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DOE OSTI2021

Isotherm, Kinetic, Process Modeling, and Techno-Economic Analysis of a Diamine-Appended Metal-Organic Framework for CO 2 Capture Using Fixed Bed Contactors

In this report, diamine-appended metal-organic frameworks exhibiting step-shaped CO 2 adsorption are exceptional candidates for energy-efficient carbon capture. However, there are few studies examining their performance in real-world capture scenarios, in part due to the challenge inherent in modeling their CO 2 uptake behavior. Here, we develop a dual-site Sips model to fit experimental CO 2 adsorption data for dmpn-Mg 2 (dobpdc) (dmpn = 2,2-dimethyl-1,3-diaminopropane; dobpdc 4- = 4,4'-dioxidobiphenyl-3,3'-dicarboxylate) and develop a linear driving force model for the adsorption kinetics based on available experimental data. These models are used to develop a dynamic, fixed bed, nonisothermal contactor model using shaped particles of the material, which is validated with experimental breakthrough data. We also examine the effects of the high heat of adsorption of the material on CO 2 uptake performance and find that heat removal is essential to maximize capture performance. We finally investigate "basic"(no bed cooling during adsorption) and "modified"(bed cooling during adsorption) temperature swing adsorption (TSA) processes using dmpn-Mg 2 (dobpdc), and their process economics are compared to a state-of-the-art monoethanolamine (MEA) capture system with and without heat recovery. In the absence of heat recovery, the adsorbent systems are more costly than established technology. However, with 85% heat recovery, both adsorbent-based TSA processes are projected to cost less than the MEA system. This work highlights that thermal management is vital for implementation of dmpn-Mg 2 (dobpdc) as a viable CO 2 capture technology. Investigation of other contactor technologies that can provide unique ways to manage system heat represent promising future areas of study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Implementation of a feature selection algorithm in FARM to identify important state variables and time-invariant matrices

The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM aids the HERON software module in the evaluation of the optimal dispatch for the different IES components. Set-point trajectories are required to meet limits on both production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). To evaluate the feasibility of HERON generated set-points and to do so in an acceptable time, FARM employs reduced order models to represent the dynamic behavior of the systems to be dispatched. These surrogate models take the form of a linear dynamic system with sets of Linear Parameter Varying (LPV) matrices that are mapped to the system operating space. These matrices are derived from the trajectories of system state variables and system output variables during transients. The accuracy of LPV matrices depends on the selection of state variables. In previous reports, state variables were selected by adopting a complicated workflow requiring multiple software licenses and an advanced level of user expertise. In this report, a new workflow that automates the state variable selection process is presented. It significantly reduces the frequency of user interventions and does not require multiple software licenses. Each module in the new workflow is described in detail, and the input / output examples in each step of the workflow are provided. It was demonstrated that this workflow can greatly reduce the complexity of the state variable selection process, and that the updated FARM-Gamma and FARM-Delta validators can benefit from this workflow when solving the power dispatch problem of a representative IES test case. Finally, some code improvements that can further enhance the efficiency are suggested.

42 ENGINEERING↗

DOE OSTI2026

Documentation, assessment, and improvements of fission gas modeling capabilities in BISON

Fission gas behavior critically affects nuclear fuel performance, influencing thermal conductivity, microstructure, swelling, and fuel-cladding interactions. The Simple Integrated Fission Gas Release and Swelling (Sifgrs) model, implemented within the BISON fuel performance code, provides advanced capabilities for modeling these phenomena. This manuscript reviews and assesses Sifgrs’ modular structure and submodels describing intragranular, intergranular, and release mechanisms. The modular implementation enables rapid improvements as our understanding advances and new fuel forms emerge. After an overview of capabilities, we present a comprehensive validation suite, comparing predictions to experimental data from tests under normal and transient conditions, including reproduction of the Vitanza curve. Results demonstrate Sifgrs’ accuracy, robustness, and applicability. Current limitations and ongoing efforts are discussed, including mechanistic, multiscale modeling, uncertainty quantification, and validation within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, supporting the safe and efficient use of nuclear fuels.

