Engineering Papers⌕ Search

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Revisiting of Multiscale Static Analysis of Notched Laminates Using the Generalized Method of Cells

Composite material systems generally exhibit a range of behavior on different length scales (from constituent level to macro); therefore, a multiscale framework is beneficial for the design and engineering of these material systems. The complex nature of the observed composite failure during experiments suggests the need for a three-dimensional (3D) multiscale model to attain a reliable prediction. However, the size of a multiscale three-dimensional finite element model can become prohibitively large and computationally costly. Two-dimensional (2D) models are preferred due to computational efficiency, especially if many different configurations have to be analyzed for an in-depth damage tolerance and durability design study. In this study, various 2D and 3D multiscale analyses will be employed to conduct a detailed investigation into the tensile failure of a given multidirectional, notched carbon fiber reinforced polymer laminate. Threedimensional finite element analysis is typically considered more accurate than a 2D finite element model, as compared with experiments. Nevertheless, in the absence of adequate mesh refinement, large differences may be observed between a 2D and 3D analysis, especially for a shear-dominated layup. This observed difference has not been widely addressed in previous literature and is the main focus of this paper.

Materials Engineering↗
NASA NTRSDate not supplied

Geometric Requirements for Modeling Polycrystalline Representative Volume Elements Using the Generalized Method of Cells

To accurately predict the mechanical response of advanced metallic parts, the grain structure of the metal must be considered and coupled to the macroscale in a multiscale model. The size of the microscale representative volume element must be sufficiently large, and the grid used to discretize the microscale must be adequately refined, in order to ensure an accurate and converged solution. The generalized method of cells is used to predict the mechanical properties of polycrystalline representative volume elements of copper containing randomly oriented grains with the properties of a single face-centered-cubic crystal. Moreover, the results of a parametric study are used to conclude the geometric requirements for an objective microscale model, with respect to the elastic stiffness.

Multiscale Modeling↗
DOE OSTI2018

Multiscale finite element modeling of sheet molding compound (SMC) composite structure based on stochastic mesostructure reconstruction

Predicting the mechanical behavior of the chopped carbon fiber Sheet Molding Compound (SMC) due to spatial variations in local material properties is critical for the structural performance analysis but is computationally challenging. Such spatial variations are induced by the material flow in the compression molding process. In this work, a new multiscale SMC modeling framework and the associated computational techniques are developed to provide accurate and efficient predictions of SMC mechanical performance. The proposed multiscale modeling framework contains three modules. First, a stochastic algorithm for 3D chip-packing reconstruction is developed to efficiently generate the SMC mesoscale Representative Volume Element (RVE) model for Finite Element Analysis (FEA). A new fiber orientation tensor recovery function is embedded in the reconstruction algorithm to match reconstructions with the target characteristics of fiber orientation distribution. Second, a metamodeling module is established to improve the computational efficiency by creating the surrogates of mesoscale analyses. Third, the macroscale behaviors are predicted by an efficient multiscale model, in which the spatially varying material properties are obtained based on the local fiber orientation tensors. Our approach is further validated through experiments at both meso- and macro-scales, such as tensile tests assisted by Digital Image Correlation (DIC) and mesostructure imaging.

36 MATERIALS SCIENCE↗
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

Single-size and cluster dynamics modeling of intra-granular fission gas bubbles in UO 2

For this work, we perform simulations of intra-granular fission gas bubble evolution in UO 2 using both a relatively simple, computationally inexpensive single-size model and a detailed cluster dynamics model. Simulations encompass 36 experimental cases from 4 different databases, covering various temperature and burnup levels. We systematically compare results from the two models to each other and to post-irradiation experimental data of bubble average size and number density. Overall, the model-to-model comparisons reveal an excellent agreement across the set of simulations. This outcome indicates that, in spite of the underlying assumptions, the single-size model provides a good approximation of the complex physical behavior that is more rigorously described by the cluster dynamics model. Qualitatively, both models reproduce the trends of the experimental data with temperature and burnup correctly. Quantitatively, calculated results are either in good agreement with the data or within errors that appear consistent with the inherent uncertainties. Moreover, for the single-size model, we demonstrate and assess a multiscale approach whereby values for the fission gas atom diffusion coefficient from separate atomistic calculations are used. Systematic comparisons to experimental data point out a credible accuracy of the multiscale model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Sensitivity of pore collapse heating to the melting temperature and shear viscosity of HMX

