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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Modeling aerosol bolus inhalations in the human lung with the multiple path particle deposition model: Comparison with experimental data

Existing one-dimensional (1D) models of aerosol dosimetry often ignore mixing mechanisms of inhaled aerosols during their transport in the lung. This mixing or aerosol dispersion results from different physical mechanisms in different regions of the lung. It is a higher order effect, which cannot be directly captured in 1D modeling approaches, and thus is sometimes modeled as a diffusive process. Here, in this study, we improved our recently developed alveolar mixing module incorporated in the multiple path particle dosimetry model (MPPD) to account for flow irreversibility and particle trapping in the alveolar spaces, as well as mixing occurring in the tracheobronchial region. This new version of MPPD was coupled with CFPD-based predictions of aerosol bolus dispersion in the oral airway. The model was used to predict the deposition, dispersion, and mode shift of aerosol bolus inhaled at different penetration depths within the lung for breathing patterns and particle size matching those used in a previous experimental study (Darquenne et al., 2016). Even though a quite simplified approach was used, the computations appear to describe subject-specific and test-specific experimental data reasonably well. The proposed combined dispersion-deposition model can be a useful tool for targeted drug delivery and also for exposure health risk assessment.

MPPD↗
DOE OSTI2025

A Practical Comparison of Data-Driven Prognostics Methods for Energy Systems

This study explores data-driven prognostics for nuclear power plant (NPP) condensers, focusing on tube fouling. We utilized the Asherah nuclear power plant simulator (ANS) to compare four methods: Random Forest (RF), Support Vector Regressor (SVR), Fully Connected Neural Network (FCNN), and Long Short-Term Memory Neural Network (LSTM). By simulating various fouling scenarios in the ANS, we generated data with different degradation rates under transient operations. The models were trained and tested on these data, with performance evaluated visually and numerically including uncertainty assessment. The LSTM model excelled, exhibiting minimal prediction noise and the most accurate remaining useful life estimates across all degradation levels. Its ability to capture long-term dependencies and produce cleaner outputs makes it a strong candidate, although accurate training data across the entire component lifespan are crucial. The RF model emerged as a robust alternative, providing reliable predictions with high confidence. The FCNN and SVR models, while less effective overall, showed potential under specific conditions. FCNN offers a less complex alternative to LSTM and might benefit from larger datasets. SVR excels in precision when the quality of the training data is high. Furthermore, this study highlights the operational benefits of advanced prognostics in the energy sector and emphasizes the need for further research in NPP condenser health management through real-life experiments.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Hanging String Cuts in SPR Caverns: Modeling Investigation and Comparison with Sonar Data

Investigation of leaching for oil sales includes looking closely at cavern geometries. Anomalous cavern "features" have been observed near the foot of some caverns subsequent to partial drawdowns. One potential mitigation approach to reducing further growth of preexisting features is based on the hypothesis that reducing the brine string length via a "string cue' would serve to move the zone associated with additional leaching to a location higher up in the cavern and thus away from the preexisting feature. Cutting of the hanging string is expected to provide a control of leaching depth that could be used to "smooth" existing features and thus reduce geomechanical instability in that region of the cavern. The SANSMIC code has been used to predict cavern geometry changes (i.e., the extent of cavern growth with depth) based on variable input parameters for four caverns: West Hackberry 11 (WH11), West Hackberry 113 (WH113), Big Hill 104 (BH104), Big Hill 114 (BH114). By comparing the initial sonar geometry with resultant geometries calculated by the SANSMIC code, conclusions may be drawn about the potential impact of these variables on future cavern growth. Ultimately, these conclusions can be used to assess possible mitigation strategies such as the potential advantage of cutting versus not cutting a brine string. This work has resulted in a recommendation that a hanging string cut of 80 ft in WH11 would be beneficial to future cavern geometry, while there would be little to no benefit to string cuts in the other three caverns investigated here. The WH11 recommendation was followed in 2019, resulting in an operational string cut. A sonar performed after the string cut showed no adverse leaching in the area of the preexisting flare, as expected from the results of the preliminary SANSMIC runs described in this report. Additional SANSMIC modeling of the actual amount of injected raw water resulted in good agreement with the post-cut sonar.