BISON↗

DOE OSTI2020

Observation of Unconventional Dynamics of Domain Walls in Uniaxial Ferroelectric Lead Germanate

Application of scanning probe microscopy techniques such as piezoresponse force microscopy (PFM) opens the possibility to re-visit the ferroelectrics previously studied by the macroscopic electrical testing methods and establish a link between their local nanoscale characteristics and integral response. It is reported that the nanoscale PFM studies and phase-field modeling of the static and dynamic behavior of the domain structure in the well-known ferroelectric material lead germanate Pb 5 Ge 3 O 11 . Here, several unusual phenomena are revealed: 1) domain formation during the paraelectric-to-ferroelectric phase transition, which exhibits an atypical cooling rate dependence; 2) unexpected electrically induced formation of the oblate domains due to the preferential domain walls motion in the directions perpendicular to the polar axis, contrary to the typical domain growth behavior observed so far; 3) absence of the bound charges at the 180 degrees head-to-head (H-H) and tail-totail (T-T) domain walls, which typically exhibit a significant charge density in other ferroelectrics due to the polarization discontinuity. This strikingly different behavior is rationalized by the phase field modeling of the dynamics of uncharged H-H and T-T domain walls. The results provide a new insight into the emergent physics of the ferroelectric domain boundaries, revealing unusual properties not exhibited by conventional Ising-type walls.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Isotherm modeling and techno-economic analysis of a TSA moving bed process using a tetraamine-appended MOF for NGCC applications

Tetraamine-appended metal-organic frameworks (MOF) are a new family of amine-functionalized MOF materials that show potential for CO 2 capture from flue gas conditions relevant to natural gas combined cycle (NGCC) power plants. This work presents isotherm modeling of the tetraamine-appended MOF Mg 2 (dobpdc)(3-4-3)(dobpdc 4- =4,4'-dioxidobiphenyl-3,3'dicarboxylate;3-4-3=N,N'bis(3-aminopropyl)-1,4-diaminobutane), process modeling, scale up, and a techno-economic optimization of a moving bed Temperature Swing Adsorption (TSA) process for carbon capture using this sorbent. This MOF exhibits a unique two-step CO 2 adsorption profile in three different pressure ranges. Thus, arctangent-based logistic functions and, quadratic and Langmuir models were employed to represent such isotherm behavior. The results of the isotherm model show good fitting vs the experimental data with an RMSE of 0.41. Here, to model the carbon capture process, the isotherm model was embedded into a moving bed contactor model, and this was used to simulate a TSA CO 2 capture cycle and evaluate cost-optimal designs considering flue gas from a -650 MWe NGCC power plant. The capital cost model consists of CAPEX correlations for reactors, compressors, ducting, etc., while the operating costs include steam, water, chemicals, and electricity (following NETL's quality guidelines for energy systems studies). A techno-economic optimization of the capture system was performed by using NETL's Framework for the Optimization and Quantification of Uncertainty and Surrogates tool (FOQUS). Results suggest that a moving bed carbon capture system with tetraamine-appended MOF can be competitive compared to conventional MEA solvent-based capture processes for NGCC plants, when a heat recovery efficiency of at least 40% is achieved, and MOF materials can be produced at a cost below -$9/kg.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Reduced-Order Modeling of Hydrogen Releases from Vent Stacks and with Wind

This report describes computational modeling in the HyRAM+ software for study of hydrogen behavior in two common scenarios. First, models of unignited plumes exiting a vent stack were considered. It was shown that entrainment and vent backpressure were major factors in plume physics. Second, HyRAM+ was extended to include wind effects on plumes by modifying the plume momentum balance and entrainment modeling. Use of the model showed that plume shape and length changed with wind speed and direction; in all cases, wind causes a shortening of the plume along the streamline. While the no-wind case in HyRAM+ has been validated and the newly developed wind model was fitted to very limited experimental data, more controlled experimental configurations would help validate the models and ensure accurate simulation of hydrogen plume behavior for vent stack releases or in wind.

