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For this episode of the “PLM Quick 30,” Ansys expert, Craig Miller, PhD, joins Patrick Sullivan in a discussion to uncover the three pillars necessary to bring a simulated digital twin to reality. Listen to ArcherGrey’s latest podcast to hear first hand.

Contact us if you’re ready to discuss a dynamic PLM strategy.

Transcript

The transcript is close to a literal transcript of the spoken word. Please excuse any grammatical errors, spelling errors or break in the flow. The podcast is a non-scripted conversation with natural flow aimed to deliver value. 

Dr. Miller: VW wanted to enter an all electric, race car and the Pike’s Peak race. So, the heart of the
electric car is going to be the battery. Temperature state of the battery plays into the
state of charge. And then that plays into power delivery that the car needs. A reduced
order model of the air flow around the battery was done. VW not only won the race, but
they set the track record.

Patrick: Welcome everybody to another ArcherGrey’s PLM Quick 30 where we discuss all things
PLM. And there’s been a topic that’s been floating around the industry for a number of
years here. And essentially it’s all around digital transformation. Every conference we go
to, people are talking about it. And recently I was sitting with a client and they were
talking about their top initiatives and one was related to PLM and then the other was
the CIO’s initiative and it was digital transformation. Neither of those initiatives were
connected.

Patrick: So I thought, let’s dive into what this topic really is. Let’s try to define it because it
sounds like everybody has a different perspective. So can I call you Doctor?
Dr. Miller: You may.

Patrick: So we have Dr. Craig Miller on the podcast today as our guest. He is responsible for
digital twin and simulation based digital twin within the FA and D industry, federal
aerospace and defense industry. And he’s been gracious enough to be our guest today
and spread his knowledge so he can help everybody listening to this podcast further
define what digital transformation means within that context. So Craig, thanks so much
for joining today.

Dr. Miller: Great. Well thank you Patrick for inviting me to speak. Really appreciate ArcherGrey’s
invitation to ANSYS. So I’m a principal engineer with ANSYS really excited to talk about
how ANSYS and their simulation tools can kind of connect those dots between the
digital transformation into PLM systems. As you mentioned, I focus on the FA and D
industry vertical, here at ANSYS. For my career and how did I get here while I attended
university of Maryland and Penn State for my engineering degrees, make no mistake, I
bleed blue and white on football Saturdays.

Patrick: Good, good.

Dr. Miller: My career started during the .com frenzy in San Jose. I was analyzing packages for fiber
optic devices. And I started in a classic sense as a frugal grad student. I was predicting
mechanical stresses on optical fibers, piezoelectric electric disks and I was using these
classic elasticity equations and Mathcad and Excel. And pretty soon I went through a
transformation of my own.

Dr. Miller: I found I could use these commercial off the shelf FEA tools that I learned in grad school
such as ANSYS. I could do these mechanical stress predictions 10 times faster. I could
convey the conclusions and ideas to others in the organization a lot easier. I could train
other engineers so we could start working in parallel on different ideas. And I didn’t
have to explain area stress functions to my colleagues. All the commercial off the shelf
FEA tools had their own training software and maybe most importantly, we could test
design changes.

Dr. Miller: Design changes can be driven by anybody in the organization and you have to redo your
analysis at a very quick way. And these commercial off the shelf FEA tools really
facilitate that. So that was really my own personal digital transformation and I think it
lends itself well to digital twins, as we kind of dive deeper into the topic.

Patrick: Thanks for the introduction Craig. I mean that’s a lot of value that you just discussed as
far as your experience of one, learning and then having exposure to the tools and going
through your own digital transformation. I mean, prizing, as long as PLM has been
around, it seems that people continue to struggle with this idea of ROI and you hear
things like speed to market efficiency, increased collaboration, increased quality, but
being able to leverage the FEA tools which resulted in getting work done 10 times faster
and it’s taken care of what used to be manual work, sounds like a pretty compelling ROI.
That’s one of the reasons why you’re so excited about digital transformation and still in
this industry.

Dr. Miller: Absolutely, and I can remember as the FDA tools then maybe five, 10 years later we’re
starting to integrate themselves up into PLM systems such as Windchill. Then you could
do those redesigns almost in an automated way where the designers will be working
with release parts and release products and you could bring those redesigns and test
those in simulation space. I can remember what used to take me probably three or four
days to change the design, update the simulation and so on and so forth, and reapply all
the loads and boundary conditions, what then happened over those next five years was
you could automatically bring in a new part or a new sub-module and it would
automatically re-mesh and reapply the loads and get your analyses done that much
faster.

