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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Validation of Calibrated k–ε Model Parameters for Jet-in-Crossflow

Previous efforts determined a set of calibrated, optimal model parameter values for Reynolds-averaged Navier–Stokes (RANS) simulations of a compressible jet in crossflow (JIC) using a $k–ε$ turbulence model. These parameters were derived by comparing simulation results to particle image velocimetry (PIV) data of a complementary JIC experiment under a limited set of flow conditions. Here, a $k–ε$ model using both nominal and calibrated parameters is validated against PIV data acquired from a much wider variety of JIC cases, including a realistic flight vehicle. The results from the simulations using the calibrated model parameters showed considerable improvements over those using the nominal values, even for cases that were not used in the calibration procedure that defined the optimal parameters. This improvement is demonstrated using a number of quality metrics that test the spatial alignment of the jet core, the magnitudes of multiple flow variables, and the location and strengths of vortices in the counter-rotating vortex cores on the PIV planes. These results suggest that the calibrated parameters have applicability well outside the specific flow case used in defining them and that with the right model parameters, RANS solutions for the JIC can be improved significantly over those obtained from the nominal model.

42 ENGINEERING↗
DOE OSTI2022

Field Sensitivity Analysis of Turbulence Model Parameters for Flow Over a Wing

Reynolds-averaged-Navier-Stokes (RANS) turbulence models are a critical tool in computational-fluid-dynamics simulations of aerodynamic systems, but simulation results can be highly sensitive to RANS-model parameter choices. Sensitivity analysis can be used to quantify these impacts, and the objective of this study is to demonstrate field sensitivity analysis with respect to ten parameters in the 2003 Menter shear-stress-transport (SST) turbulence model. Here, the analysis is demonstrated for an application relevant to wind energy, namely, flow over a NACA 0015 wing at 12 degree angle of attack and a Reynolds number of 1.5 x 10 6 . We quantify sensitivity using Sobol indices and the mean-squared gradient, which are estimated using polynomial chaos and active subspace models, respectively. Our results indicate that there are substantial spatial variations in parameter sensitivities, with different sets of most-sensitive parameters near the wing, as well as in the downstream wake, consistent with the physical interpretations of the turbulence model inputs. We show that, for this particular turbulence model and flow, simultaneous dimension reduction is possible across all quantities of interest, enabling efficient exploration of model outcomes. Ultimately, this analysis provides new insights into turbulence model parameter sensitivities in incompressible flows, and also demonstrates the implementation of field sensitivity analysis for applications relevant to aerodynamics simulations.

17 WIND ENERGY↗
DOE OSTI2024

Viscosity Measurements at High Pressures: A Critical Appraisal of Corrections to Stokes' Law

Abstract Fluids and melts in planetary interiors significantly influence geodynamic processes from volcanism to global‐scale differentiation. The roles of these geofluids depend on their viscosities ( η ). Constraining geofluid η at relevant pressures and temperatures relies on laboratory‐based measurements and is most widely done using Stokes' Law viscometry with falling spheres. Yet small sample chambers required by high‐pressure experiments introduce significant drag on the spheres. Several correction schemes are available for Stokes' Law but there is no consensus on the best scheme(s) for high‐pressure experiments. We completed high‐pressure experiments to test the effects of (a) the relative size of the sphere diameter to the chamber diameter and (b) the top and bottom of the chamber, that is, the ends, on the sphere velocities. We examined the influence of current correction schemes on the estimated viscosity using Monte Carlo simulations. We also compared previous viscometry work on various geofluids in different experimental setups/geometries. We find the common schemes for Stokes' Law produce statistically distinct values of η . When inertia of the sphere is negligible, the most appropriate scheme may be the Faxén correction for the chamber walls. Correction for drag due to the chamber ends depends on the precision in the sinking distance and may be ineffective with decreasing sphere size. Combining the wall and end corrections may overcorrect η . We also suggest the uncertainty in η is best captured by the correction rather than propagated errors from experimental parameters. We develop an overlying view of Stokes' Law viscometry at high pressures.