08 HYDROGEN↗

DOE OSTI2024

Versatile TRISO fuel particle modeling in Bison

Tri-structural isotropic (TRISO) fuel particles are a key component in several previous and current reactors as well as in a variety of novel nuclear reactor designs. Interest in TRISO fuel is on the rise, necessitating considerable computer modeling of TRISO fuel behavior in order to support related design and licensing activities. The Bison nuclear fuel performance code, which offers a full set of capabilities for modeling TRISO fuels, makes it easier to explore the various important aspects of TRISO fuel behavior. One key advantage of Bison is its ability to create meshes in 1D, 2D, and 3D. Users can customize these meshes for specific geometries, mesh densities, and use cases. This enables a wide variety of analyses, including thermal, structural, mass diffusion, homogenization, and statistical failure analyses. Furthermore, the meshing capability simplifies analysts’ workflows. The inherent mesh generation capability eliminates the need for separate mesh-generating software and mesh file management. Also, the fact that the meshes are customizable makes it straightforward to automate an investigation over a range of geometric parameters or mesh densities. Here, the present paper highlights the ease with which Bison may be used to create meshes for both simple and relatively complex TRISO fuel particles, and it explores the types of analyses enabled by these meshes.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Stochastic agent-based model for predicting turbine-scale raptor movements during updraft-subsidized directional flights

Rapid expansion of wind energy development across the world has highlighted the need to better understand turbine-caused avian mortality. The risk to golden eagles (Aquila chrysaetos) is of particular concern due to their small population size and conservation status. Golden eagles subsidize their flight in part by soaring in orographic updrafts, which can place them in conflict with wind turbines utilizing the same low-altitude wind resource. Understanding the behavior of soaring raptors in varying atmospheric conditions can therefore be relevant to predicting and mitigating their risk of collision. We present a predictive movement model that simulates individual paths of golden eagles during directional flight (such as migration) that is subsidized by orographic updraft. We modeled eagles in a 50 km by 50 km study area in Wyoming containing three wind power plants with documented golden eagle collisions with turbines. The movement model is applicable to any region where ground elevation is known at turbine scale (50 m) and wind conditions are known at facility scale (3 km). For a given set of atmospheric conditions, the model simulates movements of thousands of orographic soaring eagles to produce a density map quantifying the relative probability of eagle presence. We validated the simulated tracks with GPS telemetry data showing four directional tracks made by golden eagles transiting through the area in 2019 and 2020. For each eagle track, validation was performed using the ratio of the model-simulated eagle presence likelihood with uniform eagle presence and the presence computed using directed random-walk movements. We found that the predictive performance of the model was significantly better (likelihood ratio 1) for low-altitude movements than high-altitude movements that can involve thermal-soaring. We employed the model to produce seasonal presence maps for migrating golden eagles. We found significant turbine-level variations in eagle presence between northerly and southerly migration routes through the study area. Overall, the proposed model offers a generalizable, probabilistic, and predictive tool to assist wind energy developers, ecologists, wildlife managers, and industry consultants in estimating the potential for conflict between soaring birds and wind turbines, thereby reducing the need for site-specific data on golden eagle movements.

17 WIND ENERGY↗

DOE OSTI2021

Effect of the Addition of a Low Equivalent Stress Mechanism to the Analysis of Geomechanical Behavior of the SPR West Hackberry Site

