Accident Tolerant Fuel Modeling in Bison
An overview of accident tolerant fuel modeling capabilities in Bison.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
SEARCH · Engineering Papers
Results for “multiscale modeling”
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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Effective Stochastic Model For Reactive Transport
We propose an effective stochastic advection-diffusion-reaction (SADR) model. Unlike traditional advection-dispersion-reaction models, the SADR model describes mechanical and diffusive mixing as two separate processes. In the SADR model, the mechanical mixing is driven by random advective velocity with the variance given by the coefficient of mechanical dispersion. The diffusive mixing is modeled as a fickian diffusion with the effective diffusion coefficient. Both coefficients are given in terms of Peclet number (Pe) and the coefficient of molecular diffusion. We use the experimental results of \cite{Gramling} to demonstrate that for transport and bimolecular reactions in porous media the SADR model is significantly more accurate than the traditional dispersion model, which overestimates the mass of the reaction product by as much as 25\%.
stochastic equations, mixing controlled reactions,↗
Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine
Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.
60 APPLIED LIFE SCIENCES↗
A nonlinear data-driven reduced order model for computational homogenization with physics/pattern-guided sampling
Developing an accurate nonlinear reduced order model from simulation data has been an outstanding research topic for many years. For many physical systems, data collection is very expensive and the optimal data distribution is not known in advance. Thus, maximizing the information gain remains a grand challenge. In a recent paper, Bhattacharjee and Matouš (2016) proposed a manifold-based nonlinear reduced order model for multiscale problems in mechanics of materials. Expanding this work here, we develop a novel sampling strategy based on the physics/pattern-guided data distribution. Our adaptive sampling strategy relies on enrichment of sub-manifolds based on the principal stretches and rotational sensitivity analysis. This novel sampling strategy substantially decreases the number of snapshots needed for accurate reduced order model construction (i.e., ~5 x reduction of snapshots over Bhattacharjee and Matouš (2016)). Moreover, we build the nonlinear manifold using the displacement rather than deformation gradient data. We provide rigorous verification and error assessment. We demonstrate both localization and homogenization of the multiscale solution on a large particulate composite unit cell.
97 MATHEMATICS AND COMPUTING↗
Effect of Material Porosity on Residual Stress in an Additive Manufacturing Simulation using the Generalized Method of Cells
The effect of material porosity on final part distortion and residual stresses in a selective laser sintering manufacturing simulation is presented here. A time-dependent thermo-mechanical model is used with the open-source FEA software CalculiX. Effective homogenized material properties for Inconel 625 are precomputed using NASA’s Micromechanics Analysis Code with the Generalized Method of Cells. The evolving porosity of the material is estimated with each pass of the laser beam during simulation runtime. A comparison with a homogenous model and the evolving model shows that the model with evolving porosity predicts larger distortions with greater residual stresses.
Additive Manufactuirng↗
solveME: fast and reliable solution of nonlinear ME models
Background Genome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. ME models present considerable computational challenges: they are much (>30 times) larger than corresponding metabolic reconstructions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, mainly due to macromolecule dilution constraints. Results Here, we address these computational challenges. We develop a fast and numerically reliable solution method for growth maximization in ME models using a quad-precision NLP solver (Quad MINOS). Our method was up to 45 % faster than binary search for six significant digits in growth rate. We also develop a fast, quad-precision flux variability analysis that is accelerated (up to 60× speedup) via solver warm-starts. Finally, we employ the tools developed to investigate growth-coupled succinate overproduction, accounting for proteome constraints. Conclusions Just as genome-scale metabolic reconstructions have become an invaluable tool for computational and systems biologists, we anticipate that these fast and numerically reliable ME solution methods will accelerate the wide-spread adoption of ME models for researchers in these fields.