A multiscale modeling strategy is used to quantify factors governing the temperature rise in hot spots formed by pore collapse from supported and unsupported shock waves in the high explosive HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine). Two physical aspects are examined in detail, namely the melting temperature and liquid shear viscosity. All-atom molecular dynamics simulations of phase coexistence are used to predict the pressure-dependent melting temperature up to 5 GPa. Equilibrium simulations and the Green–Kubo formalism are used to obtain the temperature- and pressure-dependent liquid shear viscosity. Starting from a simplified continuum-based grain-scale model of HMX, in this study we systematically increase the complexity of treatments for the solid–liquid phase transition and liquid shear viscosity in simulations of pore collapse. Using a realistic pressure-dependent melting temperature completely suppresses melting for supported shocks, which is otherwise predicted when treating it as a constant determined at atmospheric pressure. Alternatively, melt pools form around collapsed pores when the pressure (and melting temperature) are reduced during the release stage of unsupported shocks. Capturing the pressure dependence of the shear viscosity increases the peak temperature of melt pools by hundreds of Kelvin through viscous work. The complicated interplay of the solid-phase plastic work, solid–liquid phase transition, and liquid-phase viscous work identified here motivate taking a systematic approach to building increasingly complex grain-scale models.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multiscale Evaluation of Acetohydroxamic Acid (AHA) Radiolysis Under Used Nuclear Fuel Reprocessing Solvent System Conditions

Acetohydroxamic acid (AHA) has been proposed as an alternative agent for the selective separation of plutonium and neptunium from co-extracted uranium during the reprocessing of used nuclear fuel. However, the fundamental radiolytic behavior of this molecule under envisioned process conditions – i.e., acidic biphasic solvent systems – is not sufficiently understood to support process applications. Here we present a systematic irradiation study (steady-state gamma and time-resolved pulsed electron) into the radiolytic integrity of AHA and formation of degradation products in aqueous nitric acid (HNO3) solutions (0.2 M) in presence and absence of an organic phase, comprising current (tri-butyl phosphate - TBP) and future (N,N-di-(2-ethylhexyl)butyramide - DEHBA and di-2-ethylhexylisobutyramide - DEHiBA) reprocessing ligands dissolved in n-dodecane diluent. Experimental data are complimented by predictive multiscale model calculations for the elucidation of underpinning mechanisms.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS2014

A Technique for Mapping Characteristic Lengths to Preserve Energy Dissipated via Strain Softening in a Multiscale Analysis

It is often advantageous to account for the microstructure of the material directly using multiscale modeling. For computational tractability, an idealized repeating unit cell (RUC) is used to capture all of the pertinent features of the microstructure. Typically, the RUC is dimensionless and depends only on the relative volume fractions of the different phases in the material. This works well for non-linear and inelastic behavior exhibiting a positive-definite constitutive response. Although, once the material exhibits strain softening, or localization, a mesh objective failure theories, such as smeared fracture theories, nodal and element enrichment theories (XFEM), cohesive elements or virtual crack closure technique (VCCT), can be utilized at the microscale, but the dimensions of the RUC must then be defined. One major challenge in multiscale progressive damage modeling is relating the characteristic lengths across the scales in order to preserve the energy that is dissipated via localization at the microscale. If there is no effort to relate the size of the macroscale element to the microscale RUC, then the energy that is dissipated will remain mesh dependent at the macroscale, even if it is regularized at the microscale. Here, a technique for mapping characteristic lengths across the scales is proposed. The RUC will be modeled using the generalized method of cells (GMC) micromechanics theory, and local failure in the matrix constituent subcells will be modeled using the crack band theory. The subcell characteristic lengths used in the crack band calculations will be mapped to the macroscale finite element in order to regularize the local energy in a manner consistent with the global length scale. Examples will be provided with and without the regularization, and they will be compared to a baseline case where the size and shape of the element and RUC are coincident (ensuring energy is preserved across the scales).

softening↗
DOE OSTI2024

Ab initio generalized Langevin equation

We introduce a machine learning–based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables (CVs) in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed in the context of the Mori–Zwanzig formalism, under the constraint of the fluctuation–dissipation theorem. Combined with deep potential molecular dynamics and electronic density functional theory, this approach opens the way to multiscale modeling in a variety of situations. Here, we demonstrate this capability with a study of two mesoscale processes in crystalline lead titanate, namely the field-driven dynamics of a planar ferroelectric domain wall, and the dynamics of an extensive lattice of coarse-grained electric dipoles. In the first case, AIGLE extends the reach of ab initio simulations to a regime of noise-driven motions not accessible to molecular dynamics. In the second case, AIGLE deals with an extensive set of CVs by adopting a local approximation for the memory kernel and retaining only short-range noise correlations. The scheme is computationally more efficient than molecular dynamics by several orders of magnitude and mimics the microscopic dynamics at low frequencies where it reproduces accurately the dominant far-infrared absorption frequency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Data Driven Approach to Dislocation-Based Plasticity Models of Face-Centered Cubic Metals