Sandia National Laboratories has long used the Munson-Dawson (M-D) model to predict the geomechanical behavior of salt caverns used to store oil at the Strategic Petroleum Reserve (SPR). Salt creep causes storage caverns to deform inward, thus losing volume. This loss of volume affects the salt above and around the caverns, puts stresses and strains on borehole casings, and creates surface subsidence which affects surface infrastructure. Therefore, accurate evaluation of salt creep behavior drives decisions about cavern operations. Parameters for the M-D model are typically fit against laboratory creep tests, but nearly all historic creep tests have been performed at equivalent stresses of 8 MPa or higher. Creep rates at lower equivalent stresses are very slow, such that tests take months or years to run, and the tests are sensitive to small temperature perturbations (<0.1°C). A recent collaboration between US and German researchers, recently characterized the creep behavior at low equivalent (deviatoric) stresses (<8 MPa) of salt from the Waste Isolation Pilot Plant. In addition, the M-D model was recently extended to include a low stress creep “mechanism”. This paper details new simulations of SPR caverns that use this extended M-D model, called the M-D Viscoplastic model. The results show that the inclusion of low stress creep significantly alters the prediction of steady-state cavern closure behavior and indicate that low stress creep is the dominant displacement mechanism at the dome scale. The implications for evaluating cavern and well integrity are demonstrated by investigating three phenomena: the extent of stress changes around the cavern; the predicted vertical strains applied to wellbore casings; and the evaluation of oscillating stress changes around the cavern due to oil sale cycles and their potential effect on salt fatigue.

58 GEOSCIENCES↗

DOE OSTI2025

MOSCATO Development and Integration in Fiscal Year 2025: Implementation of Multiphase, Multiphysics Modeling Capabilities for Molten Salt Systems

MOSCATO (Molten Salt Chemistry and Transport) is a multiphysics code that provides high-fidelity, coupled simulations of fluid flow, heat transfer, mass transfer, chemistry, electrochemical phenomena, and alloy corrosion for molten salt systems. In FY25, significant developments were made to the code package, enhancing its capabilities for modeling all relevant phenomena within operating moltens salt reactors (MSRs). The developments and activities in FY25 included: 1. Implementation of Level-Set methods to enable modeling of single-bubble behavior in molten salts. In FY25, the Level-Set two-phase flow modeling implementation was improved to simulate single bubble behavior with molten salt media. The large density and viscosity ratios between typical gases and molten salt liquids present challenges for these types of numerical solvers. With enhancements to the pressure projection method, MOSCATO’s Level-Set solver was able to be successfully validated to experiments related to helium bubble rise in stagnant molten salt. The simulated bubble rising velocity showed reasonable good agreement with experimental measurements. The bubble shape and dynamics were also visually compared with experimental snapshots, demonstrating a good qualitative match. 2. Generation of mass transfer correlations for multiphase flow systems. To enable calculations of the tritium transport across the interface between gas bubbles and salt, we modeled high- Schmidt-number mass transfer around a sphere across a broad range of Reynolds numbers. The mesh near the sphere surface was highly refined to resolve steep concentration gradients caused by the low diffusion coefficient. Literature-based mass transfer correlations were compared with the numerical results, and modifications were proposed to improve agreement, particularly at higher Schmidt numbers. These mass transfer correlations were subsequently provided to other national laboratories to help enable high quality mass transfer simulations using lower-order solvers under development within the NEAMS program. 3. Preliminary implementation of a bubbly flow solver. To model bubbly flow in molten salt, we implemented a bubbly flow solver for void fractions less than 5%. To do so, an algebraic relative velocity model that assumes small bubbles with rapid momentum equilibration was added to MOSCATO to compute bubble velocities. Preliminary comparisons with experimental data showed reasonable agreement, and further improvements are underway. 4. Generation of mass transfer correlations for MSRE subchannel The Molten-Salt Reactor Experiment (MSRE) was a landmark historical project that demonstrated the feasibility of molten-salt reactor technology. The MSRE campaign also generated a significant body of experimental data and reports that continue to support molten-salt–related research. In this report, the MSRE core subchannel was used as the reference geometry for a mass transfer study performed with MOSCATO. The geometry and computational mesh were adapted from a previous study, providing adequate resolution for the relatively low Reynolds number in this case. Additional mesh refinement was applied to reach higher Schmidt numbers, enabling the derivation of a reliable mass-transfer correlation for the present scenario. 5. Simulations of oxygen ingressions into molten salt. In the previous fiscal year, we initiated a study to simulate oxygen ingression in stagnant salt. As oxygen enters the salt through its surface, it reacts with Ce 3+ to form solid CeO 2 and other reaction products. To more fully capture the complex diffusion-convection-reaction mechanisms, capabilities for modeling natural convection in the salt vessel were added. These were needed as the flow of the ingressed gas induced flow in the salt caused by surface shear and non-isothermal effects. With these updated physics in place, we were able to successfully reproduce the experimental results for the rate of change of CeCl 3 concentrations versus time. 6. Flow corrosion model validation. In FY24, MOSCATO’s corrosion model was validated against static corrosion experiments. In FY25, this work was extended to a flow corrosion experiment, where FLiNaK salt was driven by natural convection, with initial salt impurities to initiate corrosion. Despite uncertainties in parameters such as elemental diffusion coefficients in the alloy and unknown H + concentrations, the simulations achieved good agreement with experimental results, especially in predicting sample mass losses.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Ruthenium solubility and its impact on the crystallization behavior and electrical conductivity of MoO 3 -containing borosilicate-based model high-level nuclear waste glasses

