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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Heat release surrogates for NH 3 /H 2 /N 2 –air premixed flames

The present study investigates the performance of NH, NH 2 , O-atom, and H-atom as heat release rate (HRR) surrogates for NH 3 /H 2 /N 2 –air premixed flame through simulations. The simulations are conducted across different pressures, reactant inlet temperatures, fuel blend compositions, and equivalence ratios. Cantera is used to simulate one-dimensional (1D) freely propagating flames to investigate the spatial correlations of the species with the HRR. PeleLMeX, a low-Mach direct numerical simulation (DNS) code with Adaptive Mesh Refinement (AMR), is used to simulate two-dimensional (2D) flame-vortex interactions to investigate the temporal correlations including stretch effects. Three different mechanisms (Jiang et al. 2020; Glarborg et al. 2018; Okafor et al. 2018) were considered in the 1D flame simulations, whereas only the Jiang mechanism was considered in the flame-vortex simulations. The HRR surrogate performance for the 2D flames is evaluated at two different locations: (1) the centerline and (2) the flame cusp. The cusp is defined as the region in the flame front with the greatest curvature and the centerline encounters the highest tangential strain rate. The 1D flame results suggest that, although there is not uniformly good spatial correlation for HRR across all flame conditions, NH is the best overall as a HRR surrogate for laminar flames. The 2D flame results, however, suggest that O-atom and H-atom have satisfactory temporal correlations at different conditions—the former for rich flames, the latter for high-pressure flames. Furthermore, these simulations provide guidance to experimental measurements of surrogate HRR markers in unsteady multi-dimensional flames using laser diagnostics to detect species such as NH, O-atom, and H-atom.

Ammonia↗
DOE OSTI2025

A Model Intercomparison Study of Aerosol‐Cloud‐Turbulence Interactions in a Cloud Chamber: 1. Model Results

This study presents the first model intercomparison of aerosol‐cloud‐turbulence interactions in a controlled cloudy Rayleigh‐Bénard Convection chamber environment, utilizing the Pi Chamber at Michigan Technological University. We analyzed simulated cloud chamber‐averaged statistics of microphysics and thermodynamics in a warm‐phase, cloudy environment under steady‐state conditions at varying aerosol injection rates. Simulation results from seven distinct models (DNS, LES, and a 1D turbulence model) were compared. Our findings demonstrate that while all models qualitatively capture observed trends in droplet number concentration, mean radius, and droplet size distributions at both high and low aerosol injection rates, significant quantitative differences were observed. Notably, droplet number concentrations varied by over two orders of magnitude between models for the same injection rates, indicating sensitivities to the model treatments in droplet activation and removal and wall fluxes. Furthermore, inconsistencies in vertical relative humidity profiles and in achieving steady‐state liquid water content suggest the need for further investigation into the mechanisms driving these variations. Despite these discrepancies, the models generally reproduced consistent power‐law relationships between the microphysical variables. This model intercomparison underscores the importance of controlled cloud chamber experiments for validating and improving cloud microphysical parameterizations. Recommendations for future modeling studies are also highlighted, including constraining wall conditions and processes, investigating droplet/aerosol removal (including sidewall losses), and conducting simplified experiments to isolate specific processes contributing to model divergence and reduce model uncertainties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Rethinking materials simulations: Blending direct numerical simulations with neural operators

Abstract Materials simulations based on direct numerical solvers are accurate but computationally expensive for predicting materials evolution across length- and time-scales, due to the complexity of the underlying evolution equations, the nature of multiscale spatiotemporal interactions, and the need to reach long-time integration. We develop a method that blends direct numerical solvers with neural operators to accelerate such simulations. This methodology is based on the integration of a community numerical solver with a U-Net neural operator, enhanced by a temporal-conditioning mechanism to enable accurate extrapolation and efficient time-to-solution predictions of the dynamics. We demonstrate the effectiveness of this hybrid framework on simulations of microstructure evolution via the phase-field method. Such simulations exhibit high spatial gradients and the co-evolution of different material phases with simultaneous slow and fast materials dynamics. We establish accurate extrapolation of the coupled solver with large speed-up compared to DNS depending on the hybrid strategy utilized. This methodology is generalizable to a broad range of materials simulations, from solid mechanics to fluid dynamics, geophysics, climate, and more.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Direct numerical simulation of supercritical oxy-methane mixing layers with CO 2 substituted counterparts

