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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Simulation scaling studies of reactor core two-phase flow using direct numerical simulation

Tremendous growth in supercomputing power in recent years has resulted in the emergence of high-resolution flow analysis methods as an advanced research tool to evaluate single and two-phase flow behavior. In particular, unstructured mesh-based methods have been applied to analyze flows in complex reactor core geometries, including those of light water reactors (LWR). The finite-element based code, PHASTA, is utilized to perform large-scale simulations of two-phase bubbly flows in LWR geometries. Given the large computational cost of direct numerical simulation (DNS) coupled with interface tracking methods (ITM), typical domains encompass a portion of a single subchannel. In the presented research, the state-of-the-art analysis of turbulent two-phase flows in complex LWR subchannel geometries are demonstrated at both prototypical reactor parameters as well as scaled low pressure conditions. Three different cases are studied, a high-pressure simulation in prototypical reactor subchannel geometry, a low-pressure case in prototypical geometry and a final low-pressure case in a geometry scaled up to conserve the ratio between the bubble size and the domain pitch. Utilizing advanced statistical processing tools, these simulation conditions are compared to shed light on the relevancy of two-phase flow characteristics given the significant differences between LWR and low-pressure conditions. These findings can lead to the generation of useful guiding principles when researchers need to scale the two-phase flow behavior captured at low pressure and temperature conditions to those at reactor operating conditions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Interface capturing simulations of bubble population effects in PWR subchannels

As the computational power of high-performance computing (HPC) facilities grows, so too does the feasibility of using first principle based simulation to study turbulent two-phase flows within complex pressurized water reactor (PWR) geometries. Direct numerical simulation (DNS), integrated with an interface capturing method, allows for the collection of high-fidelity numerical data using advanced analysis techniques. The research presented here employs the massively parallel, finite-element based, unstructured mesh code, PHASTA, to simulate a set of two-phase bubbly flows through PWR subchannel geometries including auxiliary structures (spacer grids and mixing vanes). The main objective of the presented work is to analyze bubble dynamics and turbulence interactions at varying bubble concentrations to support the development of advanced two-phase flow closure models. Turbulent two-phase flows in PWR subchannels were simulated at hydraulic Reynolds numbers of 81,000 with bubble concentrations of 3%–15% by gas volume fraction (768–3928 resolved bubbles, respectively) and compared against a 1% void fraction case (262 bubbles) that had been previously simulated. The finite element mesh utilized for the study at higher bubble concentrations was composed of 1.55 billion elements, compared to the previous study which employed 1.11 billion elements, ensuring all turbulence scales and individual bubbles within the flow are fully resolved. For each case, the resolved initial bubble size was 0.65 mm in diameter (resolved with 25 grid points across the diameter). The simulations were analyzed to find flow features such as the mean velocity profile, bubble relative velocity and the effect of the bubbles on the turbulent conditions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

A statistical analysis of developing knock intensity in a mixture with temperature inhomogeneities

Knock formation and its intensity for a stoichiometric ethanol/air mixture under a representative end-gas auto-ignition condition in IC engines with temperature inhomogeneities are investigated using multi-dimensional direct numerical simulations (DNS) with a 40-species skeletal mechanism of ethanol. Two- and three-dimensional simulations are performed by systematically varying temperature fluctuations and its most energetic length scale, l T . The volumetric fraction of the mixture regions that have the propensity to detonation development, F D , is proposed as a metric to predict the amplitude of knock intensity. It is found that with increasing l T , F D shows a good agreement with the heat release fraction of the mixture regions with pressure greater than equilibrium pressure, F H . The detonation peninsula is well captured by F D and F H when plotting them as a function of the volume-averaged ξ, ξ, (ξ= a/S sp is the ratio of the acoustic speed, a to the ignition front speed, S sp ) Decreasing l T is found to significantly reduce the super-knock intensity. The results suggest that decreasing l T , as in engines with tumble designs resulting in a smaller turbulence scale, will be effective in mitigating the the super-knock development.

