Hello my name is Bruno Loyer with Siemens PLM software. In a previous video I had
demonstrated an optimization with LMS Imagine.Lab Amesim 3 00:00:23,050 --> 00:00:30,130 built in design exploration tool. Now in the second video still based
on our injector system,
I will demonstrate a Monte Carlo analysis done on this injector system.
Coming back to our injector model, we will assume that it has been optimized
using LMS Amesim optimization features.
We will now study the variability of its performance with respect to the
variability of two diameters: diameter A and diameter Z. So we will start our
design optimization tool here, as in the previous video, but now we have defined a
MonteCarlo analysis. When we open this design exploration study we can see that
we can find several parameters number of runs here thousand, the sampling
method, here we have selected Latin Hypercube which is a good compromise,
and here we see that as controls parameters we can find diameter A and
diameters Z. We assume that these parameters were following a normal law,
which means a Gaussian distribution and we can see here the mean values and
standard deviations associated with these
normal laws. And then we can see the responses of our model and here we will
specifically take a look at pre-injected quantity and main injected quantity that
are crucial especially in the case of multiple injections.
So here, we are able to start the optimization by clicking on this button.
Now that the Monte Carlo analysis is complete we can see that we dispose of
various kinds of specialized plots that can be used for instance histograms here
but we also have scatter plots history plots and response surfaces.
So for instance we can check that diameter A was indeed following
normal law. And here we see that indeed the distribution is very similar to a
Gaussian.
We can also plot several scatter plots and while doing so on the quantities of
interest,
meaning the pre injected quantity and the main injected quantity, we can see
the correlations between these quantities and diameter A or diameters Z.
And what actually is very clearly regarding the pre injected quantity is
that it is very well spread over the many runs which shows that it is
difficult to control especially by mins of diameter A, whereas some clearer
correlation seems to appear with diameters Z.
However we can see that the main injected quantity seems to be more
controllable with diameters Z and to a lesser extent with diameter A.
So this concludes this second video, and what needs to be retained from the design
exploration features of LMS Amesim is that you can define DoEs to get a
better understanding of the physical phenomena that are being put into play.
You can also optimize your desig,n and you can use Monte Carlo analysis to
assess its robustness. Thus engineers can discover and rectify sensitive element
in their design, I would even say
failure prone elements. Also engineers can proactively suggest improvement
directions and also it is possible to manage costs by balancing functional
tolerances with system operation. And lastly I would say that with the design
exploration you can handle the trade off between the pure performance of your
design and its robustness. Last but not least 49 00:05:01,820 --> 00:05:05,780 note that all the design exploration features that we have demonstrated in
these two videos can be used
offline meaning without even opening the LMS Amesim GUI. We have powerful
API's for that
available in various scripting languages and also these powerful features can
then use on high performance computing.
Thank you for watching and for more information please visit our website