The present study focuses on investigating the solubility of RuO 2 in a borosilicate-based model high-level waste glass and understanding its impact on the crystallization behavior and electrical conductivity of the resulting vitrified waste forms. The solubility limit of RuO 2 in the investigated glass composition has been determined to be 460 ppm by weight. The higher concentration of RuO 2 results in sub-micron sized Ru-rich inclusions in the glassy matrix, which eventually agglomerate to form needle-like and polyhedral RuO 2 crystals. It is observed that RuO 2 selectively promotes the crystallization of the rare-earth apatite phase over the powellite phase. The as-synthesized RuO 2 -containing glasses exhibit semiconducting behavior with a similar level of electrical conductivity below the glass transition. Here, the percolation of non-uniformly distributed RuO 2 inclusions may result in a formation of short-range low-resistive conduction pathways in the host glass matrix leading to an apparent metallic-like behavior of selected thin samples with the highest ruthenium content.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Simulation of dynamic crystal plasticity with a Lagrangian discontinuous Galerkin hydrodynamic method

Here we present a new Lagrangian modal discontinuous Galerkin (DG) hydrodynamic method that supports a dynamic dislocation based crystal plasticity model for simulating the mechanical behavior of crystallographic materials, both single crystal and polycrystalline, under dynamic conditions. A modal DG approach is used to evolve fields relevant to conservation laws. These fields are approximated by Taylor series polynomials of varying degree. These polynomials describe macro-scale hydrodynamic behavior while their evolution is determined by evaluating the dynamic crystal plasticity model at material points within the element. The dynamic crystal plasticity model is sensitive to the time increment size, with too large of time increments leading to instability in the model. To mitigate this, the temporal evolution of the dynamic crystal plasticity model is achieved with the combination of a sub-incrementing scheme with Heun’s third-order time integration scheme, which is also used to temporally evolve the governing equations. The implementation of the dynamic crystal plasticity model within the DG framework is tested using a 2D approximation of the Taylor impact test with a single crystal material, using quadratic elements that have faces that can bend. In addition to the standard continuous material modeling, we propose a new simulation method that would represent the heterogeneous behavior of polycrystalline microstructures within an element by varying the position and material properties of the material points within the element. This method is demonstrated using random orientation distributions on materials points that are arranged in both structured and random configurations.

42 ENGINEERING↗

DOE OSTI2021

Explaining and predicting human behavior and social dynamics in simulated virtual worlds: reproducibility, generalizability, and robustness of causal discovery methods

