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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Machine learning accelerated discrete element modeling of granular flows

Granular flows are widely encountered in many industrial processes and natural phenomena. Discrete Element Modeling (DEM) is a useful tool for understanding and troubleshooting devices, which handle granular materials. However, its applicability is significantly limited by the huge computational cost associated with detecting and computing collisions. In this research, the computation speed of DEM was accelerated by orders of magnitude using a convolutional neural network to replace the direct calculation of particle-particle and particle-boundary collisions. The MFiX software was used to generate the training and testing dataset. Additionally, a GPU accelerated TensorFlow model was used to train the neural network and test the results. The model fluctuations caused by different training steps were reduced with a multi-scale loss function. The accuracy was improved with more frames within one training step. The modeling of a rotating drum and a hopper demonstrated the accuracy and efficiency of this machine learning accelerated DEM in the simulation of granular flows.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Measurement of flow properties coupled to experimental and numerical analyses of dense, granular flows for solar thermal energy storage

Granular flows of sintered bauxite proppants were researched along an inclined plane for solar thermal energy storage applications. Granular flow properties needed to drive numeric granular models were measured for improved numerical model predictions for Carbobead CP 50/140, 40/100, and 30/60 particles. Particle shape and size distributions were determined by coupling optical microscopy to an in-house image processing algorithm. The impulse excitation technique was used to measure elastic and shear moduli, and compute Poisson’s ratio. The coefficient of static sliding friction was measured using the slip-stick method, and the static rolling friction was determined from measured shear on particles positioned between two hot-pressed plates. The coefficient of restitution was measured by dropping particles on a surface and determining the kinetic energy before and after impact with the surface using high resolution particle tracking velocimetry. Particle size did not significantly impact the coefficients of restitution and static rolling friction, however, particle shape distribution resulted in a large variation in measurements. An inclined flow experiment was performed to characterize granular flows of Carbobead CP 30/60 particles using particle image velocimetry. Numerical models of the experiment using discrete element method were generated with the measured mechanical properties as inputs for comparison with experimental results. A constant directional torque rolling friction model best predicted bulk granular flow behavior. Furthermore, good agreement between the model and experiment was achieved at ambient, steady state conditions, with average velocity differences <10%.

14 SOLAR ENERGY↗
DOE OSTI2024

Micromechanical origin of heat transfer to granular flow

Heat transfer across a granular flow is comprised of two resistances in series : near the wall and within the bulk particle bed, neither of which is well understood due to the lack of experimental probes to separate their respective contribution. Here, we use a frequency modulated photothermal technique to separately quantify the thermal resistances in the near-wall and the bulk bed regions of particles in flowing states. Compared to the stationary state, the flowing leads to a higher near-wall resistance and a lower thermal conductivity of bulk beds. As a result, coupled with discrete element method simulation, we show that the near-wall resistance can be explained by particle diffusion in granular flows.

14 SOLAR ENERGY↗
DOE OSTI2025

Pseudo-viscous modeling of transport in dense granular flows for thermal energy storage applications

Dense, granular flows were examined to effectively capture and model bulk viscous properties in thin packed beds. A modified Couette cell with particle image velocimetry was used to experimentally determine pseudo-viscosity properties of four particulate media with varying morphologies: (1) iron oxide-coated SiO 2 particles, (2) CARBOBEAD CP30-60 particles, (3) CARBOBEAD CP40-100 particles, and (4) Al 2 O 3 beads. The pseudo-viscosity functions were fitted using a power law to correlate the measured shear stress as a function of measured shear rate. The pseudo-viscous functions were used as inputs to computation fluid dynamics models for a single-phase viscous fluid to predict granular flow profiles. Steady-state free surface velocity profiles at angular velocities <7 rad/s predicted by the model were in good agreement with the experimental particle image velocimetry measurements, resulting in Pearson correlation coefficients of 0.97 for iron-oxide coated SiO 2 particles and 0.95 for CP30-60 particles. As a result, this alternative approach to measuring pseudo-viscous properties under shearing and modeling bulk transport behavior of granular flow using computation fluid dynamics model offered significant reduction in computational load compared to discrete element methods.

