Hello my name is Bruno Loyer with Siemens PLM software. Today I will show you the
design exploration features that are available in the LMS Imagine.Lab
Amesim environment. The design exploration module allows you to do
three kinds of things.Firstly you can define design of experiments also known
as DoEs. DoEs are very useful to create response surfaces that could
replace your model. DoEs can also be used to get the respective influences of
your parameters on your design.
Secondly you can do optimizations with two different algorithms, we have the
genetic algorithm that is for the global search of a large design space.
We also have a local gradiant based algorithm called NLPQL and lastly
through the Monte Carlo analysis you can assess the robustness of your design
with respect to uncertainties related to the design variables themselves or to
the operating conditions of your system. In this first video I will show an
example of optimization applied to the use case of an injector model.
An injector system is a complex system because it needs to satisfy precise
requirements in terms of injected quantity but also it is subjected to
tight manufacturing tolerances.
If we simulate this first model, here, we can see that we have the complete
injected volume here,
currently its value is slightly below 40 cubic millimeters and in this example
we'll try to optimize it, such that it reaches the value of 50 cubic
millimeters,
and for this purpose we will optimize some geometrical parameters.
So here I will just hold this curve that is the previous curve before optimization
and now I will define my optimization study. So we assume that our
configuration has already been set, so when opening this window we see that we
have our input parameters, where we can find our geometrical parameters: diameter
of the hole, diameter of the puppet
also the puppet half angle parameter. Here we have a second tab called simple
output parameters.
Those are the responses of your model and both the inputs and the outputs can
be used for defining what we call compounds output parameters that can be
the objective functions that you are trying to minimize as well as some auxilliary
functions that could serve for defining feasibility constraints. So in
this example we have set an objective function to be the absolute difference
between the target value of 50 cubic millimeters and the current injected
quantity,
and here we have specified a difference between the puppet diameter and the hole
diameter that needs to be above a certain value.
Once this is done we can start the design exploration tool.
So here we can see that we have three possibilities for studies, we can either
have DoEs optimization or Monte Carlo. In this first video we are dealing with
optimization so here if I edit this study we have selected NLPQL algorithm
we can change the relative gradient step. We can also add at the final desired
accuracy
knowing that lower value needs to longer optimization times and here we can find
the inputs, the design valuables that we had previously selected, with their
default values lower and upper bands. Here on the second tab we can see the
objective function so here when we tick it we mean that we want to minimize
the difference with the target value that we had specified here we will see
that we have specified alower band for this difference between those two
diameters.
So now we can start the optimization as such and the NPQL algorithm
will compute the best solution within several iterations.
By the way note that the gradient calculation takes benefits from the
parallelization because it is paralize as all other design exploration features:
such as genetic algorithm for which the population is spread out on the many
cause that you might have at disposal either locally or remotely, and also of
course the Monte Carlo studies that we will see in the second video take
benefits from the pultiple capabilities of your machines. Now that the
optimization is complete we can see that we have reached a solution that
satisfies the constraint that we had previously defined and we can see the
value of the objective here and we have a specialized plot here that
for instance we can plot the history plot of our objective function here,
and when selecting a logarithmic scale we can see how far the value of the
difference
absolute difference with our target value for injected volume has been
reached here.
Another nice feature of the optimization is that you can apply these values to
your system through the "Apply best results" function here and we will see
what happens.
Now I restart simulating my model and we will see now that the results are as
expected the value of 50 cubic millimeters is reached.
so this concludes our first video if you're interested in seeing an example
of Monte Carlo analysis then please take a look at our second video