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DOE OSTI2017

Two-scale model for the effect of physical aging in elastomers filled with hard nanoparticles

A two-scale model is developed, and solved numerically, to describe the mechanical behavior of elastomers filled with hard nanoparticles. Of particular interest is the slow recovery of the elastic modulus after large-amplitude oscillatory deformation. To account for this effect, the physical aging of the glassy bridges between the filler particles is captured with two thermal degrees of freedom for the matrix material, namely a kinetic and a configurational one. Formulating the two-scale model enriched with aging in a nonequilibrium thermodynamics context, first results in a constitutive relation for the Cauchy stress tensor. Second, the dynamics of physical aging is described, which eventually results in the slow recovery of the elastic modulus with waiting time. The proposed model is investigated numerically under large amplitude oscillatory shear deformation. Of particular interest in this respect is the coupling of the micro-scale dynamics with the physical aging on the macroscopic scale. This coupling is examined in detail, both in an approximate way using a Gaussian approximation, as well as numerically, under specific conditions. It turns out that the CONNFFESSIT approach (Laso and Öttinger 1993 ) can not be employed for the numerical solution of the model under arbitrary loading conditions because of the novel structure of the two-level coupling term. While a procedure for solving the model numerically for the case of strong applied deformation is presented in this paper, other solution methodologies need to be sought for the cases of weak and no applied deformation.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Non-Boussinesq subgrid-scale model with dynamic tensorial coefficients

A major drawback of Boussinesq-type subgrid-scale stress models used in large-eddy simulations is the inherent assumption of alignment between large-scale strain rates and filtered subgrid-stresses. A priori analyses using direct numerical simulation (DNS) data have shown that this assumption is invalid locally as subgrid-scale stresses are poorly correlated with the large-scale strain rates [J. Bardina, J. Ferziger, and W. Reynolds, Improved subgrid-scale models for large-eddy simulation, in Proceedings of the 13th Fluid and Plasmadynamics Conference, AIAA (1980); C. Meneveau and K. Katz, Scale-invariance and turbulence models for large-eddy simulation, Ann. Rev. Fluid Mech. 32, 1 (2000)]. In the present work, a new, non-Boussinesq subgrid-scale model is presented where the model coefficients are computed dynamically. Some previous non-Boussinesq models have observed issues in providing adequate dissipation of turbulent kinetic energy [e.g., Bardina et al., Proceedings of the 13th Fluid and Plasmadynamics Conference (1980); R. A. Clark, J. Ferziger, and W.C. Reynolds. Evaluation of subgrid-scale models using an accurately simulated turbulent flow, J. Fluid Mech. 91, 1 (1979); S. Stolz and N. A. Adams, An approximate deconvolution procedure for large-eddy simulation, Phys. Fluids 11, 1699 (1999)]; however, the present model is shown to provide sufficient dissipation using dynamic coefficients. Modeled subgrid-scale Reynolds stresses satisfy the consistency requirements of the governing equations for large-eddy simulation (LES), vanish in laminar flow and at solid boundaries, and have the correct asymptotic behavior in the near-wall region of a turbulent boundary layer. The new model, referred to as the dynamic tensor-coefficient Smagorinsky model (DTCSM), has been tested in simulations of canonical flows: decaying and forced homogeneous isotropic turbulence, and wall-modeled turbulent channel flow at high Reynolds numbers. The results show favorable agreement with DNS data. It has been shown that DTCSM offers similar predictive capabilities as the dynamic Smagorinsky model for canonical flows. In order to assess the performance of DTCSM in more complex flows, wall-modeled simulations of high Reynolds number flow over a Gaussian bump (Boeing speed bump) exhibiting smooth-body flow separation are performed. Predictions of surface pressure and skin friction, compared against DNS and experimental data, show improved accuracy from DTCSM in comparison to existing static coefficient (Vreman) and dynamic Smagorinsky model. The computational cost of performing LES with this model is up to 15% higher than the dynamic Smagorinsky model.