Ground Truth program was designed to evaluate social science modeling approaches using simulation test beds with ground truth intentionally and systematically embedded to understand and model complex Human Domain systems and their dynamics Lazer et al. (Science 369:1060–1062, 2020). Our multidisciplinary team of data scientists, statisticians, experts in Artificial Intelligence (AI) and visual analytics had a unique role on the program to investigate accuracy, reproducibility, generalizability, and robustness of the state-of-the-art (SOTA) causal structure learning approaches applied to fully observed and sampled simulated data across virtual worlds. In addition, we analyzed the feasibility of using machine learning models to predict future social behavior with and without causal knowledge explicitly embedded. In this paper, we first present our causal modeling approach to discover the causal structure of four virtual worlds produced by the simulation teams—Urban Life, Financial Governance, Disaster and Geopolitical Conflict. Our approach adapts the state-of-the-art causal discovery (including ensemble models), machine learning, data analytics, and visualization techniques to allow a human-machine team to reverse-engineer the true causal relations from sampled and fully observed data. We next present our reproducibility analysis of two research methods team’s performance using a range of causal discovery models applied to both sampled and fully observed data, and analyze their effectiveness and limitations. We further investigate the generalizability and robustness to sampling of the SOTA causal discovery approaches on additional simulated datasets with known ground truth. Our results reveal the limitations of existing causal modeling approaches when applied to large-scale, noisy, high-dimensional data with unobserved variables and unknown relationships between them. We show that the SOTA causal models explored in our experiments are not designed to take advantage from vasts amounts of data and have difficulty recovering ground truth when latent confounders are present; they do not generalize well across simulation scenarios and are not robust to sampling; they are vulnerable to data and modeling assumptions, and therefore, the results are hard to reproduce. Finally, when we outline lessons learned and provide recommendations to improve models for causal discovery and prediction of human social behavior from observational data, we highlight the importance of learning data to knowledge representations or transformations to improve causal discovery and describe the benefit of causal feature selection for predictive and prescriptive modeling.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

In-situ magnetic alignment model for additive manufacturing of anisotropic bonded magnets

In this work, we report a mathematical framework which predicts the degree of alignment (DoA) in an in-situ aligned additively manufactured 3D printed bonded magnets. A multiphysics model is developed which couples the harmonious interactions of magnetic particles in a viscous flowing polymer under the presence of an external magnetic field. The hydrodynamic fluid-particle interaction is paired with the magnetophoretic force to predict the particle trajectory and distribution during extrusion through a 3D printer nozzle. Succeeding the force balance, a magnetohydrodynamic torque equilibrium analysis is performed to predict the net-orientation of the magnetic particles as a function of the applied field strength, viscous forces, and particle-to-particle interactions (P2P). Experimental validation of the DoA predictions is performed using 65 vol% Nd-Fe-B+Sm-Fe-N in Nylon12 (DoA exp = 0.620 and DoA theory = 0.686) and 15 vol% Sm-Co in PLA (DoA exp = 0.830 and DoA theory = 0.863). A parametric analysis is performed to analyze the effect of operating and design parameters like alignment field strength, magnetic loading fraction, extrusion load, and particle size. The model predicts a competing behavior between particle-fluid and particle-particle interactions under the presence of an applied field. Lastly, the model provides a framework to efficiently predict the DoA in tandem with a functionalized-magnetic 3D printer and allows the user to adjust the operating parameters according to the desired DoA.

36 MATERIALS SCIENCE↗

DOE OSTI2020

BISON Development and Validation for Priority LWR-ATF concepts

Over the years, the Nuclear Energy Advanced Modeling and Simulation (NEAMS) (2015-2018, 2020) and Consortium for Advanced Simulation of Light Water Reactors (CASL) (2019) programs have provided support for development of Accident Tolerant Fuel (ATF) material models in the BISON fuel performance code. Since the beginning, the goal has been to utilize a multiscale modeling approach to gain a physical understanding of the fuel concepts of interest and to develop mechanistic models in the absence of a large amount of experimental data. This work builds upon that of previous years. In particular we present newly updated fission gas release models for both gas behavior in Cr 2 O 3 -doped UO 2 and U 3 Si 2 fuels, and a new creep model for U 3 Si 2 . The validation exercises completed last year are revisited with the latest models and the results updated. A brief summary of recent modeling activities for FeCrAl cladding is also provided.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

A MPET 2 -mPBPK model for subcutaneous injection of biotherapeutics with different molecular weights: From local scale to whole-body scale

