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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

PINN surrogate of Li-ion battery models for parameter inference, Part I: Implementation and multi-fidelity hierarchies for the single-particle model

To plan and optimize energy storage demands that account for Li-ion battery aging dynamics, techniques need to be developed to diagnose battery internal states accurately and rapidly. Here, this study seeks to reduce the computational resources needed to determine a battery's internal states by replacing physics-based Li-ion battery models - such as the single-particle model (SPM) and the pseudo-2D (P2D) model - with a physics-informed neural network (PINN) surrogate. The surrogate model makes high-throughput techniques, such as Bayesian calibration, tractable to determine battery internal parameters from voltage responses. This manuscript is the first of a two-part series that introduces PINN surrogates of Li-ion battery models for parameter inference (i.e., state-of-health diagnostics). In this first part, a method is presented for constructing a PINN surrogate of the SPM. A multi-fidelity hierarchical training, where several neural nets are trained with multiple physics-loss fidelities is shown to significantly improve the surrogate accuracy when only training on the governing equation residuals. The implementation is made available in a companion repository (https://github.com/NREL/PINNSTRIPES). The techniques used to develop a PINN surrogate of the SPM are extended in Part II for the PINN surrogate for the P2D battery model, and explore the Bayesian calibration capabilities of both surrogates.

25 ENERGY STORAGE↗
NASA NTRS1985

Isotopic overabundances and the energetic particle model of solar flares

According to the energetic particle model of solar flares particles are efficiently accelerated in the magnetic field loop of an active region (AR) by hydromagnetic turbulence. It is demonstrated that the isotopic overabundances observed in some flares are not a consequence of the flare itself but are characteristic of the plasma in the AR. Only when a flare releases the plasma into the interplanetary space it is possible to observe this anomalous composition at spacecraft locations.

Perez-Enriquez, R.↗
NASA NTRS2022

Review of Solar Energetic Particle Models

Solar Energetic Particle (SEP) events are interesting from a scientific perspective as they are the product of a broad set of physical processes from the corona out through the extent of the heliosphere, and provide insight into processes of particle acceleration and transport that are widely applicable in astrophysics. From the operations perspective, SEP events pose a radiation hazard for aviation, electronics in space, and human space exploration, in particular for missions outside of the Earth’s protective magnetosphere including to the Moon and Mars. Thus, it is critical to improve the scientific understanding of SEP events and use this understanding to develop and improve SEP forecasting capabilities to support operations. Many SEP models exist or are in development using a wide variety of approaches and with differing goals. These include computationally intensive physics-based models, fast and light empirical models, machine learning-based models, and mixed-model approaches. The aim of this paper is to summarize all of the SEP models currently developed in the scientific community, including a description of model approach, inputs and outputs, free parameters, and any published validations or comparisons with data.

Kathryn Whitman↗
DOE OSTI2021

Multiscale CFD simulation of biomass fast pyrolysis with a machine learning derived intra-particle model and detailed pyrolysis kinetics

Coupling particle and reactor scale models is as essential as reactor fluid dynamics and particle motion for accurate Computational Fluid Dynamic (CFD) simulations of biomass fast pyrolysis reactors due to intraparticle heat transfer and chemical reactions controlling conversion time and product distributions. Direct online coupling of a particle model with a reactor model is computationally expensive, while offline coupling is case-dependent. In this research, solutions from a series of particle pyrolysis simulations were regressed with Artificial Neural Network (ANN). Furthermoer, this machine learning-derived model predicted the same temperature and conversion profiles compared with particle resolved simulation while the isothermal approach overpredicted the temperature by 130 K and underpredicted the conversion time by 30 s. The ANN model was then integrated into CFD simulations of fluidized bed biomass fast pyrolysis with varied feedstocks via coupling PyTorch and MFiX. The averaged error of simulation predicted bio-oil yields with four feedstocks is 6.4%. This multi-scale approach provides an efficient tool for the coupled particle and reactor scale simulations of biomass pyrolysis.

09 BIOMASS FUELS↗
NASA NTRS1992

A particle model for magnetotail neutral sheet equilibria

A particle model of the magnetotail neutral sheet self-consistent structure was developed by assuming that the thickness of the neutral sheet is much less than the thickness of the plasma sheet. This made it possible to approximate the neutral sheet as an x-independent structure. The role of nonlinear particle motions in determining the conductivity and the global current sheet structure is investigated.