Geochemistry & Geophysics↗
DOE OSTI2023

Large eddy simulation of atmospheric boundary layer flow over complex terrain in comparison with RANS simulation and on-site measurements under neutral stability condition

Large eddy simulation (LES) of the atmospheric boundary layer (ABL) flow over complex terrain is presented with a validation using meteorological tower (met-tower) data through an improved neutral stability sampling approach. The proposed stability sampling procedure includes a condition based on the most-likely occurrence time-periods of the neutral ABL and reduces the variabilities of the conditional wind statistics calculated at the met-towers in comparison to our previous work. The ABL flow simulations are carried out over a potential wind site with a prominent hill based using the OpenFOAM-based simulator for on/off-shore wind farm applications by applying the Lagrangian-averaged scale-invariant dynamic sub-grid scale turbulence model. A low-dissipative scale-selective discretization scheme for the non-linear convection term in the LES governing equation is adopted implicitly to ensure both the second-order accuracy and bounded solution. The LES inflow is generated through a precursor method with a “tiling” approach based on the flow driving parameters obtained from a corresponding Reynolds-averaged Navier–Stokes (RANS) simulation. Overall, the averaged wind velocity profiles predicted by the LES approach at all met-tower locations show a similar tendency as the RANS results, which are also in reasonable agreement with the met-tower data. An obvious difference in wind speed standard deviation profiles is seen between LES and RANS, especially at regions downstream of the hill edge, where the LES shows under-predicted results at the highest measurement levels in comparison to the tower data. The computational costs of the LES are found to be about 20 times higher than the RANS simulations.

Energy & Fuels↗
DOE OSTI2025

Openpronghorn

OpenPronghorn is a simulation tool specifically tailored for modeling thermal-hydraulic phenomena in advanced nuclear reactors. It is built on the Multiphysics Object-Oriented Simulation Environment (MOOSE), an open-source platform that facilitates the development of high-performance scientific computing applications. OpenPronghorn solves the Navier-Stokes equations, which describe the conservation of mass, momentum, and energy in fluid flows, using the finite volume numerical method. The code supports a wide range of fluid flow conditions that are applicable to nuclear reactors, including incompressible and weakly compressible flows, as well as single-phase and multiphase flows. It is capable of modeling diverse flow regimes, including laminar and turbulent flows, using various turbulence models such as the standard k-epsilon models, the v2f model, and the mixing length model. For multiphase flows, OpenPronghorn employs a mixture a Eulerian modeling approach with mixture, drift-flux, and full Eulerian models, and includes open-sourced interfacial transfer correlations for drag, exchange, and heat transfer coming from the scientific literature. OpenPronghorn's modular design allows it to handle multiscale simulations, ranging from detailed Reynolds-Averaged Navier Stokes (RANS) simulations to coarse-mesh and lumped parameter models. This flexibility enables users to perform high-fidelity simulations of specific reactor components as well as system-level analyses of entire reactor circuits. The code can be coupled with other MOOSE-based tools using the MultiApp system, allowing for the transfer of coupling quantities such as mass flow rates, heat fluxes, and boundary conditions between different simulation scales. One of the main features of OpenPronghorn is the it includes built-in validation cases from the open-source scientific literature and supports the implementation of user-defined models and correlations through MOOSE's FunctorMaterial system. OpenPronghorn is designed to be computationally efficient, leveraging the SIMPLE projection method for large-scale problems, and can be run on high-performance computing systems to handle the extensive computational demands of detailed reactor simulations. Overall, OpenPronghorn is a versatile and robust tool that provides critical insights into the thermal-hydraulic behavior of advanced nuclear reactors, supporting the design, safety, and optimization of next-generation nuclear energy systems.

Retamales, Mauricio Eduardo Tano [Idaho National L↗
DOE OSTI2021

Low-order preconditioning of the Stokes equations

A well-known strategy for building effective preconditioners for higher-order discretizations of some PDEs, such as Poisson's equation, is to leverage effective preconditioners for their low-order analogs. In this work, we show that high-quality preconditioners can also be derived for the Taylor–Hood discretization of the Stokes equations in much the same manner. In particular, we investigate the use of geometric multigrid based on the Q 1 iso Q 2 /Q 1 discretization of the Stokes operator as a preconditioner for the Q 2 /Q 1 discretization of the Stokes system. We utilize local Fourier analysis to optimize the damping parameters for Vanka and Braess–Sarazin relaxation schemes and to achieve robust convergence. Furthermore, these results are then verified and compared against the measured multigrid performance. While geometric multigrid can be applied directly to the Q 2 /Q 1 system, our ultimate motivation is to apply algebraic multigrid within solvers for Q 2 /Q 1 systems via the Q 1 iso Q 2 /Q 1 discretization, which will be considered in a companion paper.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Anharmonic Exciton‐Phonon Coupling in Metal‐Organic Chalcogenides Hybrid Quantum Wells

