Numerical Simulation of the Dynamics of Turbulent Swirling Flames
Numerical Simulation of the Dynamics of Turbulent Swirling Flames
Numerical Simulation of the Dynamics of Turbulent Swirling Flames
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Stability Analysis with Low-Order Network Models<br />
Figure 6.3: UIR Method<br />
To overcome this difficulty with elements that are defined only for purely real<br />
frequencies, it is proposed to first compute <strong>the</strong> UIR <strong>of</strong> <strong>the</strong> element by <strong>the</strong><br />
inverse z-transform using Eq. (5.7). After obtaining <strong>the</strong> different UIR coefficients,<br />
which describe <strong>the</strong> element in <strong>the</strong> time domain, a forward z-transform<br />
(see Eq. (4.32)) is applied evaluating <strong>the</strong> element with complex frequencies<br />
(ω ∈ C). A similar procedure was applied by Schuermans in [177]. A scheme <strong>of</strong><br />
<strong>the</strong> procedure is shown in Fig. 6.3.<br />
6.3 Stability Analysis <strong>of</strong> <strong>the</strong> System<br />
To look at <strong>the</strong> impact <strong>of</strong> <strong>the</strong> different conditions on predicting stability limits<br />
<strong>of</strong> a system, a stability analysis is carried out with <strong>the</strong> network model tool<br />
“taX” [117] developed at TU Munich to evaluate and compare <strong>the</strong>ir eigenfrequencies<br />
and cycle increments.<br />
6.3.1 Network Model <strong>of</strong> <strong>the</strong> System<br />
The low-order model <strong>of</strong> <strong>the</strong> premix burner test rig (see Fig. 5.1) is shown<br />
in Fig. 6.4. The network model consists <strong>of</strong> different elements as ducts, area<br />
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