Dislocation dynamics controls plastic deformation, mechanical strength, and failure of crystalline materials. It also governs fatigue resistance under cyclic loading, creep resistance at elevated-temperature, and radiation resistance for reactor applications. There is a compelling need for understanding fundamental dislocation mechanisms for deformation because virtually all structural metals used in energy systems are fabricated to desired forms and shapes by deformation processes. To date, the most outstanding problem in a physics-based multiscale model of crystal plasticity is the lack of quantitative connections between continuum plasticity (CP) models with the lower scale dislocation models. As a result, existing CP models used in engineering applications are still phenomenological, while evidence continues to mount that they can make inaccurate predictions under realistically complex scenarios. This project takes advantage of the recent advances in high-performance discrete dislocation dynamics (DDD) simulations and data science approaches to establish the first fully connected multiscale plasticity model for pure face-centered cubic (FCC) single crystals.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

The Nasa Multiscale Analysis Tool: an Enabling Platform for Achieving Vision 2040

Vision 2040 is a community-driven consensus document, written in 2018, aimed at defining the potential 25-year future state required for performing integrated multiscale modeling of materials and systems for future aerospace and aeronautical applications. Nine Vision Key Elements (KEs) were defined along with associated technical gaps. This paper will address current NASA GRC research efforts utilizing the NASA Multiscale Analysis Tool (NASMAT). This paper will specifically focus on NASMAT’s ability to address gaps in three of the nine Vision 2040 KEs: 1) Models and Methods, 2) Multiscale Measurements and Characterization Tools and Methods, and 6) Data, Informatics, and Visualization. NASMAT is a versatile platform for performing computationally efficient multiscale analyses of heterogeneous materials. NASMAT offers the user flexibility to define an arbitrary number of length scales (levels) where a variety of micromechanics theories can be implemented at each level. Micromechanics theories can be selected to balance accuracy and computational efficiency and range from analytical (Mori-Tanaka) to several semi-analytical (method of cells) formulations. NASMAT can also be coupled with external software and used to perform multiscale analyses of more complex structures. The paper will include a recent application of NASMAT to model a complex, three-dimensional woven composite, with a particular emphasis placed on multiscale measurements utilized to enhance the quality of the multiscale analysis. Since typical NASMAT analyses can be completed in on the order of seconds to minutes, a second example will demonstrate NASMAT’s ability to generate large quantities of data useful for sensitivity analysis, uncertainty quantification, or machine learning applications. Current progress on developing multiscale data visualization tools will also be addressed along with the challenges associated with and proposed solutions for sifting through large amounts of data. These examples will demonstrate that NASMAT is an enabling platform for achieving the goals in Vision 2040.

Vision 2040↗
DOE OSTI2015

Mesoscale modeling of solute precipitation and radiation damage

This report summarizes the low length scale effort during FY 2014 in developing mesoscale capabilities for microstructure evolution in reactor pressure vessels. During operation, reactor pressure vessels are subject to hardening and embrittlement caused by irradiation-induced defect accumulation and irradiation-enhanced solute precipitation. Both defect production and solute precipitation start from the atomic scale, and manifest their eventual effects as degradation in engineering-scale properties. To predict the property degradation, multiscale modeling and simulation are needed to deal with the microstructure evolution, and to link the microstructure feature to material properties. In this report, the development of mesoscale capabilities for defect accumulation and solute precipitation are summarized. Atomic-scale efforts that supply information for the mesoscale capabilities are also included.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Resistor-Network Modeling of Ionic Conduction in Polymer Electrolytes

A resistor- and pore-network methodology is used here to examine transport of ions in various ion-conducting polymers. The model is used to examine ion conduction in random and correlated (at the mesoscale) distributions of high and low conductive domains showing the impact that defects or different conduction modes have on overall effective conductivity and percolation. The specific case of Nafion is modeled where swelling is accounted for as well as a spatially varying conductivity within the nanodomains. The model is also used to investigate conduction in thin-films, where a substantial drop in conductivity is witnessed for films less than 50 nm thick. The model shows good agreement with experimental data and provides a methodology for efficient multiscale modeling of transport in ion-conducting polymers from the nanoscale morphology through the mesoscale transport pathways to the observable macroscale properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Machine learning predictions for local electronic properties of disordered correlated electron systems