02 PETROLEUM↗
DOE OSTI2022

Impact of Changes in ENDF/B-VII.1 and ENDF/B-VIII.0 235U Nuclear Data Indicated by NDSE Studies on LLNL Pulsed Sphere Simulations

A recent journal article by J.A. Gomez et al. measured gamma-ray die-off curves of three subcritical static highly-enriched uranium (HEU) assemblies driven by an external neutron source with a new detector system. This new detector system was developed for being used in dynamically driven subcritical assemblies as part of the Neutron Diagnosed Subcritical Experiments (NDSE) program. Simulations of these die-off curves with various nuclear data and comparison to experimental data indicated that a decrease of the ENDF/B-VII.1 235 U(n,inl) cross section by a factor 0.8 and 0.85 for ENDF/B-VIII.0 would lead to better predictions of experimental data. Here, we test the proposed changes with another type of measurement response, namely neutron-leakage spectra emitted in LLNL pulsed sphere measurements. These spheres were pulsed by 14-MeV neutrons produced via the D+T reaction in their center. The proposed changes in nuclear data have a distinctly smaller impact on predicting pulsed-sphere neutron-leakage spectra than for the die-off curves; they lead to a worsened prediction of the inelastic valley of LLNL pulsed-sphere neutron spectra indicating that the proposed change could constitute a compensating error. Changes in the inelastic angular distributions along with the cross section might be worthwhile to study

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Multi-scale fission product release model with comparison to AGR data

TRistructural ISOtropic (TRISO) particle fuel is central to several advanced, high-temperature reactor designs. Each particle consists of a fuel kernel encapsulated by three layers of carbon and ceramics that prevent the release of fission products and ensure physical integrity. Despite outstanding retention properties, fission product release has been observed from intact particles. To better understand and quantify fission product release from TRISO particles, a multiscale, mechanistic model of fission product transport is being developed by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. Previous work focused on silver (Ag) transport and improved Ag release predictions. The work described in this report builds on this experience to better understand cesium (Cs) transport in silicon carbide (SiC), the main barrier to the release of fission products. Atomistic simulations provide bulk and grain boundary (GB) Cs diffusivities in SiC, which are used by phase field simulations in the mesoscale code Marmot to determine the temperature, microstructure, and irradiation-dependent Cs diffusivity at the mesoscale in SiC. This approach attributes the different temperature regimes experimentally observed for Cs diffusivities in SiC to a transition from bulk-dominated diffusivity at high temperatures to a GB-dominated regime at low temperatures, providing new insight. The multiscale, mechanistic effective diffusivity is then implemented in the fuel performance code BISON and further validated by comparing Cs release predictions from Advanced Gas Reactor (AGR)-1 and AGR-2 post-irradiation measurements. The new model improves BISON’s predictability. This document also reports improvements made on Ag transport modeling by accounting for different GB types having different diffusivities. Moreover, this report details preliminary efforts to model palladium (Pd) attack of the SiC at the mesoscale using a phase field approach. Pd attack and its impact on accelerated Ag transport remains a misunderstood phenomenon, and we use the model to demonstrate that the formation of lamellae that has been observed in experiments can be explained by the reaction of Pd with SiC to form alternating layers of graphite and Pd 2 Si. This effort aims to improve our understanding of the reaction and eventually provide a model for BISON to account for Pd penetration and its effects on fission product release.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