Dr. Miller: And again, I could do in a morning, it took me several days, previously. So the
advancement of the simulation tools and how they integrate with all the other digital
artifacts of engineering processes, has made life so much easier through the years.
Patrick: So what kind of products have you worked on throughout your career?

Dr. Miller: So a whole spectrum of products. I’ve mentioned some photonic devices to telescopes,
all the way up to a fusion based, nuclear energy reactors. The common thread for me
through all those size scales has been engineering simulation. My personal brand is to
learn and teach, and that’s to learn the product or the system behavior through
simulation and make that learning then in turn useful to others.

Patrick: So I mean this may be a redundant question, but I think it’s worth asking, so can you
explain how you got to be the point person for the digital twin simulation based digital
twin at ANSYS?

Dr. Miller: Sure. So there’s a team really focused on the platform and part of that is the digital twin.
Myself, I’ve been the technical lead for federal accounts for the last several years. Now,
the digital twin has been talked about for over 20 years by various branches of the
armed services for various reasons. But really in the last couple of years, we now have
the computational power as well as the connectivity capabilities. So that would be the
IoT internet of things, capabilities for all the sensor data. And so we have the
computational power interconnectivity capabilities and those two things are now ready
to be integrated to make these digital twin concepts a reality. So it’s been natural that
I’ve been involved in a lot of engagement with the armed services around deploying
these simulation based digital twins.

Patrick: So okay, you said a three letter word here, IoT, right? And so what I started with was the
word digital transformation is being thrown around a lot and oftentimes with those
words are digital thread, IoT, IIoT, Industry 4.0. from your own perspective, is there a
single definition for digital transformation?

Dr. Miller: So the digital transformation to me is a broader goal. So it’s two-fold. One is a business
transformation and two is a cultural transformation. So one, the business
transformation, as you had mentioned, ROI for my adopting FEA tools in an analogous
way, the digital transformation is to look to bring efficiencies into the engineering
processes. And again that can approve the time to market, reduce costs and all these
different KPIs that that people can strive for.

Dr. Miller: Workforce development is the cultural transformation that’s going to be required to
adopt these new digital processes and interconnected digital processes. And ultimately
that digital transformation could follow more of a software engineering approach to
product development. That would be one of continuous insight or continuous oversight
over programs or projects, that really facilitates achieving those goals of the digital
transformation.

Patrick: So you talked about the engineering process side of things, you also spoke of the
cultural transformation of it and then ultimately the software side of it as well. So if all
of that is happening to some degree now, what’s so different when people are talking
about digital transformation?

Dr. Miller: Well, to various degrees, we’ve been doing digital engineering for a long time. Many
tens if not 30 or 40 years. Mostly these tasks have replaced steps in the existing product
development processes that are typically Stage-Gate processes using traditional
documentation, centric processes and piece by piece digital engineering is displacing the
existing again, Stage-Gate process that companies have in place.

Dr. Miller: They require time-consuming design reviews and all the different sign offs that that
need to happen to kind of go through the milestones in that Stage-Gate process.
Patrick: So can you elaborate a bit? I mean how is that accomplished?

Dr. Miller: So one perspective to have continuous oversight rather than having the oversight stay
at milestones or the Stage-Gate, would be to follow the model based systems
engineering. Another acronym MBSE. That MBSE design paradigm provides the
mechanics to achieve the continuous oversight. And that can be done by connecting
your mission system or your product requirements in a requirements management tool
into the use cases. And that would include physical testing as well as the simulation. And
those use cases are used to validate the engineering designs against those
requirements.

Dr. Miller: So the anybody can dive into as deep as they want to at any time that they would want
to and get the “now” state of the product development, rather than having to wait for
one of these milestones or the Stage-Gate design reviews.

Patrick: So, that lends itself into one of the main topics of where we started in this discussion is
talking about the digital twin. So I assume when you’re, well I shouldn’t assume, I’ll ask.
So when you brought up model based systems engineering, can you tell us how digital
twin fits within that type of transformation under the example of MBSE.

Dr. Miller: Sure. Patrick. So you know, it’s important to realize that that digital twin is not static. It
evolves throughout that life cycle. And so the digital twin can extend the connections, so
it can be defined at the design stage and then it can effectively make those connections
from requirements to design. Again, the use cases are used to validate the design
against the requirements and then digital twin would evolve through the manufacturing
and into operations.

