MFIX-Exa: CFD-DEM Simulations of Thermodynamics and Chemical Reactions in Multiphase Flows
For presentation at the AIChE Annual Meeting, Orlando, FL, November 5-10, 2023.
Porcu, Roberto↗
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Revisiting the empirical particle-fluid coupling model used in DEM-CFD by high-resolution DEM-LBM-IMB simulations: A 2D perspective
The work investigates the applicability of the unresolved Computational Fluid Dynamics and Discrete Element Method (CFDDEM) technique based on empirical equations for fluid-particle coupling. We first carry out a series of representative volume element simulations using the high-resolution particle-resolved Lattice Boltzmann method and Discrete Element Method (LBMDEM) coupled by an Immersed Moving Boundary (IMB) scheme. Then, we compare the results obtained by both LBMDEM and empirical equations used in unresolved CFDDEM with analytical solutions. It is found that the existing empirical equations used in solving fluid-particle interactions in 2D CFDDEM fail to accurately calculate the hydrodynamic force applied to solid particles. The underlying reason is that the existing empirical models are obtained based on 3D experimental results and thus are not applicable to 2D problems. Based on the simulation results, a new drag coefficient model is then proposed. The estimated drag forces using the new model are compared favourably with the simulated ones, indicating the good performance of the proposed model.
42 ENGINEERING↗
CFD-DEM Modeling of Autothermal Pyrolysis of Corn Stover with a Coupled Particle- and Reactor-Scale Framework
Autothermal operation of fast pyrolysis is an efficient process-intensification technique wherein exothermic oxidation reactions are used to overcome the heat-transfer bottleneck of conventional pyrolysis. The development of accurate, reliable modeling toolsets is imperative to generating a deeper understanding of biomass autothermal pyrolysis systems to support scale-up and industrial deployment. This modeling effort describes the development of single-particle and reactor models which incorporate detailed reaction schemes and simultaneous exothermic oxidation reactions. The particle-scale model was parameterized for corn stover feedstock with particle morphology, density, ash content, and biopolymer composition, all of which impact the emergent conversion characteristics during pyrolysis. Results were then used to parameterize a reactor-scale autothermal pyrolysis model, which was developed using a coarse-grained computational fluid dynamic-discrete element method. The simulation results compared well with experimental results, with the predicted bio-oil, light gas, and biochar yield within 3.0 wt% of the experimental yields. Further analyses were performed to test the influence of equivalence ratio, biomass injection position, and particle size distribution on autothermal pyrolysis. The analysis of the physio-chemical properties of the fluid and solid phase inside the reactor and at the reactor outlet help reveal important process interactions of autothermal pyrolysis.
BIOMASS FUELS,INORGANIC, ORGANIC, PHYSICAL, AND AN↗
Simulation of Mechanical Fractionation of Chopped Whole-Plant Corn (WPC) Using Discrete Element Method (DEM)
Fractionating whole-plant corn (WPC) in a single-pass harvesting system requires studies on the WPC-to-equipment interaction for improved property control, as well as mechanical and air-driven separation processes compared to the traditional multi-pass grain and stover harvesting system. The discrete element method (DEM) technique has the potential to simulate WPC mechanical fractionation and support simulation-based design of WPC separation processes. In this study, methods to develop DEM particle models of WPC (kernel, cob, stalk, and husk) and their material properties for simulating mass fractionation using the ASABE standard mechanical shaker were proposed. Measurement was done on the axial dimensions (major, intermediate, and minor) and mass of each WPC type (mean sample size is 56), sampled from single-pass harvesting. Applying gaussian multivariate regression and bootstrapping re-sampling techniques, a DEM particle approximate to each WPC was developed. Sensitivity analysis of the DEM Young‘s modulus, Poisson‘s ratio, and interaction parameters of coefficient of restitution, coefficient of rolling friction, and coefficient of static friction on mass fraction was performed after 156 ASABE sieve-shaking DEM simulation runs, generated using Latin Hypercube Design (LHD) design of experiment (DOE) from 19 DEM material parameters. DEM simulation using Hertz-Mindlin with flexible bond contact laws and DOE optimized material properties successfully reproduced the mass fractions retained in ASABE sieves at 9.8% mean relative error and a coefficient of determination of R2 = 0.87. Here, the DEM methodology developed for mechanical WPC mass fractionation could be deployed to perform virtual design of feedstock handling equipment and performance analysis of mechanical fraction systems.