Biochemistry & Molecular Biology↗
Predicted Melt Curve and Liquid Shear Viscosity of RDX up to 30 GPa
Recent grain-scale simulations of HMX and TATB have shown that predictions for hot spot formation in high explosives are particularly sensitive to accurate determinations of the pressure-dependent melt curve and the shear viscosity of the liquid phase. These physics terms are poorly constrained beyond ambient pressure for the explosive RDX. Here we adopt an all-atom modeling approach using molecular dynamics (MD) simulations to predict the melt curve of RDX near to detonation conditions (30 GPa) and determine the shear viscosity of the liquid as a function of temperature and pressure above the melt curve. Phase-coexistence simulations were used to determine the melt curve, which is predicted to vary by almost 1100 K as the pressure increases from 0 GPa to 30 GPa. Equilibrium MD simulations and the Green-Kubo formalism were used to obtain the pressure-temperature-dependent shear viscosity. The shear viscosity of RDX is predicted to be of similar magnitude to the viscosity of TATB at low GPa-range pressures, and to be roughly an order of magnitude lower than the viscosity of HMX. The temperature dependence of the shear viscosity is Arrhenius at a given pressure, and the exponential pre-factor and activation term exhibit a strong, yet complicated, pressure dependence. An empirical pressure-temperature dependent function for RDX shear viscosity is developed that simultaneously captures a wide range of MD predictions while taking an analytic form that extrapolates smoothly beyond the fitted regime. The relative strength of the pressure and temperature dependencies of these two physics terms is found to be of similar magnitude for RDX, HMX, and TATB, which motivates incorporating these results in future RDX grain-scale modeling.
36 MATERIALS SCIENCE↗
Pairwise Force Smoothed Particle Hydrodynamics model for multiphase flow: Surface tension and contact line dynamics
Not Available
Tartakovsky, Alexandre M.↗
Computational Micromechanics Model for the Analysis of Fiber Kinking in Unidirectional FRP
A computational micromechanics (CMM) model is developed to analyze fiber kinking, which is a failure mechanism that takes place in fiber-reinforced composites when they are loaded under longitudinal compression. The CMM model consists of a single AS4 carbon fiber with an initial misalignment embedded in an 8552 polymer matrix. The deformation of the model is governed by periodic boundary conditions (PBC). The relatively simple CMM model enables the evaluation of the role played by initial misalignment of the fiber, shear yielding of the matrix and fiber-matrix debonding. A novel microscale experimental technique devoted to the characterization of the longitudinal compressive strength of the fibers, X(sup f)(sub c) , was developed. By exercising the model and comparing it with several existing models in the literature, the nonlinear shear response of the composite lamina was shown to play a fundamental role not only in the prediction of the compressive strength X(sub c), but also during the post-peak regime in terms of residual stress σ(sub r) and fiber rotation φ. Finally, the influence of the fiber-matrix interface damage (not considered in most other fiber kinking models) on the fiber kinking phenomenon was assessed through a parametric study.
Polymer-matrix composites (PMC)↗
SU-E-J-273: Simulation of the Radiation Response of Hypoxic Tumors
Purpose: In radiotherapy, it is important to predict the response of tumour to irradiation prior to the treatment. Mathematical modelling of tumour control probability (TCP) based on the dose distribution, medical imaging and other biological information may help to improve this prediction and to optimize the treatment plan. The aim of this work is to develop an image based 3D multiscale radiobiological model, which describes the growth and the response to radiotherapy of hypoxic tumors. Methods: The computer model is based on voxels, containing tumour, normal (including capillary) and dead cells. Killing of tumour cells due to irradiation is calculated by the Linear Quadratic Model (extended for hypoxia), and the proliferation and resorption of cells are modelled by exponential laws. The initial shape of the tumours is taken from CT images and the initial vascular and cell density information from PET and/or MR images. Including the fractionation regime and the physical dose distribution of the radiation treatment, the model simulates the spatial-temporal evolution of the tumor. Additionally, the dose distribution may be biologically optimized. Results: The model describes the appearance of hypoxia during tumour growth and the reoxygenation processes during radiotherapy. Among other parameters, the TCP is calculated for different dose distributions. The results are in accordance with published results. Conclusion: The simulation model may contribute to the understanding of the influence of biological parameters on tumor response during treatment, and specifically on TCP. It may be used to implement dose-painting approaches. Experimental and clinical validation is needed. This study is supported by a grant from the Ministry of Education of Chile, Programa Mece Educacion Superior (2)
60 APPLIED LIFE SCIENCES↗
A Many-Task Parallel Approach for Multiscale Simulations of Subsurface Flow and Reactive Transport