42 ENGINEERING↗
DOE OSTI2023

Parameterization and Explicit Modeling of Cloud Microphysics: Approaches, Challenges, and Future Directions

Cloud microphysical processes occur at the smallest end of scales among cloud-related processes and thus must be parameterized not only in large-scale global circulation models (GCMs) but also in various higher-resolution limited-area models such as cloud-resolving models (CRMs) and large-eddy simulation (LES) models. Instead of giving a comprehensive review of existing microphysical parameterizations that have been developed over the years, this study concentrates purposely on several topics that we believe are understudied but hold great potential for further advancing bulk microphysics parameterizations: multi-moment bulk microphysics parameterizations and the role of the spectral shape of hydrometeor size distributions; discrete vs “continuous” representation of hydrometeor types; turbulence-microphysics interactions including turbulent entrainment-mixing processes and stochastic condensation; theoretical foundations for the mathematical expressions used to describe hydrometeor size distributions and hydrometeor morphology; and approaches for developing bulk microphysics parameterizations. Also presented are the spectral bin scheme and particle-based scheme (especially, super-droplet method) for representing explicit microphysics. Their advantages and disadvantages are elucidated for constructing cloud models with detailed microphysics that are essential to developing processes understanding and bulk microphysics parameterizations. Particle-resolved direct numerical simulation (DNS) models are described as an emerging technique to investigate turbulence-microphysics interactions at the most fundamental level by tracking individual particles and resolving the smallest turbulent eddies in turbulent clouds. Outstanding challenges and future research directions are explored as well.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Front propagation formulation for large eddy simulation of turbulent premixed flames

The front propagation formulation (FPF) for large eddy simulation (LES) of turbulent premixed flames is presented and discussed. The FPF method adopts a regularized Dirac delta function to minimize the spurious propagation of under-resolved filtered flame fronts in LES, while reproducing the reaction characteristics of premixed flame fronts. Here, the extension of the FPF method to LES and the relation to existing sub-filter combustion models are discussed. An algebraic model for the regularized Dirac delta function is presented and validated using direct numerical simulation (DNS) data for statistically planar premixed flames propagating in homogeneous isotropic turbulence. A posteriori validation for a filtered 1-D planar laminar flame is performed to demonstrate the characteristics of FPF in reproducing the propagation speed and the thickness of the under-resolved flame front. LES of a turbulent Bunsen flame is also performed to demonstrate the performance of the proposed method.

42 ENGINEERING↗
DOE OSTI2021

Direct Numerical Simulation of hydrogen combustion at auto-ignitive conditions: Ignition, stability and turbulent reaction-front velocity

Direct Numerical Simulations (DNS) are performed to investigate the process of spontaneous ignition of hydrogen flames at laminar, turbulent, adiabatic and non-adiabatic conditions. Mixtures of hydrogen and vitiated air at temperatures representing gas-turbine reheat combustion are considered. Adiabatic spontaneous ignition processes are investigated first, providing a quantitative characterization of stable and unstable flames. Results indicate that, in hydrogen reheat combustion, compressibility effects play a key role in flame stability and that unstable ignition and combustion are consistently encountered for reactant temperatures close to the mixture’s characteristic crossover temperature. Furthermore, it is also found that the characterization of the adiabatic processes is also valid in the presence of non-adiabaticity due to wall heat-loss. Finally, a quantitative characterization of the instantaneous fuel consumption rate within the reaction front is obtained and of its ability, at auto-ignitive conditions, to advance against the approaching turbulent flow of the reactants, for a range of different turbulence intensities, temperatures and pressure levels.

42 ENGINEERING↗
DOE OSTI2021

Machine learning assisted modeling of mixing timescale for LES/PDF of high-Karlovitz turbulent premixed combustion

Accurate modeling of mixing in the transported probability density function (PDF) method remains a great challenge, especially for turbulent premixed combustion under extreme conditions such as high Karlovitz number Ka. Recently, a power-law based mixing timescale model was developed for the large-eddy simulations (LES)/PDF modeling of high-Ka number turbulent premixed flames. It is found in this work that the power-law mixing timescale model is highly sensitive to the model parameters. It is thus critically needed to develop accurate calibration of these model parameters. The empirical specification of the model parameters developed in Zhang et. al. is found to be inadequate for accurate modeling of the mixing timescale. Here, machine learning is introduced as an attractive alternative in this work for the specification of the model parameters. A high-Ka number DNS jet flame is used as the training and validation of the machine learning models. The choices of the input parameters are discussed and compared for the machine learning models. The effect of differential molecular diffusion on mixing is examined by including the effect of the Lewis number in the training of the machine learning models. The performance of different machine learning algorithms is compared for the specification of the mixing model parameters. Overall, excellent performance of the machine learning models is observed for assisting the mixing modeling. The feasibility, interpretability, applicability, generality, and portability of using machine learning are discussed in general to provide a perspective on applying data-driven machine learning for turbulent combustion modeling studies.