Direct numerical simulations (DNS) of temporally developing, three dimensional, CH 4 /CO 2 , CH 4 /O 2 and CO 2 /O 2 mixing layers, are conducted at a supercritical pressure of 300 atm. To effectively model the supercritical regime, the employed formulation includes the compressible form of the governing equations, the cubic Peng-Robinson equation of state and a generalized formulation for heat and mass flux vectors derived from non-equilibrium thermodynamics and fluctuation theory. A linear inviscid stability analysis is also performed for each case, to determine its most unstable wavelength. Flow visualizations reveal the presence of high density gradient magnitude regions for all three mixing layers, with conditional averages indicating increased presence of heavier fluid species within these regions. No significant departures are observed from perfect gas behavior, with compressibility factors very close to unity for all three mixing cases. Applicability of presumed probability density function methods (PDF) is examined for the three supercritical mixing layers. An a priori analysis is also conducted to investigate various simplifying assumptions employed in modeling various subgrid scale (SGS) flux models. Two additional terms are identified in the large eddy simulations (LES) equations, the gradient of SGS contribution of pressure in the momentum equation and the gradient of SGS contribution of heat flux in energy equation, whose magnitudes are similar and comparable with their respective resolved terms. The performance of the scale similarity model to represent these additional terms is investigated. Lastly, the performance of Smagorinsky, gradient and scale similarity models are also investigated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

An intercomparison of wall fluxes in a turbulent thermal convection chamber: Direct numerical simulations and wall-modeled large-eddy simulations enhanced by machine learning

Thermal convection in a closed chamber is driven by a warm bottom, a cold top, and side walls at various temperatures. Although wall fluxes are the source of convection energy, accurately modeling these fluxes (i.e., the wall model) is challenging. In large-eddy simulations (LESs), many wall models are traditionally derived from the canonical boundary layer, which may be unsuitable for thermal convection bounded by both horizontal and vertical walls. This study conducts a model intercomparison of dry convection in a cubic-meter chamber using three direct numerical simulations (DNSs) and four LESs with different wall models. The LESs employ traditional wall models, a new wall model employing physics-aware neural networks, and a refined grid near the walls. The experiment involves four cases with varying sidewall temperatures. Our results show that LESs capture the main flow features and the trends of mean fluxes. The physics-aware neural networks and refined wall grids can improve the temporally averaged local fluxes when the large-scale circulation has a preferred direction. Even without the local improvement of wall fluxes, the LES flow quantities (temperature and velocities) can still largely match those in DNSs, provided the mean flux largely matches the DNSs. Additionally, DNSs reveal that a variation in corner treatments has minimal impacts on the flow quantities away from corners. Finally, LESs underestimate the mean fluxes of the entire wall due to their inability to resolve corner regions, but their mean flux away from the corner can better match DNS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Boundary-layer receptivity to oblique freestream vorticity waves for a high-enthalpy hypersonic flow

The receptivity of a Mach 15 straight-cone boundary layer to oblique freestream vorticity waves is investigated using direct numerical simulation (DNS) alongside linear stability theory and the linear parabolized stability equations. A thermochemical nonequilibrium gas model is used. Oblique freestream vorticity waves at frequencies of 400, 800, and 1200 kHz are considered, with incident angles ranging from 0° to 29.4° at 400 kHz, 0° to 15.7° at 800 kHz, and 0° to 20.6° at 1200 kHz. The 400 kHz case is of primary interest due to the strong second-mode amplification at this frequency. Although the underlying base flow is axisymmetric, the oblique vorticity waves lead to a fully three-dimensional boundary-layer disturbance whose characteristics vary depending on the azimuthal ray relative to the freestream wave. Moving downstream within the second-mode instability region, some clear trends emerge in terms of the boundary-layer disturbance amplitudes; that is, disturbance amplitudes are highest at the leeward ray (relative to the freestream wave), but weakest about halfway between the windward and leeward rays. Increasing the incident angle causes the amplitudes to increase on the leeward ray and decrease on the windward ray. Moreover, the boundary-layer disturbance throughout contains a wide spectrum of azimuthal wavenumbers in which the disturbance energy falls off at higher wavenumbers. Increasing the incident angle causes the azimuthal spectrum of the boundary-layer disturbance to broaden overall. Qualitatively similar results are found for the two higher frequencies leading up to the peak-amplitude locations corresponding to the second-mode instability.