Continuum-scale models have long been used to study subsurface flow, transport, and reactions but lack the ability to resolve processes that are governed by pore-scale mixing. Recently, pore-scale models, which explicitly resolve individual pores and soil grains, have been developed to more accurately model pore-scale phenomena, particularly reaction processes that are controlled by local mixing. However, pore-scale models are prohibitively expensive for modeling application-scale domains. This motivates the use of a hybrid multiscale approach in which continuum- and pore-scale codes are coupled either hierarchically or concurrently within an overall simulation domain (time and space). This approach is naturally suited to an adaptive, loosely-coupled many-task methodology with three potential levels of concurrency. Each individual code (pore- and continuum-scale) can be implemented in parallel; multiple semi-independent instances of the pore-scale code are required at each time step providing a second level of concurrency; and Monte Carlo simulations of the overall system to represent uncertainty in material property distributions provide a third level of concurrency. We have developed a hybrid multiscale model of a mixing-controlled reaction in a porous medium wherein the reaction occurs only over a limited portion of the domain. Loose, minimally-invasive coupling of pre-existing parallel continuum- and pore-scale codes has been accomplished by an adaptive script-based workflow implemented in the Swift workflow system. We describe here the methods used to create the model system, adaptively control multiple coupled instances of pore- and continuum-scale simulations, and maximize the scalability of the overall system. We present results of numerical experiments conducted on NERSC supercomputing systems; our results demonstrate that loose many-task coupling provides a scalable solution for multiscale subsurface simulations with minimal overhead.
high performance computing↗
Effective Pore-Scale Dispersion Upscaling with the Correlated Continuous Time Random Walk Approach
We propose a general framework for upscaling dispersion in porous media. A key challenge of the upscaling procedure is to relate the temporal evolution of spreading to the small scale velocity field properties. The representation of the Lagrangian velocity transition process as a Markovian process in space provides a simple way to quantify complex correlation properties, i.e. non-Gaussian velocity distributions. The resulting effective transport model is a correlated CTRW. We use this framework to upscale pore scale dispersion for a periodic pore geometry. The correlated CTRW model is defined by the transit time distribution across one pore and the transition probability density quantifying the correlation between successive transit times. The latter is of central importance since it accounts for incomplete mixing at the pore throats. The predictions of the correlated CTRW model are in good agreement with the pore scale simulations over the pre-asymptotic and asymptotic regimes. We investigate the representation of this effective dispersion model in phase space (position, velocity) in a form similar to a Boltzmann transport equation.
multiscale modeling, dimension reduction, transpor↗
Southern Regional Center for Lightweight Innovative Design
The three major objectives of this Phase III project are: To develop experimentally validated cradle-to-grave modeling and simulation tools to optimize automotive and truck components for lightweighting materials (aluminum, steel, and Mg alloys and polymer-based composites) with consideration of uncertainty to decrease weight and cost, yet increase the performance and safety in impact scenarios; To develop multiscale computational models that quantify microstructure-property relations by evaluating various length scales, from the atomic through component levels, for each step of the manufacturing process for vehicles; and To develop an integrated K-12 educational program to educate students on lightweighting designs and impact scenarios.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Southern Regional Center for Lightweight Innovative Design
The Southern Regional Center for Lightweight Innovative Design (SRCLID) has developed an experimentally validated cradle-to-grave modeling and simulation effort to optimize automotive components in order to decrease weight and cost, yet increase performance and safety in crash scenarios. In summary, the three major objectives of this project are accomplished: To develop experimentally validated cradle-to-grave modeling and simulation tools to optimize automotive and truck components for lightweighting materials (aluminum, steel, and Mg alloys and polymer-based composites) with consideration of uncertainty to decrease weight and cost, yet increase the performance and safety in impact scenarios; To develop multiscale computational models that quantify microstructure-property relations by evaluating various length scales, from the atomic through component levels, for each step of the manufacturing process for vehicles; and To develop an integrated K-12 educational program to educate students on lightweighting designs and impact scenarios. In this final report, we divided the content into two parts: the first part contains the development of building blocks for the project, including materials and process models, process-structure-property (PSP) relationship, and experimental validation capabilities; the second part presents the demonstration task for Mg front-end work associated with USAMP projects.
