Hierarchical Coupling of Molecular Dynamics and Micromechanics to Predict the Elastic Properties of Three-Phase and Four-Phase Silicon Carbide Composites

The results obtained from previously conducted molecular dynamics analysis of silicon carbide (-SiC (6H, 4H, & 2H-SiC), -SiC (3C SiC)), silicon and boron nitride, were utilized as inputs in the MAC/GMC micromechanics software to model and evaluate the elastic properties of three-phase SiC/BN/SiC and four-phase SiC/BN/Si/SiC composites. This method of analysis eliminates the need for back-calculation of the apparent properties of the base constituents from the measured ceramic matrix composites properties. The multiscale models are validated against the available data in literature

A Comparison of Different Modeling Strategies for Predicting Effective Properties of 3D Woven Composites

Three-dimensional (3D) woven composites are an attractive means of achieving superior mechanical performance in aerospace structures. Limited analysis capability currently exists to predict both effective elastic and strength properties for these complex composites. In this study, a comparison of three modeling strategies was performed to assess the ability of the different methods to predict the effective elastic properties of four distinct 3D orthogonal woven composites. Two finite element techniques (in-plane and triply-periodic boundary conditions) and one method of cells technique, the Multiscale Generalized Method of Cells, were considered

Computationally-efficient Structural Models for Analysis of Woven Composites

The paper presents a novel approach to model woven composite using the computationally efficient one-dimensional models. The framework is built within the scheme of the Carrera Unified Formulation (CUF), a generalized hierarchical formulation that generates variable kinematic structural theories. Various components of the woven composite unit cell are modeled using a combination of straight and curved one-dimensional CUF models. By employing a component-wise approach, a modeling technique within CUF, the complex geometry of the woven composite components is modeled precisely. The ability of CUF models to accurately resolve stress and strain fields are exploited to capture complex deformation within a woven composite unit cell. Numerical results include analyses of a non-crimped textile composite, a curved tow under tension, and a dry woven textile unit cell

Order-Reduced Solution of the Nonlinear High-Fidelity Generalized Method of Cells Micromechanics Relations

The High-Fidelity Generalized Method of Cells (HFGMC) is one technique for accurately simulating nonlinear composite material behavior. The HFGMC uses a higher-order approximation for the subcell displacement field that allows for a more accurate determination of the subcell stressstrain fields at the cost of some computational efficiency. In order to reduce computational costs associated with the solution of the ensuing system of simultaneous equations, the HFGMC global system of equations for doubly-periodic repeating unit cells with nonlinear constituents was reduced in size through the use of a Petrov-Galerkin-based Proper Orthogonal Decomposition order-reduction scheme. A number of cases were presented that address the computational feasibility of using order-reduction techniques to solve solid mechanics problems involving complex microstructures

Spatially Resolved PAH Emission Features in Nearby, Low Metallicity, Star-Forming Galaxies

Low-resolution, mid-infrared Spitzer/IRS spectral maps are presented for three nearby, low-metallicity dwarf galaxies (NGC 55, NGC 3109 and IC 5152) for the purpose of examining the spatial distribution and variation of polycyclic aromatic hydrocarbon (PAH) emission. The sample straddles a metallicity of 12+log(O/H)~8.0, a transition point below which PAH intensity empirically drops and the character of the interstellar medium changes. We derive quantitative radiances of PAH features and atomic lines on both global and spatially-resolved scales. The Spitzer spectra, combined with extensive ancillary data from the UV through the mid-infrared, allow us to examine changes in the physical environments and in PAH feature radiances down to a physical scale of 50 pc. We discuss correlations between various PAH emission feature and atomic line radiances. The (6.2 micron)/(11.3 micron), (7.7 micron)/(11.3 micron), (8.6 micron)/(11.3 micron), (7.7 micron)/(6.2 micron), and (8.6 micron)/(6.2 micron) PAH radiance ratios are found to be independent of position across all three galaxies, although the ratios do vary from galaxy to galaxy. As seen in other galaxies, we find no variation in the grain size distribution as a function of local radiation field strength. Absolute PAH feature intensities as measured by a ratio of PAH/(24 micron) radiances are seen to vary both positionally within a given galaxy, and from one galaxy to another when integrated over the full observed extent of each system. We examine direct comparisons of CC mode PAH ratios (7.7 micron)/(6.2 micron) and (8.6 micron)/(6.2 micron) to the mixed (CC/CH) mode PAH ratio (7.7 micron)/(11.3 micron). We find little variation in either mode, and no difference in trends between modes. While the local conditions change markedly over the observed regions of these galaxies, the properties of PAH emission show a remarkable degree of uniformity.Comment: Astrophysical Journal, in pres