42 ENGINEERING↗
DOE OSTI2026

Data-based filtered dissipation rate modelling for multi-modal turbulent combustion: evaluating a priori model generalizability

Manifold-based models offer a computationally efficient alternative to directly transporting the thermochemical state in computational simulations of turbulent reacting flows, projecting the high-dimensional thermochemical state-space onto a low-dimensional manifold. Recent efforts have yielded a manifold-based model applicable to multi-modal combustion, enabling reconstruction of the thermochemical state from solutions to two-dimensional manifold equations in mixture fraction and generalized progress variable that are parameterised by three scalar dissipation rates. In coarse-grained simulations such as Large Eddy Simulation (LES), closure of the multi-modal manifold equations and subfilter variances/covariance requires closure of three filtered scalar dissipation rates. Here, the present work adopts a data-based approach, providing closure for the three filtered scalar dissipation rates via deep neural networks (DNNs). High-fidelity datasets corresponding to an autoigniting n-dodecane jet flame and a bluff body swirl-stabilized confined lifted spray flame of two aviation fuels (Jet-A and C1) with different ignition propensities are leveraged to generate training data that spans a diverse range of thermodynamic conditions and combustion modes, including low- and high-temperature ignition regimes in addition to premixed and nonpremixed behaviour. A final DNN model is trained to enforce inherent physical constraints by learning nonlinear functional transformations of the three filtered scalar dissipation rates. The generalizability of this constrained DNN model is demonstrated a priori via conditional statistics evaluated on the lifted spray flame with C1–a configuration that had not been included in the training data. Excellent DNN agreement with conditional DNS statistics is observed, and integrated gradients are computed to identify the most sensitive input variables. The similarity of the marginal PDFs of the most informative input variables and outputs across configurations are quantified via the Wasserstein metric, demonstrating that data-based models may successfully generalize to unseen parametric conditions so long as the most informative input variables share similar distributions across training and testing datasets.

Data-based modelling↗
DOE OSTI2022

Toward full simulations for a liquid metal blanket: part 2. Computations of MHD flows with volumetric heating for a PbLi blanket prototype at Ha ~10 4 and Gr ~10 12

On the pathway toward full simulations for a liquid metal (LM) blanket, this part 2 extends a previous study of purely magnetohydrodynamic (MHD) flows in a DCLL blanket in reference Chen et al (2020 Nucl. Fusion 60 076003) to more general conditions when the MHD flow is coupled with heat transfer. The simulated prototypic blanket module includes all components of a real LM blanket system, such as supply ducts, inlet and outlet manifolds, multiple poloidal ducts and a U-turn zone. Volumetric heating generated by fusion neutrons is added to simulate thermal effects in the flowing lead–lithium (PbLi) breeder. The MHD flow equations and the energy equation are solved with a DNS-type finite-volume code ‘MHD-UCAS’ on a very fine mesh of 470 × 10 6 cells. The applied magnetic field is 5 T (Hartmann number Ha ~ 10 4 ), the PbLi velocity in the poloidal ducts is 10 cm s –1 (Reynolds number Re ~10 5 ), whereas the maximum volumetric heating is 30 MW m –3 (Grashof number Gr ~ 10 12 ). Four cases have been simulated, including forced- and mixed-convection flows, and either an electrically conducting or insulating blanket structure. Various comparisons are made between the four computed cases and also against the purely MHD flows computed earlier in reference Chen et al (2020 Nucl. Fusion 60 076003) with regards to the (1) MHD pressure drop, (2) flow balancing, (3) temperature field, (4) flows in particular blanket components, and (5) 3D and turbulent flow effects. The strongest buoyancy effects were found in the poloidal ducts. In the electrically non-conducting blanket, the buoyancy forces lead to significant modifications of the flow structure, such as formation of reverse flows, whereas their effect on the MHD pressure drop is relatively small. In the electrically conducting blanket case, the buoyancy effects on the flow and MHD pressure drop are almost negligible.

Physics↗
DOE OSTI2025

Quantum dynamics simulation of the advection-diffusion equation

The advection-diffusion equation is simulated via several quantum algorithms. Three formulations are considered: (1) Trotterization, (2) variational quantum time evolution (VarQTE), and (3) adaptive variational quantum dynamics simulation (AVQDS). These schemes were originally developed for the Hamiltonian simulation of many-body quantum systems. The finite-difference discretized operator of the transport equation is formulated as a Hamiltonian and solved without the need for ancillary qubits. Computations are conducted on a quantum simulator (IBM Qiskit Aer) and a superconducting quantum hardware (IBM Fez). The former emulates the latter without the noise. The actual hardware implementation experiences significant noise. The results of the quantum simulator are compared with data from direct numerical simulation (DNS) with infidelities of the order 10 −5 . In the quantum simulator, Trotterization is observed to have the lowest infidelity and is suitable for fault-tolerant computation. The AVQDS algorithm requires the lowest gate count and circuit depth. The VarQTE algorithm is the next best in terms of gate counts, but the number of its optimization variables is directly proportional to the number of qubits. Due to current hardware limitations, Trotterization cannot be implemented, as it has an overwhelmingly large number of operations. Meanwhile, AVQDS and VarQTE can be executed at the hardware level. These algorithms present a new paradigm for computational transport phenomena on quantum computers.