09 BIOMASS FUELS↗
DEM Modeling and Validation of Pebble Bed Packing Using Chrono::GPU
Accurate prediction of pebble packing structure is important for pebble bed reactors because the spatial distribution of void fraction directly affects coolant flow, pressure drop, heat transfer, and neutronic behavior. However, experimentally validated DEM studies that directly evaluate local void-fraction structure in reactor-relevant pebble beds remain limited. In this work, the pebble bed experiment conducted at Missouri University of Science and Technology is simulated using the graphics processing unit (GPU)-based discrete element method (DEM) code Chrono::GPU. The study focuses on evaluating the ability of Chrono::GPU to reproduce the packing arrangement and void-fraction distribution of a randomly packed spherical pebble bed. The DEM results are first verified against established radial void-fraction correlations, including the Mueller and Vortmeyer-Schuster models, to assess the predicted bulk porosity, near-wall behavior, and oscillatory packing structure. The simulation is then verified against reference DEM data and validated against gamma-ray computed tomography (CT) experimental data at three axial locations. The Chrono::GPU results reproduce the main features of the experimental packing, including the high void fraction near the wall, the first near-wall trough, and the damped oscillatory radial profile caused by wall-induced ordering. Quantitative comparison with DEM data and the CT-based radial profiles shows good agreement, with mean absolute errors on the order of 0.07 and root-mean-square errors below 0.09 for the averaged profiles. These results demonstrate that Chrono::GPU can accurately capture the void-fraction structure of spherical pebble beds and provides a reliable DEM framework for future pebble bed reactor packing, recycling, and thermal-hydraulic studies.
97 - MATHEMATICS AND COMPUTING↗
The New Landsat Global Land Survey (GLS) DEM
The Landsat project, for its Collection 2 distribution, is going to introduce a new global Digital Elevation Model (DEM) for scene orthorectification. The new global DEM is a composite of the latest and greatest available DEM sources and will include reprocessed SRTM (called NASADEM), hi-resolution stereo optical data (ArcticDEM), and various nationally produced datasets. With exception of a few places, most of the world will have the updated DEM, with improvements in absolute vertical accuracy and horizontal improvements of up to 360 meters (12 Landsat 30m pixels). This new DEM will be used to process all the scenes from Landsat 1-8 in Collection 2 processing and will be made available to public in Summer 2020.
Landsat↗
MFIX DEM Enhancement for Industry-Relevant Flows (Final Report)
The overall goal of this two-phase project is to implement performance improvements of the Multiphase Flow with Interphase Exchanges (MFIX) Discrete Element Model (DEM) code that enable a transformative shift for industrial use. Prior to this effort, the largest simulations performed using MFIX are O(10 7 ) particles. This falls short of the O(10 9 ) particle simulations that must be completed on a timescale of days or weeks (vs. months or years) to enable simulations with physically-relevant domain sizes to be incorporated into industrial design cycles within five years. This was accomplished by tailoring best-in-class practices to bear on the unique challenges posed by the MFIX-DEM algorithm and code base. Scientific simulations (e.g., in cosmology, turbulent combustion) routinely use massively parallel computing to update far more particles in short wall clock times. Results from Phase 1 (1.5 years in duration) indicated significant gains in speed were possible for a wide range of benchmark cases. Moreover, a survey sent to >35 companies indicates that the timing is ideal for such an enhanced tool, with >80% of the respondents indicating that DEM is already value-added or will be within the next 5 years, and >70% of the respondents indicating that improved speed is the top computational priority. In Phase 2 (3.5 years in duration), the two major barriers that hinder industry from effectively using multiphase Computational Fluid Dynamics (CFD) to cut costs and improve performance, namely computational overhead and confidence in predictions, continued to be addressed. Regarding the former, the results from Phase 1 to guide the effort, with enhancements focused on an improved time-stepping algorithm and particle sorting. Four target problems of 1 billion particles each and increasing complexity were identified: homogeneous cooling, tumbler with continuous particle size distribution, discharge from a rectangular hopper and a cylindrical riser. Each of these were successfully simulated for relevant time scales (on order of seconds) using less than 24 hours of wall clock time. These represent the first 1-billion particle DEM simulations performed with MFIX, namely using the MFIX-Exa code. This code is currently under development at NETL in collaboration with Lawrence Berkeley National Laboratory. Regarding the second barrier on predictive uncertainty, experiments from Phase 1 (interacting nozzles - hydrodynamics only) and Phase 2 (very small-scale segregation experiments) were used to demonstrate the ability of two simplified approaches to uncertainty quantification (UQ). By limiting the number of particles, UQ based on the simplified treatment was compared to standard UQ, which was shown to have much higher computational demands. Experiments were also performed on a pilot-scale stripper unit to provide validation data for future CFD-DEM simulations and UQ.