GROWdb US River Systems - Samples

GROW Overview We developed the Genome Resolved Open Watersheds database (GROWdb), which aims to increase genomic sampling and understanding of global river microbiomes. An emphasis of GROWdb is to create a publicly available and ever-expanding microbial genome database that is focused on rivers while being interoperable with databases from other ecosystems. GROWdb is based on a network-of-networks approach to move beyond a small collection of well-studied rivers, towards a spatially distributed, global network of systematic observations. GROWdb represents the first microbial, river-focused resource parsed at various scales from genes to MAGs to community level including expression and potential based measurements that will be of interest to microbiologists, ecologists, geochemists, hydrologists, and modelers. Dataset Acknowledgement GROWdb contains data from various research campaigns, please acknowledge the following data generators, as appropriate: WHONDRS derived genomes or samples - include this statement in your acknowledgements: “This study used data from the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) under the River Corridor Science Focus Area (SFA) at the Pacific Northwest National Laboratory (PNNL) that was generated at the U.S. Department of Energy (DOE) Joint Genome Institute User Facility. PNNL is operated by Battelle Memorial Institute for the U.S. DOE under Contract No. DE-AC05-76RL01830. The SFA is supported by the U.S. DOE, Office of Biological and Environmental Research (BER), Environmental System Science (ESS) Program.” Total Samples loaded onto this Narrative: 178 Note: Not all GROW samples may be loaded into KBase Data Availability The data underlying GROWdb are accessible across various platforms to ensure all levels of data structure are widely available. First, all reads and MAGs are publicly hosted on National Center for Biotechnology (NCBI) under Bioproject PRJNA946291. Second, all data related data presented here including MAG annotations, extended data tables, phylogenetic tree files, antibiotic resistance gene database files, and MAG abundance tables are available in Zenodo (link). Beyond the flat database files listed above, our aim for GROWdb was to maximize data use by making the data available in searchable and interactive platforms including the National Microbiome Data Collaborative (NMDC) data portal, the Department of Energy’s Systems Biology Knowledgebase (KBase), and a GROW specific user interface released here, GROWdb Explorer. Each platform provides different ways to interact with GROWdb: NMDC GROWdb formed a pilot project for the NMDC. Specifically, individual GROWdb datasets (metagenomes, metatranscriptomes, etc) are easily accessible and searchable through the NMDC data portal, where they are systematically connected to each other and to a rich suite of sample information and standard analysis results, following Findable, Accessible, Interoperable, and Reusable (FAIR) data practices. KBase GROWdb is publicly available within KBase, including samples (this Narrative), MAGs, and corresponding genome scale metabolic models. Access within KBase allows for immediate access and reuse of data, including comparison to private data using KBase’s 500+ analysis tools. Other linked narratives in KBase: GROW Metagenome Assembled Genomes (MAGs) GROW Metabolic Models GROWdb Explorer GROWdb data is also explorable through a graphical user interface built through the Colorado State University Geospatial Centroid (https://geocentroid.shinyapps.io/GROWdatabase/), allowing users to search and graph microbial and spatial data simultaneously. In summary, this microbial genome resource represents the first publicly available genome collection from rivers and offers data that can be leveraged across microbiome studies. GROWdb is an expanding repository to incorporate and unify global river multi-omic data for the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Comparison of GOES16 Data with the TRACER-ESCAPE Field Campaign Dataset for Convection Characterization: A Selection of Case Studies and Lessons Learnt

Convective updrafts are one of the main characteristics of convective clouds, responsible for the convective mass flux and the redistribution of energy and condensate in the atmosphere. During the early stages of their lifecycle, convective clouds experience rapid cloud-top ascent manifested by a decrease in the geostationary IR brightness temperature (𝑇⁢𝐵 𝐼⁢𝑅 ). Under the assumption that the convective cloud top behaves like a black body, the ascent rate of the convective cloud top can be estimated as ($\frac{∂𝑇⁢𝐵_{𝐼⁢𝑅}}{∂𝑡}$), and it can be used to infer the near cloud-top convective updraft. The temporal resolution of the geostationary IR measurements and non-uniform beam-filling effects can influence the convective updraft estimation. However, the main shortcoming until today was the lack of independent verification of the strength of the convective updraft. Here, Doppler radar observations from the ESCAPE and TRACER field experiments provide independent estimates of the convective updraft velocity at higher spatiotemporal resolution throughout the convective core column and can be used to evaluate the updraft velocity estimates from the IR cooling rate for limited samples. Isolated convective cells were tracked with dedicated radar (RHIs and PPIs) scans throughout their lifecycle. Radial Doppler velocity measurements near the convective cloud top are used to provide estimates of convective updrafts. These data are compared with the geostationary IR and VIS channels (from the GOES satellite) to characterize the convection evolution and lifecycle based on cloud-top cooling rates.