42 ENGINEERING↗

DOE OSTI2016

Overview of lower length scale model development for accident tolerant fuels regarding U 3 Si 2 fuel and FeCrAl cladding

U 3 Si 2 and FeCrAl have been proposed as fuel and cladding concepts, respectively, for accident tolerance fuels with higher tolerance to accident scenarios compared to UO 2 . However, a lot of key physics and material properties regarding their in-pile performance are yet to be explored. To accelerate the understanding and reduce the cost of experimental studies, multiscale modeling and simulation are used to develop physics-based materials models to assist engineering scale fuel performance modeling. In this report, the lower-length-scale efforts in method and material model development supported by the Accident Tolerance Fuel (ATF) high-impact-problem (HIP) under the NEAMS program are summarized. Significant progresses have been made regarding interatomic potential, phase field models for phase decomposition and gas bubble formation, and thermal conductivity for U 3 Si 2 fuel, and precipitation in FeCrAl cladding. The accomplishments are very useful by providing atomistic and mesoscale tools, improving the current understanding, and delivering engineering scale models for these two ATF concepts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2016

Fragmentation Prediction for a Brittle Ceramic: a Two-Scale Model Approach

Glass and oxide-ceramic radioactive material forms (e.g., PuO{sub 2}) are susceptible to brittle fracture during handling and transportation. Fracture of these materials may result in the generation of fragments small enough to become airborne, and thus easily dispersed and inhaled. Entrainment and dispersal of particles with dimensions less than 10 μm is generally anticipated under normal aerodynamic conditions. The assessment of the risk to the public and the environment from these materials due to events resulting in their fracture is based on the determination of the source term for environmental release. This source term is typically quantified by an airborne release fraction (ARF) and a respirable fraction (RF) of material generated by a given event. While the recommended empirical bounding values prove satisfactory to comply with the regulatory process for handling and transportation of these materials, they are based on test data for only a few materials subjected to a limited range of loading conditions. Because the final state of fragmentation (in terms of the size and distribution of fragments) is strongly dependent not only on the properties of the material and the energetic loading conditions that lead to breakup, but also on the many intrinsic length scales associated with fragmentation, it is difficult to precisely predict fragmentation characteristics for other brittle solid wastes forms subjected to a wider range of loading conditions based on the test data alone. The purpose of this manuscript is to present a two-scale modeling approach that may be used to simulate the dynamic fragmentation of brittle solid radioactive material forms subjected to general loading and boundary conditions. The method provides the resulting fragment characteristics (average size and size distribution) across the entire fragment range of interest. At the macro-scale, the approach uses a gradient damage elastic continuum mechanics material model within a finite element framework to simulate dynamic fracture under general loading and boundary conditions. Results from the macroscopic calculations are passed to a one-dimensional (1-D) lower length scale model that incorporates the relevant fine scale effects to determine the micro-scale fragmentation. This 1-D micro-scale fragmentation model simulates the internal crack nucleation and opening processes to predict the average fragment size and the fragment size distribution as a function of the material properties, loading conditions, and microscopic characteristic length scale of the material. Results from the lower length scale model are then combined with the macro-scale model fragmentation information to determine the resulting fragment size distribution spanning both length scales. This two-scale model has been exercised on a representative problem drawn from experimental studies, consisting of an impact of a falling weight onto a brittle ceramic. (authors)

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2026

Advancing SiC clad fuel performance model: bridging micro- and macro-scale models and experimental validation

This report presents a workflow for advancing fuel performance modeling of SiC composite cladding for light-water reactors by linking microscale, experimental data-informed finite element analysis with rod-scale fuel performance codes such as BISON. The workflow uses X-ray computed tomography (XCT) to capture the actual geometry and processing-induced defects of as-fabricated SiC composite tube specimens, particularly porosity and wall-thickness variations, and converts the segmented XCT volumes into image-based finite element meshes for high fidelity structural analysis.

Koyanagi, Takaaki [Oak Ridge National Laboratory (↗

DOE OSTI2015

Developing a physically-based tantalum strength model: Integrating fundamental concepts with macro scale models.

