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Design Rules for Membrane-Embedded Voltage-Sensing Nanoparticles
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Orientation embedded high order shape functions for the exact sequence elements of all shapes
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Application of gamma-ray radiography and gravimetric measurements after accelerated corrosion tests of steel embedded in mortar
The accelerated corrosion by the impressed current technique is widely used in studies of concrete durability since it has the advantage that tests can be carried out within reasonable periods of time. In the present work the relationship between the applied current density and the resulting damage on the reinforcing steel, by applying optical microscopy, scanning electron microscopy, gamma-ray radiography and gravimetric measurements, was studied by means of the implementation of accelerated corrosion tests on reinforced mortar. The results show that the efficiency of the applied current is between 1 and 77%, regardless of the applied current density, the water/cement ratio and the mortar cover depth of the specimens. The results show the applicability of the gamma-ray radiography technique to detect localized corrosion of steel rebars in laboratory specimens.
Modeling and simulation of vascular tumors embedded in evolving capillary networks
Here in this work, we present a coupled 3D–1D model of solid tumor growth within a dynamically changing vascular network to facilitate realistic simulations of angiogenesis. Additionally, the model includes erosion of the extracellular matrix, interstitial flow, and coupled flow in blood vessels and tissue. We employ continuum mixture theory with stochastic Cahn–Hilliard type phase-field models of tumor growth. The interstitial flow is governed by a mesoscale version of Darcy’s law. The flow in the blood vessels is controlled by Poiseuille flow, and Starling’s law is applied to model the mass transfer in and out of blood vessels. The evolution of the network of blood vessels is orchestrated by the concentration of the tumor angiogenesis factors (TAFs); blood vessels grow towards the increasing TAFs concentrations. This process is not deterministic, allowing random growth of blood vessels and, therefore, due to the coupling of nutrients in tissue and vessels, makes the growth of tumors stochastic. We demonstrate the performance of the model by applying it to a variety of scenarios. Numerical experiments illustrate the flexibility of the model and its ability to generate satellite tumors. Simulations of the effects of angiogenesis on tumor growth are presented as well as sample-independent features of cancer.
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
Uncertainty propagation in complex engineering systems often poses significant computational challenges related to modeling and quantifying probability distributions of model outputs, as those emerge as the result of various sources of uncertainty that are inherent in the system under investigation. Gaussian Processes regression (GPs) is a robust meta-modeling technique that allows for fast model prediction and exploration of response surfaces. Multi-fidelity variations of GPs further leverage information from cheap and low fidelity model simulations in order to improve their predictive performance on the high fidelity model. In order to cope with the high volume of data required to train GPs in high dimensional design spaces, a common practice is to introduce latent design variables that are typically projections of the original input space to a lower dimensional subspace, and therefore substitute the problem of learning the initial high dimensional mapping, with that of training a GP on a low dimensional space. Here in this paper, we present a Bayesian approach to identify optimal transformations that map the input points to low dimensional latent variables. The \projection" mapping consists of an orthonormal matrix that is considered a priori unknown and needs to be inferred jointly with the GP parameters, conditioned on the available training data. The proposed Bayesian inference scheme relies on a two-step iterative algorithm that samples from the marginal posteriors of the GP parameters and the projection matrix respectively, both using Markov Chain Monte Carlo (MCMC) sampling. In order to take into account the orthogonality constraints imposed on the orthonormal projection matrix, a Geodesic Monte Carlo sampling algorithm is employed, that is suitable for exploiting probability measures on manifolds. We extend the proposed framework to multi-fidelity models using GPs including the scenarios of training multiple outputs together. We validate our framework on three synthetic problems with a known lower-dimensional subspace. The benefits of our proposed framework, are illustrated on the computationally challenging aerodynamic optimization of a last-stage blade for an industrial gas turbine, where we study the effect of an 85-dimensional shape parameterization of a three-dimensional airfoil on two output quantities of interest, specifically on the aerodynamic efficiency and the degree of reaction
A quasi-conforming embedded reproducing kernel particle method for heterogeneous materials
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A concentration dependent embedded atom method potential for the Mg–Li system
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Nonparasitic behavior of embedded triboluminescent sensor in multifunctional composites
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Portobello - Quantum embedding in correlated materials made accessible
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