TRACER/ESCAPE field campaign↗
DOE OSTI2025

The final WaZP galaxy cluster catalog of the Dark Energy Survey and comparison with SZE data

In this work, we present and characterize the galaxy cluster catalog detected by the WaZP cluster finder, which is not based on red-sequence identification, on the full six years of observations of the Dark Energy Survey (DES-Y6). The full catalog contains over 400k detected clusters with richnesses, Ngals, above 5 and that reach redshifts up to 1.3. We also provide a version of the catalog where the observation depth and richness computation are homogenized to be used for cosmology, containing 33k rich (Ngals >25) clusters. We compare our results with the previous WaZP catalog obtained from the DES first-year data release (DES-Y1). We find that essentially all clusters within the common footprint and depth limit are recovered. The deeper observations on DES-Y6 and the more complete available spectroscopic redshift sample lead to improvements in the redshifts of the clusters, resulting in an average scatter of 1.4% and offset of 0.2%. The optical clusters are also cross-matched with Sunyaev Zel'dovich Effect (SZE) cluster samples detected by the South Pole Telescope (SPT) and the Atacama Cosmology Telescope (ACT). We find that essentially all SZE clusters with reasonable overlapping footprint have a corresponding WaZP cluster. Conversely, 90% of the optical detections with richness greater than 150 have a counterpart in the deeper regions of the SZE surveys. Based on cross-match with the SZE catalogs, we also find that 15-20% of the SZE matched systems have more than one possible WaZP counterpart at the same redshift and within the SZE R500c, indicating possible interacting or unrelaxed systems. Finally, given the optical and SZE beams, WaZP and SZE centerings are found to be consistent. A more detailed study of the SZE-WaZP mass-richness relation will be presented in a separate paper.

Benoist, C. [OCA, Nice, Lab. Lagrange; LIneA, Rio ↗
DOE OSTI2021

ESS-DIVE Reporting Format for File-level Metadata

The ESS-DIVE reporting format for file-level metadata (FLMD) provides granular information at the data file level to describe the contents, scope, and structure of the data file to enable comparison of data files within a data package. The FLMD are fully consistent with and augment the metadata collected at the data package level. We developed the FLMD template based on a review of a small number of existing FLMD in use at other agencies and repositories with valuable input from the Environmental Systems Science (ESS) Community. Also included is a template for a CSV Data Dictionary where users can provide file-level information about the contents of a CSV data file (e.g., define column names, provide units). Files are in .csv, .xlsx, and .md. Templates are in both .csv and .xlsx (open with e.g. Microsoft Excel, LibreOffice, or Google Sheets). Open the .md files by downloading and using a text editor (e.g. Notepad or TextEdit). Though we provide Excel templates for the file-level metadata reporting format, our instructions encourage users to 'Save the FLMD template as a CSV following the CSV Reporting Format guidance'. In addition, we developed the ESS-DIVE File Level Metadata Extractor which is a lightweight python script that can extract some FLMD fields following the recommended FLMD format and structure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Randomized Algorithms for Scientific Computing (RASC)

Randomized algorithms have propelled advances in artificial intelligence (AI) and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office of Science priority areas such as climate science, astrophysics, fusion, advanced materials, combustion, and quantum computing all require randomized algorithms for surmounting challenges of complexity, robustness, and scalability. Advances in data collection and numerical simulation have changed the dynamics of scientific research and motivate the need for randomized algorithms. For instance, advances in imaging technologies such as X-ray ptychography, electron microscopy, electron energy loss spectroscopy, or adaptive optics lattice light-sheet microscopy collect hyperspectral imaging and scattering data in terabytes, at breakneck speed enabled by state-of-the-art detectors. The data collection is exceptionally fast compared with its analysis. Likewise, advances in high-performance architectures have made exascale computing a reality and changed the economies of scientific computing in the process. Floating-point operations that create data are essentially free in comparison with data movement. Thus far, most approaches have focused on creating faster hardware. Ironically, this faster hardware has exacerbated the problem by making data still easier to create. Under such an onslaught, scientists often resort to heuristic deterministic sampling schemes (e.g., low-precision arithmetic, sampling every nth element) and sacrifice potentially valuable accuracy. Dramatically better results can be achieved via randomized algorithms, reducing the data size as much as or more than naive deterministic subsampling can achieve, while retaining the high accuracy of computing on the full data set. By randomized algorithms we mean those algorithms that employ some form of randomness in internal algorithmic decisions to accelerate time to solution, increase scalability, or improve reliability. Examples include matrix sketching for solving large-scale least-squares problems (see Figure 1) and stochastic gradient descent for training machine learning models. We are not recommending heuristic methods but rather randomized algorithms that have certificates of correctness and probabilistic guarantees of optimality and near-optimality. Such approaches can be useful beyond acceleration, for example, in understanding how to avoid measure zero worst-case scenarios that plague methods such as QR matrix factorization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Thermal expansion of 4H and 6H SiC from 5 K to 340 K