Dr. Miller: And once that’s done then you can create the feedback loops from the field back to the
design teams and that can be used for engineering change modification of the existing
systems or to spec out new systems. And then those feedback loops then in turn create
additional efficiencies and value.

Dr. Miller: And we’ve seen just here at the precipice of these pervasive adoption of these pervasive
simulations already a 30% improvement and MRO maintenance reliability operation
cycles by implanting these digital twins into these feedback loops.

Patrick: Now you had brought up a little bit earlier about the transformation. It’s one thing to
implement technology, and a new process for people to follow. And this whole idea of a
simulated digital twin is really intriguing and a lot of our clients talk about it. What about
the adoption side of things? How do you see that fitting in? Because when you say a
30% improvement in an MRO cycle, ultimately I assume people have to adopt it and
embrace it and incorporate it into their processes.

Dr. Miller: Oh, absolutely. So there are foundational building blocks that need to be put in place to
enable the adoption. No doubt about it. And there are three pillars to build that digital
transformation and one is senior level management, sponsorship. Two is a data IoT,
internal data, IoT strategy and three is simulation competence. So to walk through each
of those three your senior level management’s sponsorship is without a doubt a
foundational block for building or deploying or adopting the digital transformation.

Dr. Miller: And because that’s going to require working across business units, silos, divisions
internally, and you definitely need somebody above and can oversee those and drive
the adoption. Also adoptions most likely, you’re going to require partnerships. There’s
no one stop shop for the digital transformation. And being able to identify and develop
those corporate partnerships for a successful adoption is going to happen at that senior
level management. They can also act as mediators to facilitate the cultural internal
transformation.

Patrick: So like a mediator’s … There’s going to be disputes? What’s a mediator?

Dr. Miller: Yeah. And in my experience, we were a adopting simulation about 15 years ago and the
chief reliability engineer was threatened that simulation would displace the testing, AKA
their jobs. So initially they would pass their lack of understanding and threat as a lack of
understanding and they’d spread the disbelief in any of these simulations that were
presented and ultimately that’s resolved by communicating, instilling the purpose of
those simulations in that case, was to increase the confidence and passing the tests. Not
necessarily they’re going to preclude the need for the test.

Dr. Miller: And then to convince them and show them, look, the simulations are in place, if there
are issues and root cause analysis, if there’s a failed test and these kinds of things can be
more readily be done. And again, that can be more efficiently and best suited for maybe
some senior level managers to kind of mediate and discuss the purposes. Because if the
digital transformation is done kind of from the ground up, you’re always going to have
different interpretations and you’ll ultimately kind of butt heads sometimes between
BUs and divisions.

Patrick: I like that. You actually started with that first one because that 30% improvement inMRO cycle, essentially you’re saying, and I’m taking some Liberty here in assuming that
you said that first because really you don’t achieve that ROI if somebody’s not taking
accountability to standardize and implement in a way where it’s going to ultimately be
used, refined and actually deliver the value that they set out in the first place.

Dr. Miller: Absolutely. And there is a curve to achieve that ROI. There’s definitely up front
investment, not just in software and hardware, but in training the people in that cultural
transformation that has to be coordinated with those business transformations. The ROI
happens as those feedback loops are being created, then the ROI just falls in place.
Patrick: All right, so you said three pillars. What are the other two?

Dr. Miller: Sure. So the other two, the second would be a data strategy for the IoT sensor data
that’s going to be used to connect that physical asset out in the field to the digital or the
simulation based asset, that’s being run in the cloud or whatever the infrastructure,
however it’s deployed. There’s 10 gigabits of data coming in from a modern engine
every second. So how to manage that data and how to use that data, really again, there
has to be a strategy and a vision for component of the digital transformation and digital
twin. That’s going to be driven by the specific customer’s business goals.

Dr. Miller: But when we talk about data and IoT strategy we’re combining the low cost sensors,
internet connectivity and the data analytics, which we think, well obviously, ANSYS
thinks simulation can augment and extend those data analytics and really significantly
improve the ROI for the transformation.

Patrick: So you brought up that there’s 10 gig of data coming from a modern day engine every
second and having a strategy around how to consume that data and what to do with it, I
assume feeds into the third building block. But you know, I wanted to ask the question,
when you say a strategy around IoT, can you elaborate on how or what an example of a
strategy is related to a modern day engine? What can be done with the simulation?