33 ADVANCED PROPULSION SYSTEMS↗
Core energies of dislocations in bcc metals
Accurate methods and an efficient workflow for computing and documenting dislocation core energies are developed and applied to 1 2 ( 111 ) and ( 100 ) dislocations in five body-centered cubic (bcc) metals W, Ta, V, Mo, and α -Fe represented by 13 model interatomic potentials. For each dislocation type, dislocation core energies are extracted for a large number of dislocation characters thoroughly sampling the entire 2-space of crystallographic line orientations of the bcc lattice. Of particular interest, core energies of the 1 2 ( 111 ) { 110 } dislocations are found to be distinctly asymmetric with respect to the sign of the character angle, whereas core energies of ( 100 ) { 110 } junction dislocations exhibit marked cusps for line orientations vicinal to the closed-packed ( 111 ) directions. Overall, our findings furnish substantial insights for developing accurate models of dislocation core energies employed in mesoscale dislocation dynamics simulations of crystal plasticity.
36 MATERIALS SCIENCE↗
Atomistic modeling of the coupling between electric field and bulk plastic deformation in fcc metals
Not Available
36 MATERIALS SCIENCE↗
Topologically derived dislocation theory for twist and stretch moiré superlattices in bilayer graphene
In this work, we develop a continuum dislocation description of twist and stretch moiré superlattices in two-dimensional material bilayers. The continuum formulation is based on the topological constraints introduced by the periodic dislocation network associated with the moiré structure. The approach is based on solving analytically for the structural distortion and displacement fields that satisfy the topological constraints and that minimize the total energy. The total energy is described by both the strain energy of each individual distorted layer and a Peierls-Nabarro-like interfacial contribution arising from stacking disregistry. The dislocation core emerges naturally within the formalism as a result of the competition between the two contributions. The approach presented here captures the structure and energetics of twist and stretch moiré superlattices of dislocations with arbitrary direction and character, without assuming an analytical solution a priori and while accounting naturally for dislocation-dislocation image interactions. In comparisons to atomistic simulations using classical potentials, the maximum structure deviation is 6%, while the maximum line energy deviation is 0.019 eV/Å. Several applications of our model are shown, including predicting the variation of structure with twist angle and describing dislocation line tension and junction energies.
36 MATERIALS SCIENCE↗
Discrete models of fluids: spatial averaging, closure and model reduction
We consider semidiscrete ODE models of single-phase fluids and two-fluid mixtures. In the presence of multiple fine-scale heterogeneities, the size of these ODE systems can be very large. Spatial averaging is then a useful tool for reducing computational complexity of the problem. The averages satisfy exact balance equations of mass, momentum, and energy. These equations do not form a satisfactory continuum model because evaluation of stress and heat flux requires solving the underlying ODEs. To produce continuum equations that can be simulated without resolving microscale dynamics, we recently proposed a closure method based on the use of regularized deconvolution. Here we continue the investigation of deconvolution closure with the long term objective of developing consistent computational upscaling for multiphase particle methods. The structure of the fine-scale particle solvers is reminiscent of molecular dynamics. For this reason we use nonlinear averaging introduced for atomistic systems by Noll, Hardy, and Murdoch-Bedeaux. We also consider a simpler linear averaging originally developed in large eddy simulation of turbulence. We present several simple but representative examples of spatially averaged ODEs, where the closure error can be analyzed. Based on this analysis we suggest a general strategy for reducing the relative error of approximate closure. For problems with periodic highly oscillatory material parameters we propose a spectral boosting technique that augments the standard deconvolution and helps to correctly account for dispersion effects. We also conduct several numerical experiments, one of which is a complete mesoscale simulation of a stratified two-fluid flow in a channel. In this simulation, the operation count per coarse time step scales sublinearly with the number of particles.
Multiscale modeling, discrete models, fluid flow↗