Geometrically nonlinear analysis of thin-walled open-section composite beams

A geometrically nonlinear model for general thin-walled open-section composite beams with arbitrary lay-ups under various types of loadings based on the classical lamination theory is presented. It accounts for all structural coupling coming from the material anisotropy and geometric nonlinearity. Nonlinear governing equations are derived and solved by means of an incremental Newton–Raphson method. The finite element model that accounts for the geometric nonlinearity in the von Kármán sense is developed to solve the problem. Numerical results are obtained for thin-walled composite Z-beam and I-beam to investigate effects of geometric nonlinearity, fiber orientation and warping restraint on the flexural–torsional response

Numerical static analysis of the curtain wall with light steel structure

This paper presents a numerical analysis of the effect of wind on the curtain wall in a high building. Two types of curtain wall were adopted: made of thin-wall C sections with nominal dimensions and the section that takes into consideration lower limits specified by the manufacturer

FDTD modeling of thin impedance sheets

Thin sheets of resistive or dielectric material are commonly encountered in radar cross section calculations. Analysis of such sheets is simplified by using sheet impedances. In this paper it is shown that sheet impedances can be modeled easily and accurately using Finite Difference Time Domain (FDTD) methods

Pathways to clinical CLARITY: volumetric analysis of irregular, soft, and heterogeneous tissues in development and disease

AbstractThree-dimensional tissue-structural relationships are not well captured by typical thin-section histology, posing challenges for the study of tissue physiology and pathology. Moreover, while recent progress has been made with intact methods for clearing, labeling, and imaging whole organs such as the mature brain, these approaches are generally unsuitable for soft, irregular, and heterogeneous tissues that account for the vast majority of clinical samples and biopsies. Here we develop a biphasic hydrogel methodology, which along with automated analysis, provides for high-throughput quantitative volumetric interrogation of spatially-irregular and friable tissue structures. We validate and apply this approach in the examination of a variety of developing and diseased tissues, with specific focus on the dynamics of normal and pathological pancreatic innervation and development, including in clinical samples. Quantitative advantages of the intact-tissue approach were demonstrated compared to conventional thin-section histology, pointing to broad applications in both research and clinical settings.</jats:p

Sensitivity Analysis of Steel Box-Section Girders.

The paper deals with the load–carrying capacity stochastic variance based sensitivity analysis of thin–walled box–section girder subjected to pure bending. The lower– and uppe-r-bound load–capacity estimation is performed. The methodology is based on the Monte-Carlo method . The exemplary results are presented in diagrams and pie charts showing the sensitivity of load–capacity to different random input variables. The analysis is focused on the variance of the yield stress of the girder material and girder’s wall thickness. Some final conclusions, concerning an efficiency of the applied models and the sensitivity analysis are derived

Resonance testing of space shuttle thermoacoustic structural specimen

The resonance testing of a structural specimen related to the space shuttle vehicle is described. The specimen consisted of a thin aluminum skin reinforced by hat-section stringers and supported by two ribs or bulkheads of corrugated web. A representative section of the space shuttle thermal protection system was bonded to the outer surface of the skin. The tests were completed by using miniature accelerometers to collect vibration data from locations forming a predetermined mesh over the tiles and base structure. The signals were recorded on FM magnetic tape and subsequently analyzed on a modal analysis system

Unsupervised Classification of Intrusive Igneous Rock Thin Section Images using Edge Detection and Colour Analysis

Classification of rocks is one of the fundamental tasks in a geological study. The process requires a human expert to examine sampled thin section images under a microscope. In this study, we propose a method that uses microscope automation, digital image acquisition, edge detection and colour analysis (histogram). We collected 60 digital images from 20 standard thin sections using a digital camera mounted on a conventional microscope. Each image is partitioned into a finite number of cells that form a grid structure. Edge and colour profile of pixels inside each cell determine its classification. The individual cells then determine the thin section image classification via a majority voting scheme. Our method yielded successful results as high as 90% to 100% precision.Comment: To appear in 2017 IEEE International Conference On Signal and Image Processing Application

Histogram-based models on non-thin section chest CT predict invasiveness of primary lung adenocarcinoma subsolid nodules.

109 pathologically proven subsolid nodules (SSN) were segmented by 2 readers on non-thin section chest CT with a lung nodule analysis software followed by extraction of CT attenuation histogram and geometric features. Functional data analysis of histograms provided data driven features (FPC1,2,3) used in further model building. Nodules were classified as pre-invasive (P1, atypical adenomatous hyperplasia and adenocarcinoma in situ), minimally invasive (P2) and invasive adenocarcinomas (P3). P1 and P2 were grouped together (T1) versus P3 (T2). Various combinations of features were compared in predictive models for binary nodule classification (T1/T2), using multiple logistic regression and non-linear classifiers. Area under ROC curve (AUC) was used as diagnostic performance criteria. Inter-reader variability was assessed using Cohen's Kappa and intra-class coefficient (ICC). Three models predicting invasiveness of SSN were selected based on AUC. First model included 87.5 percentile of CT lesion attenuation (Q.875), interquartile range (IQR), volume and maximum/minimum diameter ratio (AUC:0.89, 95%CI:[0.75 1]). Second model included FPC1, volume and diameter ratio (AUC:0.91, 95%CI:[0.77 1]). Third model included FPC1, FPC2 and volume (AUC:0.89, 95%CI:[0.73 1]). Inter-reader variability was excellent (Kappa:0.95, ICC:0.98). Parsimonious models using histogram and geometric features differentiated invasive from minimally invasive/pre-invasive SSN with good predictive performance in non-thin section CT