Alipanah, Hirad [Univ. of Pittsburgh, PA (United S↗
DOE OSTI2020

Multinucleon transfer in the interaction of 977 MeV and 1143 MeV Hg 204 with Pb 208

A previous study of symmetric collisions of massive nuclei has shown that current models of multinucleon transfer (MNT) reactions do not adequately describe the transfer product yields. To gain further insight into this problem, we have measured the yields of MNT products in the interaction of 977 (E/A = 4.79 MeV) and 1143 MeV (E/A = 5.60 MeV) 204 Hg with 208 Pb. We find that the yield of multinucleon transfer products are similar in these two reactions and are substantially lower than those observed in the reaction of 1257 MeV (E/A = 6.16 MeV) 204 Hg+ 198 Pt. We compare our measurements with the predictions of the GRAZING-F, dinuclear systems (DNS), and improved quantum molecular dynamics (ImQMD) models. We report that for the observed isotopes of the elements Au, Hg, Tl, Pb, and Bi, the measured values of the MNT cross sections are orders of magnitude larger than the predicted values. Furthermore, the various models predict the formation of nuclides near the N = 126 shell, which are not observed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Ephemeral antibubbles: Spatiotemporal evolution from direct numerical simulations

Antibubbles, which consist of a shell of a low-density fluid inside a high-density fluid, have several promising applications. We show, via extensive direct numerical simulations (DNSs), in both two and three dimensions, that the spatiotemporal evolution of antibubbles can be described naturally by the coupled Cahn-Hilliard-Navier-Stokes (CHNS) equations for a binary fluid. Our DNSs capture elegantly the gravity-induced thinning and breakup of an antibubble via the time evolution of the Cahn-Hilliard scalar-order-parameter field $\phi$, which varies continuously across interfaces, so we do not have to enforce complicated boundary conditions at the moving antibubble interfaces. To ensure that our results are robust, we supplement our CHNS simulations with sharp-interface volume-of-fluid DNSs. We track the thickness of the antibubble and calculate the dependence of the lifetime of an antibubble on several parameters; we show that our DNS results agree with various experimental results; in particular, the velocity with which the arms of the antibubble retract after breakup scales as σ 1/2 , where σ is the surface tension.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Leveraging In-Network Computing and Programmable Switches for Streaming Analysis of Scientific Data

With the emergence of programmable network devices that match the performance of fixed function devices, several recent projects have explored in-network computing, where the processing that is traditionally done outside the network is offloaded to the network devices. In-network computing has typically been applied to network functions (e.g., load balancing, NAT, and DNS), caching, data reduction/aggregation, and coordination/consensus functions. In some cases it has been used to accelerate stream-processing tasks that involve small payloads and simple operations. In this work we focus on leveraging in-network computing for stream processing of scientific datasets with large payloads that require complex operations such as floating-point computations and logarithmic functions. We demonstrate in-network computing for a real-world scientific application performing streaming normalization of a 2-D image from a light source experiment. We discuss the challenges we encountered and potential approaches to address them.

Sankaran, Ganesh↗
DOE OSTI2024

Effects of temperature inversion on densification in chemical vapor infiltration

In a classical chemical vapor infiltration (CVI) process, the competing effects of chemical kinetics and reagent gas transport lead to non–uniform depositions such that outer layers of a preform densify faster leaving the core highly porous. Currently, CVI must be performed at a sufficiently low temperature to achieve good densification quality which leads to high processing time and cost. Volumetric heating of the preform, especially through microwaves, can create temperature inversion such that the core is hotter than the outer surface and potentially, overcome the challenges associated with isothermal CVI. Direct numerical simulations (DNS) of densification under various such temperature distributions indicate that microwave heating in CVI processing can lead to better (uniform) densification of porous preforms. Here the role of key parameters describing the temperature distributions on the densification behavior is investigated. Strategic temporal control of the temperature distribution shows that processing times can be reduced by almost half while maintaining a good densification quality similar to that of low–temperature isothermal processing. Inside–out densification due to the inverted temperature profile is a key distinguishing characteristic of microwave assisted CVI.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Large Eddy Simulation of Convective Heat Transfer in a Random Pebble Bed Using the Spectral Element Method