Dr. Miller: Sure. So it would be a kind of, I would say prioritizing and optimizing the data that’s
collected and the sensors that are deployed out into the field. So you don’t have all this
superfluous data coming in. It does feed into the third building block as simulation
capabilities.

Dr. Miller: So this includes not only the detailed engineering simulations that ANSYS is known for,
but also integrating these into the system level simulations using a tool like ANSYS’s
Twin Builder. And what that will do is it will integrate the detailed physics through
reduced order modeling in with the embedded software for the controllers, but also it
facilitates the direct connection to these IoT platforms.

Dr. Miller: And once that system simulation is put in place, then you can do optimization of what
sensors that you need to put out in the field. So you might only need three sensors
instead of a hundred sensors. And so that would prioritize and streamline that data
that’s coming in. And absolutely that third pillar, which is a simulation capability, can
feed back into the second pillar for the data strategy of trying to maintain and
streamline that incoming data.

Patrick: That makes a lot of sense. So just for everybody’s benefit listening, we kind of went on a
journey here of talking about how the digital twin fits into digital transformation. And
Craig was mentioning the foundational building blocks around that, first being senior
level management sponsorships. Second, having a data and IoT strategy and then third,
having simulation competence internally to be able to consume that data and actually
do something with it to enhance the development of the product.

Patrick: So thanks for taking the time to explain those pillars. Since you brought up simulation
capabilities, why is it valuable? I mean, just in your explanation of ANSYS Twin Builder it
sounds like there’s a lot of value certainly in doing simulation, but why digital twin?

Dr. Miller: So let’s use the example of changing the oil in a car. And we can use that to convey this condition based maintenance, which would be one reason for using a digital twin. The
oil industry likes to promote to just blindly change your oil every 3000 miles. Well,
maybe it’s more or less often depending on your driving conditions that could be
monitored, recorded and analyzed. Those data analytics, just using that data alone, use
the historical data, statistical methods to generate insights, provide the driver with that
most efficient oil change interval.

Dr. Miller: Now that would be a reason of using the digital twin. But if you’re just using those
traditional based analytical methods to analyze that data that that kind of traditional
digital twin has a few challenges. And so you know, to rattle those off. I can think of
four.

Dr. Miller: One would be, how long do you have to acquire data before it’s useful? In the field,
we’ve found that’s usually six or 12 months. A second would be, what if the data doesn’t
have enough context? So for example, what if you’re driving in Alaska and there’s no
data from anyone driving in Alaska. How do we extrapolate out from the data that’s
being recorded to provide that driver with a more efficient oil change schedule? And
third, what if there are issues? What if we find that more and more cars are changing
the oil more and more often? What corrective action can be taken. And forth, what if
there’s not enough data or if the critical data that’s required, if it just can’t be
monitored? How are we going to obtain that data?

Patrick: And so what do you do in those scenarios?

Dr. Miller: So if you can augment that digital twin with computer simulation and that simulation
based digital twin, again it connects that IoT sensor data coming in from the physical
asset to the simulation based digital asset, then you can overcome those. And you just
think about these four topics or four limitations that we listed.

Dr. Miller: So first your physics-based models, most companies have those in their design teams
and they can be reused. Various scenarios covering that operational space can be
simulated, which can reduce the amount of field data required before actionable
insights can be made. So instead of six or 12 months, you can significantly reduce that.
Maybe it’s just one or two months. So that would be one limitation. You could address.

Dr. Miller: A second, once that simulation based digital twin is in place, it can be used to extend
that operational space. So going back to Alaska, you can simulate those cold weather
conditions that may not be initially in that operational space. And you can extend that
very easily with the simulation. So that limitation can be addressed. The third limitation,
was what happens if the cars need more and more oil changes? What kind of design
changes can be validated and then deployed? So once that simulation digital twin is in
place, you can explore those design changes, validate those and then go ahead and
deploy those. And keep in mind that could either be a hardware or a software change,
to be deployed out in the field. So that would be a third limitation that could be
addressed.

Dr. Miller: And the fourth is one of the key takeaways for a simulation based digital twin. And
that’s a virtual sensor. So you would use simulation to make these cross correlations
between the sensor data that you need to feed in maybe remaining useful life
calculations, and the field data that’s being acquired. So a lot of times the data that you
need and monitored has to happen at a specific location. But for example, let’s say the
motor oil is it determined by a temperature. At a specific point on the connecting rod or
the piston.

