- For the example of using a digital twin in design,
I'm going to briefly discuss
how it's been used in the design of new cars.
I'll also touch on its use in preparing
for autonomous, or self-driving vehicles.
Taking a car from concept to production
has historically taken anywhere from two to five years.
In recent years, through the use of digital technologies,
this time has been quickly shrinking.
In some instances, it has become over 50% quicker.
The competitive nature of the marketplace
and the car market is hyper-competitive,
insists that development life cycles must be reduced.
Rather than just basing new car production
on physical mockups,
3D CAD and simulators have transformed
the automobile product lifecycle.
With the use of data, sensors,
artificial intelligence, and collaboration,
using the terminology of a car digital twin
is becoming more commonplace.
The car marketplace is undergoing significant changes.
First, there are demands for more sustainable designs
that improve performance, use better materials,
and reduce carbon energy use.
Second, there is a trend towards electrification,
so new plans must include hybrid or fully electric motors.
Third, there's pressure to increase value
by slowing the growth of costs and adding more innovation.
Finally, more autonomous features are being included
that range from automated parking
to self driving on a motorway.
A car company today must design
with these areas and more in mind.
The stakes and complexity have never been higher.
Today, car designers and engineers
are placing emphasis on virtual simulations.
Each new innovation alone,
and then as a whole car system,
must be simulated and tested under thousands of scenarios.
A typical virtual design cycle will include
build, test, explore, and assess.
Build is the actual model design step.
Next, it is tested under various simulations.
Then results are explored and modified
based on test results.
Finally, outcomes are assessed.
This cycle repeats.
At some point requirements will be met,
and the design lifecycle will conclude.
Digital twin innovation can optimize this process
by varying simulations, producing high-quality data,
and predicting real world outcomes.
The simulation software can support multi-physics.
This is the study
of multiple interacting physical properties.
Coupling these generates accurate simulations.
Areas include electrical, mechanical, fluid, and chemical.
A car will have thousands of parts,
many subjected to liquids, gases,
heat, friction, pressure, humidity,
and all manner of movement.
Of course, in addition to the vehicle,
the environment in which it will operate
must be tested in parallel.
This will include weather, altitude, turns,
road damage, braking, and more.
Finally, how might the design
of self driving or autonomous vehicles differ in design?
Bottom line, they are far more complex,
and thus, simulators and the digital twin
must be more sophisticated.
In fact, it's digital twin technology
that is largely enabling the rapid pace
of autonomous vehicle innovation.
These cars must be equipped with a lot of sensors
to understand the world around them.
After all, these sensors are replacing humans as sensors,
and also augmenting the experience to be safer.
The digital twin must simulate and help with the placement
and function of these sensors.
It also needs to simulate the data being processed
as if it is operating in the real world.
Dimensions such as how artificial intelligence
will deal with the unpredictable nature of driving
and the impact on the car's behavior,
and how well the car will process and execute a decision.
These must be all emulated.
The future of cars and autonomous driving is exciting,
and also a little uncertain.
But digital twins are helping to support
a rapid pace of innovation.