Model-based systems engineering
is a holistic approach to product design
based on the definition of requirements.
All design and analysis steps target the requirements,
which are also used to validate the final design.
Maintaining traceability between requirements,
analysis, and verification through the digital thread
enables continuous performance targeting.
It greatly simplifies the process
for reviewing and modifying designs,
or even reacting to changes in the original requirements.
In this video, we will see the end-to-end process
from defining requirements
in the systems engineering environment,
to launching a multi-domain design exploration study
to target those requirements, all using Siemens tools.
The first step in this process
is to allocate requirements defined in Teamcenter
to the relevant system component.
Here we see this done
in the Teamcenter system modeling environment
using drag and drop from the integrated Teamcenter browser.
Ed, the systems engineer,
has now built out the component further,
linking other requirements
and defining design parameters and ranges.
This provides a holistic systems engineering view,
and we can see that a number of design parameters
are needed to describe the brake discs as the outputs.
Some analysis is needed
to select the brake disc characteristics.
So Ed selects the relevant system parts
and the requirements being targeted
and creates a new optimization study
to explore the possibilities.
Ed sets out the scope of the study
by selecting the parameters he wishes to vary,
reviewing ranges and resolution,
and focusing on the geometry of the cooling channels.
In this step, Ed uses the requirements
to set objectives and constraints of the study.
This allows rotor temperature, braking distance,
and system cost to be minimized
while meeting the requirements on all three parameters.
With the optimization study defined,
he passes it to his colleague,
Sam, a simulation engineer, via a workflow in Teamcenter.
This ensures that Sam has all the data she needs
to build the study
while also providing traceability for other stakeholders.
Sam opens her task from the Teamcenter Homepage
and familiarizes herself with the task.
System architecture, requirements, parameters,
and verification activities are all connected.
By reviewing the associated diagrams,
she understands which simulation models are needed
to explore and verify performance of the breaking system.
After a final review of the study parameters,
she opens the Simcenter MDAO environment
directly from Teamcenter
to begin setting up the analysis workflow.
The study parameters are automatically passed to Simcenter
and identified as variables and responses.
Sam adds a simulation step using Simcenter
using a parameterized 3D thermal model
of the rotor and caliper.
The model is automatically inspected
and input and output parameters extracted.
The next step is to connect these
to the study parameters and responses.
She searches for the relevant values in the simulation model
and tags them accordingly.
To calculate the overall cost,
she adds a second step to the workflow
using an Excel spreadsheet.
This function takes the mass calculated by Simcenter
and computes a cost metric,
which is also included in the study responses.
Once fully set up,
the MDAO project is saved back to Teamcenter.
Next, Ed will execute
the optimization study created by Sam
and review the simulation results.
He runs the study interactively,
which provides him with continuous feedback on progress.
Simcenter's intelligent optimization algorithm
explores many different designs
to learn the relationships between variables and responses
while driving towards the study objectives.
Back in Teamcenter, Ed can immediately see
that the best design found provides output parameters,
which are all within their targeted ranges.
And these are automatically verified
against the requirements originally defined.
A benefit of this fully integrated and traceable approach
is that input and output parameters
are tied to a particular design revision.
If any changes are made elsewhere in the design
which impact on this component,
it is fast and easy to repeat the analysis
and optimization with updated data.
In this case, the product cost requirement
is reduced from $6 to $4.50.
This is below the cost of the current design
so the optimization study needs to be repeated
to find an alternative design candidate,
which is cheaper to produce.
Once the requirement is changed,
it is flagged as Unsynchronized in the optimization study.
Ed synchronizes the requirement and parameter
and looks for cheaper design candidates
by expanding the parameter ranges
used in the optimization step
based on insight gained from the previous design iteration.
Now, the second optimization can be executed
by reusing the existing study setup.
Ed launches the study for a second time,
and now we see the optimizer
following a different path
to find designs meeting the updated,
more stringent requirements.
Back in the system modeling environment,
Ed uses the integrated Teamcenter viewer
to access the latest optimization study
and the resulting design parameters.
This information and the status of the verification
is visible to all stakeholders.
In this case,
all requirements were passed by the design selected.
Ed returns to the system model
and synchronizes the design parameters
with the latest verified values.
His model is now up to date.
In this workflow, we have seen how design, analysis,
and performance targeting can be conducted
in a joined up way using MBSE.
Central to this methodology is Teamcenter,
maintaining full traceability of inputs and outputs,
revisions, and verification status at each step,
and orchestrating the modeling and simulation tools
shown in this case with the Simcenter portfolio
of simulation and test solutions.
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