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In early 2020 the world changed considerably, and students, professors, and the learning/teaching communities were among those most affected. Many learning labs were closed worldwide with no notice, interrupting engineering educations. OPAL-RT has a long history and investment of time and cooperation with the educational community, and we were able to marshal our resources in short order to assist.

In Fall 2020, a Virtual Lab pilot program was launched, consisting of OPAL-RT courseware, to help mediate the physical distancing constraints as a first step–but also to help implement the concept of reverse teaching, namely flipped classrooms and labs. In this context, that means applying hands-on practice sessions to the classroom portion of the course, while assigning readings and other classwork to students at home.

It has long been a cornerstone of pedagogical theory that we retain a great deal more of what we actively participate in when learning. The Cone of Learning, below, illustrates these concepts.


In practice, what this looks like is: students do their lab sessions first, in the safety of their own homes, on their own computers, and at their own learning pace—and allow themselves in the process to make mistakes, break virtual fuses, etc. Once they are later in front of the physical test bench, they know exactly what to expect as a result, and how to deal with the hardware they have at hand.

OPAL-RT has been developing courseware since 2014 and incorporating these principles. We started initially with our suite of Power Electronics courseware. Then in 2017, we pooled our efforts to team up with Professor Viarouge and his students from Laval University in Quebec, Canada, to further develop courseware in Electric Motors and Power Systems. It should go without saying that these efforts are win-win for all parties, as we learn more about how students themselves learn, and they are able to avail themselves of world-class real-time interactive simulation concepts in action and solvers/platforms. (This is as opposed to the offline non-interactive simulation they might usually have access to in classrooms and labs.) Over the course of the pilot program, the professors adopted our courseware, adapting it to their needs, and developing new experiments, and sharing that further development with us.

Five universities and colleges have so far participated in our pilot project: Laval University and Collège Montmorency in Quebec, École Navale in France, École supérieure d’ingénieurs de Beyrouth, and the Lebanese University in Lebanon.

For more on participating in the program, please see below.

Testimonials

Prof. Philippe Viarouge

“When we designed this interactive courseware on electric motors in 2017 in collaboration with the OPAL-RT team, we never thought that two years later, we would be using it in the context of distance education imposed by the health crisis. From September to December 2020, a pilot experiment was conducted in the Electrical Machine course of the BSc program in Electrical Engineering at Laval University in Quebec City with 33 students. Everyone had access at home to their personal virtual laboratory to perform their laboratory experiments and their own investigations for the assimilation of learning.

I systematically used the virtual laboratory during the Tutorials of synchronous virtual classes in a reverse teaching approach. Several exercises in the written exams were based on analyzing performance and identifying specific training operating points from images of the adjustment and instrumentation panels. Finally, 33 one-hour individual laboratory examinations were carried out entirely remotely by videoconference with the teacher. On the occasion of the success of this experience which elicited unanimous comments among the students, I discovered with a certain enthusiasm in this versatile educational tool the possibilities of use and assimilation that I didn’t expect during its initial conception. This generic concept constitutes a powerful tool at all levels of training and in many fields.”



Prof. Flavia Khatounian

“The obvious advantage of this courseware is the concrete comparison with the lab experiments that the students would usually carry out in the laboratory and on many levels: protection against excessive current draws, variable and reduced voltage power supplies, different assemblies achievable by simple changes of connections… This gives a certain autonomy to the students who thus prepare themselves by being more at ease since handling errors are detected and interrupted without damaging any equipment.

Finally, given that the exercises are provided with a lot of details and explanations as well as with the course concepts covered in each simulation, this gives a relatively complete overview for my 22 students.”







Prof. Jean-Frédéric Charpentier

“Even if we give the course face to face at the naval school, these courseware represent an excellent advantage because they constitute the intermediate step between the theoretical course and laboratory sessions on real benches. In addition, our 80 students are familiar with the concept of simulation, using boat navigation simulators. Why not extend this same concept to electric machine laboratories? Finally, with the courseware, we can push the systems to their limits and perform experiments that we would not do on real machines.”