Dr. Miller: Well attaching and connecting sensors to these moving parts really isn’t feasible. Maybe
the remaining useful life is a pressure, the back pressure that again you just can’t
measure reliably. But the temperature can. So you could set up a simulation to create
that cross correlation between what can be measured i.e. the temperature at specific
locations on the engine and the data required, which might be pressure or temperature
at these hard to reach locations. And then those cross correlations can be made to feed
the calculations for our remaining useful life, let’s say of the oil and the crankcase at this
for this example. So again, virtual sensor is a key concept for the value of the simulation
based digital twin.

Patrick: Thanks for walking through the explanation because, well I learned a lot just in the
discussion, first off. And then secondly, a lot of clients are trying to … Well for the clients
that hear the word IoT, they’re trying to figure out what strategy to implement, to take
advantage of IoT because they’ll hear different aspects of I guess what is fully defined as
digital transformation from a PLM perspective of IoT and the overall digital thread. And
ultimately what we’re looking to accomplish is how can we help our clients develop a
strategy that they can implement to take advantage of this digital transformation? So
ultimately the digital thread is providing that information. So related to the digital twin,
are your clients starting to create digital twins just with what they have in place today?
How do you ultimately end up creating digital twins?

Dr. Miller: Oh, absolutely. There are several several examples out in the field now. As you start and
as these digital twins are created as somewhat of an oxymoron because you really have
to have your end state in mind. And that end state is typically very complex and
complicated. You have to have that end state in mind. But on the other end you need
start simple and build that complexity into your system.

Dr. Miller: And to start, we have kind of a three step approach to build and deploy the digital twins.
And that’s assembly, validation and deployment. And so the first is the assembly. We’ve
mentioned the ANSYS Twin Builder where we can integrate embedded software ROMs,
resenting the physics behavior.

Patrick: So I don’t know if you know this, I’m in business development. Okay, so when you say
ROM, I think rough order of magnitude, what’s the level of effort? Is that what you’re
talking about or,

Dr. Miller: Yeah. Thanks for grounding me Patrick. I guess submersed in the digital twin, but ROM
in this context is a same acronym, but it’s a reduced order model. And that reduced
order model is a quick runtime representation of the asset behavior derived from the
detailed engineering simulations. AndPatrick: Yeah, that’s a little different than a rough order of magnitude.

Dr. Miller: Sure. And so for example, a robotic arm might have a structural ROM, or a reduced
ordered model representing the kinematics and the inertia, and the applied forces
among the arm and the grippers and the object being manipulated. The motors, if
they’re electric, they could have power supplies and switching electronics and the
embedded software for the controllers. You would assemble all these artifacts together
logically in a circuit schematic representation. And that would be the assembly of the
system. And that would be your first step to create the digital twin.

Patrick: Okay. So I’m glad I asked. All right. So that’s the first step.

Dr. Miller: And the second step is validation. Just like in any simulation when you create it, you
need to validate it. You need to test it. And this entails co-stimulation, model in the loop
software in the loop. And for those just think of tuning control parameters, are really
what they’re done. So you’re comparing that to a test data and deploying parametric
sweeps to tune those control parameters. And these are the kinds of capabilities that
are needed for that second step.

Dr. Miller: So you assemble, you validate, and that third step is to deploy. And that deployment is
really where the connection to the IoT platforms is going to happen. And ultimately, you
need that simulation. Digital twin, again, that’s a simulation of the system that needs to
happen as a standalone executable out on the IoT platform. Because it’s not feasible for
running these out in the field to, to require connections back into wherever their
clusters or their clouds are, that are being used to run these engineering and system
level simulations.

Dr. Miller: You need to be able to create a standalone executable that can plug into your IoT
platform. Okay, and that’s exactly what the ANSYS Twin Builder does. Those platforms,
PTC ThingWorx and SAP Leonardo would be examples of these IoT platforms. Those
provide the user interface and they have dashboards for system operators. They
integrate with it, data analytics, artificial intelligence, and they execute that digital twin
executable quote, under the hood.

Patrick: All right. So now I feel like I’ve got a decent background or foundation on a simulated
digital twins. All right. So it always helps me to put some real life examples in place. Can
you talk about any?

Dr. Miller: Sure. We’ve got three, maybe for the sake of time I focus on one. But regardless, the
first one, VW. They wanted to enter an all electric race car in the Pike’s Peak Race. And
for you know, the heart of the electric car is going to be the battery and how big and
heavy the battery is. It needs to store enough energy to go all the way up the mountain.
It needs to have the power delivery for the acceleration when it needs it to come out of
the turns and so on and so forth. And so you could imagine looking at the airflow around
the car plays into the down force on the car, but it also plays into the cooling, the air
cooling of the battery. And that is significant because the temperature state of the
battery plays into the state of charge.