Abstract not provided.

Lim, Hojun↗

DOE OSTI2024

Improving Stratocumulus Cloud Amounts in a 200‐m Resolution Multi‐Scale Modeling Framework Through Tuning of Its Interior Physics

Abstract High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due to reduced parameterization and ability for multidecadal throughput on modern computing hardware. However low clouds in past HR have suffered a stubborn problem of over‐entrainment due to an uncontrolled source of mixing across the marine subtropical inversion manifesting as stratocumulus dim biases in present‐day climate, limiting their scientific utility. We report new results showing that this over‐entrainment can be partly offset by using hyperviscosity and cloud droplet sedimentation. Hyperviscosity damps small‐scale momentum fluctuations associated with the formulation of the momentum solver of the embedded large eddy simulation. By considering the sedimentation process adjacent to default one‐moment microphysics in HR, condensed phase particles can be removed from the entrainment zone, which further reduces entrainment efficiency. The result is an HR that can produce more low clouds with a higher liquid water path and a reduced stratocumulus dim bias. Associated improvements in the explicitly simulated sub‐cloud eddy spectrum are observed. We report these sensitivities in multi‐week tests and then explore their operational potential alongside microphysical retuning in decadal simulations at operational 1.5° exterior resolution. The result is a new HR having desired improvements in the baseline present‐day low cloud climatology, and a reduced global mean bias and root mean squared error of absorbed shortwave radiation. We suggest it should be promising for examining low cloud feedbacks with minimal approximation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Multi-scale modeling of the electric field assisted sintering process

The electric field assisted sintering (EFAS) process involves tightly coupled physics that influence microstructural evolution in the particles being compacted. It is also an inherently multi-scale phenomenon, with the microstructure of the compact influencing the subsequent engineering-scale response of the sintering system. To improve understanding of how processing parameters influence microstructural evolution, we have developed a multi-scale modeling approach that couples a continuum-level model of the sintering system with a phase-field model for microstructural evolution of particles within the compact. The phase-field model couples the effect of chemical and electrical driving forces on microstructural evolution and includes the effect of charged defect segregation to surfaces and grain boundaries; this segregation leads to enhanced defect transport and heat generation at these interfaces in response to applied electric field. The effect of enhanced heat generation on particle neck growth and the influence of microstructural evolution on the engineering-scale model are demonstrated.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Multi-scale modeling of the electric field assisted sintering process

36 - MATERIALS SCIENCE↗

DOE OSTI2019

Integrating terrestrial and aquatic processes toward watershed scale modeling of dissolved organic carbon fluxes

Dissolved organic carbon (DOC) is not only a critical component of global and regional carbon budgets, but also an important precursor for carcinogenic disinfection byproducts (DBP) generated during drinking water disinfection process. The lack of process based watershed scale model for carbon cycling has been a limiting factor impeding effective watershed management to control DOC fluxes to source waters. Here, we integrated terrestrial and aquatic carbon processes into the widely tested Soil and Water Assessment Tool (SWAT) watershed model to enable watershed-scale DOC modeling (referred to as SWAT-DOC hereafter). The modifications to SWAT mainly fall into two groups: (1) DOC production in soils and its transport to aquatic environment by different hydrologic processes, and (2) riverine transformation of DOC and their interactions with particular organic carbon (POC), inorganic carbon and algae (floating and bottom). We tested the new SWAT-DOC model in the Cannonsville watershed, which is part of the New York City (NYC) water supply system, using long-term DOC load data (from 1998 to 2012) derived from 1399 DOC samplings. The calibration and verification results indicate that SWAT-DOC achieved satisfactory performance for both streamflow and DOC at daily and monthly temporal scales. The parameter sensitivity analysis indicates that DOC loads in the Cannonsville watershed are controlled by the DOC production in soils and its transport in both terrestrial and aquatic environments. Further model uncertainty analysis indicates high uncertainties associated with peak DOC loads, which are attributed to underestimation of high streamflows. Therefore, future efforts to enhance SWAT-DOC to better represent runoff generation processes hold promise to further improve DOC load simulation. Overall, the wide use of SWAT and the satisfactory performance of SWAT-DOC make it a useful tool for DOC modeling and mitigation at the watershed scale.