14 SOLAR ENERGY↗
DOE OSTI2020

Numerical analyses of high temperature dense, granular flows coupled to high temperature flow property measurements for solar thermal energy storage

High temperature particle flow properties necessary to predict granular flow behavior for solar thermal energy storage applications were measured and calculated for Carbobead CP 30/60 up to 800 °C. Here, the measured properties included elastic and shear moduli, particle-particle coefficients of static sliding and rolling friction, and particle-particle coefficients of restitution. Poisson’s ratio was calculated with elastic and shear moduli. The flow properties were used as inputs for a numerical model using the discrete element method to examine granular flows along an inclined plane at high temperature. The flow behavior was strongly influenced by the coefficients of static friction, which impacted the particle residence time, shear effects from the side walls, and particle flow mass flux. An 8.7%, 15.6%, and 8.5% increase and 37.9% decrease in steady state mass flow rate was observed for 200 °C, 400 °C, 600 °C, and 800 °C, respectively, when compared to room temperature simulations. A 52%, 59%, and 33% decrease in the time to reach steady state was observed for 200 °C, 400 °C, and 600 °C, respectively, while a 53% increase in time was observed for 800 °C. A significant delay in the flow development at 800 °C was observed due to significantly higher frictional forces.

14 SOLAR ENERGY↗
DOE OSTI2025

An adaptive, data-driven multiscale approach for dense granular flows

The accuracy of coarse-grained continuum models of dense granular flows is limited by the lack of high-fidelity closure models for granular rheology. One approach to addressing this issue, referred to as the hierarchical multiscale method, is to use a high-fidelity fine-grained model to compute the closure terms needed by the coarse-grained model. The difficulty with this approach is that the overall model can become computationally intractable due to the high computational cost of the high-fidelity model. In this work, we describe a multiscale modeling approach for dense granular flows that utilizes neural networks trained using high-fidelity discrete element method (DEM) simulations to approximate the constitutive granular rheology for a continuum incompressible flow model. Our approach leverages an ensemble of neural networks to estimate predictive uncertainty that allows us to determine whether the rheology at a given point is accurately represented by the neural network model. Additional DEM simulations are only performed when needed, minimizing the number of additional DEM simulations required when updating the rheology. This adaptive coupling significantly reduces the overall computational cost of the approach while controlling the error. In addition, the neural networks are customized to learn regularized rheological behavior to ensure well-posedness of the continuum solution. We first validate the approach using two-dimensional steady-state and decelerating inclined flows. We then demonstrate the efficiency of our approach by modeling three-dimensional sub-aerial granular column collapse for varying initial column aspect ratios, where our multiscale method compares well with the computationally expensive computational fluid dynamics (CFD)-DEM simulation.

Dense granular flows↗
DOE OSTI2022

Experimental characterization of extreme temperature granular flows for solar thermal energy transport and storage

High-temperature, dense granular flows along an inclined plane were considered for solar thermal energy transport and storage with sintered bauxite particles. A series of experiments was performed for particle inlet temperatures of ~ 200, 400, 600, and 800 °C to understand the mechanisms of granular flows at extreme temperatures. Mass flow rates were measured using a load cell and free-surface velocities were measured and computed using particle image velocimetry. Surface temperatures were measured using infrared cameras. A significant decrease in steady-state particle mass flow rate was observed with increasing temperature due to changing flow properties. A decrease in bulk particle free-surface velocities was observed at higher temperatures. Free-surface velocity measurement error between experiments were within 20% of the average. The particle surface temperatures decreased from inlet to outlet with larger gradients at higher temperatures observed due to increasing convection and radiative heat losses. Here, a decrease in temperature was observed along the side walls due to a decrease in particle velocities.

14 SOLAR ENERGY↗
DOE OSTI2024

Impact of Janssen effect on thermal transport in granular flow

Using a modulated photothermal radiometry (MPR) technique capable of measuring thermal transport in particle beds, we find asymptotically increasing effective thermal conductivity and decreasing near-wall thermal resistance along gravity-driven downward granular flows through a 1-meter-long narrow vertical channel owing to the Janssen effect. The Janssen effect is confirmed by similar asymptotic trends from particle bed apparent mass measurements as well as separate MPR heat transfer measurements on stationary particle beds under compression at different heights of the channel with and without pressure screening. Furthermore, local heat transfer coefficient along the 1-meter-long channel was modeled based on measured spatially resolved thermal conductivity and near-wall thermal resistance due to the Janssen effect. This work reveals for the first time the impact of the over century-old Janssen effect on heat transfer in granular media. Furthermore, the results from this work can lead to a better understanding of heat transfer in granular flow.

14 SOLAR ENERGY↗
DOE OSTI2024

SPH Modeling of Biomass Granular Flow: Engineering Application in Hoppers and Augers

Numerical modeling of granular biomass material flow in handling operations is indispensable to decipher flow upsets, commonly manifested as clogging and jamming in hoppers and augers. With a computational tool developed based on smoothed particle hydrodynamics (SPH), we simulated the hopper flow and auger feeding of six granular biomass materials. The good agreement between the experimental and numerical flow rates demonstrated the capability of the developed SPH solver in modeling the complicated flow of granular biomass materials. Further, the impact of physical and numerical parameters is investigated, and the major results show that the hopper flow pattern is controlled by shear band evolution; hopper clogging is collectively influenced by the opening size, wall friction, and material packing, and the impact of material compressibility on the auger feed rate is minimal. These parametric studies validate the solver’s robustness in simulating biomass flow in handling equipment and demonstrate that the SPH computational tool can provide insights about granular flow mechanics to facilitate handling equipment design and optimize handling operations.

