Development of a Neural Network Simulator for Studying the Constitutive Behavior of Structural Composite Materials

This paper introduces a recent development and application of a noncommercial artificial neural network (ANN) simulator with graphical user interface (GUI) to assist in rapid data modeling and analysis in the engineering diffraction field. The real-time network training/simulation monitoring tool has been customized for the study of constitutive behavior of engineering materials, and it has improved data mining and forecasting capabilities of neural networks. This software has been used to train and simulate the finite element modeling (FEM) data for a fiber composite system, both forward and inverse. The forward neural network simulation precisely reduplicates FEM results several orders of magnitude faster than the slow original FEM. The inverse simulation is more challenging; yet, material parameters can be meaningfully determined with the aid of parameter sensitivity information. The simulator GUI also reveals that output node size for materials parameter and input normalization method for strain data are critical train conditions in inverse network. The successful use of ANN modeling and simulator GUI has been validated through engineering neutron diffraction experimental data by determining constitutive laws of the real fiber composite materials via a mathematically rigorous and physically meaningful parameter search process, once the networks are successfully trained from the FEM database

Decreased glutathione levels and impaired antioxidant enzyme activities in drug-naive first-episode schizophrenic patients

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to determine glutathione levels and antioxidant enzyme activities in the drug-naive first-episode patients with schizophrenia in comparison with healthy control subjects.</p> <p>Methods</p> <p>It was a case-controlled study carried on twenty-three patients (20 men and 3 women, mean age = 29.3 ± 7.5 years) recruited in their first-episode of schizophrenia and 40 healthy control subjects (36 men and 9 women, mean age = 29.6 ± 6.2 years). In patients, the blood samples were obtained prior to the initiation of neuroleptic treatments. Glutathione levels: total glutathione (GSHt), reduced glutathione (GSHr) and oxidized glutathione (GSSG) and antioxidant enzyme activities: superoxide dismutase (SOD), glutathione peroxidase (GPx), catalase (CAT) were determined by spectrophotometry.</p> <p>Results</p> <p>GSHt and reduced GSHr were significantly lower in patients than in controls, whereas GSSG was significantly higher in patients. GPx activity was significantly higher in patients compared to control subjects. CAT activity was significantly lower in patients, whereas the SOD activity was comparable to that of controls.</p> <p>Conclusion</p> <p>This is a report of decreased plasma levels of GSHt and GSHr, and impaired antioxidant enzyme activities in drug-naive first-episode patients with schizophrenia. The GSH deficit seems to be implicated in psychosis, and may be an important indirect biomarker of oxidative stress in schizophrenia early in the course of illness. Finally, our results provide support for further studies of the possible role of antioxidants as neuroprotective therapeutic strategies for schizophrenia from early stages.</p

In-Situ Investigation of Advanced Structural Coatings and Composites

The premise of this project is a comprehensive study that involves the in-situ characterization of advanced coatings and composites by employing both neutron and x-ray diffraction techniques in a complementary manner. The diffraction data would then be interpreted and used in developing or validating advanced micromechanics models with life prediction capability. In the period covered by this report, basic work was conducted to establish the experimental conditions for various specimens and techniques. In addition, equipment was developed that will allow the in-situ studies under a range of conditions (stress, temperature, atmosphere, etc.)

PMU-Based Dynamic Model Calibration of Type 4 Wind Turbine Generators

In today’s power system where the share of renewables is rapidly increasing, the system now exhibits a more dynamic behavior compared to the past. Therefore, the importance of dynamic simulations at every level of the power system is crucial for the system operators. However, calibration of model parameters and their regular controlling are required to simulate the real-life behavior of the system correctly. This paper aims to improve the dynamic simulations by calibrating the parameters of the Type 4 wind turbine generator model. The employed method uses an ensemble Kalman filter to estimate the model states and calibrate parameters. For the simulation environment, SIEMENS PSS®E software (v35.5) and its PythonTM API are utilized. After the sensitivity and collinearity analyses, during the transient event, the erroneous model parameters are calibrated

Tae-Soon Park A Comparison of X-Ray Microdiffraction and Coherent Gradient Sensing in Measuring Discontinuous Curvatures in Thin Film: Substrate Systems Introduction and Motivation

The coherent gradient sensor (CGS) is a shearing interferometer which has been pro

Development of a Neural Network Simulator for Studying the Constitutive Behavior of Structural Composite Materials

This paper introduces a recent development and application of a noncommercial artificial neural network (ANN) simulator with graphical user interface (GUI) to assist in rapid data modeling and analysis in the engineering diffraction field. The real-time network training/simulation monitoring tool has been customized for the study of constitutive behavior of engineering materials, and it has improved data mining and forecasting capabilities of neural networks. This software has been used to train and simulate the finite element modeling (FEM) data for a fiber composite system, both forward and inverse. The forward neural network simulation precisely reduplicates FEM results several orders of magnitude faster than the slow original FEM. The inverse simulation is more challenging; yet, material parameters can be meaningfully determined with the aid of parameter sensitivity information. The simulator GUI also reveals that output node size for materials parameter and input normalization method for strain data are critical train conditions in inverse network. The successful use of ANN modeling and simulator GUI has been validated through engineering neutron diffraction experimental data by determining constitutive laws of the real fiber composite materials via a mathematically rigorous and physically meaningful parameter search process, once the networks are successfully trained from the FEM database.This article is from ISRN Materials Science 2013: 147086, doi: 10.1155/2013/147086</p

Evolution of ferroelectric domain structures embedded inside polychrystalline BaTiO3 during heating

The evolution of ferroelectric domains inside a single grain of a polycrystalline BaTiO{sub 3} ceramic was investigated under quasistatic heating by using polychromatic scanning x-ray microdiffraction. Four domain orientations were observed, three of which exhibited a classic of {approx}90{sup o} ferroelastic relationship. The fourth domain orientation was found to be crystallographically related with one of the other orientations by a rotation of either 180.47{sup o} or 0.47{sup o}. While heating the polycrystalline BaTiO{sub 3} from room temperature to above the Curie temperature (125 C), all four ferroelectric domain orientations rotated toward a paraelectric cubic orientation which was found to be at an intermediate orientation relative to the four domain orientations. The crystallographic relationships of the domains with respect to paraelectric phase were explained using a domain structure model by Nepochatenko

In-Situ Characterization of Matrix Response to Fiber Fractures

Successful application of metal matrix composites often requires strength and lifetime predictions that account for the deformation of each constituent. However, the deformation of individual phases in composites usually differs significantly from their respective monolithic behaviors. For instance, generally little is known about the in-situ deformation of the metal matrix and fiber/matrix interface region, other than that it likely differs from the bulk material response. This article describes an approach to quantifying the in-situ deformation parameters using neutron diffraction measurements of matrix failure around a fiber fracture in a model composite consisting of an Al matrix and a single Al{sub 2}O{sub 3} fiber. We also study the shear sliding resistance as it evolves through fiber fracture upon loading and unloading. Matching the stress/strain distributions predicted from micromechanical models to the measured strain distributions determined by neutron diffraction under applied tensile loading results in an estimate of the typically non-linear, stress-strain behavior of the metal matrix