Browsing by Subject "Multiscale modeling"

Now showing 1 - 4 of 4
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    Atomistically-informed Finite Element Simulations of Phase Transformations and Fracture in Materials
    (2020-01) Fan, Jiadi
    Multiscale material modeling is a powerful computational method to investigate materials at disparate length and/or time scales, and has been widely employed to study a large variety of problems in science and engineering. In this dissertation, an atomistically-informed finite element method (AFEM) is introduced, which involves two scales of calculations: the finite element method (FEM) and atomistic simulation. The FEM as a powerful tool to simulate material response in the continuum scale is widely used in solid mechanics field. However, phenomenological model is usually employed as constitutive law, which lacks the fundamental insights of material. Atomistic simulation can provide us with thermomechanical properties of material based on the interactions between atoms, but is limited to small model size due to the computational efficiency. In the AFEM presented in this dissertation, the material properties are calculated from the atomistic scale simulations, and are employed in the continuum scale FEM simulations as material parameters. Using such a modeling method, we can predict the large scale mechanical response of a system without losing atomistic insights of materials. The AFEM is implemented in an \emph{in situ} simulation of a diamond anvil cell to predict the phase transformation of silicon under pressure, and a cohesive element simulation of epoxy--graphene composite to study the fracture mechanism at small graphene loading.
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    Bioengineered tissue mechanics: experimental characterization and a multi-component model
    (2013-08) Lai, Victor
    In the last few decades, tissue engineering has emerged as an interdisciplinary field of research which holds much promise as a complement to clinical medicine towards the overall improvement of personal health. Despite significant advances in this field, much work in TE continues to rely on an Edisonian approach of employing ad hoc methods to engineer tissues with desired properties without fundamental knowledge of the problem at hand. This thesis presents the development of a comprehensive model that predicts the mechanical properties of bioengineered tissue equivalents (TEs) based on its structure and composition, to enhance the understanding of the contribution of various biological components (e.g. biopolymeric fibers, cells, etc) to macroscopic mechanical properties of a tissue at different stages of tissue growth. The project framework considered bioengineered tissues as being composed of three components: fibrous networks, an interstitial matrix, and cells. The following interactions between different components were investigated: (a) multiple fiber networks, (b), fiber network + interstitial matrix, and (c) fiber network + cells. Experimentally, mechanical tests such as stress relaxation and tensile stretch to failure were coupled with electron microscopy, confocal microscopy, and biochemical analyses to probe tissue microstructure and composition. Constructs were formulated with varying compositions of the different components in a TE. These experimental results guided the development of the theoretical model. Modeling work built upon an existing single-component microstructural model by incorporating other components and morphological features as observed from experiment. Improvements to the model combined two approaches: (1) a microstructural approach via incorporation of morphological features observed from micrographs, and (2) a phenomenological approach using constitutive relations commonly employed for various biological structures. Model validation was done by comparing model predictions of mechanical behavior with experimental results; agreements and discrepancies alike shed insight into the complex interactions between different components the comprise a TE. Overall, the work presented in this thesis represented significant improvements to the predictive capabilities of our computational model, and established the foundation for further modifications to capture better the microstructure and mechanics of different components within a TE.
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    The explicit polarization theory as a quantum mechanical force field and the development of coarse-grained models for simulating crowded systems of many proteins
    (2014-01) Mazack, Michael John Morgan
    This dissertation consists of two parts. The first part concerns the use of explicit polarization theory (X-Pol), the semiempirical polarized molecular orbital (PMO) method, and the dipole preserving, polarization consistent (DPPC) charge model as a quantum mechanical force field (QMFF). A detailed discussion of Hartree-Fock theory and X-Pol is provided, along with expressions for the energy and the analytical first derivative of this QMFF. Test cases for this QMFF with extensive comparisons to experimental data and other models are provided for water (XP3P) and hydrogen fluoride (XPHF), showing that the PMO/X-Pol/DPPC approach discussed in this dissertation is competitive with the most accurate models for those two chemical species over a wide range of chemical and physical properties.The second part of this dissertation concerns the development and application of coarse-grained models for protein dynamics. First, a coarse-grained force field (CGFF) for macromolecules in crowded environments is introduced and described along with a visualization environment for the cartoon-like rendering of biomolecules in vivo. This CGFF is tested against experimental diffusion coefficients for myoglobin (Mb) at a wide range of concentrations, including volume fractions as high as 40%, finding it to be surprisingly accurate for its simplicity and level of coarseness. Second, an analytical coarse-grained (ACG) model for mapping the internal dynamics of proteins into a spherical harmonic expansion is described.
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    On some PDF based moment closure approximations of micro-macro models for viscoelastic polymeric fluids
    (University of Minnesota. Institute for Mathematics and Its Applications, 2009-01) Hyon, Yunkyong; Du, Qiang; Liu, Chun