20 FOSSIL-FUELED POWER PLANTS↗
Separability of soils in a tallgrass prairie using SPOT and DEM data
An investigation is conducted which uses a canonical transformation technique to reduce the features from SPOT and DEM data and evaluates the statistical separability of several prairie soils from the canonically transformed variables. Both SPOT and DEM data was gathered for a tallgrass prairie near Manhattan, Kansas, and high resolution SPOT satellite images were integrated with DEM data. Two canonical variables derived from training samples were selected and it is suggested that canonically transformed data were superior to combined SPOT and DEM data. High resolution SPOT images and DEM data can be used to aid second-order soil surveys in grasslands.
Su, Haiping↗
DEM and MELT In-Can Vitrification Process for Fukushima Daiichi Water Treatment Secondary Waste - 20034
Fukushima Daiichi Nuclear Power Station was severely damaged by a tsunami induced by the earthquake on March 11, 2011. A large amount of water treatment secondary waste has been generated from the purification work of contaminated water that consists of the water flowing into the reactor building. The Mid and Long-Term Road map specifies that solid waste at Fukushima Daiichi Nuclear Power Station should be stored safely and reasonably according to its characteristics. In order to meet this goal, a versatile treatment solution is needed, and the In-Can vitrification seems to be a suitable solution. The DEM and MELT In-Can vitrification process is being developed and qualified in France to treat waste arising from decommissioning and dismantling operations. This qualification is carried out through the consortium of the DEM and MELT project coordinated by CEA which gathers Orano, ECM technologies and Andra. The DEM and MELT In-Can vitrification process is a robust, simple and versatile in situ vitrification process. It is designed for high or intermediate level waste and is compact enough to be implemented in an existing facility or close to the waste to be treated. It is developed to treat liquid and solid waste, to produce a small amount of secondary waste and to minimize investment and operating costs. The DEM and MELT In-Can vitrification process is also developed with a modular design in order to be adapted to nuclear operators needs and requirements. The In-Can melting process has already been developed and fully qualified by the CEA for Alpha liquid waste and can be rated TRL 7 for this scope. The In-Can vitrification technology is considered as a qualified solution that could be applied for the treatment of waste resulting from the D and D work in Fukushima Daiichi Nuclear Power Station. The feasibility evaluation of the DEM and MELT In-Can vitrification process for Fukushima Daiichi water treatment secondary waste is a project led by Orano, CEA and ANADEC. This R and D project is performed by the funds from the Ministry of Economy, Trade and Industry as The Subsidy Program 'Project of Decommissioning and Contaminated Water Management'. This paper presents the first results of this feasibility evaluation, from the scenarios and glass formulation definition to the industrial implementation feasibility. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
CFD-DEM and PR-DNS studies of low-temperature densely packed beds
Over the past few decades, granular media is gaining attention as a viable option for heat transfer fluids (HTFs). Several research efforts are studying the use of particle-based heat transfer fluids in a wide variety of applications. With this motivation, the current work focusses on analyzing the different heat transfer mechanisms in low-temperature mono-sized densely packed granular media. To study the heat transfer behavior of granular media at different scales, the current work employs a two-way coupled computational strategy. The motion of particles is solved using the Discrete Element Method (DEM) and the interstitial air is solved using a Finite-Volume (CFD) approach. The Open-Source library CFDEM Coupling® is used in the current study to join the Finite Volume PISO solver of OpenFOAM® and the DEM solver of LIGGGHTS®. Typically, particle-particle contact conduction and particle-air convection are the most popular closure models. But recent research identified a different heat transfer phenomenon in packed beds that cannot be identified by conduction or convection models. While closure models were developed to implement this on a CFD-DEM framework, they did not capture the effect of intra-particulate thermal gradients on this phenomenon. Hence the current work also employs Particle-Resolved Direct Numerical Simulations (PR-DNS) to gain valuable insights allowing for the modification of existing models. A new closure model is then proposed here and is implemented in the CFD-DEM framework. This model provides key insights into the different heat transfer mechanism of packed beds.
