Modeling a Periodic Heat Load
Walter Frei February 16, 2015
One of the more common questions we are asked is about the modeling of periodic, or pulsed, heat loads. That is, a heat load that turns on and off repeatedly at known times. Modeling such a situation accurately and efficiently in COMSOL Multiphysics is quite easy to do with the Events interface. The techniques we will introduce are applicable to many classes of time-dependent simulations in which you have changes in loads that occur at known times.
A Brief Introduction to Time-Dependent Simulations
First, let’s take a (very) brief conceptual look at the implicit time-stepping algorithms used when you are solving a time-dependent problem in COMSOL Multiphysics. These algorithms choose a time step based upon a user-specified tolerance. While this allows the software to take very large time steps when there are gradual variations in the solution, the drawback is that using too loose of a tolerance can skip over certain transient events.
To understand this, consider the ordinary differential equation:
where the forcing function f(t) is a square unit pulse starting at t_s and ending at t_e. Given an initial condition, u_0, we can solve this problem for any length of time, either analytically or numerically. Here is the analytic solution for u_0=1:
In the above plot, we can observe the exponential decay and rise as the forcing function is zero or one. Let’s now look at the numerical solution to this problem for two different user-specified tolerances:
The numeric solution (red dots) is shown for a relative tolerance of 0.2 and 0.01 and is compared to the analytical result (grey line).
We can see from the plot above that a very loose relative tolerance of 0.2 does not accurately capture the switching of the load. At a tighter relative tolerance of 0.01 (the solver default), the solution is reasonably well resolved. We can also observe that the spacing of the points shows the varying time steps used by the solver. It is apparent that the solver takes larger time steps where the solution changes slowly and finer time steps when the heat load switches on and off.
However, if the tolerance is set too loosely, the solver may skip over the heat load change entirely when the width of the heat load gets very small. That is, if t_s and t_e move very close to each other, the magnitude of the total heat load is too small for the specified tolerance. We can of course mitigate this by using tighter tolerances, but a better option exists.
We can avoid having to tighten the tolerances by using Explicit Events, which are a way of letting the solver know that it should evaluate the solution at a specified point in time. From that point in time forward, the solver will continue as before until the next event is reached. Let’s look at the numeric solution to the above problem, with Explicit Events at t_s and t_e and solved with a relative tolerance of 0.2 (a very loose tolerance):
When using Explicit Events, the numerical solution — even with a very loose relative tolerance of 0.2 — compares quite well with the analytical result. Away from the events, large time steps are taken.
The above plot illustrates that the Explicit Events force a solution evaluation when the load switches on or off. The loose relative tolerance allows the solver to take large time steps when the solution varies gradually. Small time steps are taken immediately after the events to give good resolution of the variation in the solution. Thus, we have both good resolution of the heat load switching on or off and we take large time steps to minimize the overall computational cost.
Now that we’ve introduced the concepts, we will take a look at implementing these Explicit Events.
An Example from Heat Transfer
We will begin with an existing example from the COMSOL Multiphysics Model Library and modify it slightly to include a periodic heat load and the Events interface. We will look at an example of the Laser Heating of a Silicon Wafer, where a laser is modeled as a distributed heat source moving back and forth across the surface of a spinning silicon wafer.
The laser heat source itself traverses back and forth over the wafer with a period of 10 seconds along the centerline. To minimize the temperature variation over the wafer during the heating process, we want to turn the laser off periodically, while the heat source is in the center of the wafer. To model this, we will introduce an Analytic function, pulse (x), that uses the Boolean expression:
(x<2)||(x > 3)
to evaluate pulse (t) to zero between t=2-3 seconds, and one otherwise. The Periodic Extension option is used to repeat this pattern every five seconds, as shown in the screenshot below.
We can use this function to modify the applied heat flux representing the laser heat source, as illustrated below:
The settings for the applied heat flux boundary condition.
The last thing that we need to do is to add the Events interface. This physics interface is found within Mathematics > ODE and DAE interfaces when using the Add Physics browser. Within the Events interface, add two Explicit Events with the settings shown below to define a periodic event starting at two and three seconds and repeating every five seconds.
The Explicit Events settings. The second of these events starts at 3 s.
No other changes are needed, but we can take a quick look at the solver settings:
The settings for the time-dependent solver.
Note that the entries in the Times field are the output times. These settings do not directly control the actual time steps taken by the solver. The Relative Tolerance field (default value of 0.01) along with the Events — if they are in the model — control these time steps.
You can compare the results of this simulation to the original model to see the differences in temperature across the wafer. With a periodic heat load, the temperature rise is more gradual and the temperature variations at any point in time are smaller.
We have looked at using the Events interface for modeling a periodic heat load over time and introduced why it provides a good combination of accuracy and low computational requirements. There is a great deal more that you can do with the Events interface — if you would like to learn more, we encourage you to consult the documentation. An extended demonstration of the usage of the Events interface is featured in the Capacity Fade of a Li-ion Battery example from the Model Library.
On the other hand, when dealing with problems that are either convection dominated or wave-type problems (e.g., fluid flow models or transient structural response, respectively), then we would not want to introduce instantaneous changes in the loads. The reasons behind that — and alternative modeling techniques for such situations — will be the topic of an upcoming blog. Stay tuned!