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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 OSTI2020

Comparison of Eulerian Bin and Lagrangian Particle-Based Schemes in Simulations of Pi Chamber Dynamics and Microphysics

This paper presents a comparison of simulations applying either a traditional Eulerian bin microphysics or a novel particle-based Lagrangian approach to represent CCN activation and cloud droplet growth. The Eulerian microphysics solve the evolution equation for the spectral density function, whereas the Lagrangian approach follows computational particles referred to as superdroplets. Each superdroplet represents a multiplicity of natural droplets that makes the Lagrangian approach computationally feasible. The two schemes apply identical representation of CCN activation and use the same droplet growth equation; these make direct comparison between the two schemes practical. The comparison, the first of its kind, applies an idealized simulation setup motivated by laboratory experiments with the Pi Chamber and previous model simulations of the Pi Chamber dynamics and microphysics. The Pi Chamber laboratory apparatus considers interactions between turbulence, CCN activation, and cloud droplet growth in moist Rayleigh–Bénard convection. Simulated steady-state droplet spectra averaged over the entire chamber are similar, with the mean droplet concentration, mean radius, and spectral width close in Eulerian and Lagrangian simulations. Small differences that do exist are explained by the inherent differences between the two schemes and their numerical implementation. The local droplet spectra differ substantially, again in agreement with the inherent limitations of the theoretical foundation behind each approach. There is a general agreement between simulations and Pi Chamber observations, with simplifications of the CCN activation and droplet growth equation used in the simulations likely explaining specific differences.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1996

Decoupling the Role of Inertia and Gravity on Particle Dispersion

Turbulent gas flows laden with small, dense particles are encountered in a wide number of important applications in both industrial settings and aerodynamics applications. Particle interactions with the underlying turbulent flow are exceedingly complex and, consequently, difficult to accurately model. The difficulty arises primarily due to the fact that response of a particle to the local environment is dictated by turbulence properties in the reference frame moving with the particle (particle-Lagrangian). The particle-Lagrangian reference frame is in turn dependent upon the particle relaxation time (time constant) as well as gravitational drift. The combination of inertial and gravitational effects in this frame complicates our ability to accurately predict particle-laden flows since measurements in the particle-Lagrangian reference frame are difficult to obtain. Therefore, in this work we will examine separately the effects of inertia and gravitational drift on particle dispersion through a combination of physical and numerical experiments. In this study, particle-Lagrangian measurements will be obtained in physical experiments using stereo image velocimetry. Gravitational drift will be varied in the variable-g environments of the NASA DC-9 and in the zero-g environment at the drop tower at NASA-Lewis. Direct numerical simulations will be used to corroborate the measurements from the variable-g experiments. We expect that this work will generate new insight into the underlying physics of particle dispersion and will, in turn, lead to more accurate models of particle transport in turbulent flows.

Rogers, Chris↗
NASA NTRS1977

Development and application of a three dimensional numerical model for predicting pollutant and sediment transport using an Eulerian-Lagrangian marker particle technique

A computer coded Lagrangian marker particle in Eulerian finite difference cell solution to the three dimensional incompressible mass transport equation, Water Advective Particle in Cell Technique, WAPIC, was developed, verified against analytic solutions, and subsequently applied in the prediction of long term transport of a suspended sediment cloud resulting from an instantaneous dredge spoil release. Numerical results from WAPIC were verified against analytic solutions to the three dimensional incompressible mass transport equation for turbulent diffusion and advection of Gaussian dye releases in unbounded uniform and uniformly sheared uni-directional flow, and for steady-uniform plug channel flow. WAPIC was utilized to simulate an analytic solution for non-equilibrium sediment dropout from an initially vertically uniform particle distribution in one dimensional turbulent channel flow.

Pavish, D. L.↗
NASA NTRS1992

Electromagnetic potential vectors and the Lagrangian of a charged particle

Maxwell's equations can be shown to imply the existence of two independent three-dimensional potential vectors. A comparison between the potential vectors and the electric and magnetic field vectors, using a spatial Fourier transformation, reveals six independent potential components but only four independent electromagnetic field components for each mode. Although the electromagnetic fields determined by Maxwell's equations give a complete description of all possible classical electromagnetic phenomena, potential vectors contains more information and allow for a description of such quantum mechanical phenomena as the Aharonov-Bohm effect. A new result is that a charged particle Lagrangian written in terms of potential vectors automatically contains a 'spontaneous symmetry breaking' potential.

