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DOE OSTI2025

Probabilistic flux limiters

The stable numerical integration of shocks in compressible flow simulations relies on the reduction or elimination of Gibbs phenomena (unstable, spurious oscillations). A popular method to virtually eliminate Gibbs oscillations caused by numerical discretization in under-resolved simulations is to use a flux limiter. A wide range of flux limiters have been studied in the literature, with recent interest in their optimization via machine learning methods trained on high-resolution datasets. The common use of flux limiters in numerical codes as plug-and-play blackbox components makes them key targets for design improvement. Even for deterministic dynamical models, numerical uncertainty is introduced via coarse-graining required by insufficient computational power to solve all scales of motion. Conventional flux limiters are deterministic and lack the capacity to address uncertainties, both aleatoric (inherent randomness) and epistemic (modeling uncertainty due to limited knowledge), which arise in coarse-grained numerical simulations. Here, we introduce a conceptually distinct type of flux limiter that is designed to handle the effects of randomness in the model and uncertainty in model parameters. Unlike traditional single-function flux limiters, these new probabilistic flux limiters incorporate multiple flux limiting functions, each applied with a learned probability drawn from high-resolution data to mitigate the effects of uncertainty in numerical simulations. This approach departs from traditional single-function limiters by explicitly modeling and incorporating uncertainty into the shock capturing process. Using the example of Burgers' equation as a testbed, we show that a machine learned, probabilistic flux limiter may be used in a shock capturing code to more accurately capture shock profiles. In particular, we show that our probabilistic flux limiter outperforms standard limiters and can be successively improved upon (up to a point) by expanding the set of probabilistically chosen flux limiting functions.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1995

Use of an adjoint model for finding triggers for Alpine lee cyclogenesis

The authors propose a new procedure, designated the adjoint-based genesis diagnostic (AGD) procedure, for studying triggering mechanisms and the subsequent genesis of the synoptic phenomena of interest. This procedure makes use of a numerical model sensitivity to initial conditions and the nonlinear evolution of the initial perturbations that are designed using this sensitivity. The model sensitivity is evaluated using the associated adjoint model. This study uses the dry version of the National Center for Atmospheric Research Mesoscale Adjoint Modeling System (MAMS) for the numerical experiments. The authors apply the AGD procedure to two cases of Alpine lee cyclogenesis that were observed during the Alpine Experiment special observations period. The results show that the sensitivity fields that are produced by the adjoint model and the associated initial perturbations are readily related to the probable triggering mechanisms for these cyclones. Additionally, the nonlinear evolution of these initial perturbations points toward the physical processes involved in the lee cyclone formation. The AGD experiments for a weak cyclone case indicate that the MAMS forecast model has an underrepresented topographic forcing due to the sigma vertical coordinate and that this model error can be compensated by adjustments in the initial conditions that are related to the triggering mechanisms, which is not associated with the topographic blocking mechanism.

Vukicevic, Tomislava↗

DOE OSTI2021

A Poisson–Nernst–Planck Model of Ion Transport and Interface Segregation in Metal–Insulator–Semiconductor Structures and Solar Cells

A numerical model that describes the transport of mobile ionic species in metal–insulator–semiconductor (MIS) and photovoltaic (PV) devices subject to temperature and voltage stress is presented. The finite element method (FEM) is used to solve the Nernst–Planck equation while imposing Poisson's equation self-consistently as a restriction for the electrostatic potential. This allows the contribution of the ionic species to the potential to be taken into account. Using a variational formulation eases the implementation of diverse boundary conditions, including the incorporation of segregation kinetics at the device interfaces. Segregation across the dielectric–semiconductor interface is relevant to modeling the electronic device degradation in systems where contamination reaches the semiconductor. The model in closed systems with no-flux boundary conditions is validated first. In the limiting case of low contamination levels with respect to the gate bias, the FEM solution matches analytically derived approximations. Then, the implementation is broadened to include an open boundary at the dielectric–semiconductor interface to account for leakage of ions. The predicted time dependence of the flatband voltage in Na-contaminated MIS test structures agrees well with measurements. This model successfully captures the role of long-range ion transport at concentrations of relevance to electronic and PV device instability and neuromorphic computing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Modeling the Charge and Exciton Distributions in Phosphorescent White Organic Light-Emitting Diodes

A comprehensive numerical model is developed to quantitatively analyze the charge and exciton distributions in phosphorescent white organic light emitting diodes (WOLEDs). The model encompasses bipolar charge drift-diffusion transport with doping-dependent charge mobilities, field-dependent charge hopping across multiple interfaces, exciton generation, diffusion, energy transfer, radiative decay, and non-radiative exciton annihilation. The model is validated by direct measurement of the exciton distribution in monochromatic phosphorescent OLEDs. It is then applied to complex, multilayer WOLEDs where it is used to accurately predict their intensity and current-dependent spectral shifts. The magnitudes of different exciton loss channels in WOLEDs are calculated and quantitatively compared. This model provides an alternative for the many experimental iterations commonly used to optimize the color balance of complex OLEDs. In this context, the model inspired the addition of a hole blocking layer in the WOLED to balance the charge conduction and reduce spectral shifts – a design that is verified by experiment.