Abstract In contrast to inorganic quantum wells, hybrid quantum wells (HQWs) based on metal‐organic semiconductors are characterized by relatively soft lattices, in which excitonic states can strongly couple to lattice phonons. Therefore, understanding the lattice's impact on exciton dynamics is essential for harnessing the optoelectronic potential of HQWs. Beyond 2D metal halide perovskites, layered metal‐organic chalcogenides (MOCs), which are an air‐stable, underexplored material class hosting room‐temperature excitons, can be exploited as photodetectors, light emitting devices, and ultrafast photoswitches. Here, the role of phonons in the optical transitions of the prototypical MOC [AgSePh] ∞ is elucidated. Impulsive stimulated Raman scattering (ISRS) allows the detection of coherent exciton oscillations driven by Fröhlich interaction with low‐energy optical phonons. Steady state absorption and Raman spectroscopies reveal a strong exciton‐phonon coupling (Huang‐Rhys parameter ≈1.7) and its anharmonicity, manifested as a nontrivial temperature‐dependent Stokes shift. The ab initio calculations support these observations, hinting at an anharmonic behavior of the low‐energy phonons <200 cm −1 . These results untangle complex exciton‐phonon interactions in MOCs, establishing an ideal testbed for room‐temperature many‐body phenomena.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Probabilistic learning on manifolds constrained by nonlinear partial differential equations for small datasets

A novel extension of the Probabilistic Learning on Manifolds (PLoM) is presented. It makes it possible to synthesize solutions to a wide range of nonlinear stochastic boundary value problems described by partial differential equations (PDEs) for which a stochastic computational model (SCM) is available and which depend on a vector-valued random control parameter. The cost of a single numerical evaluation of this SCM is assumed to be such that only a limited number of points can be computed for constructing the training dataset (small data). Each point of the training dataset is made up of realizations from a vector-valued stochastic process (the stochastic solution) and the associated random control parameter on which it depends. The presented PLoM constrained by PDE allows for generating a large number of learned realizations of the stochastic process and its corresponding random control parameter. These learned realizations are generated so as to minimize the vector-valued random residual of the PDE in the mean-square sense. Appropriate novel methods are developed to solve this challenging problem. Three applications are presented. The first one is a simple uncertain nonlinear dynamical system with a nonstationary stochastic excitation. The second one concerns the 2D nonlinear unsteady Navier–Stokes equations for incompressible flows in which the Reynolds number is the random control parameter. Here, the last one deals with the nonlinear dynamics of a 3D elastic structure with uncertainties. The results obtained make it possible to validate the PLoM constrained by stochastic PDE but also provide further validation of the PLoM without constraint.

Machine learning↗
DOE OSTI2025

Dust-void evolution driven by turbulent dust flux can induce runaway migration of Earth-mass planets

Torques from asymmetric dust structures (so-called dust-void and filamentary structures) formed around low-mass planets embedded in a nonturbulent dust-gas disk can exceed the torques produced by the gas disk component and then go on to dominate the planet’s orbital dynamics. Here, we investigate how these structures (hence the dust torque) change when the effect of turbulent dust diffusion and dust feedback are included, along with the direct implications on the migration of Earth-like planets. Using the FARGO3D code, we performed 2D and 3D multifluid hydrodynamic simulations, focusing on a non-migrating planet with a mass of M p = 1.5 M ⊕ in 2D and on migrating planets with M p ∈ [1.5, 12] M ⊕ in 3D. We varied the δ-dimensionless diffusivity parameter in the range [0, 3 × 10 −3 ] and considered three different Stokes numbers, St = {0.04, 0.26, 0.55}, which are representative of the gas-dominated, the transitional, and the gravity-dominated regimes, respectively. In our 2D models, we find that turbulent diffusion of dust prevents the formation of the dust-void and filamentary structures when δ > 3 × 10 −4 . Otherwise, dust structures survive turbulent diffusion flow. However, dust and total torques become positive only in transitional and gravity-dominated regimes. In our 3D models, we find that the dust-void is drastically modified and the high-density ring-shaped barrier delineating the dust-void disappears if δ ≳ 10 −4 , due to the effect of dust turbulent diffusion along with the back-reaction of the dust. For all values of δ, the filament in front of the planet is replaced by a low-density trench. Remarkably, as we have allowed the planets to migrate, the evolving dust-void can drive either runaway migration or outward (inward) oscillatory-torque migration. Our study thus suggests that low-mass Earth-like planets can undergo runaway migration in dusty disks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.