42 ENGINEERING↗
DOE OSTI2024

a priori uncertainty quantification of reacting turbulence closure models using Bayesian neural networks

While many physics-based closure model forms have been posited for the sub-filter scale (SFS) in large eddy simulation (LES), vast amounts of data available from direct numerical simulations (DNS) create opportunities to leverage data-driven modeling techniques. Albeit flexible, data-driven models still depend on the dataset and the functional form of the model chosen. Increased adoption of such models requires reliable uncertainty estimates both in the data-informed and out-of-distribution regimes. Here, in this work, we employ Bayesian neural networks (BNNs) to capture both epistemic and aleatoric uncertainties in a reacting flow model. In particular, we model the filtered progress variable scalar dissipation rate which plays a key role in the dynamics of turbulent premixed flames. We demonstrate that BNN models can provide unique insights about the structure of uncertainty of the data-driven closure models. We also propose a method for the incorporation of out-of-distribution information in a BNN, which can be used for out-of-distribution query detection. The efficacy of the model is demonstrated by a priori evaluation on a dataset consisting of a variety of flame conditions and fuels.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Joint scheduling of energy, fast and primary frequency response reserves in integrated transmission–distribution networks

Inverter-based distributed energy resources (DERs) connected to distribution networks (DNs) can provide fast frequency support, but their reserve deliverability depends on feeder constraints and differs from synchronous primary frequency response (PFR). Existing transmission–distribution coordination studies usually treat reserve generically or neglect feeder-level feasibility, while frequency-security scheduling studies rarely represent distribution feeders explicitly. This paper develops a bi-level day-ahead scheduling framework for integrated transmission–distribution networks that jointly clears energy, transmission-side PFR, and distribution-side fast frequency response (FFR) under exogenous hourly inertia and largest-loss inputs from an external unit commitment (UC) schedule. The transmission problem is modeled with DC-optimal power flow (OPF) and closed-form second-order cone (SOC) frequency-security constraints, whereas each DN is represented by a reserve-aware branch-flow AC-OPF so that scheduled fast reserves remain deliverable during activation. The bi-level problem is reformulated through Karush–Kuhn–Tucker (KKT) conditions into a mixed-integer SOC program, and a penalty term is used to tighten the distribution-network relaxation. In the reduced test system, lower exogenous inertia increased the required primary response from 179.64 MW to 191.08 MW, distribution-side fast response reduced total frequency-response procurement by up to 4.9%, and neglecting distribution constraints overstated the combined distribution-side energy and reserve award by up to 18%. In the expanded study, the largest case was solved in 2.02 s with a 0.00% optimality gap. Time-domain simulations kept the frequency nadir above 59.0 Hz in all tested hours. These results demonstrate the value of fast-response modeling and distribution-feasible reserve delivery in coordinated market clearing.

Noh, Seung-Gil↗
DOE OSTI2020

Some observations concerning "laminarization" in heated vertical tubes

For low "turbulent" Reynolds numbers in strongly-heated vertical gas flow, fundamental results of existing direct numerical simulation (DNS) databases have been examined to deduce differences between cases which "revert" to turbulent and those that yield integral parameters which correspond to laminar flow (henceforth laminarizing or laminarized). The objectives of this paper are (1) to examine the streamwise evolution of near-wall flow structures and (2) to determine which, if any, proposed laminarization parameters could be used to discriminate between turbulent and laminarizing flows resulting. Views of streamlines in r-θ cross sections showed evidence of a ring of irregular-shaped streamwise vortices near the wall at all locations. The transverse advection of these vortices is hypothesized to provide a path of least resistance to the transport of streamwise momentum in the wall-normal direction. It is demonstrated that a steady streamwise laminar vortex can provide apparent turbulent momentum transport (as a consequence of the Reynolds decomposition) such that a reasonable mean velocity profile is predicted near the wall. For the present cases, the values of the non-dimensional radius (y c,w + = Re τ,w ) and of the modified wall Reynolds number did discriminate whether turbulent or laminarizing flow resulted.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

Development of a CPU/GPU portable software library for Lagrangian–Eulerian simulations of liquid sprays