42 ENGINEERING↗

DOE OSTI2020

Modeling Spatial Heterogeneity in Surface Turbulent Heat Flux in the U.S. Southern Great Plains

Advances in numerical modeling of cloud dynamics are driving a need for improved land model prediction at convective storm scales. Here, satellite and ground-based vegetation remote sensing data were combined with land model experiments to more accurately characterize land surface spatial heterogeneity in the Community Land Model (CLM4.0). The new subgrid classification of plant functional types (PFT) and leaf area index (LAI) enables consistent comparison between models and ground-based flux measurements in the U.S. southern Great Plains. Errors in vegetation data sets (inferred from comparison between 250 m satellite and ground-based LAI), while large, had less impact on the simulated characteristics of spatial heterogeneity compared to errors in model representation of surface energy partitioning (between latent and sensible heat flux) and its relationship to LAI. Predicted spatial heterogeneity in surface energy partitioning was enhanced after replacing soil and stomatal resistance parameters with a new set that better predicts the observed relationship to LAI. These modifications increase the number of smaller (mesoscale) dry land patches having higher sensible heat flux. The parameter experiments suggest that vegetation state and processes (transpiration) act to broaden the size spectrum of surface heat flux heterogeneity, which can influence clouds and convective initiation. Moreover, improvements in vegetation input data and model parameters had partially compensating effects on surface flux heterogeneity, indicating the importance of evaluating input data and parameterizations together to improve prediction at higher spatial resolutions.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1985

A coupled ice-ocean model of ice breakup and banding in the marginal ice zone

A coupled ice-ocean numerical model for the marginal ice zone is considered. The model consists of a nonlinear sea ice model and a two-layer (reduced gravity) ocean model. The dependence of the upwelling response on wind stress direction is discussed. The results confirm earlier analytical work. It is shown that there exist directions for which there is no upwelling, while other directions give maximum upwelling in terms of the volume of uplifted water. The ice and ocean is coupled directly through the stress at the ice-ocean interface. An interesting consequence of the coupling is found in cases when the ice edge is almost stationary. In these cases the ice tends to break up a few tenths of kilometers inside of the ice edge.

Smedstad, O. M.↗

NASA NTRS1995

Assimilation of Altimeter Data into a Quasigeostrophic Model of the Gulf Stream System: Dynamical Considerations - Part 1

The dynamical consequences of constraining a numerical model with sea surface height data have been investigated. The model used for this study is a quasigeostrophic model of the Gulf Stream region. The data that have been assimilated are maps of sea surface height obtained as the superposition of sea surface height variability deduced from the Geosat altimeter measurements and a mean field constructed from historical hydrographic data. The method used for assimilating the data is the nudging technique. Nudging has been implemented in such a way as to achieve a high degree of convergence of the surface model fields toward the observations. The assimilation of the surface data is thus equivalent to the prescription of a surface pressure boundary condition. The authors analyzed the mechanisms of the model adjustment and the characteristics of the resultant equilibrium state when the surface data are assimilated. Since the surface data are the superposition of a mean component and an eddy component, in order to understand the relative role of these two components in determining the characteristics of the final equilibrium state, two different experiments have been considered: in the first experiment only the climatological mean field is assimilated, while in the second experiment the total surface streamfunction field (mean plus eddies) has been used. It is shown that the model behavior in the presence of the surface data constraint can be conveniently described in terms of baroclinic Fofonoff modes. The prescribed mean component of the surface data acts as a 'surface topography' in this problem. Its presence determines a distortion of the geostrophic contours in the subsurface layers, thus constraining the mean circulation in those layers. The intensity of the mean flow is determined by the inflow/outflow conditions at the open boundaries, as well as by eddy forcing and dissipation.