42 ENGINEERING↗
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↗
Radar and Lidar Radar DEM
Using radar and lidar data, the aim is to improve 3D rendering of terrain, including digital elevation models (DEM) and estimates of vegetation height and biomass in a variety of forest types and terrains. The 3D mapping of vegetation structure and the analysis are useful to determine the role of forest in climate change (carbon cycle), in providing habitat and as a provider of socio-economic services. This in turn will lead to potential for development of more effective land-use management. The first part of the project was to characterize the Shuttle Radar Topography Mission DEM error with respect to ICESat/GLAS point estimates of elevation. We investigated potential trends with latitude, canopy height, signal to noise ratio (SNR), number of LiDAR waveform peaks, and maximum peak width. Scatter plots were produced for each variable and were fitted with 1st and 2nd degree polynomials. Higher order trends were visually inspected through filtering with a mean and median filter. We also assessed trends in the DEM error variance. Finally, a map showing how DEM error was geographically distributed globally was created.
remote sensing↗
Reverse scaling of a bonded-sphere DEM model: Formulation and application to lignocellulosic biomass microstructures
We explore scaling laws for adapting a bonded-sphere discrete element method (BS-DEM) model developed for woody structural mechanics at the millimeter scale to model the mechanics of realistic lignocellulosic biomass microstructures. Two scaling approaches, i.e., the reverse coarse graining (RCG) and equivalent bulk behavior (EBB), are proposed based on the classical mechanics principles and assessed in the single-particle compression and rectangular cuboid block tension tests. The EBB approach is recommended for BS-DEM models with general 3D geometries and is applied to simulate the microindentation test on a realistic microscale pinewood specimen. Simulations are performed to elucidate the impact of specimen thickness and loading position on the specimen’s force–deformation behavior. Furthermore, the range of Young’s modulus obtained from the calibrated BS-DEM simulations can match the experimental measurements. This is the first-of-its-kind study that has explored scale-bridging modeling approaches in biomass micromechanics and has proposed solutions based on BS-DEM models in the microscale.
59 BASIC BIOLOGICAL SCIENCES↗
BOREAS HYP-8 DEM Data Over The NSA-MSA and SSA-MSA in The AEAC Projection
These data were derived from the original Digital Elevation Models (DEMs) produced by the Boreal Ecosystem-Atmosphere Study (BOREAS) Hydrology (HYD)-8 team. The original DEMs were in the Universal Transverse Mercator (UTM) projection, while this product is projected in the Albers Equal-Area Conic (AEAC) projection. The pixel size of the data is 100 meters, which is appropriate for the 1:50,000-scale contours from which the DEMs were made. The original data were compiled from information available in the 1970s and 1980s. This data set covers the two Modeling Sub-Areas (MSAs) that are contained within the Southern Study Area (SSA) and the Northern Study Area (NSA). The data are stored in binary, image format files. The DEM data over the NSA-MSA and SSA-MSA in the AEAC projection are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).
Knapp, David E.↗
Discrete-Element and Material-Point Method (DEM and MPM) Based Solvers for Sustainable Technologies
We present the use of discrete element method (DEM) and material point method (MPM) in three relevant green technology applications that include biomass feedstock handling, lithium-ion battery manufacturing, and high-pressure reverse osmosis. Our open-source DEM and MPM solvers are developed using performance portable grid and particle management library, AMReX, thus enabling superior performance on NVIDIA and AMD GPUs with > 100 million particles. Our DEM solver resolves the motion of individual particles in a granular system and includes a bonded sphere method for modeling non-spherical particles along with Hertzian and liquid bridge-based contact models. We simulate highly variable biomass feedstock flows in large-scale hoppers for biofuel production and electrode calendering in battery manufacturing using DEM. Our simulations predict flow blockage in large scale biomass hoppers and electrode microstructure variations, thus providing valuable information for biofuel and battery manufacturers, respectively. The second half of the talk will be on MPM and its application towards pore resolved simulations of reverse osmosis membranes under compressive loads. We present a validation study of our MPM simulations with membrane microscopy imaging thus providing useful insights on membrane stability under high pressure conditions. We also present a spectral stability analysis of using linear hat, quadratic and cubic spline basis in MPM indicating regions of numerical stability.