The first thermal expansion measurements of the 4H and 6H polytypes of SiC below room temperature are reported. The measurements were carried out on single-crystal specimens using high-resolution capacitive-based dilatometry. For both polytypes, the thermal expansion coefficient is below 2.4 × 10 -6 1/K near room temperature. No phase transitions are observed over the 5 K to 340 K temperature range of the measurements. The thermal expansion coefficient α of 4H SiC is slightly anisotropic for measurements parallel and perpendicular to the crystallographic c axis with α ∥ about 2.2 × 10 -7 1/K larger than α ⟂ near room temperature. For 6H SiC no discernible anisotropy is observed. The differences in anisotropy can be understood by considering the ratio of hexagonal to cubic bonds of each polytype. Narrow regions with negative thermal expansion that are within the limits of our resolution (~ 1 x 10 -8 ) are observed in the vicinity of 30 K for both specimens. In conclusion, tabulated data, polynomial fits, fit parameters, and comparison to data based on lattice-parameter measurements are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2022

AXEAP : a software package for X-ray emission data analysis using unsupervised machine learning

The Argonne X-ray Emission Analysis Package ( AXEAP ) has been developed to calibrate and process X-ray emission spectroscopy (XES) data collected with a two-dimensional (2D) position-sensitive detector. AXEAP is designed to convert a 2D XES image into an XES spectrum in real time using both calculations and unsupervised machine learning. AXEAP is capable of making this transformation at a rate similar to data collection, allowing real-time comparisons during data collection, reducing the amount of data stored from gigabyte-sized image files to kilobyte-sized text files. With a user-friendly interface, AXEAP includes data processing for non-resonant and resonant XES images from multiple edges and elements. AXEAP is written in MATLAB and can run on common operating systems, including Linux, Windows, and MacOS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Serial femtosecond and serial synchrotron crystallography can yield data of equivalent quality: A systematic comparison

For the two proteins myoglobin and fluoroacetate dehalogenase, we present a systematic comparison of crystallographic diffraction data collected by serial femtosecond (SFX) and serial synchrotron crystallography (SSX). To maximize comparability, we used the same batch of micron-sized crystals, the same sample delivery device, and the same data analysis software. Overall figures of merit indicate that the data of both radiation sources are of equivalent quality. For both proteins, reasonable data statistics can be obtained with approximately 5000 room-temperature diffraction images irrespective of the radiation source. The direct comparability of SSX and SFX data indicates that the quality of diffraction data obtained from these samples is linked to the properties of the crystals rather than to the radiation source. Therefore, for other systems with similar properties, time-resolved experiments can be conducted at the radiation source that best matches the desired time resolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Inclusion of $K\Lambda $ electroproduction data in a coupled channel analysis

Exclusive electroproduction reactions provide an access to the structure of excited baryons. To extract electroproduction multipoles encoding this information, the Julich-Bonn-Washington (JBW) analysis framework is extended to the analysis of differential cross sections in KΛ electroproduction. Here this update enlarges the scope of previous coupled-channel analyses of pions and eta mesons, with photoproduction reactions as boundary condition in all analyzed electroproduction reactions. Polarization observables are predicted and compared to recent CLAS data. The comparison shows the relevance of these data to pin down baryon properties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Jet wake from linearized hydrodynamics