Capotondi, Antonietta↗

NASA NTRS1992

Modelling the Phanerozoic carbon cycle and climate: constraints from the 87Sr/86Sr isotopic ratio of seawater

A numerical model describing the coupled evolution of the biogeochemical cycles of carbon, sulfur, calcium, magnesium, phosphorus, and strontium has been developed to describe the long-term changes of atmospheric carbon dioxide and climate during the Phanerozoic. The emphasis is on the effects of coupling the cycles of carbon and strontium. Various interpretations of the observed Phanerozoic history of the seawater 87Sr/86Sr ratio are investigated with the model. More specifically, the abilities of continental weathering, volcanism, and surface lithology in generating that signal are tested and compared. It is suggested that the observed fluctuations are mostly due to a changing weatherability over time. It is shown that such a conclusion is very important for the modelling of the carbon cycle. Indeed, it implies that the conventional belief that the evolution of atmospheric carbon dioxide and climate on a long time scale is governed by the balance between the volcanic input of CO2 and the rate of silicate weathering is not true. Rather carbon exchanges between the mantle and the exogenic system are likely to have played a key role too. Further, the increase of the global weathering rates with increasing surface temperature and/or atmospheric CO2 pressure usually postulated in long-term carbon cycle and climate modelling is also inconsistent with the new model. Other factors appear to have modulated the weatherability of the continents through time, such as mountain building and the existence of glaciers and ice sheets. Based on these observations, a history of atmospheric carbon dioxide and climate during Phanerozoic time, consistent with the strontium isotopic data, is reconstructed with the model and is shown to be compatible with paleoclimatic indicators, such as the timing of glaciation and the estimates of Cretaceous paleotemperatures.

NASA Discipline Number 52-30↗

DOE OSTI2022

Physics-Informed Neural Network Modeling of Li-Ion Batteries: Preprint

Li-ion batteries (LIB) are a promising solution to enable storage of intermittent energy sources due to their high energy density. However, LIBs are known to significantly degrade after about 1000 charge-discharge cycles. LIBs degrade following different degradation modes and at a rate that depends on the operating conditions (external temperature, load). To plan the installation of batteries, appropriate understanding and prediction capabilities of their lifecycle is needed. High-fidelity numerical models of LIBs such as the pseudo-two-dimensional (P2D) model have been shown to accurately represent the charge-discharge-cycle of an LIB given ac- curate choice of the physical parameters. Given the large number of P2D parameters, adjusting them using forward runs is intractable. This work describes the development of a physics-informed neural network (PINN) as a surrogate substitute of the P2D model that captures parameter dependence. The PINN is advantageous as it needs little to no data, and can naturally encode the dependence of every model parameter. Here, a specific training procedure is adopted to efficiently cover parameter space, handle model stiffness and enforce boundary conditions. The trained PINN is validated against numerical solutions of the P2D model, and its applicability to battery degradation modeling is discussed.

battery degradation↗

DOE OSTI2022

Effect of Thermal Expansion Coefficient, Viscosity and Melting Range in Simulation of PCM Embedded Heat Exchangers With and Without Fins

Phase change material heat exchangers (PCMHX) have an important role in integrating renewable energy systems. PCMHX can offer high storage density at various temperatures of interest and can be used for grid load shifting purposes. Numerical models enable engineers to estimate PCMHX performance for different design parameters and operating conditions. Modeling phase change phenomena is challenging due to the complex time-dependent nature of the process. The accuracy of models is highly sensitive to PCM thermo-physical properties. Thermal expansion coefficient (β), viscosity (μ) and melting temperature range (MR) of a PCM are important properties, especially when natural convection is not negligible. In PCMHX modeling, using less than accurate values for these properties can have significant impact on the simulation outcomes. These properties and discussions thereof are not readily available in the literature. This paper presents a brief review of the literature and a numerical study investigating the model sensitivity to the above-mentioned properties for PCMHX with and without fins. CFD is used to evaluate the charging (melting) phenomena. The study quantifies the impact of uncertainty in these properties on the melting rate and temperature profile. Constant wall temperature was considered as heat source with no heat loss to ambient. Here, the results show that β and μ has significant effect on the melting rate and evolution of the melting front. For a non-finned domain when comparing results for different published values of β and μ, the deviation in melting time can be up to 12.9% and 57.6% respectively. For high wall temperatures, change in melting range did not impact melting time. But when the wall temperature is reduced, up to 9.8% deviation in melting time is observed.

