English Summary
The research presented in this Ph.D. thesis aims at the numerical calculation
of reference test cases for wake-induced transition. By extension, the method
can be applied to calculate flow in the low pressure part of a gasturbine engine.
Gasturbine engines are mainly used for propulsion of airplanes, and for
generation of electricity. They consist of three major parts. The flow enters the
gasturbine through the compressor. Compressed air is heated and converted
into combustion gas in the combustion chambers. In the final part, combustion
gas at high temperature is expanded in the turbine. The turbine consists
of several parts. The high pressure turbine has small dimensions, but the
velocities are high, and so is the Reynolds number. The combustion gas is
further expanded in the low pressure turbine. Because of the low rotational
speed, the dimensions of this turbine are rather big. The rotational speed is low
because the low pressure turbine drives the fan. An example of a gasturbine
for aviation purposes is shown in Figure 1.
Figure 1: Gasturbine for aviation purposes.
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For aero applications working at high altitude, the air density is low. So
in the low pressure turbine the Reynolds number is low. This means that,
although the free-stream flow is turbulent, the boundary layer remains laminar
over a substantial part of the profile. Due to weight considerations, the number
of blades is limited, with a high loading on each blade. In steady flow, the
laminar boundary layer would separate in the decelerating part on the suction
side. This would lead to very high losses. In real engine conditions, the wakes
from upstream blade rows enter the blade passage, and periodically suppress
the separation. This makes the time averaged losses decrease.
For applications on ground level, the air density is higher, and reduction
of the turbine weight is not essential. This means that the Reynolds number
is higher and the loading is lower. So the flow has considerably less tendency
to separate.
Computational fluid dynamics that are industrially applicable average out
all turbulent fluctuations (RANS), and model their influence on the flow in a
turbulent viscosity assumption. RANS simulations provide accurate laminar
flow solutions. Further, large amount effort has been put into the accurate description of turbulent flow, for a broad range of flow configurations. Generally,
the accuracy of turbulent simulations increases with Reynolds number. In the
open literature, few models can be found that are able to describe transition
from a laminar to a turbulent flow. An overview is given in Chapter 2.
To design a new turbine, computational simulation is a rapid and cheap
tool. The other option is to perform experimental measurements. But due
to the complex geometry of a gasturbine, these measurements are expensive,
not extensive or simply impossible. Most measurements are performed on
reference configurations in a laboratory setup. These measurements neglect
three dimensional effects (often important in LP turbines) and influence of
rotation. Nevertheless, these measurements are very interesting, and are used
here for validation of the model, in Chapter 6. Up to now, the only method
to actually ’know’ what is going on in the engine is via numerical simulation.
The development of the model is a continuation of the work from Steelant
and Dick [76] performed at the department of the author. They developed a
steady transition model using conditional averaging of the flow. This technique
takes into account the interactions between the laminar and the turbulent flow
parts. This was considered too expensive for industrial applications, and is
therefore not used in the present model. The model, used in RANS context, is
extended to unsteady flow in Chapter 3. The intermittency is divided into two
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components, a near-wall intermittency factor, and a free-stream factor. This
was necessary because their time scales are much different. The velocity scale
in the equation for near-wall intermittency is altered for the case of kinematic
wake impact on a separation bubble.
The model is based on steady criteria which often rely on the leading edge
turbulence intensity. These steady criteria were derived from experiments on
flat plates. Flat plate test cases have also been used during development of the
present model. The results are discussed in Chapter 4. Afterward, the model
has been applied to steady cascade test cases in Chapter 5. Their geometry
is much more complex. Due to the strong acceleration on the leading edge,
the oncoming turbulence level is no longer characteristic for the turbulence
intensity level at transition. So the local value is used. Finally, these criteria,
made dependent on the local turbulence intensity, are used in unsteady cascade
test cases. This is proved to be successful.
In the unsteady cases, periodic wake passage is a trigger to transition.
The wake passage has two components, a kinematic impact followed by an
increased level of free-stream turbulence intensity. In between the wakes a
background turbulence intensity is seen. Three different types of unsteady
behaviour are seen. A first type is that the flow is attached, and transition
moves forward under the increased free-stream turbulence of the wake. A
second type is that the flow is separated in between the wakes, but reattaches
under wake passage. The kinematic wake impact on the separation bubble
induces a large scale roll-up of the separation bubble. The roll-up is followed
by wake turbulence induced transition. A third type is that the flow is always
separated on the suction side, but that the size of the separation bubble reduces
under the wake passage.
Some shortcomings are revealed. The calculations are two-dimensional,
and consequentely, the break-up of roll-up vortices is not well captured. The
turbulence model is seen to overpredict turbulence production in the complex
geometry between the blades. Therefore, we conclude that further improvement of the simulation result can be obtained by improving the turbulence
model. It seems very appealing to use the model in a hybrid RANS/LES
context. This would require some adaptation of the model.
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