Dissolved Organic Carbon, non-point source polluti↗

DOE OSTI2024

Pore‐Scale Modeling of Reactive Transport with Coupled Mineral Dissolution and Precipitation

Abstract We present a new pore‐scale model for multicomponent advective‐diffusive transport with coupled mineral dissolution and precipitation. Both dissolution and precipitation are captured simultaneously by introducing a phase transformation vector field representing the direction and magnitude of the overall phase change. An effective viscosity model is adopted in simulating fluid flow during mineral dissolution‐precipitation that can accurately capture the velocity field without introducing any empirical parameters. The proposed approach is validated against analytical solutions and interface tracking simulations in simplified structures. After validation, the proposed approach is employed in modeling realistic rocks where mineral dissolution and precipitation are dominant at different locations. We have identified three regimes for mineral dissolution‐precipitation coupling: (a) compact dissolution‐precipitation where dissolution is dominant near the inlet and precipitation is dominant near the outlet, (b) wormhole dissolution with clustered precipitation where dissolution generates wormholes in the main flow paths and precipitation clogs the secondary flow paths, and (c) dissolution dominant where all solid grains are gradually dissolved. In the three regimes, the proposed approach provides reliable porosity‐permeability relationships that cannot be described well by traditional macroscale models. We find that the permeability can increase while the overall porosity decreases when the main flow paths are expanded by dissolution and adjacent pore spaces are clogged by precipitation.

58 GEOSCIENCES↗

DOE OSTI2015

Assessment of current atomic scale modelling methods for the investigation of nuclear fuels under irradiation: Example of uranium dioxide

Multiscale approaches are developed to build more physically based kinetic and mechanical mesoscale models to enhance the predictive capability of fuel performance codes and increase the efficiency of the development of the safer and more innovative nuclear materials needed in the future. Atomic scale methods, and in particular electronic structure and empirical potential methods, form the basis of this multiscale approach. It is therefore essential to know the accuracy of the results computed at this scale if we want to feed them into higher scale models. We focus here on the assessment of the description of interatomic interactions in uranium dioxide using on the one hand electronic structure methods, in particular in the density functional theory (DFT) framework and on the other hand empirical potential methods. These two types of methods are complementary, the former enabling to get results from a minimal amount of input data and further insight into the electronic and magnetic properties, while the latter are irreplaceable for studies where a large number of atoms needs to be considered. We consider basic properties as well as specific ones, which are important for the description of nuclear fuel under irradiation. These are especially energies, which are the main data passed to higher scale models. We limit ourselves to uranium dioxide.

multiscale modeling↗

DOE OSTI2015

A Method to Constrain Genome-Scale Models with 13 C Labeling Data

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from 13C labeling experiments and genome-scale models. The data from 13 C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by 13 C labeling data. A comparison shows that the results of this new method are similar to those found through 13 C Metabolic Flux Analysis ( 13 C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Optimization of nutrient utilization efficiency and productivity for algal cultures under light and dark cycles using genome-scale model process control

Abstract Algal cultivations are strongly influenced by light and dark cycles. In this study, genome-scale metabolic models were applied to optimize nutrient supply during alternating light and dark cycles of Chlorella vulgaris . This approach lowered the glucose requirement by 75% and nitrate requirement by 23%, respectively, while maintaining high final biomass densities that were more than 80% of glucose-fed heterotrophic culture. Furthermore, by strictly controlling glucose feeding during the alternating cycles based on model-input, yields of biomass, lutein, and fatty acids per gram of glucose were more than threefold higher with cycling compared to heterotrophic cultivation. Next, the model was incorporated into open-loop and closed-loop control systems and compared with traditional fed-batch systems. Closed-loop systems which incorporated a feed-optimizing algorithm increased biomass yield on glucose more than twofold compared to standard fed-batch cultures for cycling cultures. Finally, the performance was compared to conventional proportional-integral-derivative (PID) controllers. Both simulation and experimental results exhibited superior performance for genome-scale model process control (GMPC) compared to traditional PID systems, reducing the overall measured value and setpoint error by 80% over 8 h. Overall, this approach provides researchers with the capability to enhance nutrient utilization and productivity of cell factories systematically by combining genome-scale models and controllers into an integrated platform with superior performance to conventional fed-batch and PID methodologies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

