Presence Of Imperfection Research Articles

Instability of Conical Shells with Multiple Dimples under Axial Compression

Thin-walled conical shells are primary structures in offshore application. Presence of imperfection can considerably reduce the load carrying capacity of such structures when in use. This study examines the buckling behavior of axially compressed imperfect steel cones using the multiple perturbation load analysis (MPLA). This is both a numerical and experimental study. Eight conical shell test models were manufactured in pairs and collapsed under axial compression: two perfect, and the remaining six with MPLA imperfection amplitude, A, of 0.56, 1.12 and 1.68 having two equally-spaced dimples on each cones. Experimental test results for all the conical shell models and the accompanying numerical predictions are given in this paper. Repeatability of experimental data was good. The errors within each pair were 3%, 13%, 1% and 0%. In addition, there was a good comparison between experimental and numerical data. The ratio of experimental to numerical buckling loads varies from 0.91 to 1.13.

Non-Linear Analysis of Precast Concrete Element for Airport Pavement

The paper focuses on structural design of a precast concrete element used for an innovative precast concrete pavement system for airports which is being developed within an ongoing project at Faculty of Civil Engineering CTU in Prague. The object of the structural design consists in designing reinforcement for the selected element with respect to crack width, tensile stress limitation in reinforcement and compressive stress limitation in concrete. The amount of reinforcement is highly affected by both applied load and the presence of imperfection in pavement bedding. In order to examine the behavior of the element subject to the load from Airbus A380800F, a three-dimensional model was created and analyzed by using Newton-Raphson's method and FEM. The data obtained from the numerical simulation served for the optimization of the element reinforcement.

Postbuckling and Free Vibration of Multilayer Imperfect Nanobeams under a Pre-Stress Load

This paper investigates the postbuckling and free vibration response of geometrically imperfect multilayer nanobeams. The beam is assumed to be subjected to a pre-stress compressive load due to the manufacturing and its ends are kept at a fixed distance in space. The small-size effect is modeled according to the nonlocal elasticity differential model of Eringen within the nonlinear Bernoulli-Euler beam theory. The constitutive equations relating the stress resultants to the cross-section stiffness constants for a nonlocal multilayer beam are developed. The governing nonlinear equation of motion is derived and then manipulated to be given in terms of only the lateral displacement. The static problem is solved for the buckling load and the postbuckling deflection in terms of three parameters: Imperfection amplitude, size, and lamination. A closed-form solution for the buckling load in terms of all of the beam parameters is developed. With the presence of imperfection and size effects, it has been shown that the buckling load can be either less or greater than the Euler buckling load. Moreover, the free vibration in the pre and postbuckling domains are investigated for the first five modes. Numerical results show that the effects of imperfection, the nonlocal parameter, and layup on buckling loads and natural frequencies of the nanobeams are significant.

Source Localization by a Binary Sensor Network in the Presence of Imperfection, Noise, and Outliers

In this paper, source localization by a network of primitive binary sensors under various imperfections are studied. Detailed analysis and mathematical modeling of imperfect binary sensors are presented. Imperfections include sensor failures of two types, drifting, uncertainty, and heterogeneity in binary sensor trigger thresholds, presence of noise, and nonradial symmetry of sensing ranges. Theoretical results, including asymptotical convergence, are established, in particular in the presence of substantial outliers due to sensor failure and large noise. Efficient numerical algorithms are proposed and simulated supporting the theoretical analysis.

Influence of surface states on the reverse and noise properties of silicon power diodes

This contribution investigates transient degradation of reverse characteristics of diodes by means of a noise measurement. This effect appears immediately after external heating or after a long time on-state polarisation of diodes (without a significant temperature growth of the device in this case). Simultaneously with the reverse characteristics degradation, the noise power measured under a low voltage DC reverse bias is influenced. The first possible cause of these effects is connected with so called slow surface states (SSS). The SSS are caused by the presence of material process induced defects in the region of a p-n junction surface termination. SSS have fundamental impact on the reverse properties of diodes and their low frequency noise behaviour. The second cause is connected with so called volume structural defects (VSD). Their origin can be ‘genetic’ (e.g. the presence of imperfection inside a silicon crystal) or they can be induced during technological processing. These defects are not repairable and under the reverse bias they will form so called ‘hot spots’ that is, places with a local high current density. Rapid and operative measurements of the noise power can reveal latent instabilities of reverse characteristics invisible during a standard inspection process during the production.

Iterative Detection Scheme for Turbo-BLAST System with Adaptive Power Allocation in the Presence of Channel State Information Imperfection

We propose an iterative detection scheme for Turbo-BLAST system with optimal power allocation in the presence of channel state information imperfection. The proposed scheme uses the channel estimation matrix for detection and treats the interference caused by channel estimation errors and additive white Gaussian noise as equivalent noise where the channel estimation matrix and the statistical characteristic of channel estimation errors are necessitated. Simulation results show the proposed algorithm is effective to improve bit error rate (BER) performance through iterative detection for modified Turbo-BLAST system with optimal power allocation in the presence of imperfect channel state information.