BIOMASS FUELS,MATHEMATICS AND COMPUTING↗
DEM, DSM, and Cleaned LiDAR Point Cloud Data from the NGEE Arctic UAS Campaigns at the Teller 27 Field Site from 2017 and 2018, Seward Peninsula, Alaska
A Digital Elevation Model (DEM) and Digital Surface Model (DSM) were derived from airborne Light Detection and Ranging (LiDAR) data collected from Los Alamos National Laboratory's (LANL) heavy-lift unoccupied aerial system (UAS) quadcopter and hexacopter platforms operated by Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) scientists from the EES-14 group at LANL. These data were collected in August 2017 and July 2018 at the NGEE Arctic field site near mile marker 27 of the Bob Blodgett Nome-Teller Memorial Highway between Nome, Alaska and Teller, Alaska. A Vulcan Raven X8 Airframe (Mitcheldean, Gloucestershire, UK), DJI Matrice 600 Pro Airframe (Shenzhen, China), and Routescene UAV LiDARSystem (Edinburgh, Scotland, UK) were used to collect LiDAR data. Following pre-processing in Routescene LidarViewer Pro software, the LiDAR point clouds were cleaned and processed using CloudCompare software to separate ground and off-ground points. A high resolution DEM and DSM were then created using ArcGIS Pro software. This data package contains fully cleaned point clouds of ground and off-ground points (.las), a 25 cm DEM (.tif), and a 25 cm DSM (.tif) for the Teller 27 field site. Ancillary aircraft data, flight mission parameters, weather conditions, and raw lidar data and imagery can be found in the L0 datasets for these campaigns: NGA299 (2017) and NGA297 (2018). Minimally processed point clouds and auxiliary files can be found in the L1 dataset: NGA304 (2017 and 2018).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
54 ENVIRONMENTAL SCIENCES↗
BOREAS HYD-8 DEM Data Over the NSA-MSA and SSA-MSA in the UTM Projection
The BOREAS HYD-8 team focused on describing the scaling behavior of water and carbon flux processes at local and regional scales. These DEMs were produced from digitized contours at a cell resolution of 100 meters. Vector contours of the area were used as input to a software package that interpolates between contours to create a DEM representing the terrain surface. The vector contours had a contour interval of 25 feet. The data cover the BOREAS MSAs of the SSA and NSA and are given in a UTM map projection. Most of the elevation data from which the DEM was produced were collected in the 1970s or 1980s. The data are stored in binary, image format files. The data files are available on a CD-ROM (see document number 20010000884) or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).
Wang, Xue-Wen↗
A Review of Discrete Element Method (DEM) Particle Shapes and Size Distributions for Lunar Soil
As part of ongoing efforts to develop models of lunar soil mechanics, this report reviews two topics that are important to discrete element method (DEM) modeling the behavior of soils (such as lunar soils): (1) methods of modeling particle shapes and (2) analytical representations of particle size distribution. The choice of particle shape complexity is driven primarily by opposing tradeoffs with total number of particles, computer memory, and total simulation computer processing time. The choice is also dependent on available DEM software capabilities. For example, PFC2D/PFC3D and EDEM support clustering of spheres; MIMES incorporates superquadric particle shapes; and BLOKS3D provides polyhedra shapes. Most commercial and custom DEM software supports some type of complex particle shape beyond the standard sphere. Convex polyhedra, clusters of spheres and single parametric particle shapes such as the ellipsoid, polyellipsoid, and superquadric, are all motivated by the desire to introduce asymmetry into the particle shape, as well as edges and corners, in order to better simulate actual granular particle shapes and behavior. An empirical particle size distribution (PSD) formula is shown to fit desert sand data from Bagnold. Particle size data of JSC-1a obtained from a fine particle analyzer at the NASA Kennedy Space Center is also fitted to a similar empirical PSD function.
Lane, John E.↗