Dr. Miller: And then that plays into the power delivery that the car needs. And we were able to
integrate all these aspects together. Again, a reduced order model of the airflow around
the battery was done so that you weren’t having to run these fluid simulations. It might
take days to run. You could encapsulate the behavior of different airspeeds in different
flow regimes around the battery and factor in its temperature state and how it performs
to drive the motors in the car.

Dr. Miller: And that can be done once and now that reduced order model can operate in seconds
as you look at your control systems and you do your multidisciplinary optimization of all
these aspects of the car. And ultimately, VW not only won the race, but they set the
track record. They reduced it by 15 seconds from eight minutes. And it was quite an
accomplishment of, not only the VW team, but also how ANSYS and it’s a digital twin of
the automobile, really our teams were able to work together and really accomplish a
remarkable goal.

Patrick: I mean, that’s a great example. I mean, especially with huge results. A lot of folks don’t
get the publicity around winning a race and setting a course record. So having that as an
example and a model for justification as to the results that can deliver. I mean that’s a
huge accomplishment. Congratulations.

Dr. Miller: Absolutely. Thank you.

Patrick: Yeah, so well, I mean I’m just looking at the time check here. I mean we’ve covered such
great topics associated to all of this around digital transformation, and simulated digital
twin and we just capped it here with the example with VW. So thanks for running
through everything. I’m sure there’s a lot of different areas that we could dive into.

Dr. Miller: Maybe we can dive deeper at a followup podcast on some different applications. To me
the future of the digital twin, I mean currently the digital twins are being adopted and
deployed for, I would say simpler systems. Not to say a car is a simpler system, but
again, we were looking at the battery and airflow. And these low hanging fruit are really
where companies are being able to adopt the digital twin.

Dr. Miller: Weaving that digital twin into that digital thread or digital tapestry as some are calling it,
that’s going to require that digital twin to really integrate into the PLM and ERP systems.
And I think that’s kind of where we started our discussion. And really that’s kind of the
next step because to create those feedback loops and that evolving digital twin that we
talked about, is really going to require connecting the simulation into the PLM systems
for configuration management, version control, all these aspects that people rely on
PLM systems for.

Dr. Miller: And longterm future of the digital twin? It’s really exciting concepts such as uncertainty
quantification. So we can try to really start looking at defining what are the required
levels of fidelity for the underlying simulations? That’s really interesting as well as
autonomous simulation driven by artificial intelligence. Hopefully they won’t completely
remove the human in the loop where I’ll be the chief reliability engineer was 15 years
ago. But those kinds of projections and speculations are always fun to, to kick around.

Patrick: I mean as everything is going digital and organizations are transforming, as you had
mentioned with the simulation aspect of everything, one, there’s a whole bunch of
efficiency to be gained by doing it and doing it well. And especially with this idea of
autonomous vehicles, there are so many different scenarios that need simulation. I
mean, it’s becoming a point where it’s needed versus the alternative of doing the
physical simulation, before something goes out to the market. So you’re involved with
some exciting stuff and very grateful that you decided to spend some time with us and
not only educate me, but educate everybody that’s listening to this podcast and I’m
looking forward to hearing all the questions and comments that come from this. So
Craig, thanks so much for taking the time to spend with me.

Dr. Miller: Oh, well you’re welcome. And again, I really appreciate the invitation. I hope my passion
for the topic comes through and I look forward to maybe fielding the questions with you
and seeing how it evolves.

Patrick: So if somebody wants to reach out, how can they follow up with you?

Dr. Miller: Sure. Yeah. Electronically is the easiest, most efficient. It’s Craig, C R A I G.Miller@ A N S
Y S.com and for further research, ansys.com are systems products really encapsulate
some of the things we talked about as well as the detailed physics solvers. There are
electronic publications that are available on the website as well.

Patrick: So Craig, any final words before we end the podcast?

Dr. Miller: Well, thanks again for the invitation. You know, Accenture, their research found that
90% of A and D companies believe they’ve entered an era of exponential change. And I
look forward to discussions and participation in that evolution, especially as ANSYS and
the digital twin. I’ll definitely be playing a big role.

Patrick: Oh, I have no doubt that they will. And well, I look forward to collaborating with you on
it Craig. Thanks so much, I appreciate it.

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