CFD↗

NASA NTRS1993

The effects on the ionosphere of inertia in the high latitude neutral thermosphere

High-latitude ionospheric currents, plasma temperatures, densities, and composition are all affected by the time-dependent response of the neutral thermosphere to ion drag and Joule heating through a variety of complex feedback processes. These processes can best be studied numerically using the appropriate nonlinear numerical modeling techniques in conjunction with experimental case studies. In particular, the basic physics of these processes can be understood using a model, and these concepts can then be applied to more complex realistic situations by developing the appropriate simulations of real events. Finally, these model results can be compared with satellite-derived data from the thermosphere. We used numerical simulations from the National Center of Atmospheric Research Thermosphere/Ionosphere General Circulation Model (NCAR TIGCM) and data from the Dynamic Explorer 2 (DE 2) satellite to study the time-dependent effects of the inertia of the neutral thermosphere on ionospheric currents, plasma temperatures, densities, and composition. One particular case of these inertial effects is the so-called 'fly-wheel effect'. This effect occurs when the neutral gas, that has been spun-up by the large ionospheric winds associated with a geomagnetic storm, moves faster than the ions in the period after the end of the main phase of the storm. In these circumstances, the neutral gas can drag the ions along with them. It is this last effect, which is described in the next section, that we have studied under this grant.

Burns, Alan↗

DOE OSTI2015

A rigorous two-dimensional model for the stripline ferromagnetic resonance response of metallic ferromagnetic films

In this work, we constructed a two-dimensional numerical model for calculation of the stripline ferromagnetic resonance (FMR) response of metallic ferromagnetic films. We also conducted numerical calculations by using this software. The calculations demonstrated that the eddy current contribution to the FMR response decreases with a decrease in the stripline width. The most important manifestations of the conductivity (eddy current) effect are excitation of the higher-order standing spin waves across the film thickness in the materials for which the standing spin wave peaks would be absent in cavity FMR measurements and strong dependence of the off-resonance series conductance of the stripline on the stripline width. Whereas the contribution of the eddy currents to the stripline FMR response can be very significant, because wide striplines (100 μm+) are conventionally used for the FMR measurements, it is negligible in the case of excitation of spin waves, just because very narrow stripline transducers (0.5–5 μm wide) are required in order to excite spin waves in metallic ferromagnetic films in a noticeable frequency/applied field range.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2018

Development of a Virtual CFR Engine Model for Knocking Combustion Analysis

Knock is a major bottleneck to achieving higher thermal efficiency in spark ignition (SI) engines. The overall tendency to knock is highly dependent on fuel anti-knock quality as well as engine operating conditions. It is, therefore, critical to gain a better understanding of fuel-engine interactions in order to develop robust knock mitigation strategies. In the present work, a numerical model based on three-dimensional (3-D) computational fluid dynamics (CFD) was developed to capture knock in a Cooperative Fuel Research (CFR) engine. For combustion modeling, a hybrid approach incorporating the G-equation model to track turbulent flame propagation, and a homogeneous reactor multi-zone model to predict end-gas auto-ignition ahead of the flame front and post-flame oxidation in the burned zone, was employed. In addition, a hybrid methodology was implemented wherein a laminar flame speed lookup table generated a priori from a chemical kinetic mechanism could be used to provide flame speed as an input to the G-equation model, instead of using conventional empirical correlations. Multi-cycle Reynolds-averaged Navier-Stokes (RANS) simulations were performed for two different spark timings (STs) corresponding to non-knocking and knocking conditions, with other operating conditions kept the same as those of a standard research octane number (RON) test. Isooctane was considered as the fuel for the numerical study. Two different reduced kinetic mechanisms were employed to describe end-gas auto-ignition chemistry and to generate the flame speed lookup table. Experimental data, including intake/exhaust boundary conditions, was provided by an isooctane ST sweep study conducted in an in-house CFR engine. Moreover, cylinder wall/valve/port surface temperatures and residual gas fraction (RGF) were estimated using a well-calibrated one-dimensional (1-D) model. On the other hand, a novel methodology was also developed to analyze experimental data for the knocking case and identify the most representative cycle. For the non-knocking case, a good agreement was found between experiment and CFD simulation, with respect to cycle-averaged values of 10% burn point (CA10), 50% burn point (CA50), and peak pressure magnitude/location. The virtual CFR engine model was also demonstrated to be capable of predicting average knock characteristics for the knocking case, such as knock point, knock intensity, and energy of resonance, with good accuracy.