We explore how to improve the hybrid model description of the particles originating from the wake that a jet produced in a heavy ion collision leaves in the droplet of quark-gluon plasma (QGP) through which it propagates, using linearized hydrodynamics on a background Bjorken flow. Jet energy and momentum loss described by the hybrid model become currents sourcing linearized hydrodynamics. By solving the linearized hydrodynamic equations numerically, we investigate the development of the wake in the dynamically evolving droplet of QGP, study the effect of viscosity, scrutinize energy-momentum conservation, and check the validity of the linear approximation. We find that linearized hydrodynamics works better in the viscous case because diffusive modes damp the energy-momentum perturbation produced by the jet. We calculate the distribution of particles produced from the jet wake by using the Cooper-Frye prescription and find that both the transverse momentum spectrum and the distribution of particles in azimuthal angle are similar in shape in linearized hydrodynamics and in the hybrid model. Their normalizations are different because the momentum-rapidity distribution in the linearized hydrodynamics analysis is more spread out, due to sound modes. Since the Bjorken flow has no transverse expansion, we explore the effect of transverse flow by using local boosts to add it into the Cooper-Frye formula. After including the effects of transverse flow in this way, the transverse momentum spectrum becomes harder: more particles with transverse momenta bigger than 2 GeV are produced than in the hybrid model. Although we defer implementing this analysis in a jet Monte Carlo, as would be needed to make quantitative comparisons to data, we gain a qualitative sense of how the jet wake may modify jet observables by computing proxies for two example observables: the lost energy recovered in a cone of varying open angle, and the fragmentation function. We find that linearized hydrodynamics with transverse flow effects added improves the description of the jet wake in the hybrid model in just the way that comparison to data indicates is needed. Our study illuminates a path to improving the description of the wake in the hybrid model, highlighting the need to take into account the effects of both transverse flow and the broadening of the energy-momentum perturbation in spacetime rapidity on particle production.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Development and formulation of physics based metallic fuel models and comparison to integral irradiation data

Metallic fuel has an important historical significance in the development of nuclear reactors and continues to be relevant to the progression of advanced test and power reactors. A number of models, ranging from empirical to mechanistic, have been developed and implemented in various fuel performance codes to describe U-Zr and U-Pu-Zr fuel and typical fast reactor cladding materials. One challenge of using these models to simulate fuel performance is the inevitable tangling of coupled phenomena that can cloud proper implementation, calibration, and eventual utilization of new models. Here in an effort to provide a baseline capability that will facilitate the use of advanced models, new capabilities have been implemented into the fuel performance code BISON specific to metallic fuel simulations, ranging from materials properties, fission gas release and swelling calculations, coolant channel models, and cladding correlations. These models have been applied to the X441/X441A EBR-II experimental assembly data, a set of irradiated metallic UPuZr fuel rods of varying pin designs. The models implemented in BISON are able to capture the general trend of the expected response of the fuel and cladding to irradiation in EBR-II, especially when considering the spread in experimental measurements and the uncertainties inherited from the historical material models. Ultimately, the models outlined here provide the baseline capabilities on which new models can build upon in order to improve the prediction of metallic fuel performance simulations in off-normal designs or operations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

CFD-DEM study of bubble properties in a cylindrical fluidized bed of Geldart Group D particles and comparison with prior MRI data

Numerical simulations of a freely bubbling cylindrical fluidized bed are carried out using a coupled computational fluid dynamics and discrete element method (CFD-DEM) model and compared to recent experimental data. The experiments were conducted using high-resolution and high-frequency magnetic resonance imaging providing high-fidelity data of the bubbling within a central 10 mm slice of the bed. Qualitatively, we nd more regular (i.e., less chaotic) structures in the simulated beds than observed experimentally. Quantitatively, however, the bubble diameter and number of bubbles as a function of height within the bed is predicted well by the base model. Unfortunately, the regularity in the simulations manifests as a considerable discrepancy in the speed of the (dense) emulsion phase. The simulated velocity probability distribution functions show an accumulation of low-speed regions and deficiency of high-speed regions. A simple parametric study of the base model is also carried out considering many of the most common CFD-DEM modeling parameters. It is found that the fluid grid size, geometry resolution, transfer kernel and drag law did not have a significant effect on bubble or particle dynamics.

42 ENGINEERING↗