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DOE OSTI2022

Heat transfer in directly-irradiated high-temperature solid–gas flows laden with polydisperse particles

Heat transfer in directly-irradiated high-temperature solid–gas flows laden with polydisperse particles is investigated using a novel transient three-dimensional computational fluid dynamics model. The model couples particle–gas hydrodynamics of solid–gas flows laden with polydisperse particles, radiative heat transfer in non-grey absorbing, emitting and anisotropically-scattering multi-component participating media, conduction heat transfer in the gas phase, and interfacial convection heat transfer. The multiphase particle-in-cell method is used to predict high-fidelity solid–gas flow characteristics, such as the local discrete particle size distribution, with increased computational efficiency by combining the advantages of both Eulerian and Lagrangian methods. The multi-component radiative transfer model is implemented using an advanced collision-based Monte Carlo ray-tracing method. The number of the prescribed discrete particle components is found to be the key parameter affecting the computational accuracy and efficiency, which primarily depends on the size distribution of the particles. For the model particle–gas flow featuring free-falling Gamma-distributed ceramic particles exposed to concentrated solar irradiation, the particle volume fraction, radiative, fluid flow and thermal characteristics appear to converge with the increasing number of the discrete particle components. Five particle components are sufficient to obtain physically meaningful results. A further increase in the number of the particle components only slightly increases the accuracy of the numerical predictions at the expense of a rapidly increasing computational time. For five particle components, the particle vertical velocity at the receiver exit for particles with the diameter of 43.4 μm is 57% of that for the particles with the diameter of 202.8 μm. The temperatures of these two particle components increase from the initial ambient values by factors of 2 and 1.2, respectively, during the simulation time. In conclusion, the model developed allows for increased fidelity of particle–gas flow simulations with significant radiative effects.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Equation of state and strength of diamond in high-pressure ramp loading

Diamond is used extensively as a component in high-energy-density experiments, but existing equation of state (EOS) models do not capture its observed response to dynamic loading. In particular, in contrast with first-principles theoretical models of equilibrium multiphase EOS, no solid-solid phase changes have been detected, and no general-purpose EOS models match the measured ambient isotherm. We have performed density functional theory (DFT) calculations of the diamond phase to ~10 TPa, well beyond its predicted range of thermodynamic stability, and used these results as the basis of a Mie-Grüneisen EOS. We also performed DFT calculations of the elastic moduli, and calibrated an algebraic elasticity model for use in simulations. We then estimated the flow stress of diamond by comparison with the stress-density relation measured experimentally in ramp-loading experiments. Furthermore, the resulting constitutive model allows us to place a constraint on the Taylor-Quinney factor (the fraction of plastic work converted to heat) from the observation that diamond does not melt on ramp compression.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Numerical investigation of laser-driven shock interaction with a deformable particle

A laser-driven shock propagating through an isolated particle embedded in a plastic (CH) target was studied using the radiation-hydrodynamic code FLASH. Preliminary simulations using IONMIX equations of state (EOS) showed significant differences in the shock Hugoniot of aluminum compared to experimental data in the low-pressure regime [O(10) GPa], resulting in higher streamwise compression and deformation of an aluminum particle. Hence, a simple modification to the ideal gas EOS was developed and employed to describe the target materials and examine the particle dynamics. The evolution of the pressure field demonstrated a complex wave interaction, resulting in a highly unsteady particle drag which featured two drag minima due to shock focusing at the rear end of the particle and rarefaction stretching due to laser shut-off. Although ~30% lateral expansion and ~25% streamwise compression were observed, the aluminum particle maintained considerable integrity without significant distortion. Additional simulations examined the particle response for a range of particle densities, sizes, and acoustic impedances. The data revealed that lighter particles such as aluminum gained significant momentum, reaching up to ~96% of the shocked CH's speed, compared to ~29% for the heavier tungsten particles. Despite the differences seen in the early stage of shock interaction, particles with varying acoustic impedances ultimately reached the same peak velocity. This identified particle-to-host density ratio is an important factor in determining the inviscid terminal velocity of the particle. In addition, the modified EOS model presented in this study could be used to approximate solid materials in hydrocodes that lack material strength models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Preliminary parameter studies to form pellets of hard materials for optical constants derived via single-angle reflectance spectroscopy