Shebalin, John V.↗
DOE OSTI2024

Utilizing HYSPLIT for Emergency Response Modeling at SRS

At SRS, emergency responders use a variety of tools to detect, track, and mitigate hazardous material releases into the atmosphere. Two models currently used at SRS are Puff-Plume and the Lagrangian Particle Dispersion Model (LPDM), a Gaussian and Lagrangian model, respectively. A decision has been made to replace LPDM with the more widely-supported Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model for evaluating inhalation and ingestion doses following a release. HYSPLIT is designed to compute complex dispersion and deposition simulation To achieve the implementation of HYSPLIT, we have developed a preliminary UI framework that will allow HYSPLIT to be run on ATG computers without the need for active network connections, thus avoiding the loss of capabilities in the event of a network outage during an emergency.

Earley, Ian↗
DOE OSTI2023

Lagrangian large eddy simulations via physics-informed machine learning

High-Reynolds number homogeneous isotropic turbulence (HIT) is fully described within the Navier–Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as large eddy simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about effects of subgrid scales on the resolved scales. Here, we take an alternative approach and design LES heuristics stated in terms of Lagrangian particles moving with the flow. Our Lagrangian LES, thus L-LES, is described by equations generalizing the weakly compressible smoothed particle hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from direct numerical simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The subgrid-scale contributions are modeled separately with physical constraints to account for the effects from unresolved scales. We build the resulting model under the differentiable programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our L-LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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 OSTI2023

Code for the manuscript "Lagrangian Large Eddy Simulations vis Physics-informed Machine Learning"

We would like to disclose a code based on Python/Pytorch, in which we implement the data-driven physics-informed machine learning of Lagrangian large eddy simulation that is described in the manuscript "Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning ". High-Reynolds number Homogeneous Isotropic Turbulence is fully described within the Navier-Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of resolved scales, have designed heuristics, known as Large Eddy Simulation (LES). LES is described in terms of the temporally evolving Eulerian velocity field defined over a spatial grid with the mean-spacing correspondent to the resolved scale. This classic Eulerian LES depends on assumptions about the effects of sub-grid scales on the resolved scales. Here, we take an alternative approach and design novel LES heuristics stated in terms of Lagrangian particles moving with the flow. Our \emph{Lagrangian LES}, thus L-LES, is described by equations generalizing the weakly compressible Smoothed Particle Hydrodynamics formulation with extended parametric and functional freedom, which is then resolved via Machine Learning training on Lagrangian data from Direct Numerical Simulations of the NS equations. The L-LES model includes physics-informed parameterization and functional form, by combining physics-based parameters and physics-inspired Neural Networks to describe the evolution of turbulence within the resolved range of scales. The sub-grid scale contributions are modeled separately with physical constraints to account for the effects from un-resolved scales. We build the resulting model under the Differentiable Programming framework to facilitate efficient training. We experiment with loss functions of different types, including physics-informed ones accounting for statistics of Lagrangian particles. We show that our Lagrangian LES model is capable of reproducing Eulerian and unique Lagrangian turbulence structures and statistics over a range of turbulent Mach numbers.

Tian, Yifeng↗
DOE OSTI2024

Electron energization in reconnection: Eulerian vs Lagrangian perspectives

Particle energization due to magnetic reconnection is an important unsolved problem for myriad space and astrophysical plasmas. Electron energization in magnetic reconnection has traditionally been examined from a particle, or Lagrangian, perspective using particle-in-cell (PIC) simulations. Guiding-center analyses of ensembles of PIC particles have suggested that Fermi (curvature drift) acceleration and direct acceleration via the reconnection electric field are the primary electron energization mechanisms. However, both PIC guiding-center ensemble analyses and spacecraft observations are performed in an Eulerian perspective. For this work, we employ the continuum Vlasov–Maxwell solver within the Gkeyll simulation framework to reexamine electron energization from a kinetic continuum, Eulerian, perspective. We separately examine the contribution of each drift energization component to determine the dominant electron energization mechanisms in a moderate guide-field Gkeyll reconnection simulation. In the Eulerian perspective, we find that the diamagnetic and agyrotropic drifts are the primary electron energization mechanisms away from the reconnection x-point, where direct acceleration dominates. We compare the Eulerian (Vlasov Gkeyll) results with the wisdom gained from Lagrangian (PIC) analyses.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