In this paper we study the efficacy of combining machine-learning methods with projection-based model reduction techniques for creating data-driven surrogate models of computationally expensive, high-fidelity physics models. Such surrogate models are essential for many-query applications e.g., engineering design optimization and parameter estimation, where it is necessary to invoke the high-fidelity model sequentially, many times. Surrogate models are usually constructed for individual scalar quantities. However there are scenarios where a spatially varying field needs to be modeled as a function of the model’s input parameters. Here we develop a method to do so, using projections to represent spatial variability while a machine-learned model captures the dependence of the model’s response on the inputs. The method is demonstrated on modeling the heat flux and pressure on the surface of the HIFiRE-1 geometry in a Mach 7.16 turbulent flow. The surrogate model is then used to perform Bayesian estimation of freestream conditions and parameters of the SST (Shear Stress Transport) turbulence model embedded in the high-fidelity (Reynolds-Averaged Navier–Stokes) flow simulator, using shock-tunnel data. The paper provides the first-ever Bayesian calibration of a turbulence model for complex hypersonic turbulent flows. We find that the primary issues in estimating the SST model parameters are the limited information content of the heat flux and pressure measurements and the large model-form error encountered in a certain part of the flow.

42 ENGINEERING↗
DOE OSTI2025

Aerodynamic Sensitivities over Separable Shape Tensors

Here, we present a comprehensive aerodynamic sensitivity analysis of airfoil parameterization informed by separable shape tensors. This parameterization approach uniquely benefits the design process by isolating various well-studied shape characteristics, such as airfoil thickness, and providing a well-regulated low-dimensional parameter domain for aerodynamic designs. Exploring the aerodynamic sensitivities of this novel parameterization can provide valuable insights for more robust designs and future manufacturing efforts. We construct a data-driven parameter space of airfoils using principal geodesic analysis of separable shape tensors informed by a curated database containing almost 20,000 suitable engineering airfoils. Analyzing the shape reconstruction error and the maximum mean discrepancy between joint distributions of aerodynamic quantities, we study the dimensionality of the learned parameter space. This simple numerical experiment demonstrates a dramatic dimension reduction that retains design effectiveness and promotes regularity of the shape representations. Finally, we generate new airfoils and use the HAM2D Reynolds-averaged Navier–Stokes solver to predict lift, drag, and moment coefficients. We compute multiple sensitivity metrics to quantify and assert the consistency of parameter influence on the aerodynamic quantities. We also explore low-dimensional polynomial ridge approximations to motivate physical intuitions and offer explanations of the approximated sensitivities.

17 WIND ENERGY↗
DOE OSTI2025

Numerical simulations of liquid jetting with solid inclusions

The dynamics of finite-sized particles in fluids, and their influence on the overall flow, are of great interest across several industrial, environmental, and medical fields. In the context of inkjet printing, the presence of solid inclusions can be either intentional, as in additive manufacturing, or unintentional, as in standard printing processes. These inclusions can strongly impact the jetting process, causing effects such as jet asymmetry, bubble entrapment, and the formation of satellite droplets. Understanding and controlling particle behavior is therefore essential, particularly to predict how and when particles are ejected over multiple jetting cycles. It is therefore critical to develop reliable models that allow for a deeper understanding of the complex interplay between particle and fluid during the whole printing process. To address this, we present a tailored implementation of the Color-Gradient multicomponent Lattice Boltzmann Method for fully resolved three-dimensional (3D) simulations of multicycle liquid jetting with particles. Our method supports realistic parameter settings aligned with industrial inkjet systems, and we provide both qualitative and quantitative validation against experimental data. Additionally, we introduce a simplified model based on the Stokes drag law, in which solid particles are represented as point particles and do not influence the fluid flow. Despite this limitation, the model offers a computationally efficient means to explore the vast parameter space typically encountered in industrial applications, allowing, e.g., identifying critical ejection regions and estimating the number of cycles required for particle release. These qualitative insights are valuable for guiding and complement fully two-way coupled simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
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 OSTI2020

Parameter space mapping of the Princeton magnetorotational instability experiment