We present a scalable machine learning (ML) model to predict local electronic properties such as on-site electron number and double occupation for disordered correlated electron systems. Our approach is based on the locality principle, or the nearsightedness nature, of many-electron systems, which means local electronic properties depend mainly on the immediate environment. A ML model is developed to encode this complex dependence of local quantities on the neighborhood. We demonstrate our approach using the square-lattice Anderson-Hubbard model, which is a paradigmatic system for studying the interplay between Mott transition and Anderson localization. We develop a lattice descriptor based on the group-theoretical method to represent the on-site random potentials within a finite region. The resultant feature variables are used as input to a multilayer fully connected neural network, which is trained from data sets of variational Monte Carlo (VMC) simulations on small systems. We show that the ML predictions agree reasonably well with the VMC data. Our work underscores the promising potential of ML methods for multiscale modeling of correlated electron systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Predicting encapsulant delamination in photovoltaic modules bridging photochemical reaction kinetics and fracture mechanics

Abstract Photovoltaic (PV) modules are subjected to environmental stressors (UV exposure, temperature, and humidity) that cause degradation within the encapsulant and its interfaces with adjacent glass and cell substrates. To save experimental time and to enable long‐term assessment with intensive degradation only taking place after many years, the development of predictive models is indispensable. Previous works have modeled the delamination of the ethylene vinyl acetate (EVA) encapsulant/glass and encapsulant/cell interfaces under field aging conditions with fundamental photochemical degradation reactions that lead to molecular scission and loss of interfacial adhesion, characterized by the fracture resistance, G c . However, these models were fundamentally limited in that the following aspects were not incorporated: (i) molecular crosslinking in the field, (ii) synergistic autocatalytic interactions of degradation mechanisms, (iii) connection between degraded encapsulant structure and its mechanical properties, and (iv) rigorous treatment of the plasticity contribution to G c with finite element models. Here, we present a time‐dependent multiscale model that addresses these limitations and is applicable to a wide range of encapsulants and interfaces. For the reference EVA encapsulant and its interfaces with the glass and cell, the presented model predicts an initial rise in G c in the first 3 years of field aging from crosslinking, then a subsequent sharp decline from degradation mechanisms. We used nanoindentation to measure the changes in EVA mechanical properties over exposure time to tune the model parameters. The model predictions of G c and mechanical properties match with experimental data and show an improvement compared to previous models. The model can even predict switches in failure interfaces, such as the observed EVA/cell to EVA/glass transition. We also conducted a sensitivity analysis study by varying the degradation and crosslinking kinetic parameters to demonstrate their effects on G c . Model extensions to polyolefin elastomer‐ and silicone‐encapsulants and their interfaces are also demonstrated.

Liu, Kuan↗
DOE OSTI2019

Circumventing mesh bias by r- and h-adaptive techniques for variational eigenfracture

This work introduces and compares mesh r- and h-adaptivity for the eigenfracture model originally proposed in Schmidt et al. (Multiscale Model Simul 7:1237–1266, 2009), Pandolfi and Ortiz (J Numer Methods Eng 92:694–714, 2012), with the goal of suppressing potential mesh bias due to the element deletion. In the r-adaptive approach, we compute the configurational force at each incremental step and move nodes near the crack tip parallel to the normalized configurational forces field such that the crack propagation direction can be captured more accurately within each incremental step. In the h-adapative approach, we introduce mesh refinement via a quad-tree algorithm to introduce more degrees of freedom within the nonlocal ϵ-neighborhood such that a more refined crack path can be reproduced with a higher mesh resolution. Our numerical examples indicate that the r-adaptive approach is able to replicate curved cracks and complex geometrical features, whereas the h-adaptive approach is advantageous in simulating sub-scale fracture when the nonlocal regions are smaller than the un-refined coarse mesh.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Necessity of capillary modes in a minimal model of nanoscale hydrophobic solvation

Modern theories of the hydrophobic effect highlight its dependence on length scale, emphasizing the importance of interfaces in the vicinity of sizable hydrophobes. We recently showed that a faithful treatment of such nanoscale interfaces requires careful attention to the statistics of capillary waves, with significant quantitative implications for the calculation of solvation thermodynamics. Here, we show that a coarse-grained lattice model like that of Chandler [Chandler D (2005) Nature 437(7059):640-647], when informed by this understanding, can capture a broad range of hydrophobic behaviors with striking accuracy. Specifically, we calculate probability distributions for microscopic density fluctuations that agree very well with results of atomistic simulations, even many SDs from the mean and even for probe volumes in highly heterogeneous environments. This accuracy is achieved without adjustment of free parameters, because the model is fully specified by well-known properties of liquid water. As examples of its utility, we compute the free-energy profile for a solute crossing the air-water interface, as well as the thermodynamic cost of evacuating the space between extended nanoscale surfaces. These calculations suggest that a highly reduced model for aqueous solvation can enable efficient multiscale modeling of spatial organization driven by hydrophobic and interfacial forces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