09 BIOMASS FUELS↗
DOE OSTI2024

Shear rate dependency on flowing granular biomass material

The commercialization of bioenergy has been significantly limited by various material handling issues due to the poor flowability of granular biomass materials. A good understanding of flow physics and robust constitutive models to predict flow behavior across multiple regimes are essential to address these issues. In this study, we investigated the multi-regime flow behavior of loblolly pine chips, a widely used bioenergy feedstock, through comprehensive inclined plane flow experiments and simulations. A quasi-static hypoplastic model and a cross-regime Drucker-Prager-µ(I) model were calibrated and validated against the physical experiments to investigate the quasi-static and dense flow behavior. The results show that for granular biomass, 1) plane flow (iso-thickness along the plane) exists within a smaller range of inclination while heap flow (varying thickness along the plane) exists within a broader range of inclination, as compared with conventional granular materials (e.g., glass beads); 2) the scaling law of granular biomass flowing on an inclined plane (Froude Number versus dimensionless thickness) forms a bi-linear trend with the turning point governed by the quasi-static and dense flow regimes; 3) the multi-regime DP-µ(I) model can capture the flow behavior in both regimes well at the cost of extra calibration. In conclusion, these findings advance the scientific understanding of the multi-regime flow behavior of granular biomass materials and shed light on formulating novel constitutive models to assist granular biomass handling in the bioenergy industry.

09 BIOMASS FUELS↗
DOE OSTI2025

Dense granular flows with MFIX-Exa

This report extends the linear spring dashpot collision model the discrete element method available in MFIX-Exa to include static a static tangential friction force. Additionally, two rolling friction models frequently used in the literature are also implemented. The governing equations are provided with an emphasis on the new terms. The new model is validated by comparison to existing experimental data of single particle oblique collisions. The model is then tested on three dense granular flow problems: the formation of static piles, the discharge from a flat-bottom hopper and the self-induced granular Rayleigh-Taylor instability.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

BDEM (Discrete-element-simulator for high-solids granular flows) [SWR-22-72]

BDEM is a discrete element method based simulation tool developed specifically for modeling high-solids granular flows that include polydispersity, heat-transfer, moving boundaries and chemistry. Our solver provides facilities for simulating spherical/non-spherical particles with modified contact and friction models in complex dynamic geometries defined using level-sets or triangulated files. The solver is developed on top of NREL's open-source performance portable library, AMReX, providing parallel execution capabilities on current and upcoming high-performance-computing (HPC) architectures. Simulations at the scale of several millions to billion particles have been performed using this software on large scale computing resources. This software can be applied to non-reacting solids dominant flows in silos, hoppers and screw conveyors as well as in high temperature reacting systems such as screw kilns and auger reactors.

Sitaraman, Hariswaran↗
DOE OSTI2020

Experimental demonstration of a 5 kW th granular-flow reactor for solar thermochemical energy storage with aluminum-doped calcium manganite particles

A two-step cycle was discussed for solar thermochemical energy storage based on aluminum-doped calcium manganite reduction/oxidation reactions for direct integration into Air Brayton cycles. The two steps encompassed 1) the storage of concentrated solar direct irradiation via the thermal reduction of aluminum-doped calcium manganite and 2) the delivery of heat to an Air-Brayton cycle via re-oxidation of oxygen-deficient aluminum-doped calcium manganite. The re-oxidized aluminum-doped calcium manganite was fed back to the first step to complete the cycle. A 5 kWth solar thermochemical reactor operating under vacuum was fabricated and tested to examine the first cycle reduction step. Reactor operating conditions and high-flux solar simulator control were tuned for continuous reactor operation with particle temperatures > 1073 K. Continuous operation was achieved using intermittent, dense granular flows. A maximum absorption efficiency of 64.7% was shown, accounting for both sensible and chemical heat storage.

25 ENERGY STORAGE↗
DOE OSTI2021

Granular Flow Constitutive Model Add-ons For Abaqus

The software is a list of Abaqus User MATerial subroutines (VUMAT) for modeling granular flow physics. It includes (1) density dependent Mohr-Coulomb model, (2) density dependent Drager-Prager\Cap model, (3)Gudehus-Bauer hypoplastic model, and (4) critical state-based NorSand model.