Infrared reflectance spectra can be influenced by many factors: the substrate and the thickness of the layer for liquids and the surface micromorphology, the form (powder, crystal) and the particle size for solids. All these parameters can play a role in the appearance of the measured spectrum. To avoid collecting multitudes of spectra to cover all such scenarios, the optical constants n and k, which are intrinsic properties of a material, can instead be used to model the reflectance spectrum. For solids, two techniques are often used to derive optical constants: ellipsometry and single-angle reflectance spectroscopy. For both methods, best results are usually obtained from single crystals. We have recently demonstrated for ammonium sulfate (a relatively soft material) that by optimizing certain conditions, high quality pellets with specularly reflective surfaces can be used in lieu of crystals. This was confirmed by the excellent agreement between the optical constants derived by these two methods. This work focuses on the possible extension of these methods to harder materials, starting with sodium sulfate. The first goal is to see if high quality pellet surfaces can be obtained as for ammonium sulfate. The reflectance values and the associated optical constants can also be obtained.

Diaz, Emmanuela↗

DOE OSTI2019

A Thermodynamic Model Toward the Comprehension of Ferrous Burden Softening and Melting Using FactSage Macro-Processing

The softening and melting properties of iron-bearing materials play a decisive role in the formation of the cohesive zone, which greatly affects blast furnace gas flow distribution and heat-transfer efficiency. To improve the understanding regarding the evolution of condensed phases during reduction, softening, and melting, a thermodynamic model has been developed using FactSage™ thermodynamic software and macro-processing. The model was constructed using a series of equilibrium stages and splitters to determine flow directions of streams and to consider kinetic inhibitions. For all iron-bearing materials studied, the methodology proposed for modeling was capable of obtaining reduction degrees in very good agreement to its experimental data. The evolution of solid phases was qualitatively comparable to the available literature, with Fayalite, Kirschsteinite, Melilite, and FeO as the main solid phases in equilibrium before slag formation. The comparison between the profiles of the calculated slag mass fraction and the experimental pressure drop showed a close relation between these properties. Moreover, the level of heterogeneity of each raw material may play a significant role in the interpretation of its results.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Heat transfer enhancement due to cold cap motion from bubbling in a waste glass melter

In this study, a computational fluid dynamics (CFD) model was developed to model the motion of a solid cold cap in a waste glass melter. Forced convection bubblers at the base of the melter release air into the molten glass, which forms large bubbles that travel upwards to the cold cap and augment heat transfer from the glass to the cold cap. The CFD model employs the Navier- Stokes equations to solve for the fluctuating flowfield using a rigid body motion dynamic fluid body interaction module. This allows for movement of the floating body in response to the bubbling forces calculated at each time step. Further, the heat flux delivered to the cold cap by the convective bubbling is studied as a function of the bubbling rate. Results for the moving cold cap are compared with the computed heat flux trends for a stationary cold cap. The heat flux delivered to the cold cap from the glass is 25% higher for the case with the moving cold cap. The heat flux was found to be proportional to v0.6 as opposed to v0.9 (where v is the normalized bubbling rate) for the stationary cold cap.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Accelerating Finite-Temperature Kohn-Sham Density Functional Theory with Deep Neural Networks

We present a numerical modeling workflow based on machine learning (ML) which reproduces the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially-resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Accelerating Finite-temperature Kohn-Sham Density Functional Theory\ with Deep Neural Networks

We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially-resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Calculation of mode lifetimes in weakly anharmonic solids using self-consistent ensemble eigenstates of the Liouvillian

Recent schemes for the efficient calculation of vibrational mode frequencies and lifetimes in weakly anharmonic systems such as solid crystal structures have shown promise, with simple relationships being observed between low order moments of the Liouvillian and the observed lifetime. While only shown empirically, two parameters seemed sufficient to determine the lifetime to high accuracy over a large range of temperatures for a few simple low degree of freedom models and a Lennard–Jones solid. In this paper we analytically reproduce the numerically observed relationship using a few generally applicable assumptions about the density of states. This advance is made by studying the autocorrelation based on an “ensemble eigenstate” of the Liouvillian. We propose the use of this ensemble eigenstate as the basis for autocorrelations to simplify calculations in richer systems. Finally, an analysis is also performed of the relationship between canonical and microcanonical autocorrelation functions and a long standing error in the long time behavior of such functions originally presented by Sen, Sinkovits, and Chakravarti is corrected.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Off-harmonic optical probing of high intensity laser plasma expansion dynamics in solid density hydrogen jets