Burkhart, G. R.↗
DOE OSTI2017

Model independent constraints on charges of new particles

Any particle that is charged under SU(3) C and U(1) EM can mediate the gg →γγ process through loops. Near the threshold for the new particle pair production, gauge boson exchanges necessitate the resummation of ladder diagrams. We discuss the leading log order matching of the one-loop result with the nonrelativistic effective theory resummed result. We show how the diphoton invariant mass spectrum varies depending on decay width, color representation, and electric charge of the new particle. In conclusion, the exclusion limits on the product of SU(3) C and U(1) EM charges of the new scalar or fermion particle are obtained from current LHC data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Is tokenization needed for masked particle modeling?

In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets relevant to developing foundation models for high-energy physics. In MPM, a model is trained to recover the missing elements of a set, a learning objective that requires no labels and can be applied directly to experimental data. We achieve significant performance improvements over previous work on MPM by addressing inefficiencies in the implementation and incorporating a more powerful decoder. We compare several pre-training tasks and introduce new reconstruction methods that utilize conditional generative models without data tokenization or discretization. We show that these new methods outperform the tokenized learning objective from the original MPM on a new test bed for foundation models for jets, which includes using a wide variety of downstream tasks relevant to jet physics, such as classification, secondary vertex finding, and track identification.

conditional generative models↗
DOE OSTI2015

Development of a hybrid shrinking‐core shrinking‐particle model for entrained‐flow gasifiers

Slagging entrained‐flow gasifiers operate above the melting temperature of the ash. As slag is highly nonwetting on the surface of char (carbon) particles, it is likely that it will agglomerate into one or several slag droplets and some of these droplets can detach from the char particles. If the slag exists in the form of droplets on the char surface rather than as a solid shell around the unreacted char particle, a shrinking particle model would be more physically realistic representation in comparison to the widely used shrinking core model (SCM). In the early section of the gasifier, the temperature remains below the ash melting temperature and, therefore, the SCM is more appropriate in this region. With this motivation, a novel hybrid shrinking‐core shrinking‐particle model has been developed. The model provides spatial profile of a number of important variables that are not available from the traditional SCM. © 2015 American Institute of Chemical Engineers AIChE J , 62: 659–669, 2016

Pednekar, Pratik↗
DOE OSTI2017

Pairwise interaction extended point-particle model for a random array of monodisperse spheres

This study introduces a new point-particle force model that attempts to account for the hydrodynamic influence of the neighbouring particles in an Eulerian–Lagrangian simulation. In previous point-particle models the force on a particle depends only on Reynolds number and mean volume fraction. Thus, as long as the mean local volume fraction is the same, the force on different particles will be estimated to be the same, even though the precise arrangement of neighbours can be vastly different. From direct numerical simulation (DNS) it has been observed that in a random arrangement of spheres that were distributed with uniform probability, the particle-to-particle variation in force can be as large as the mean drag. Since the Reynolds number and mean volume fraction of all the particles within the array are the same, the standard models fail to account for the significant particle-to-particle force variation within the random array. Here, we develop a model which can compute the drag and lateral forces on each particle by accounting for the precise location of a few surrounding neighbours. A pairwise interaction is assumed where the perturbation flow induced by each neighbour is considered separately, then the effects of all neighbours are linearly superposed to obtain the total perturbation. Faxén correction is used to quantify the force perturbation due to the presence of the neighbours. The single neighbour perturbations are mapped in the vicinity of a reference sphere and stored as libraries. We test the pairwise interaction extended point-particle (PIEP) model for random arrays at two different volume fractions of $\unicode[STIX]{x1D719}=0.1$ and 0.21 and Reynolds numbers in the range $16.5\leqslant Re\leqslant 170$ . The PIEP model predictions are compared against drag and lift forces obtained from the fully resolved DNS simulations performed using the immersed boundary method. Although not perfect, we observe the PIEP model prediction to correlate much better with the DNS results than the classical mean drag model prediction.

Mechanics↗
DOE OSTI2023

Compressible pairwise interaction extended point-particle model for force prediction of shock-particle bed interaction

We propose a pairwise influence framework for the complex unsteady compressible particle-laden flow problem by accounting for the scattered hydrodynamic waves emitting from neighboring particles in a Euler-Lagrange simulation. It has been observed from particle-resolved (PR) simulations of randomly dispersed particle beds under a loading shock that the compressible pseudoturbulence dominates the flow system even after the primary shock has passed, which causes fluctuations observed in the forces experienced by the particle. Moreover, the fact that each particle exists in the vicinity of a random arrangement of other particles modifies the time history of the drag force experienced by each particle during and after the passage of the shock. First, the scattering flow field due to an incoming shock interacting with a single sphere is constructed using an analysis of the flow in the acoustic limit. Then we examine the validity of the compressible Maxey-Riley-Gatignol force model by comparing the force prediction against a PR simulation of two interacting particles for various particle arrangements and incoming shock strength. Subsequently, the neighboring influences are stored as a library of maps that can be used readily in the calculation of the perturbation force. Lastly, the pairwise interaction assumption is evaluated by comparing the force predicted with the model with PR simulations of a randomly packed particle bed of 10% volume fraction for both water and air as the fluid medium for an incoming shock Mach number 1.22. With a considerably lower cost for the implementation of the model compared to PR simulations, it is verified that the model is reasonably accurate in pinpointing particles whose peak force is significantly larger or smaller than the mean drag but also to capture the prolonged fluctuations after the initial shock.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Patchy particle model of hydrated amorphous silica