Wencheng, Wencheng↗
DOE OSTI2023

SPH modeling of biomass granular flow: Theoretical implementation and experimental validation

The commercialization of biomass-derived energy is impeded by flowability challenges arising from the feeding and handling of granular biomass materials in full-scale biorefineries. To overcome these obstacles, a robust and accurate model to simulate the flow of granular biomass is indispensable. However, conventional mesh-based numerical codes are limited by inherent mesh distortion in simulating large deformation that commonly occurs in granular biomass handling. Here, in this study, we propose a graphics processing unit (GPU)-accelerated meshless Smoothed Particle Hydrodynamics (SPH) code to model the flow of granular biomass materials. A modified void ratio-based mass conversation, a hybrid particle-to-particle/surface frictional boundary treatment, and a hypoplastic constitutive model are implemented. Four numerical examples, an elastic block sliding on inclined planes, sand column collapse, Angle of Repose, and axial compression tests for pine chips, were simulated using the developed SPH code. The results demonstrate good agreement between numerical predictions and analytical and experimental data for all four examples, validating the SPH code and increasing confidence that it can be applied to simulate more complex granular biomass handling processes, such as hopper feeding or auger conveyance.

09 BIOMASS FUELS↗
DOE OSTI2023

Granular Flow in Novel Octet Shape–Based Lattice Frame Material

Moving packed-bed heat exchangers in concentrated solar power (CSP) plants involves heat transfer between heated falling particles and supercritical carbon dioxide. The overall effective thermal conductivity of the moving packed bed and particle-side channel contact resistances are still the bottlenecks in achieving the desirable thermal transport levels. To this end, a novel moving packed bed heat exchanger consisting of an Octet lattice packed between the walls of the particle-side channel is proposed in this study. Granular flow analysis in Octet lattice moving packed bed heat exchanger (OLHX) was conducted through experiments and discrete element method (DEM)-based numerical simulations. The experimental images clearly demonstrated stagnation regions upstream of lattice fibers, void regions downstream of the fiber junctions, and wavy-type unobstructed flow on the lateral sides of the fibers. DEM simulations were successful in capturing all these critical flow phenomena. Larger flow velocities were observed on the lateral sides of the fibers in the simulations. Also, when the particles in the silo were emptied, the final images showed an accumulation of particles on the inter-fiber as well as fiber–channel wall junctions. Moreover, the fiber connections resulted in some regions devoid of particle contact on the channel endwall, which means that these regions would suffer from poor thermal exchange. Finally, the overall mass flowrate increased with increasing porosity for a fixed particle diameter.

14 SOLAR ENERGY↗
DOE OSTI2026

Continuum Correlations from CFD-DEM Modeling of Conduction Heat Transfer in Granular Flows

Heat transfer between a surface and flowing particles is analyzed to improve the accuracy of continuum models for wall-to-bed heat transfer in a fluidized bed. Discrete element modeling (DEM) is used to model a fluidized bed heat exchanger where heat enters the system through a heated wall. The DEM heat transfer predictions are validated against published experimental work (Brewster et al., 2024) with less than 15% error. In previous work by Morris et al. (2015), a continuum model was developed using data from high-fidelity DEM simulations of chute flows. In the current study, the continuum model is extended and validated for fluidized beds. The sensitivity of the continuum heat transfer model parameters, which was not quantified in previous studies, is also investigated. It is observed that for a given particle with specific properties, e.g. the particle size, roughness, and conduction lens radius, the continuum correlation developed for heat transfer from a heated boundary to the particle bed depends mainly on the solid fraction or porosity of the particle bed for a given fluid. The new continuum heat transfer model is then validated over a wide range of superficial velocities via comparisons to both discrete element and experimental data. It is shown that this correlation is valid for a large range of particle flow conditions from chute flows to fluidized beds with less than 10% error as compared to DEM predictions.

14 SOLAR ENERGY↗
DOE OSTI2021

Granular Flow

LIGGGHTS-INL is a capability-extended version of LIGGGHTS. LIGGGHTS is an Open Source Discrete Element Method Particle Simulation Software (https://www.cfdem.com/media/DEM/docu/Manual.html). LIGGGHTS stands for LAMMPS improved for general granular and granular heat transfer simulations. LAMMPS is a classical molecular dynamics simulator. It is widely used in the field of Molecular Dynamics (https://lammps.sandia.gov/doc/Manual.html). Thanks to physical and algorithmic analogies, LAMMPS is a very good platform for DEM simulations. LAMMPS offers a GRANULAR package to perform these kinds of simulations. LIGGGHTS aims to improve those capability with the goal to apply it to industrial applications.

Xia, Yidong↗