Due to the non-linear nature of relativistic laser induced plasma processes, the development of laser-plasma accelerators requires precise numerical modeling. Especially high intensity laser-solid interactions are sensitive to the temporal laser rising edge and the predictive capability of simulations suffers from incomplete information on the plasma state at the onset of the relativistic interaction. Experimental diagnostics utilizing ultra-fast optical backlighters can help to ease this challenge by providing temporally resolved inside into the plasma density evolution. We present the successful implementation of an off-harmonic optical probe laser setup to investigate the interaction of a high-intensity laser at 5.4 x 10 21 W/cm 2 peak intensity with a solid-density cylindrical cryogenic hydrogen jet target of 5μm diameter as a target test bed. The temporal synchronization of pump and probe laser, spectral filtering and spectrally resolved data of the parasitic plasma self-emission are discussed. The probing technique mitigates detector saturation by self-emission and allowed to record a temporal scan of shadowgraphy data revealing details of the target ionization and expansion dynamics that were so far not accessible for the given laser intensity. Plasma expansion speeds of up to (2.3 ± 0.4)x 10 7 m/s followed by full target transparency at 1.4ps after the high intensity laser peak are observed. A three dimensional particle-in-cell simulation initiated with the diagnosed target pre-expansion at -0.2ps and post processed by ray tracing simulations supports the experimental observations and demonstrates the capability of time resolved optical diagnostics to provide quantitative input and feedback to the numerical treatment within the time frame of the relativistic laser-plasma interaction.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Modeling the hydrodynamic impact on the tool influence function during hemispherical subaperture optical polishing

To fabricate high-precision and accurate optics relative to the optical design surface, a high level of deterministic control of material removal (i.e., the tool influence function, TIF) during subaperture tool polishing is required. In this study, a detailed analysis of the pressure distribution, which is a key component of the TIF, has been performed using finite element analysis to couple together solid mechanics and fluid dynamics. Modeling experimental parameters of recently published work reveals that, when considering tool deformation, which in turn influences the fluid film thickness between the tool and workpiece, the effective pressure profile has a flat-top distribution. This flat-top pressure profile differs from the parabolic pressure distributions predicted by Hertzian mechanics. Furthermore, the shear contribution is shown here to be a key contributor to material removal, inducing the removal at the periphery of the contact edge and even outside the generally accepted contact area. Finally, the simulated fluid velocities provide evidence of mixed-mode contact polishing, supporting recent experimental findings that also suggest that onset of hydroplaning contributions lead to material removal drop-off.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Monte Carlo Simulation of Background and Source Measurements with CSG and CAD Geometries

Detecting radioactive sources in an urban environment is difficult due to the large magnitude and variability of the background radiation. To support the search mission of the National Nuclear Security Administration, a project was undertaken to determine if first-principles modeling and simulation could be used to accurately predict the response of radiation detectors in urban environments. This study demonstrates that the simulated detector responses to photon radiation from both an isotopic source and the background compare well to benchmark-quality measurements in a large urban-like test environment. Simulation results using a traditional combinatorial solid geometry and a CAD model generated from LiDAR measurements gave very similar detector responses. With this validation, further simulations could be used to predict detector responses for various situations in real urban settings.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Multiscale plasticity of geomaterials predicted via constrained optimization‐based granular micromechanics

Abstract A general framework to derive nonlinear elastic and elastoplastic material models from granular micromechanics is proposed, where a constraint‐based variational structure is introduced to classical grain contact‐based homogenization methods of hyperelasticity. Like the classical hyperelastic methods, reference solutions for closed‐form hyperelastic material models are analytically derived from the grain‐scale contact mechanics. However, unlike prior methods, the proposed homogenization framework defines closed‐form hyperelastoplastic material models that extend multiscale variational methods to granular plasticity. The proposed framework is used to develop novel granular micromechanics‐based macroscopic models for a Mises type solid, Drucker–Prager type plasticity, and grain‐contact cohesive‐debonding with a deviatorically and volumetrically coupled nonlinearly elastic response. Macroscopic plastic parameters and yield criteria are explicitly related to their microscale counterparts, for example, the friction coefficient governing intergranular slip. Numerical examples and comparison to measurements from the literature, including triaxial compaction of concrete, are provided to investigate model predictions and demonstrate calibration to experimental data.

