Figure 1
Calculation procedure to obtain the jet exit condition and the diameter downstream from the jet orifice.
Figure 2
Detailed scheme of the methodology employed for implementation of DESQr in a sonic jet.
Figure 3
Typical free jet flow obtained in the experiment conducted by Birch et al. (1987)Birch, A. D., Hughes, D. J., Swaeld, F., Velocity decay of high pressure jets. Comb. Sci. Tech., 52(1- 3), 161-171 (1987).. (a) Experimental jet setup. (b) Modelled region and the simulated region.
Figure 4
Modelling in FDS 53 mm downstream from the jet orifice using (a) constant Smagorinsky sub-grid model (b) Deardorff sub-grid model. Experimental data (Birch et al., 1987Birch, A. D., Hughes, D. J., Swaeld, F., Velocity decay of high pressure jets. Comb. Sci. Tech., 52(1- 3), 161-171 (1987).).
Figure 5
Modelling in DESQr-FDS 53 mm downstream from the jet orifice using the dynamic Smagorinsky sub-grid model. Experimental data (Birch et al., 1987Birch, A. D., Hughes, D. J., Swaeld, F., Velocity decay of high pressure jets. Comb. Sci. Tech., 52(1- 3), 161-171 (1987).).
Figure 6
Modelling in FDS 53 mm downstream from the jet orifice using. (a) Entrainment coefficient constant set to 0.25 (b) and entrainment coefficient set to 0.32. Experimental data (Birch et al., 1987Birch, A. D., Hughes, D. J., Swaeld, F., Velocity decay of high pressure jets. Comb. Sci. Tech., 52(1- 3), 161-171 (1987).).
Figure 7
Modelling of the velocity profile using DESQr-FDS. Results are compared with ANSYS-CFX and experimental data (Birch et al., 1987Birch, A. D., Hughes, D. J., Swaeld, F., Velocity decay of high pressure jets. Comb. Sci. Tech., 52(1- 3), 161-171 (1987).) (a) 53mm downstream from the jet orifice. (b) 100mm downstream from the jet orifice. (c) 150mm downstream from the jet orifice.
Figure 8
Simulation of the turbulent jet downstream from the jet orifice and its respective simulation duration for three sets of boundary conditions. (a) 53 mm modelling. (b) 100 mm modelling. (c) 150 mm modelling.
Figure 9
Level of agreement for various equivalent diameters considering in the sensitivity analysis of the DESQr model. Results are compared with experimental data. Accuracy is reduced by 0.08% while computational time is reduced 6 times.
Figure 10
Wind velocity monitoring points distributed on the platform deck.
Figure 11
Wind velocity development visualized using vector slices in a plant 2.5 m above the main deck. (a) 15 s of simulation. (b) 25 s of simulation. (c) 35 s of simulation. (d) 45 s of simulation.
Figure 12
Wind velocity profile captured by the monitoring points. (a) Point 13 and (b) point 14 (point 5 m in height over the deck).
Figure 13
Computational domain and the numerical mesh used in the simulations. Inner values are in metres and outer values represent the computational cell size in centimetres.
Figure 14
Growth of the flammable material for three different computational domains (50.0 m long x 50.0 m wide x 40.0 m in height), (60.0 m long x 60.0 m wide x 40.0 m in height) and (80.0 m long x 80.0 m wide x 40.0 m in height).
Figure 15
Development of the flammable cloud in the three different computational domains (80.0 m long x 80.0 m wide x 40.0 m in height), (85.0 m long x 85.0 m wide x 40.0 m in height) and (80.0 m long x 80.0 m wide x 50.0 m in height).
Figure 16
Geometrical model of the chemical process area considered in the computational analysis. The gas leak is placed at the centre of the area. Black arrows indicate the wind directions considered in the gas dispersion analysis.
Figure 17
Top view of methane dispersion transported by the wind blowing from the north. (a) DESQr-FDS code. (b) ANSYS-CFX code. Different turbulent length scales are well captured in the LES formulation while the RANS approach relies on average values for gas concentration.
Figure 18
Top view of methane dispersion transported by the wind blowing from the south. (a) DESQr-FDS code. (b) ANSYS-CFX code. Different turbulent length scales are well captured in the LES formulation while the RANS approach relies on average values for gas concentration.
Figure 19
Top view of methane dispersion transported by the wind blowing from the east. (a) DESQr-FDS code. (b) ANSYS-CFX code. Different turbulent length scales are well captured in the LES formulation while the RANS approach relies on average values for gas concentration.
Figure 20
Top view of methane dispersion transported by the wind blowing from the west. (a) DESQr-FDS code. (b) ANSYS-CFX code. Different turbulent length scales are well captured in the LES formulation while the RANS approach relies on average values for gas concentration.
Figure 21
Flammable cloud results for DESQr-FDS and ANSYS-CFX. Results from DESQr - FDS are shown as black dots and they do vary over time. Red dots represent the steady state solution for ANSYS-CFX. Simulations were performed with wind blowing from the (a) north, (b) south, (c) west and (d) east.