Outline of current research results by Björn Birnir and Höskuldur Ari Hauksson.  This research has been funded by NSF under grant number DMS-9704874 and in part by AFOSR under grant number F49620-95-1-0409. Equipment were funded by NSF under grants number DMS-9628606 and PHY-9601954.


In recent years a lot of attention has been devoted to the study of air flow through turbomachines.  The main reason for this interest is that when a turbomachine, such as a jet engine, operates close to its optimal operating parameter values, the flow can become unstable.  These instabilities put a large stress on the engine and in some cases the engine needs to be turned off in order to recover original operation.  For this reason jet engines are currently operated away from their optimal operating parameter values increasing both fuel consumption and the engine weight.

A jet engine can be thought of as a compressor, where the incoming air is compressed by alternating rings of rotating blades and stationary blades.  The mixture of fuel and compressed air is then ignited and the resulting combustion generates thrust that propels the aircraft.  This is better explained by a picture, for a more detailed description of how the engine works click here.  There are primarily two types of instabilities that occur in the flow through the compressor.  They are called surge and stall.  Surge is characterized by large oscillations of the mean mass flow through the engine.  During part of the cycle, the mean mass flow may become reversed, thrusting air out the front of the engine.  This puts a large stress on the components of the engine and seriously impairs its performance.  When stall occurs, there are regions of relatively low air flow that form at isolated locations around the rim of the compressor.  Here too, the phenomenon can be so pronounced that the flow in these isolatedregions is reversed.  Again this causes a large stress on the components of the engine and reduces its performance.

Moore and Greitzer published in 1986 a pde model describing the airflow through the compression system in turbomachines.  Although relatively simple, this model has been surprisingly succesful at predicting experimental outcomes.  Currently Mezic has derived a model of the three dimensional flow in jet engine compressors.  In his treatment, a diffusive term, first introduced by Adomaitis and Abed, is justified and interpreted as the inviscid process of turbulent momentum transport via Reynolds stresses.  It is this model with the additional assumption that the flow has no radial component that we analyze.  In this guise it is called the viscous Moore-Greitzer equation (vMG).

equations of motion

The vMG equation is a nonlinear nonlocal parabolic partial differential equation on the unit circle that is coupled with two ordinary differential equations.  The two odes describe the average flow through the compressor and the pressure rise from atmospheric pressure to the pressure in the plenum.  The pde describes the deviations of the flow from the average flow.  The parameter gamma is called the throttle parameter and this will later be our control parameter.

The majority of previous research in this area has been focused on a simple Galerkin projection of the Moore-Greitzer equation, namely assuming that the solution of the pde is the first Fourier mode with a varying amplitude.  Some simple extensions of this have also been considered, such as projecting onto more than one Fourier mode or a linear combination of Fourier modes.  One exception from this is a control result by Banaszuk, Hauksson and Mezic for the full Moore-Greitzer model.

Asymptotic Dynamics of the vMG Equation:

Our first results in this area were to prove the existence and uniqueness of solutions of the vMG equation.  Furthermore, the solutions are smooth in positive time and the equation has a global attractor.  We found explicit bounds on the fractal and Hausdorff dimensions of this attractor, but as is frequently the case for these type of bounds, they were fairly large.  Analysis of the attractor, or more precise the basic attractor, shows that its structure is not too complicated.  The basic attractor is the smallest part of the attractor which attracts all of state space with probability one.  In simple systems it can be thought of as all the stable orbits in the global attractor.

The basic attractor consists of design flow, surge and one or more stall solutions.  The design flow is a stationary solution and surge is a periodic orbit in the two ode variables, average flow and pressure rise.  Surge, being a two dimensional phenomenon, has for the most part been understood.  Stall, on the other hand, has until now only been analyzed in low order Galerkin projections.

By assuming that stall is a traveling wave one can use Hamiltonian methods to prove the existence of finitely many stall solutions.  In fact, one can find an explicit expression for them and they are given as rational functions of the Jacobi elliptic function ns.   These stall solutions are solitons that rotate around the annulus and depending on the parameters one can have one pulse solutions up to N pulse solutions.  Furthermore, when one linearizes the vMG equation about a stall solution, there exists a beautiful relationship between the resulting linear operator and the Schrodinger equation with Lame potential.  This in fact allows us to find explicitly the first eigenvalues and eigenvectors of the linearized operator as well as characterizing the rest of the spectrum.  It turns out that the stall solutions are stable over a large parameter range and hence belong to the basic attractor.

When one varies the parameter gamma then the shape of the stall cells changes and in fact each stall solution is a member of a one parameter family of stall solutions.  The one parameter family for the one pulse solution is shown in Figure 1.

One parameter family
Figure 1:  On the horizontal axis is the angular position around the annulus, on the vertical one the deviation of the flow from the average flow and on the axis going into the picture we vary a parameter that allows us span the whole family.  Notice that for the most part it is the width of the stall cell that is changing with the parameter, but not its amplitude.
Here we also display a movie that shows how stall evolves from a small disturbance to design flow. Stall evolution (4 Mb)

Reduced Order Model and Control

A primary goal of the current jet engine research is to understand the flow better and to be able to control the flow through the engine.  In order to facilitate this goal, a meaningful model reduction is needed.  The Galerkin approximations have been able to describe many aspects of the flow qualitatively, but not quantitatively.  For this a better model reduction is needed. It has been suggested to make a Galerkin approximation with a more general basis than the Fourier basis and some results have been obtained using multiple Fourier modes.