Bryant, E. C.↗

DOE OSTI2024

Comparative study of decay heat calculations with FLUKA and MCNP/CINDER2008

In designing a high-power tungsten target, decay heat driven temperature rise in the spallation volume is a safety concern during maintenance and in loss of coolant accidents. As tungsten hydrates and becomes volatile in steam at above 800 ◦C, it is important to keep the target temperature below this threshold when active cooling is unavailable. Decay heat in a target is calculated with particle transport simulation codes combined with transmutation codes. The calculated decay heat usually differs depending on the nuclear cross sections and the decay particle transport models built in the code architecture. In this paper, we calculated decay heat of a water-cooled tungsten target using popular particle transport codes, FLUKA and MCNP6® paired with CINDER2008 and compared the results. The target-moderator-reflector (TMR) system is modeled with a water-cooled solid tungsten target, water premoderators, liquid hydrogen cold moderators and beryllium reflectors. Water-cooled stainless-steel shielding is modeled around the TMR system. The tungsten volume is clad with a thin layer of erosion/corrosion resistant material. This study provides information about the uncertainty range in decay heat prediction of high-power spallation targets for hazard analysis.

Lee, Yong Joong↗

DOE OSTI2022

Analytical Modeling of Biomass Transport and Feeding Systems

The processing of biomass solids in a biorefinery consists of pretreatment, enzyme hydrolysis / concurrent fermentation of sugars to ethanol, product recovery, and drying. Sustainable operation requires a front end that transforms wet solids into a pumpable slurry. Otherwise the biorefinery will suffer unscheduled shut-downs and inefficient operation due to solids that obstruct pumps and other equipment and resist mixing in a bioreactor. Downtime in pioneer biorefineries due to interruptions from materials handling problems has been 50% or more, leading to unsustainable manufacturing processes. This work addresses new technology, predictive computational models, and definition of operational conditions that result in formation of slurries of corn stover at up to 300 g/L using low enzyme loadings (1 to 3 FPU cellulase/g) before the biomass (corn stover) enters the pretreatment step. A team of researchers from Purdue University, Idaho National Laboratory (INL), Forest Concepts, AdvanceBio, Argonne National Laboratory, and DOE BETO have combined their knowledge in agricultural and biological engineering, bioprocess engineering, mechanical engineering, chemical engineering, agricultural economics, materials engineering and enzyme and microbial technology to address the challenge of making lignocellulose flow. This team effort has resulted in the development and validation of conditions that employ low levels of commercial enzyme in an agitated bioreactor to which corn stover pellets are added resulting in formation of slurries at high solids loadings, before pretreatment. This approach overcomes challenges caused by handling of dry, particulate biomass materials at the front end of the biorefinery. The subsequent materials handling issues cause obstruction at pumps, pipes and valves. Formation of high loadings slurries with low yield stress, as reported here, significantly decreases the potential for process interruption and enhances plant operability. Key advances in the knowledge of how slurry formation occurs is reported here and in recently published journal papers. We found that pellets are needed to achieve high solids loading, and that commercial enzymes are effective in forming slurries of corn stover particles from pellets that have not been pretreated. Our work has resulted in models that predict solids behavior for formation of compressed solids and pellets that in turn facilitate slurries made of high concentrations of corn stover particles. A computational model was developed that gives mechanistic insights into properties of particles and mixing process that gives the slurry rheology needed to facilitate pumping. Hence, the corn stover may be pumped into a pretreatment reactor in place of auguring in solids against high pressure which is a root cause of interruptions at the front end of a biorefinery. Subsequent mixing in enzyme and microbial bioreactors results in conversion of lignocellulose to sugars in a biorefinery in agitated bioreactors, with flows in and out of the vessels being less likely to be interrupted due to plugging or materials handling problems. The obtained data coupled to process models, techno-economic assessment (TEA) and Life Cycle Analysis (LCA) were used to assess whether this approach is practical. These results are based on a foundation of laboratory characterization and pilot runs. The NREL biochemical sugar model was utilized to carry out techno-economic analysis of enzyme catalyzed liquefaction followed by enzyme hydrolysis. The minimum sugar selling price was between 17.5 and 18.3 ¢/pound or about the same as calculated by the NREL model for dilute acid pretreatment followed by enzyme hydrolysis. Life cycle analysis (LCA) based on Argonne’s Greet Model showed the enzyme catalyzed route had the lowest greenhouse gas emissions of the three combinations studied (i.e., enzyme, enzyme mimetic, and enzyme + mimetic combined). GHG emissions for enzyme-based corn stover liquefaction step, alone, were about 21 g CO 2 -equivalent/kg of liquefied slurry. We believe this approach will further enhance operability of a pioneer biorefinery, and bring large-scale conversion of lignocellulosic biomass to low carbon footprint biofuels closer to implementation.

