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Both numerical temperature and stress fields are compared with the experimental ones.
Numerical temperature profiles for both gas and solid phase, are in good agreement with experimental data.
In order to simulate real measurements, a normally distributed random error was added to the numerical temperature response.
To solve the numerical temperature model with the discrete form based on the control volume method, a new iteration method was developed.
The numerical temperature distribution in the glass was validated through the comparison with the data obtained from an experimental apparatus designed and built for the purpose.
The data were available in netCDF file format which is not a flat file construction, relatively a self-describing multi-layered structure for storing and documenting large amounts of numerical temperature data files.
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A general good agreement was obtained between numerical temperatures and test data.
Figure 9 Numerical results of thermal driven cavity flow with (pmb{kappa=1times10^{5}}) at different time, left panels the numerical streamlines, middle panels the numerical pressures, and right panels the numerical temperatures.
Figure 10 Numerical results of thermal driven cavity flow with (pmb{kappa=1times10^{6}}) at different times, left panels the numerical streamlines, middle panels the numerical pressures, and right panels the numerical temperatures.
From the numerical results, we can see that MCPFEM can simulate the fluid field, temperature field and pressure field very well, and it works well for a high Grashoff number κ. Figure 5 Numerical temperatures of Bénard convection problem with (pmb{kappa=1times10^{5}}) at different times.
A specific (numerical) growth temperature is available for 518 species (453 bacteria and 65 archaea) and can be obtained from ftp://ftp.ncbi.nlm.nih.gov/genomes/genomeprj/.
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CEO of Professional Science Editing for Scientists @ prosciediting.com