Glasses corroded in static aqueous conditions typically reach a slow, residual rate of dissolution as a result of the complex interplay between multiple processes. While intrinsically suited to address this problem, Monte Carlo (MC) models developed to date have relied on lattices to describe the amorphous structure of glass and are thus unable to reproduce the residual rate of glass corrosion. A recently developed MC approach based on amorphous structures derived from molecular dynamics simulations offers a solution [Kerisit and Du J. Non-Cryst. Solids 522 (2019) 119601], but it requires a simple model that can rapidly relax the glass-water interface upon each dissolution/condensation event to retain the ability of MC simulations to reach large spatial and temporal scales. To this end, a patchy particle model of hydrated amorphous silica was developed and evaluated against atomistic simulations in this work. Models of water and amorphous silica were first developed separately, and cross terms were then defined to enable simulations of hydrated amorphous silica structures with varying water content. While the nature of its interaction potential prevents the patchy particle model from reproducing simultaneously the structure and dynamics of the systems of interest with a single set of parameters, it described well the connectivity of hydrated amorphous silica structures and the local coordination geometry of individual species. Therefore, this model opens the door to MC simulations of the residual rate of glass corrosion and offers an alternative to atomistic models to investigate the evolution of silica gels over large spatial and temporal scales.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Estimation of heat transfer coefficients for biomass particles by direct numerical simulation using microstructured particle models in the Laminar regime

Here, direct numerical simulation of convective heat transfer from hot gas to isolated biomass particle models with realistic morphology and explicit microstructure was performed over a range of conditions with laminar flow of hot gas (500 degrees C). Steady-state results demonstrated that convective interfacial heat transfer is dependent on the wood species. The computed heat transfer coefficients were shown to vary between the pine and aspen models by nearly 20%. These differences are attributed to the species-specific variations in the exterior surface morphology of the biomass particles. We also quantify variations in heat transfer experienced by the particle when positioned in different orientations with respect to the direction of fluid flow. These results are compared to previously reported heat transfer coefficient correlations in the range of 0.1 < Pr < 1.5 and 10 < Re < 500. Comparison of these simulation results to correlations commonly used in the literature (Gunn, Ranz-Marshall, and Bird-Stewart-Lightfoot) shows that the Ranz-Marshall (sphere) correlation gave the closest h values to our steady-state simulations for both wood species, though no existing correlation was within 20% of both species at all conditions studied. In general, this work exemplifies the fact that all biomass feedstocks are not created equal, and that their species-specific characteristics must be appreciated in order to facilitate accurate simulations of conversion processes.

09 BIOMASS FUELS↗
DOE OSTI2018

Comprehensive Study of the Polarization Behavior of LiFePO 4 Electrodes Based on a Many-Particle Model

The unique polarization behavior of LiFePO 4 electrodes was investigated using mathematical simulations with a many-particle model, which have a non-monotonic potential profile for each LiFePO 4 particle, and analyzed by the active population concept. The present simulations reveal two known polarization behaviors, namely the memory effect and path dependence. Notably, a hitherto unknown polarization behavior, the so-called relaxation-induced polarization (RIP), was also identified. The memory effect requires, at a minimum, a sequential four-step operation. By comparison, RIP is triggered only by a one-step operation, namely a long rest. In effect, the polarization associated with the memory effect and RIP was caused by a reduction of the active particles in the two-phase region via relaxation during a rest. The path-dependence mechanism was attributed to kinetically inhomogeneous reactions for each particle. We further found that narrowing the particle size distribution was an effective means to reduce polarization viz. the memory effect and path dependence of LiFePO 4 electrodes. However, RIP could not be suppressed by narrowing the particle size distribution. Furthermore, a comprehensive understanding of these polarization behaviors with our model provides a more accurate way to estimate the state of charge in Li-ion batteries with LiFePO 4 electrodes.