SCALE modeling of foil irradiations at WSU’s TRIGA with sensitivity/uncertainty analysis

We report the Washington State University TRIGA reactor was modeled in the SCALE system’s KENO neutron transport code. The model includes 119 fuel rods, a central boral control rod, 4 control blades, 5 irradiation ports, and 20 graphite reflector blocks within a rectangular aluminum grid box. The model accurately simulated the irradiation of depleted uranium and highly enriched uranium foils and predicted reaction rate ratios for 238 U(n,2n) 237 U / 238 U(n,γ) 239 U and 99 Mo / fission, which compared favorably to empirical values previously measured at Los Alamos. Finally, a sensitivity/uncertainty analysis was performed using the continuous-energy TSUNAMI-3D module of SCALE.

237Np↗

DOE OSTI2019

A dynamic spectrally enriched subgrid-scale model for preferential concentration in particle-laden turbulence

A new subgrid-scale (SGS) model for turbulent velocity fluctuations is proposed for large-eddy simulations (LES) of dispersed multi-phase flows. The modeled velocity contains scales smaller than the LES grid resolution, thereby enabling the prediction of small-scale phenomena such as the preferential concentration of particles in high-strain regions. The construction of the spectrally enriched velocity field in physical space is made dynamically, and is based on (1) modeling the smallest resolved eddies of sizes comparable to the LES grid size via approximate deconvolution, and (2) reconstructing the SGS fluctuations via non-linear generation of small-scale turbulence. The model does not contain tunable parameters, can be deployed in non-uniform grids, and is applicable to inhomogeneous flows subject to arbitrary boundary conditions. Furthermore the performance of the model is assessed in LES of isotropic turbulence laden with inertial particles, where improved agreement with direct numerical simulation results is obtained for the statistics of preferential concentration.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Learning subgrid-scale models with neural ordinary differential equations

We propose a new approach to learning the subgrid-scale model when simulating partial differential equations (PDEs) solved by the method of lines and their representation in chaotic ordinary differential equations, based on neural ordinary differential equations (NODEs). Solving systems with fine temporal and spatial grid scales is an ongoing computational challenge, and closure models are generally difficult to tune. Machine learning approaches have increased the accuracy and efficiency of computational fluid dynamics solvers. In this approach neural networks are used to learn the coarse- to fine-grid map, which can be viewed as subgrid-scale parameterization. We propose a strategy that uses the NODE and partial knowledge to learn the source dynamics at a continuous level. Our method inherits the advantages of NODEs and can be used to parameterize subgrid scales, approximate coupling operators, and improve the efficiency of low-order solvers. Numerical results with the two-scale Lorenz 96 ODE, the convection-diffusion PDE, and the viscous Burgers' PDE are used to illustrate this approach.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Experimental Validation of the Earthquake Resistance of a Full-Scale Model of RBMK-1000 Graphite Stack with Post-Repair Characteristics

In providing justification for the operational safety of RBMK-1000 power units with graphite stack distortion, earthquake resistance tests were performed on a full-scale model of RBMK graphite stack under seismic platform conditions. The tests were performed for three mutually perpendicular axes of the model successively for seismic loads corresponding to magnitudes 6, 7 and 8 on the MSK-64 scale. The tests confirmed the earthquake resistance of the graphite stack for prescribed loads. The results were used to verify the FEMGR code (Kurchatov Institute) and the LEGAK-DF finite-element software system (RFYaTs – VNIIEF) for purposes of computational validation of the earthquake resistance of the post-repair restored RMBK-1000 core.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