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.

Neuro-Fuzzy genetic system for selection of construction project managers

Choosing a project manager for a construction project—particularly, large projects—is a critical project decision. The selection process involves different criteria and should be in accordance with company policies and project specifications. Traditionally, potential candidates are interviewed and the most qualified are selected in compliance with company priorities and project conditions. Precise computing models that could take various candidates’ information into consideration and then pinpoint the most qualified person with a high degree of accuracy would be beneficial. On the basis of the opinions of experienced construction company managers, this paper, through presenting a fuzzy system, identifies the important criteria in selecting a project manager. The proposed fuzzy system is based on IF-THEN rules; a genetic algorithm improves the overall accuracy as well as the functions used by the fuzzy system to make initial estimates of the cluster centers for fuzzy c-means clustering. Moreover, a back-propagation neutral network method was used to train the system. The optimal measures of the inference parameters were identified by calculating the system’s output error and propagating this error within the system. After specifying the system parameters, the membership function parameters—which by means of clustering and projection were approximated—were tuned with the genetic algorithm. Results from this system in selecting project managers show its high capability in making high-quality personnel predictions