Subcutaneous injection of biotherapeutics has attracted considerable attention in the pharmaceutical industry. However, there is limited understanding of the mechanisms underlying the absorption of drugs with different molecular weights and the delivery of drugs from the injection site to the targeted tissue. Here, we propose the MPET 2 -mPBPK model to address this issue. This multiscale model couples the MPET 2 model, which describes subcutaneous injection at the local tissue scale from a biomechanical view, with a post-injection absorption model at injection site and a minimal physiologically-based pharmacokinetic (mPBPK) model at whole-body scale. Utilizing the principles of tissue biomechanics and fluid dynamics, the local MPET 2 model provides solutions that account for tissue deformation and drug absorption in local blood vessels and initial lymphatic vessels during injection. Additionally, we introduce a model accounting for the molecular weight effect on the absorption by blood vessels, and a nonlinear model accounting for the absorption in lymphatic vessels. The post-injection model predicts drug absorption in local blood vessels and initial lymphatic vessels, which are integrated into the whole-body mPBPK model to describe the pharmacokinetic behaviors of the absorbed drug in the circulatory and lymphatic system. We establish a numerical model which links the biomechanical process of subcutaneous injection at local tissue scale and the pharmacokinetic behaviors of injected biotherapeutics at whole-body scale. With the help of the model, we propose an explicit relationship between the reflection coefficient and the molecular weight and predict the bioavalibility of biotherapeutics with varying molecular weights via subcutaneous injection. The considered drug absorption mechanisms enable us to study the differences in local drug absorption and whole-body drug distribution with varying molecular weights. This model enhances the understanding of drug absorption mechanisms and transport routes in the circulatory system for drugs of different molecular weights, and holds the potential to facilitate the application of computational modeling to drug formulation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Computational fluid dynamic analysis of a novel particle-to-air fluidized-bed heat exchanger for particle-based thermal energy storage applications

Long-duration energy storage technologies are being targeted to enable cost-effective, decarbonized energy systems. Particle-based thermal energy storage systems are one promising technology by storing excess electricity or heat as sensible thermal energy in inexpensive, solid, inert particles. These systems are only possible if an effective and economical particle-to-working fluid heat exchanger exists. This study predicts the performance of a proposed, direct-contact, particle-to-air, pressurized fluidized-bed heat exchanger using computational fluid dynamics. The common Eulerian-Eulerian framework for modeling fluidized beds is first benchmarked to experimental results at a previously untested operating condition and application. Then, the benchmarked model evaluates the performance of a proposed design for a commercial-scale version of the novel particle-to-air heat exchanger. The results show pressure drop and gas-phase approach temperatures are advantageous compared to other proposed designs for particle-to-air heat exchangers in the literature; approach temperatures were less than 5 °C and gas-phase pressure drop across the fluidized bed was 32 kPa. The model also highlights the importance of gas distributor design and representation in the Eulerian-Eulerian framework to control fluidization behavior. In conclusion, the model built and benchmarked in this study can be leveraged to advance the design and analysis of these heat exchangers critical to the deployment of a promising long-duration energy storage technology.

25 ENERGY STORAGE↗

DOE OSTI2023

Horizontal Resolution Sensitivity of the Simple Convection-Permitting E3SM Atmosphere Model in a Doubly-Periodic Configuration

We develop a doubly periodic version of the Simple Convection-Permitting E3SM Atmosphere Model (SCREAM) to provide an efficient configuration for this global convection permitting model (GCPM), akin to a single column model often found in conventional general circulation models. The design details are explained, in addition to the extensive case library associated with the doubly periodic SCREAM (DP-SCREAM) configuration. We demonstrate that doubly periodic cloud resolving models are useful tools to explore the horizontal resolution sensitivity of GCPMs, in addition to replicating biases seen in the global models. Using DP-SCREAM, we show that SCREAM exhibits behaviors of a scale aware model as it is able to naturally partition between sub-grid scale (SGS) and resolved vertical transport across the gray zone of turbulence. We show that SCREAM is reasonably scale insensitive when run at resolutions from 1 to 5 km, but can exhibit sensitivity, particularly for the shallow convective regime, when run at resolutions approaching that of large eddy simulations. We conclude that SGS parameterization improvements are likely needed to reduce this scale sensitivity.

54 ENVIRONMENTAL SCIENCES↗