42 ENGINEERING↗

DOE OSTI2024

Assessing the release, transport, and retention of radioactive aerosols from hypothetical breaches in spent fuel storage canisters

Interim dry storage of spent nuclear fuel involves storing the fuel in welded stainless-steel canisters. Under certain conditions, the canisters could be subjected to environments that may promote stress corrosion cracking leading to a risk of breach and release of aerosol-sized particulate from the interior of the canister to the external environment through the crack. Research is currently under way by several laboratories to better understand the formation and propagation of stress corrosion cracks, however little work has been done to quantitatively assess the potential aerosol release. The purpose of the present work is to introduce a reliable generic numerical model for prediction of aerosol transport, deposition, and plugging in leak paths similar to stress corrosion cracks, while accounting for potential plugging from particle deposition. The model is dynamic (changing leak path geometry due to plugging) and it relies on the numerical solution of the aerosol transport equation in one dimension using finite differences. The model’s capabilities were also incorporated into a Graphical User Interface (GUI) that was developed to enhance user accessibility. Model validation efforts presented in this paper compare the model’s predictions with recent experimental data from Sandia National Laboratories (SNL) and results available in literature. We expect this model to improve the accuracy of consequence assessments and reduce the uncertainty of radiological consequence estimations in the remote event of a through-wall breach in dry cask storage systems.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Modeling cell venting and gas-phase reactions in 18650 lithium ion batteries during thermal runaway

A numerical model is developed to study cell venting, internal pressure, and gas-phase dynamics behavior of 18650 Li-ion cells undergoing thermal runaway. A k-e Reynolds-Averaged Navier-Stokes (RANS) model is adopted to describe the turbulent flow out of the cells, while the fluid dynamics inside the cells is described by Darcy-Forchheimer's equation. Thermal abuse reactions and gas generation kinetics are described by a single-step lumped reaction model. Then, a series of computational fluid dynamics (CFD) simulations are conducted on a single 18650 cell at various states-of-charge (100%, 50%, 25%) to study detailed flow and thermal behavior as a function of quantity of gas generated during cell venting. Venting events are categorized into two stages: i) breaching of the cell container and ii) thermal runaway reactions. It is found that the cell response is dominated by the second stage since most of the gases are generated during thermal runaway. Also, the propensity for propagation is highly affected by state-of-charge (SOC). Cells at higher SOCs produce more heat and gas during the venting event, owing to higher mass and concentrations of reacting gases, and consequently reach higher internal cell pressures which increase the risk of side-wall breaching.

25 ENERGY STORAGE↗

DOE OSTI2022

PyApprox: Enabling efficient model analysis

PyApprox is a Python-based one-stop-shop for probabilistic analysis of scientific numerical models. Easy to use and extendable tools are provided for constructing surrogates, sensitivity analysis, Bayesian inference, experimental design, and forward uncertainty quantification. The algorithms implemented represent the most popular methods for model analysis developed over the past two decades, including recent advances in multi-fidelity approaches that use multiple model discretizations and/or simplified physics to significantly reduce the computational cost of various types of analyses. Simple interfaces are provided for the most commonly-used algorithms to limit a user’s need to tune the various hyper-parameters of each algorithm. However, more advanced work flows that require customization of hyper-parameters is also supported. An extensive set of Benchmarks from the literature is also provided to facilitate the easy comparison of different algorithms for a wide range of model analyses. This paper introduces PyApprox and its various features, and presents results demonstrating the utility of PyApprox on a benchmark problem modeling the advection of a tracer in ground water.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1985

A study of GLAS and UCLA model forecasts of explosive maritime cyclogenesis

The explosive development of a maritime cyclone was examined with a numeric model of a cyclone which occurred during the FGGE Special Observing Period One in January 1979. The storm exhibited a Petterssen type-system frontal wave development. The model used was a nine-level, fourth-order coarse-mesh GLAS model initialized with FGGE level II-B data. Two versions of 72 hr synoptic-scale predictions were performed for the storm: with and without the input of meteorological satellite data. Attention was focused on the heat, mass and vorticity budgets of the storm, starting 24 hr before storm onset. The inclusion of satellite data, due to the poor quality of remotely sensed wind fields, degraded the performance of the model.

Peak, J. E.↗

NASA NTRS1988

High-resolution dynamical modelling of the Antarctic stratospheric vortex

Progress is reported on the high-resolution three-dimensional numerical simulation of flows characteristic of the Antarctic wintertime stratosphere. The numerical model is a modified version of the Reading University sigma-coordinate used previously for tropospheric studies. Physical parameterizations are kept to a minimum in order to concentrate as much computing power as possible on simulating details of the dynamical processes. The major question addressed is whether the features observed in recent high-resolution two-dimensional simulations - namely: (1) the formation of a sharp edge to the vortex (seen in the potential vorticity field), (2) the survival of the polar vortex in a material entity, and (3) the formation of small-scale eddies rough the break-up of tongues of high potential vorticity drawn out from the polar vortex - are realized in three-dimensional simulations.

Haynes, P. H.↗