Applications of Deep Learning on Protein Mutations

Recent developments in Deep Learning have enabled new approaches to important prediction problems in biology. In particular, models can approximate experimental laboratory investigations at a scale that would otherwise be prohibitive in time and cost. In this work, we report on three research threads adapting deep learning methods for applications involving proteins. Our longest running thread focuses on the use of rigidity analysis to assess protein stability, most recently using multiclass classification to predict the stability change caused by a mutation in a protein, with explicit modeling of experimental uncertainty. This work was recently published at BICOB 2018. The second thread involves accelerating or approximating an exhaustive analysis of in-silico protein mutations. While an exhaustive analysis is possible using parallel computing for pairwise mutations, it is infeasible to analyze higher level of protein mutation. We are using low rank matrix factorization techniques to approximate the exhaustive results with dramatically less computation. Our newest thread involves training variational autoencoders on protein sequences to learn a fixed-size latent representation of proteins, which can then be leveraged for a variety of applications (e.g. optimizing protein properties). We are also analyzing the biological significance of our model\u27s errors when translating from the latent space back into a sequence of amino acids

Predicting the Effect of Single and Multiple Mutations on Protein Structural Stability

Predicting how a point mutation alters a protein’s stability can guide pharmaceutical drug design initiatives which aim to counter the effects of serious diseases. Conducting mutagenesis studies in physical proteins can give insights about the effects of amino acid substitutions, but such wet-lab work is prohibitive due to the time as well as financial resources needed to assess the effect of even a single amino acid substitution. Computational methods for predicting the effects of a mutation on a protein structure can complement wet-lab work, and varying approaches are available with promising accuracy rates. In this work we compare and assess the utility of several machine learning methods and their ability to predict the effects of single and double mutations. We in silico generate mutant protein structures, and compute several rigidity metrics for each of them. We use these as features for our Support Vector Regression (SVR), Random Forest (RF), and Deep Neural Network (DNN) methods. We validate the predictions of our in silico mutations against experimental Δ Δ G stability data, and attain Pearson Correlation values upwards of 0.71 for single mutations, and 0.81 for double mutations. We perform ablation studies to assess which features contribute most to a model’s success, and also introduce a voting scheme to synthesize a single prediction from the individual predictions of the three models

Solution of the Nonlinear High-Fidelity Generalized Method of Cells Micromechanics Relations via Order-Reduction Techniques

The High-Fidelity Generalized Method of Cells (HFGMC) is one technique, distinct from traditional finite-element approaches, for accurately simulating nonlinear composite material behavior. In this work, the HFGMC global system of equations for doubly periodic repeating unit cells with nonlinear constituents has been reduced in size through the novel application of a Petrov-Galerkin Proper Orthogonal Decomposition order-reduction scheme in order to improve its computational efficiency. Order-reduced models of an E-glass/Nylon 12 composite led to a 4.8–6.3x speedup in the equation assembly/solution runtime while maintaining model accuracy. This corresponded to a 21–38% reduction in total runtime. The significant difference in assembly/solution and total runtimes was attributed to the evaluation of integration point inelastic field quantities; this step was identical between the unreduced and order-reduced models. Nonetheless, order-reduced techniques offer the potential to significantly improve the computational efficiency of multiscale calculations