25 ENERGY STORAGE↗
DOE OSTI2020

A Review of Computational Models for the Flow of Milled Biomass Part I: Discrete-Particle Models

Biomass is a renewable and sustainable energy resource. Current design of biomass handling and feeding equipment leverage both experiments and numerical modeling. This paper reviews the state-of-the-art discrete element methods (DEM) for the flow of milled biomass (Part I), accompanied by a comprehensive review on continuum-based computational models (Part II). The present review on DEM is primarily focused on the features and suitability of various particle shape models for different types of milled biomass because particle shape is the predominant attribute controlling the flow behavior of complex-shaped granular material. The general strengths and weaknesses in the applicability of those models for the milled biomass modeling are summarized. In particular, comments are provided to balance the numerical model capabilities and the computational cost for the development of DEM models. To our best knowledge, this is the first-of-its-kind review on DEM specifically for biomass. Our study indicates that the current DEM models require further development, calibration, and validation based on a deep understanding of biomass particle contact mechanics and experimental data support before they can be reliably used for predictive simulations in handling and feeding systems.

42 ENGINEERING↗
DOE OSTI2018

Multiphysics Lattice Discrete Particle Modeling (M-LDPM) for the Simulation of Shale Fracture Permeability

A three-dimensional multiphysics lattice discrete particle model (M-LDPM) framework is formulated to investigate the fracture permeability behavior of shale. The framework features a dual lattice system mimicking the mesostructure of the material and simulates coupled mechanical and flow behavior. The mechanical lattice model simulates the granular internal structure of shale, and describes heterogeneous deformation by means of discrete compatibility and equilibrium equations. The network of flow lattice elements constitutes a dual graph of the mechanical lattice system. A discrete formulation of mass balance for the flow elements is presented to model fluid flow along cracks and intact materials. The overall computational framework is implemented with a mixed explicit–implicit integration scheme and a staggered coupling method that makes use of the dual lattice topology enabling the seamless two-way coupling of the mechanical and flow behaviors. The proposed model is used for the computational analysis of shale fracture permeability behavior by simulating triaxial direct shear tests on Marcellus shale specimens under various confining pressures. The simulated mechanical response is calibrated against the experimental data, and the predicted permeability values are also compared with the experimental measurements. Moreover, the paper presents the scaling analysis of both the mechanical response and permeability measurements based on simulations performed on geometrically similar specimens with increasing size. The simulated stress strain curves reflect a significant size effect in the post-peak due to the presence of localized fractures. The scaling analysis of permeability measurements enables prediction of permeability for large specimens by extrapolating the numerical results of small ones.

58 GEOSCIENCES↗
DOE OSTI2015

INTERSTELLAR NEUTRAL HELIUM IN THE HELIOSPHERE FROM IBEX OBSERVATIONS. II. THE WARSAW TEST PARTICLE MODEL (WTPM)

We have developed a refined and optimized version of the Warsaw Test Particle Model of interstellar neutral gas in the heliosphere, specially tailored for analysis of IBEX-Lo observations. The former version of the model was used in the analysis of neutral He observed by IBEX that resulted in an unexpected conclusion that the interstellar neutral He flow vector was different than previously thought and that a new population of neutral He, dubbed the Warm Breeze, exists in the heliosphere. It was also used in the reanalysis of Ulysses observations that confirmed the original findings on the flow vector, but suggested a significantly higher temperature. The present version of the model has two strains targeted for different applications, based on an identical paradigm, but differing in the implementation and in the treatment of ionization losses. We present the model in detail and discuss numerous effects related to the measurement process that potentially modify the resulting flux of ISN He observed by IBEX, and identify those of them that should not be omitted in the simulations to avoid biasing the results. This paper is part of a coordinated series of papers presenting the current state of analysis of IBEX-Lo observations of ISN He. Details of the analysis method are presented by Swaczyna et al. and results of the analysis are presented by Bzowski et al.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Aspherical particle models for molecular dynamics simulation

In traditional molecular dynamics (MD) simulations, atoms and coarse-grained particles are modeled as point masses interacting via isotropic potentials. For studies where particle shape plays a vital role, more complex models are required. Here, we describe a spectrum of approaches for modeling aspherical particles, all of which are now available (some recently) as options within the LAMMPS MD package. Broadly these include two classes of models. In the first, individual particles are aspherical, either via a pairwise anisotropic potential which implicitly assigns a simple geometric shape to each particle, or in a more general way where particles store internal state which can explicitly define a complex geometric shape. In the second class of models, individual particles are simple points or spheres, but rigid body constraints are used to create composite aspherical particles in a variety of complex shapes. This work discusses parallel algorithms and associated data structures for both kinds of models, which enable dynamics simulations of aspherical particle systems across a wide range of length and time scales. We also highlight parallel performance and scalability and give a few illustrative examples of aspherical models in different contexts.

74 ATOMIC AND MOLECULAR PHYSICS↗