermonuclear (TN) burn. A closure model for average reaction rate is proposed for use with the k - Φ - L - a - C model, and when this model is applied, improved agreement is obtained between LES and RANS in total TN neutron production.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

High-Fidelity Energy Deposition Ignition Model Coupled with Flame Propagation Models at Engine-like Flow Conditions

With the heightened pressure on car manufacturers to increase the efficiency and reduce the carbon emissions of their fleets, more challenging engine operation has become a viable option. Highly dilute, boosted, and stratified charge, among others, promise engine efficiency gains and emissions reductions. At such demanding engine conditions, the spark-ignition process is a key factor for the flame initiation propagation and the combustion event. From a computational standpoint, there exist multiple spark-ignition models that perform well under conventional conditions but are not truly predictive under strenuous engine operation modes, where the underlying physics needs to be expanded. In this paper, a hybrid Lagrangian-Eulerian spark-ignition (LESI) model is coupled with different turbulence models, grid sizes, and combustion models. The ignition model, previously developed, relies on coupling Eulerian energy deposition with a Lagrangian particle evolution of the spark channel, at every time-step. The spark channel is attached to the electrodes and allowed to elongate at a speed derived from the flow velocity. The LESI model is used to simulate spark ignition in a nonquiescent crossflow environment at engine-like conditions, using converge commercial computational fluid dynamics (CFD) solver. The results highlight the consistency, robustness, and versatility of the model in a range of engine-like setups, from typical with Reynolds-averaged Navier-Stokes (RANS) and a larger grid size to high fidelity with large-eddy simulation (LES) and a finer grid size. The flame kernel growth is then evaluated against Schlieren images from an optical constant volume ignition chamber with a focus on the performance of flame propagation models, such as G-equation and thickened flame model, versus the baseline well-stirred reactor model. Finally, future development details are discussed.

Advanced ignition modeling↗
DOE OSTI2025

Scalar flux transport models for self-similar turbulent mixing

A common approach to closing turbulent species flux in multicomponent Reynolds-averaged Navier-Stokes models is to use the standard gradient diffusion approximation. While such an approach has been shown to work well when applied to many canonical turbulent mixing configurations, a gradient diffusion approach is fundamentally limited in its ability to capture complex phenomena such as countergradient transport. For this reason, complicated mixing applications may benefit by treating the turbulent diffusivity with a model transport equation in a manner analogous to second-moment momentum closure in Reynolds-stress transport models. Here, the present work explores the development and application of two different scalar flux transport (SFT) models. Self-similarity constraints are derived for these models, and they are evaluated against gradient-diffusion-based models in several one- and two-dimensional problems of turbulent mixing. It is found that the new SFT models out-perform gradient diffusion models in problems involving rapid acceleration reversal and in problems involving anisotropic transport of materials. In addition, it is found that even a hybrid-SFT approach, in which an SFT equation is utilized along with a gradient diffusion closure, provides some measure of improvement over models that transport the mass flux rather than the scalar flux.

Reynolds-averaged Navier Stokes↗
DOE OSTI2024

Inverse-Velocity Transformation Wall Model for Reacting Turbulent Hypersonic Boundary Layers

The present study builds on prior work by taking advantage of the novel framework proposed by Griffin et al. (hereafter referred to as the GFM) as a baseline. The model is progressively extended to multicomponent reacting mixtures, accounting for differential diffusion and finite-rate chemistry in a similar fashion to Di Renzo & Urzay and Di Renzo et al. The accuracy of the present approach, as well the prior model of Di Renzo & Urzay, is assessed in an a priori sense for the first time in a turbulent reacting boundary layer, using the boundary-layer data of Williams et al. Five species are included in the present analysis, i.e., N s = 5, namely N 2 , O 2 , NO, N and O, a neutral mixture most representative of dissociation/recombination phenomena for temperatures below 6000 K. A schematic of a flow over a wedge representative of the described configuration is presented in Figure 1. The brief is organized as follows: In Section 2, the wall-model equations and the computational framework are presented. In Section 3, the a priori results of the proposed model are described and compared to the extended EWM. Finally, in Section 4, some conclusions are offered.

97 MATHEMATICS AND COMPUTING↗