The Lagrangian–Eulerian method is widely used for simulations of fuel sprays in turbulent combustion because of the advantage of treating the spray droplets as discrete points. One challenge of the Lagrangian–Eulerian method is the intense computational requirement when tracking the large number of Lagrangian particles needed for high fidelity. We have developed a performance-portable library, Grit, to track the Lagrangian particles in parallel on central processing unit (CPU) and graphics processing unit (GPU) accelerated high performance computing (HPC) architectures. Grit is a C++ library which employs Message Passing Interface (MPI) for distributed memory parallelism and Kokkos programming model for on-node shared memory parallelism with performance portability across different architectures of GPUs and multi-core/manycore CPUs. The parallel algorithms, key parallel kernels, and their performances on the pre-exascale supercomputer, Summit, are presented. Grit is coupled with a direct numerical simulation (DNS) solver, S3D, for multiphase simulations. A conservative formulation has been developed and implemented in Grit for phase coupling with thermodynamic consistency. The formulation separates the conservation of mass, momentum and energy from the physical models to prevent accidental violation of conservation laws due to inconsistent models. The formulation also enforces consistent definitions of enthalpies of the fuel for both phases and the latent heat of evaporation. Finally, simulations of turbulent particle-laden flow and the evaporation of dilute turbulent spray jet are performed to verify the software implementation, and to demonstrate the scalability of Grit for large-scale multiphase simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

DPM: A deep learning PDE augmentation method with application to large-eddy simulation

A framework is introduced that leverages known physics to reduce overfitting in machine learning for scientific applications. The partial differential equation (PDE) that expresses the physics is augmented with a neural network that uses available data to learn a description of the corresponding unknown or unrepresented physics. Training within this combined system corrects for missing, unknown, or erroneously represented physics, including discretization errors associated with the PDE's numerical solution. For optimization of the network within the PDE, an adjoint PDE is solved to provide high-dimensional gradients, and a stochastic adjoint method (SAM) further accelerates training. Additionally, the approach is demonstrated for large-eddy simulation (LES) of turbulence. High-fidelity direct numerical simulations (DNS) of decaying isotropic turbulence provide the training data used to learn sub-filter-scale closures for the filtered Navier–Stokes equations. Out-of-sample comparisons show that the deep learning PDE method outperforms widely-used models, even for filter sizes so large that they become qualitatively incorrect. It also significantly outperforms the same neural network when a priori trained based on simple data mismatch, not accounting for the full PDE. Measures of discretization errors, which are well-known to be consequential in LES, point to the importance of the unified training formulation's design, which without modification corrects for them. For comparable accuracy, simulation runtime is significantly reduced. A relaxation of the typical discrete enforcement of the divergence-free constraint in the solver is also successful, instead allowing the DPM to approximately enforce incompressibility physics. Since the training loss function is not restricted to correspond directly to the closure to be learned, training can incorporate diverse data, including experimental data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Demonstration of RANS models with wall functions in the spectral element code Nek5000

The spectral element based computational fluid dynamics (CFD) code Nek5000 has been traditionally used for high-fidelity applications, such as direct numerical simulation (DNS) and large eddy simulation (LES). These techniques require very fine numerical resolution to accurately capture turbulent fluctuations which can be prohibitively expensive for users without access to leadership class computing facilities. For broader application and adoption, significant effort has been invested to develop Reynolds-averaged Navier–Stokes (RANS) capabilities in Nek5000. Here, this work presents details of the implementation and demonstration of the standard wall functions for the κ–τ model in Nek5000. Results using the wall-modeled approach are compared to a wall-resolved approach for cases with negligible pressure gradient, viz., channel flow, pipe flow and flow in a reactor subchannel. Results show reasonably good agreement between the two approaches for friction factor and Nusselt number. Some expected differences are identified near the wall. These cases demonstrate the potential for significant computational savings by using much coarser meshes for the wall-modeled approach, with only minor differences between the predicted result. Additionally, several Reynolds numbers up to 1,000,000 are demonstrated for pipe flow and predicted friction factors and Nusselt numbers compared well to available correlations, with the worst below 10%. As the Reynolds number is increased, better agreement is observed between the correlations and the wall-modeled approach. In addition, flow in a molten salt fast reactor (MSFR) core is considered which features an adverse pressure gradient and flow separation. It showcases the inability of standard wall functions to accurately predict flows with adverse pressure gradients. The results, however, match reasonably well in trend in regions of the flow where the boundary layer is attached. Ongoing research is dedicated to include a pressure gradient correction to wall functions to improve the accuracy of flows with separation or reattachment and adverse or favorable pressure gradients.