General field evaluation in high-order meshes on GPUs

Robust and scalable function evaluation at any arbitrary point in the finite/spectral element mesh is required for querying the partial differential equation solution at points of interest, comparison of solution between different meshes, and Lagrangian particle tracking. This is a challenging problem, particularly for high-order unstructured meshes partitioned in parallel with MPI, as it requires identifying the element that overlaps a given point and computing the corresponding reference space coordinates. Here, we present a robust and efficient technique for general field evaluation in large-scale high-order meshes with quadrilaterals and hexahedra. In the proposed method, a combination of globally partitioned and processor-local maps are used to first determine a list of candidate MPI ranks, and then locally candidate elements that could contain a given point. Next, element-wise bounding boxes further reduce the list of candidate elements. Finally, Newton’s method with trust region is used to determine the overlapping element and corresponding reference space coordinates. Since GPU-based architectures have become popular for accelerating computational analyses using meshes with tensor-product elements, specialized kernels have been developed to utilize the proposed methodology on GPUs. The method is also extended to enable general field evaluation on surface meshes. The paper concludes by demonstrating the use of the proposed method in various applications ranging from mesh-to-mesh transfer during r-adaptivity to Lagrangian particle tracking.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Toward Polydisperse Flows With MFIX-EXA

In the presence of large size disparities, single-grid neighbor search algorithms lead to inflated neighbor lists that significantly degrade the performance of Lagrangian particle solvers. If Eulerian–Lagrangian (EL) frameworks are to remain performant when simulating realistic systems, improved neighbor detection approaches must be adopted. To this end, we consider the application of a multigrid neighbor search (MGNS) algorithm in the mfix-exa software package, an exascale EL solver built upon the AMReX library. Here, details regarding the implementation and verification of MGNS are provided along with speedup curves for a bidisperse mixing layer. MGNS is shown to yield up to 15$\times$ speedup on CPU and 6$\times$ speedup on GPU for the problems considered here. The mfix-exa software is then validated for a variety of polydisperse flows. Finally, a brief discussion is given for how dynamic MGNS may be completed, with application to spatially varying particle size distributions.

42 ENGINEERING↗
DOE OSTI2025

AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold networks

We propose the artificial intelligence velocimetry-thermometry (AIVT) method to reconstruct a continuous and differentiable representation of the temperature and velocity in turbulent convection from measured three-dimensional (3D) velocity data. AIVT is based on physics-informed Kolmogorov-Arnold networks and trained by optimizing a loss function that minimizes residuals of the velocity data, boundary conditions, and governing equations. We apply AIVT to a set of simultaneously measured 3D temperature and velocity data of Rayleigh-Bénard convection, obtained by combining particle image thermometry and Lagrangian particle tracking. This enables us to directly compare machine learning results to true volumetric, simultaneous temperature and velocity measurements. We demonstrate that AIVT can reconstruct and infer continuous, instantaneous velocity and temperature fields and their gradients from sparse experimental data at a high resolution, providing an additional approach for understanding thermal turbulence.

Science & Technology - Other Topics↗
NASA NTRS1976

Numerical modeling of pollutant transport using a Lagrangian marker particle technique

A derivation and code were developed for the three-dimensional mass transport equation, using a particle-in-cell solution technique, to solve coastal zone waste discharge problems where particles are a major component of the waste. Improvements in the particle movement techniques are suggested and typical examples illustrated. Preliminary model comparisons with analytic solutions for an instantaneous point release in a uniform flow show good results in resolving the waste motion. The findings to date indicate that this computational model will provide a useful technique to study the motion of sediment, dredged spoils, and other particulate waste commonly deposited in coastal waters.

Spaulding, M.↗
DOE OSTI2026

Direct numerical simulations of activation and deactivation in turbulent atmospheric clouds

Significant knowledge gaps remain in our understanding of turbulence–cloud–aerosol interactions in the Earth's atmosphere, and direct numerical simulation (DNS) has increasingly become an indispensable tool to fill such critical knowledge gaps. Here, this study is an extension of our previous DNS model [Gao et al., J. Geophys. Res.: Atmos., 123(4), 2194–2214 (2018)], with a focus on the activation of aerosol particles into cloud droplets and deactivation of cloud droplets into aerosol particles in a microscale cloud environment. The effects of turbulence intensity, particle curvature, and solute, as well as the initial distributions of the aerosol particles (monodisperse or polydisperse) are investigated. The governing equations for the flow of air, temperature, and water vapor mixing ratio are solved numerically in the Eulerian fashion, assuming homogeneous and isotropic turbulence. The dynamics of the aerosol and cloud particles are calculated with the Lagrangian particle tracking method. The results show that the deviations of the thermodynamic variables from their respective means are significantly reduced, the activation process appears to be delayed, and the deactivation process occurs more rapidly, as the turbulence intensity is increased. The inclusion of particle curvature and solute effects, as well as polydispersity, tends to retard the activation of aerosols into cloud droplets. It is also observed that fluctuations in supersaturation broaden the spread of particle radii, and the broadening is followed by a narrowing as turbulent homogenization reduces thermodynamic fluctuations over time.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1992

