Contrasting code performances for computational aeroacoustics of jets

The numerical propagation of subcritical Tollmein-Schlichting (T-S), inviscid vortical and cut-on acoustic waves is explored. For the former case, the performances of the very different NEAT, NTS, HYDRA, FLUXp and OSMIS3D codes is studied. A modest/coarse hexahedral computational grid that starkly shows differences between the different codes and schemes used in them is employed. For the same order of discretization the five codes show similar results. The unstructured codes are found to propagate vortical and acoustic waves well on triangular cell meshes but not the T-S wave. The above code contrasting exercise is then carried out using implicit LES or Smagorinsky LES for and Ma = 0.9 plane jet on modest 0.5 million cell grids moving to circa 5 million cell grids. For this case, even on the coarse grid, for all codes results were generally encouraging. In general, the spread in computational results is less than the spread of the measurements. Interestingly, the finer grid turbulence intensity levels are slightly more under-predicted than those of the coarse grid. This difference is attributed to the numerical dispersion error having a favourable coarse grid influence. For a non-isothermal jet, HYDRA and NTS also give encouraging results. Peak turbulence values along the jet centreline are in better agreement with measurements than for the isothermal jets. Copyright © 2006 by University of Wales.

A single all-optical processor for multiple spectral amplitude code label recognition using four wave mixing

We propose a novel label processor which can recognize multiple spectral-amplitude-code labels using four-wave-mixing sidebands and selective optical filtering. Ten code-labels x 10 Gbps variable-length packets are transmitted over a 200 km single-hop switched network.

An experimental model of relay deployment planning tool for a wireless sensor network system to monitor civil engineering structures

It is essential to monitor deteriorated civil engineering structures cautiously to detect symptoms of their serious disruptions. A wireless sensor network can be an effective system for monitoring civil engineering structures. It is fast to deploy sensors especially in difficult-to-access areas, and it is extendable without any cable extensions. Since our target is to monitor deteriorations of civil engineering structures such as cracks at tunnel linings, most of the locations of sensors are known, and sensors are not required to move dynamically. Therefore, we focus on developing a deployment plan of a static network in order to reduce the value of a cost function such as initial installation cost and summation of communication distances of the network. The key issue of the deployment is the location of relays that forward sensing data from sensors to a data collection device called a gateway. In this paper, we propose a relay deployment-planning tool that can be used to design a wireless sensor network for monitoring civil engineering structures. For the planning tool, we formalize the model and implement a local search based algorithm to find a quasi-optimal solution. Our solution guarantees two routings from a sensor to a gateway, which can provide higher reliability of the network. We also show the application of our experimental tool to the actual environment in the London Underground.

Visual pattern recognition models for remote sensing of civil infrastructure

As-built models have been proven useful in many project-related applications, such as progress monitoring and quality control. However, they are not widely produced in most projects because a lot of effort is still necessary to manually convert remote sensing data from photogrammetry or laser scanning to an as-built model. In order to automate the generation of as-built models, the first and fundamental step is to automatically recognize infrastructure-related elements from the remote sensing data. This paper outlines a framework for creating visual pattern recognition models that can automate the recognition of infrastructure-related elements based on their visual features. The framework starts with identifying the visual characteristics of infrastructure element types and numerically representing them using image analysis tools. The derived representations, along with their relative topology, are then used to form element visual pattern recognition (VPR) models. So far, the VPR models of four infrastructure-related elements have been created using the framework. The high recognition performance of these models validates the effectiveness of the framework in recognizing infrastructure-related elements.