42 ENGINEERING↗
DOE OSTI2024

Evaluation of RANS vs. LES simulation of fluid flow through 3 × 3 rod bundle with a simple spacer grid as a precursor to coupled fluid–structure interaction simulations

The research literature on Computational Fluid Dynamics (CFD) of coolant flow through rod bundles with spacer-grids and mixing vanes is replete, ranging from high fidelity Large Eddy Simulation (LES)/Direct Numerical Simulation (DNS) simulations to Reynolds-Averaged Navier–Stokes (RANS) modeled studies. The mixing of flow between subchannels and the pressure drop through the bundle are fundamental quantities useful for comparing and evaluating CFD methods. Less commonly observed and compared are the forces exerted onto the structure by the fluid. The present study seeks to evaluate the use of RANS simulations for predicting the structural response to fluid flow. Wall resolved RANS simulations are benchmarked against LES simulations of fluid flow at a Reynolds number of 15,000 through a 3 × 3 fuel rod bundle with a simple spacer grid. Velocity line-plots are compared showing good agreement between RANS and LES results, ascertaining that the former is capable of capturing the essential time-averaged velocity profile. Additionally, the distribution of forces on the spacer grid and fuel rods are collected as a function of time and space. The RANS methods are evaluated using the frequency and magnitude of the fluctuating forces on various portions of the structure as compared to LES. In conclusion, the power spectral density evaluation of the models reveal underprediction of force amplitude on the rod walls by RANS and also discrepancy in the prediction of high frequency spectra, especially in the immediate vicinity of spacer-grid structure, which may be attributed to the lack of random turbulence fluctuation or insufficient modeling of small-scale eddies in RANS simulation.

FIV↗
DOE OSTI2025

High-fidelity forced convection simulations of the University of Wisconsin–Madison air-cooled reactor cavity cooling system

Among the various types of advanced nuclear technologies that exist, high-temperature gas-cooled reactors (HTGRs) stand out for their co-generation capabilities and exceptional passive safety systems. HTGRs rely on a reactor cavity cooling system (RCCS) to remove decay heat from the reactor pressure vessel (RPV) during the course of long-term transients. Several studies have focused on experimental and numerical modeling of the RCCS; however, the literature reveals a notable gap when it comes to high-fidelity simulations such as direct numerical simulations (DNS) or large eddy simulation (LES) models of the RCCS. An LES model was developed for forced convection inside the RCCS so as to obtain a high-fidelity representation of the RCCS at a scale never before attempted. Furthermore, the LES model was compared against three different Reynolds-averaged Navier–Stokes (RANS) models (Realizable k − ϵ, k − ω SST, and Reynolds stress model [RSM]). This comparison verified the robustness and accuracy of the turbulence modeling approach of the RANS models for the RCCS. Finally, based on the comparison between the LES and the RANS models, the k − ω SST RANS model of the experimental facility was developed and compared against the available experimental data.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Extracting a mixing parameter from 2D radiographic imaging of variable-density turbulent flow

We extract a suitably averaged fluctuating density from the two-dimensional radiographic image of a flow. The X-ray attenuation is given by the Beer–Lambert law which exponentially damps the incident beam intensity by a factor proportional to the density, opacity and thickness of the target. By making reasonable assumptions for the mean density, opacity and effective thickness of the target flow, we estimate the density fluctuation contribution to the attenuation. The extracted density fluctuations averaged across the thickness of the flow in the direction of the beam may be used to form the density–specific-volume correlation $b$. In a statistical description of variable-density turbulence, $b$ quantifies the degree of mixedness. The ability to extract a measure of mixedness from experimental data would be a powerful tool that could be used in the validation of mix models. The scheme proposed is tested for DNS data computed for variable density buoyancy-driven mixing. We quantify the deficits in the extracted value of $b$ due to target thickness, Atwood number and modeled signal noise. This analysis justifies using the proposed scheme to infer the mix parameter from thin targets at moderate to low Atwood numbers. Furthermore, to illustrate how the scheme might be used in a practical problem, we demonstrate its application to a radiographic image of counter-shear flow obtained from experiments at the National Ignition Facility.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