The development of fluoride-cooled high-temperature reactors has drastically increased the demand for an in-depth understanding of the heat transfer (HT) in packed beds cooled by liquid salts. The complex flow fields and space-dependent porosity found in a pebble bed require a detailed understanding to ensure the proper cooling of the reactor core during normal and accident conditions. As detailed experimental data are complicated to obtain for these configurations, high-fidelity simulation such as large eddy simulation and direct numerical simulation (DNS) can be employed to create a high-resolution heat transfer numerical database that can assist in addressing industrial-driven issues associated with the heat transfer behavior of fluoride-cooled high-temperature reactors. In this paper, we performed a series of large eddy simulation using computational fluid dynamics (CFD) code NekRS to investigate the heat transfer for a bed of 1741 pebbles. Further, the characteristics of the flow, such as average, rms, and time series of velocity and temperature, have been analyzed. Porous media averages have also been performed. The simulation results show a good agreement between non-conjugate heat transfer and conjugate heat transfer. The generated data will be used to benchmark heat transfer modeling methods and local maxima/minima of heat transfer parameters. It will also be used for supporting convective heat transfer quantification for Kairos Power and benchmarking lower fidelity models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Isogeometric large-eddy simulations of turbulent particle-laden flows

In recent years, isogeometric analysis (IGA) has attracted significant attention from the computational mechanics community due to its ability to integrate design and analysis. Besides, IGA is also a higher-order discretization technique for solving partial differential equations, showing high approximation capability per degree of freedom. In this paper, we extend the application realm of IGA to particle-laden flows based on Eulerian–Eulerian description that couples Navier–Stokes equations with a density transport equation through a Boussinesq approximation. The coupled systems are solved by using quadratic non-uniform rational B-spline (NURBS) functions and a recently developed residual-based variational multiscale (VMS) formulation, which introduces coupling between the fine velocity scales and density equation residuals. We deploy the proposed approach to perform large-eddy simulations (LES) of dilute particle-laden flows over a flat surface at Reynolds number = 10,000. We compare the simulation results against direct numerical simulation (DNS) results from the literature. We find that combining VMS and IGA, the proposed approach enables accurate prediction of a wide range of flow/particle statistics with a relatively lower mesh resolution.

Mathematics↗
DOE OSTI2025

Intelligent Sampling of Extreme-Scale Turbulence Datasets for Accurate and Efficient Spatiotemporal Model Training

With the end of Moore’s law and Dennard scaling, efficient training increasingly requires rethinking data volume. Can we train better models with significantly less data via intelligent subsampling? To explore this, we develop SICKLE, a sparse intelligent curation framework for efficient learning, featuring a novel maximum entropy (MaxEnt) sampling approach, scalable training, and energy benchmarking. We compare MaxEnt with random and phase-space sampling on large direct numerical simulation (DNS) datasets of turbulence. Evaluating SICKLE at scale on Frontier, we show that subsampling as a preprocessing step can, in many cases, improve model accuracy and substantially lower energy consumption, with observed reductions of up to 38×.

Brewer, Wes [ORNL] (ORCID:0000000236393956)↗
DOE OSTI2022

PeleLMeX [SWR-22-48]

PeleLMeX is a solver for high fidelity reactive flow simulations, namely direct numerical simulation (DNS) and large eddy simulation (LES). The solver combines a low Mach number approach, adaptive mesh refinement (AMR), embedded boundary (EB) geometry treatment and high performance computing (HPC) to provide a flexible tool to address research questions on platforms ranging from small workstations to the world's largest GPU-accelerated supercomputers. PeleLMeX has been used to study complex flame/turbulence interactions in RCCI engines and hydrogen combustion or the effect of sustainable aviation fuel on gas turbine combustion. PeleLMeX is part of the Pele combustion Suite (https://amrex-combustion.github.io/)

Day, Marcus↗
DOE OSTI2024

Tardigrade, Version 1.1x

Tardigrade is a collection of functions, libraries, and stand-alone code which enables the simulation of materials using the higher-order micromorphic framework. The tools present are the interface to INL's MOOSE code which is used as the underlying FEA solver, several constitutive equations (linear elasticity, a pressure-sensitive elasto-plastic model, crystal plasticity, and additional models for geo-materials) useful for demonstration purposes and the simulation of some materials, the micromorphic filter which converts DNS information to micromorphic stress-deformation quantities, and the overlap coupling framework which enables multi-scale simulations of a coupled higher-order macroscale and a classically defined microscale.

Miller, Nathan↗