The disadvantage of making a Galerkin truncation onto a function with a fixed spatial shape is that it doesn't describe properly how the stall cells seem to grow and decay in simulations (see the movie here above).  What one observes there is that the stall cell quickly develops a square like spatial structure with a fixed depth.  This square structure then widens until it stabilizes at a fixed width or it fills the whole annulus of the compressor which goes into surge.  To capture this behaviour with modes of fixed spatial shape one needs to include many modes and the behaviour of the amplitudes of these modes can be very complicated.  A remedy for this is to try to capture the dynamics with a one parameter family of curves.  The parameter then determines not only the amplitude, but also the shape of the stall cell.  We have indeed been able to create a reduced order model in this fashion by assuming that the solution of the pde is given by a function h(omega, t-0.5*theta) where the parameter omega is timedependent.  The equations for this model are

reduced order model

Here subscripts denote partial derivatives and the pointed brackets the inner product.  This model captures remarkably well the dynamics of the vMG equation.  It captures both asymptotic behavior as well as transient behavior both qualitatively and quantitatively.  This would indicate that the one parameter family of stall cells is very strongly attracting.  On the figure here below we see the transient behavior as the engine goes into stall.  The solid curve is from the reduced order model while the dotted one is from the pde simulations.  This shows that the reduced order is quantitatively right on the mark.

stall transient comparison
Figure 2:  The vertical axis is pressure rise and the horizontal axis is average flow.  The blue line shows the compressor characteristic and the green one the throttle characteristic.  Design flow is where these two curves intersect.  We see here how a small disturbance from design flow gives rise to a transient that spirals towards a stall solution.  The solid line is calculated by the reduced order model and the dotted one by simulating the pde.  The aggreement between the two models is strikingly good.
This reduced order model can be extended to include also soliton solutions with more than one pulse in a trivial way.  Dan Fontain and Petar Kokotovic have used a simple approximation to this model, where the one parameter family is approximated by a one parameter family of trapezoidal shapes, with good success.

Armed with this reduced order model, we can now study the dynamics in more detail.  In particular, we can analyze the bifurcations of stall solutions and Figure 3 shows the branches for the one pulse stall solution in the average flow-pressure rise plane.  The parameter that one varies here is gamma.  For a large enough gamma there don't exist any stall solutions.  As gamma is decreased, there occurs a saddle-node bifurcation and two one pulse stall solutions are created, one stable and one unstable.  The branch for the stable one is denoted by the flat blue curve on Figure 3 and the branch for the unstable one is the red dotted line.  The stable stall solution persists for a large parameter range, but collides eventually with another unstable stall solution and vanishes.

bifurcation diagram for stall
Figure 3:  This figure shows the bifurcation diagram for one pulse stall solutins.  The axis are the same as above.  The flat blue curve is the branch for the stable stall solutions and the red dotted curves are the branches for the unstable stall solutions.
In normal operation of the engine, one can make a regulator keep the flow close to design flow, but as we saw in Figure 2, under certain circumstances even small disturbances can cause the engine to stall.  Therefore, a control strategy to recover from stall is needed.  Banaszuk, Hauksson and Mezic constructed a controller for the Moore-Greitzer equation which recovers design flow for any initial conditions.  This controller is however not very cost effective, in particular, it over reacts to small amplitude high frequency disturbances.  Furthermore, they considered the inviscid Moore-Greitzer equation, which is a hyperbolic equation, and it doesn't have the same asymptotic properties as the vMG equation.  Current results by Mezic have shown that the vMG model is a better physical model for the jet engine than the inviscid Moore-Greitzer equation.

Our goal is to construct a controller which is in some sense near optimal by using our analysis of the asymptotic dynamics.  Let us assume for now that the control parameter gamma can only be changed adiabatically.  In this case the basic attractor, which attracts all of state space with probability one, will remain unchanged.  I.e. it consists of stall solutions, surge and design flow.  In this case, any control strategy that doesn't make gamma large enough so that the throttle characteristic doesn't intersect any stall branch while the system is in stall, will fail to recover design flow because the stall solution will persist.  If on the other hand, one makes gamma large enough so that there is no intersection, then all of state space is attracted to the design flow.  Our control strategy will then be to make gamma large enough when the system is in stall or surge so that it we will recover design flow.  This design flow is however at a low pressure rise and one would want to operate closer to the peak of the compressor characteristic.  To obtain this goal, we propose to now track a trajectory from our current design flow setting along the characteristic to our desired design flow setting.  By linearizing the vMG equation about this curve the problem of tracking the problem in an optimal way with respect to quadratic cost can be written as a servo-mechanism problem with quadratic cost.  It turns out that this problem can be reduced to solving five odes to obtain the optimal feedback control.  This control strategy is depicted in Figure 4.

control strategy for stall
Figure 4:  This figure shows the control strategy for the adiabatic control.  First one increases the control parameter so that the throttle characteristic doesn't intersect the stall solution branch, and then one tracks a trajectory along the compressor characteristic to the desired design flow setting in an optimal way.
Although we have now found the optimal adiabatic control strategy for recovering design flow, it is clearly not optimal to restrict ourselves to adiabatic controls.  The transient behavior of the system appears however to die out fairly fast in simulations and in experimets.  This would indicate that when one considers all controls that change the control parameter gamma at moderate rates, the above control strategy should be close to the optimal strategy.  It is in this sense that we claim that our control strategy is near optimal.  A simulation of the vMG with the near optimal control strategy can be seen in this movie.


We would like to thank Petar Kokotovic, Igor Mezic, Andrzej Banaszuk and Dan Fontain for fruitful discussions