Extensive simulations of the Princeton Magnetorotational Instability (MRI) Experiment with the Spectral/Finite Element code for Maxwell and Navier-Stokes Equations (SFEMaNS) have been performed to map the MRI-unstable region as a function of inner cylinder angular velocity and applied vertical magnetic field. The angular velocities of the outer cylinder and the end-cap rings follow the inner cylinder in fixed ratios optimized for MRI. Here, we first confirm the exponential growth of the MRI linear phase using idealized conducting vertical boundaries (end caps) rotating differentially with a Taylor-Couette profile. Subsequently, we run a multitude of simulations to scan the experimental parameter space and find that the normalized volume-averaged mean-square radial magnetic field, our main instability indicator, rises significantly where MRI is expected. At various locations, the local radial components of fluid velocity and generated magnetic field are well correlated with the volume-averaged indicator. Based on this correlation, a diagnostic system that will measure the radial magnetic field at several locations on the inner cylinder is proposed as the main comparison between simulation and experiment. A detailed analysis of poloidal mode structures in the SFEMaNS code indicates that MRI, rather than Ekman circulation or Rayleigh instability, dominates the fluid behavior in the region where MRI is expected.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Experimental study of high-energy single-pulse nanosecond discharges in pin-to-pin configuration

Single-pulse pin-to-pin nanosecond pulsed discharges in ambient air (gap distances of 3–7 mm) were investigated at high pulse energies (∼20–30 mJ deposited energy per 11 ns full width at half maximum pulse). A Michelson interferometry setup (532 nm continuous-wave laser) was employed to record time-resolved interferograms of the discharge, enabling spatially resolved calculation of the electron number density. An intensified charge-coupled device camera was used to capture the spatiotemporal evolution of the discharge (streamer formation and spark channel development), and Coherent Anti-Stokes Raman Spectroscopy (CARS) was used to measure post-discharge N 2 vibrational temperatures. Discharge current and voltage were monitored with a back-current shunt. The discharge was initiated with simultaneous cathode-directed and anode-directed streamers that bridge the gap within ∼1 ns and form a luminous, filamentary plasma channel immediately after breakdown. Laser interferometry measurements showed peak electron number densities of the order of 10 1 7–10 18 cm −3 , occurring about 15–20 ns after pulse arrival at the discharge gap. Shorter gap discharges yielded higher peak electron densities, consistent with the higher energy density in the smaller gaps. Spatially, the electron density was highest near the electrodes and decreased toward the midgap region, with 5 and 7 mm gaps exhibiting a pronounced drop in the central channel. CARS indicated initial vibrational temperatures of approximately 4000–6000 K in the spark core of ∼50 ns after the discharge onset, decaying on a ∼500 ns timescale as the plasma cooled and recombined.

Coherent anti-Stokes Raman spectroscopy↗
DOE OSTI2025

Building a new multiphysics workflow in MOOSE: application to tritium migration, trapping and advection in TMAP8

Fusion devices are anticipated to produce and consume several kilograms of tritium per year. This rare fuel resource is both highly mobile and radioactive, making tracking inventories a priority for operation and safety. The fusion safety program at the Idaho National Laboratory has been developing the Tritium Migration and Analysis Program (TMAP), of which the latest version is a MOOSE-based application. TMAP8 is verified against its predecessors and possesses additional multi-dimensional tritium migration modeling capabilities. As we extend its capabilities towards both whole device (in multiple dimensions) and whole plant (with multiple components) simulations, the syntax of inputs must become compact, descriptive, compatible with quality assurance processes, and as error-proof as achievable. The new Physics system developed MOOSE can set up equations and instantiating them on plant components. The system permits the automatic definition of complex discretization with a consistency between object parameters achieved programmatically. The Physics system can currently instantiate the equations for heat conduction and Navier Stokes weakly compressible flow. In MOOSE-terms, it automates the definition of kernels, boundary conditions, and several core and helper materials and fields. As part of this effort, Physics classes were developed for tritium migration, trapping and advection within either a multi-dimensional Navier Stokes fluid dynamics simulation, or a 1D thermal hydraulics piping system. In this presentation, we will showcase the new syntax, its application to several verification and validation cases which were already studied using the classical TMAP8 syntax, and a demonstration of the new coupling capabilities for the migration of tritium into blanket coolant channels and the subsequent advection into the coolant loop.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

τ-SIMPLE Algorithm for the closure problem in homogenization of stokes flows

Estimation of the permeability tensor is crucial for many natural and industrial porous media applications. Homogenization provides a rigorous framework to calculate effective parameters from pore-scale images of a representative unit cell of the porous medium by solving a boundary value problem, also known as closure problem, subject to global constraints. However, the latter are hard to satisfy for arbitrarily complex geometries. We have developed a novel computational algorithm to calculate rigorously the permeability tensor. The approach, here referred to as τ-SIMPLE, is based on introducing an artificial time scale τ to satisfy the global constraint within the SIMPLE iteration. Here, we show that the proposed algorithm has high accuracy for both two-dimensional and three-dimensional periodically arranged geometries.

54 ENVIRONMENTAL SCIENCES↗