09 BIOMASS FUELS↗

DOE OSTI2024

A hybrid smoothed-particle hydrodynamics model of oxide skins on molten aluminum

A computational model of aluminum melting is proposed which captures both the thermal fluid-solid phase transition and the mechanical effects of oxidation. The model hybridizes ideas from smoothed particle hydrodynamics and bonded particle models to simulate both hydrodynamic flows and solid elasticity. Oxidation is represented by dynamically adding and deleting spring-like bonds between surface fluid particles to represent the formation and rupture of the oxide skin. Various complex systems are simulated to demonstrate the adaptability of the method and to illustrate the significant impact of skin properties on material flow. As a result, initial comparison to experiments of a melting aluminum cantilever highlights that the computational model can reproduce key qualitative features of aluminum relocation.

Clemmer, Joel Thomas↗

DOE OSTI2021

Assessment of model parameters in MFiX particle-in-cell approach

The limitations in numerical treatment of solids-phase in conventional methods like Discrete Element Model and Two-Fluid Model have facilitated the development of alternative techniques such as Particle-In-Cell (PIC). However, a number of parameters are involved in PIC due to its empiricism. In this work, global sensitivity analysis of PIC model parameters is performed under three distinct operating regimes common in chemical engineering applications, viz. settling bed, bubbling fluidized bed and circulating fluidized bed. Simulations were performed using the PIC method in Multiphase Flow with Interphase eXchanges (MFiX) developed by National Energy Technology Laboratory (NETL). A non-intrusive uncertainty quantification (UQ) based approach is applied using Nodeworks to first construct an adequate surrogate model and then identify the most influential parameters in each case. This knowledge will aid in developing an effective design of experiments and determine optimal parameters through techniques such as deterministic or statistical calibration.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2021

Biophysical analysis of the plant-specific GIPC sphingolipids reveals multiple modes of membrane regulation

The plant plasma membrane (PM) is an essential barrier between the cell and the external environment, controlling signal perception and transmission. It consists of an asymmetrical lipid bilayer made up of three different lipid classes: sphingolipids, sterols, and phospholipids. The glycosyl inositol phosphoryl ceramides (GIPCs), representing up to 40% of total sphingolipids, are assumed to be almost exclusively in the outer leaflet of the PM. However, their biological role and properties are poorly defined. In this study, we investigated the role of GIPCs in membrane organization. Because GIPCs are not commercially available, we developed a protocol to extract and isolate GIPC-enriched fractions from eudicots (cauliflower and tobacco) and monocots (leek and rice). Lipidomic analysis confirmed the presence of trihydroxylated long chain bases and 2-hydroxylated very long-chain fatty acids up to 26 carbon atoms. The glycan head groups of the GIPCs from monocots and dicots were analyzed by gas chromatograph-mass spectrometry, revealing different sugar moieties. Multiple biophysics tools, namely Langmuir monolayer, ζ-Potential, light scattering, neutron reflectivity, solid state 2H-NMR, and molecular modeling, were used to investigate the physical properties of the GIPCs, as well as their interaction with free and conjugated phytosterols. We showed that GIPCs increase the thickness and electronegativity of model membranes, interact differentially with the different phytosterols species, and regulate the gel-to-fluid phase transition during temperature variations. These results unveil the multiple roles played by GIPCs in the plant PM.

59 BASIC BIOLOGICAL SCIENCES↗