A coupled Eulerian/Lagrangian method for the solution of three-dimensional vortical flows

A coupled Eulerian/Lagrangian method is presented for the reduction of numerical diffusion observed in solutions of three-dimensional rotational flows using standard Eulerian finite-volume time-marching procedures. A Lagrangian particle tracking method using particle markers is added to the Eulerian time-marching procedure and provides a correction of the Eulerian solution. In turn, the Eulerian solutions is used to integrate the Lagrangian state-vector along the particles trajectories. The Lagrangian correction technique does not require any a-priori information on the structure or position of the vortical regions. While the Eulerian solution ensures the conservation of mass and sets the pressure field, the particle markers, used as 'accuracy boosters,' take advantage of the accurate convection description of the Lagrangian solution and enhance the vorticity and entropy capturing capabilities of standard Eulerian finite-volume methods. The combined solution procedures is tested in several applications. The convection of a Lamb vortex in a straight channel is used as an unsteady compressible flow preservation test case. The other test cases concern steady incompressible flow calculations and include the preservation of turbulent inlet velocity profile, the swirling flow in a pipe, and the constant stagnation pressure flow and secondary flow calculations in bends. The last application deals with the external flow past a wing with emphasis on the trailing vortex solution. The improvement due to the addition of the Lagrangian correction technique is measured by comparison with analytical solutions when available or with Eulerian solutions on finer grids. The use of the combined Eulerian/Lagrangian scheme results in substantially lower grid resolution requirements than the standard Eulerian scheme for a given solution accuracy.

Felici, Helene Marie↗
NASA NTRS2018

Southern California Megacity CO2, CH4, and CO Flux Estimates Using Ground- and Space-Based Remote Sensing and a Lagrangian Model

We estimate the overall CO2, CH4, and CO flux from the South Coast Air Basin using an inversion that couples Total Carbon Column Observing Network (TCCON) and Orbiting Carbon Observatory-2 (OCO-2) observations, with the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the Open-source Data Inventory for Anthropogenic CO2 (ODIAC). Using TCCON data we estimate the direct net CO2 flux from the So-CAB to be 104±26 Tg CO2 yr(exp -1) for the study period of July 2013–August 2016. We obtain a slightly higher estimate of 120±30 Tg CO2 yr(exp -1) using OCO-2 data. These CO2 emission estimates are on the low end of previous work. Our net CH4 (360±90 Gg CH4 y(exp -1)) flux estimate is in agreement with central values from previous top-down studies going back to 2010 (342–440 Gg CH4 yr(exp -1)). CO emissions are estimated at 487±122 Gg CO yr(exp -1), much lower than previous top-down estimates (1440 Gg CO yr(exp -1)). Given the decreasing emissions of CO, this finding is not unexpected. We perform sensitivity tests to estimate how much errors in the prior, errors in the covariance, different inversion schemes, or a coarser dynamical model influence the emission estimates. Overall, the uncertainty is estimated to be 25%, with the largest contribution from the dynamical model. Lessons learned here may help in future inversions of satellite data over urban areas.

Total Carbon Column Observing Network (TCCON)↗
DOE OSTI2023

Lagrangian dynamics of particle transport in oral and nasal breathing

Here, we present a large-eddy simulation (LES) of saliva particle transport during normal human breathing through the nose and mouth. The flow of the air–saliva mixture is modeled using an Eulerian LES that is coupled with a Lagrangian particle tracking module to obtain trajectories of saliva particles in a room with stagnant air conditions. The coupled Eulerian–Lagrangian simulation yields novel insights into the intricate dynamics of Lagrangian coherent structures (LCS) and fundamental material lines that emerge from the saliva particles' trajectories during several breathing cycles. Further, we systematically compare the quantitative LCS diagnostics of mouth breathing with those of mouth and nose normal breathing. Analyzing the simulation results of human breathing from the mouth and nose, we show that, soon after the first breathing cycle, saliva particles form a series of roll-up vortex rings that propagate forward. The forward propagation of these vortex rings leads to the formation of an asymmetrical primary forefront vortex. The individual vortex rings continuously propagate forward, merging with the forefront vortex, and ascending along the limb of the leading vortex.

42 ENGINEERING↗