Prof. Rita Mbayed 

“Virtual labs have been of great importance to us in this time of pandemic where access to the real lab has not been possible. The experiments are clear, easy to handle, well-focused, and fit perfectly with the goals of the Electric Machine Course. With that, it is obvious that this virtual lab will have its place in my course from now on.”











Mr. Sylvain Brisebois

“The OPAL-RT solution will be used to add a dynamic component during the theoretical course sessions. Students will also be invited to install the courseware on their personal computer in order to carry out laboratory preparations. This is a solution that comes just in time in the current context of physical distancing but definitely will be used even later on too.”







If you are interested in participating in the extension and expansion of this pilot project, please communicate with Dr. Danielle Nasrallah (danielle.nasrallah@opal-rt.com), manager of the pilot project and developer of much of OPAL-RT’s courseware. 


Danielle Sami Nasrallah received an Engineer’s diploma in electromechanical engineering and a Diplôme d’Études Approfondies in electrical engineering from École supérieure d’ingénieurs de Beyrouth (ÉSIB), Beirut, Lebanon in 2000 and 2002, respectively, and a Ph. D. degree in Robotics Modelling and Control from McGill University, Montreal, QC, Canada, in 2006. During her Ph. D. studies she worked on a part-time basis at Robotics Design as a control and robotics engineer. She moved to Meta Vision Systems in 2006-2007 as a control and applications engineer. In 2008 she joined the electrical department of the Royal Military College of Kingston as an assistant professor and, in 2009, she was a visiting assistant professor at the American University of Beirut. From 2010 to 2014, she worked as a consultant in control and systems engineering. In 2014 she joined OPAL-RT Technologies where she is presently a technical lead in control and intelligent mobility.  She retained links with academia as she lectures in Robotics and Control at both Concordia and McGill Universities.

Michael Grieves first uttered the term ‘digital twin’ in 2003, and since then much ink and many pixels have been spilled over it. We’ll keep it relatively brief. While ‘digital twin’ can mean many things to many people, it has a more restricted set of understandings when we speak in the context of real-time simulation and/or Hardware in the Loop (HiL) testing.

To begin to explain this closer-cropped meaning, let’s start by thinking of a mirrored copy of a hard drive. It’s a periodically-updated copy of a hard drive in current use, intended to be redundant if/when required. The contents of the mirrored drive can be validated as a duplicate of the source drive at any given time. So it’s a four-dimensional protective mechanism (length/width of data, bit depth, plus the dimension of duration, or time) of a hard drive. Yet already this metaphor lacks the complexity required to outline everything we need to speak about here adequately.

The Iron Bird in the Digital Era

An additional metaphor may serve us better here. In the aircraft design and engineering cycle, which includes MEA (More Electrical Aircraft), a concept exists known as the ‘iron bird’—an integration test rig. All the systems and subsystems of an aircraft are assembled, laid out on the floor of a hangar, say, so that the entire plane is, in essence, operational, except the chassis itself–but it is not physically in the air.

Now to clarify: the iron bird, at the time of its inception in circa 1985, was a hybrid physical/simulated plane during a time where planes evolved exponentially more numerous and more complex systems. (Reasons for more in-air tests as these ‘systems of systems’ grew not being more common should be obvious: massive expense, countless permutations and combinations, huge development times, loss of life, etc.)

All the constituent pieces of this assemblage are validated, and are receiving live stimuli and issuing reactions and outputs as though the plane itself were flying–but interactions with engines, landing apparatus, wing flaps, etc. may or may not be virtual, depending on the phase of Verification and Validation (V & V). The iron bird testing phase must be as good (validated, accurate, entirely reproducible) as ‘real life’ i.e., as a real-world flight—there is very little to no ‘wiggle room’ in aviation–or this testing phase and concept serves no purpose.

The use of iron birds for aerospace V&V, where physical components are partly replaced with digital/virtual parts and through using real-time simulation—again, depending on the V&V phase—allows aircraft manufacturers and their equipment developers to save vast fortunes of money on expensive prototyping. Though of course, at the end of the day, virtual models need some more extensively validated dynamics and response validation before they are ready to jet you off to your next sunspot vacation.

A Digital Twin: A Great Deal More Than Just a Simulator

Now, if we add some other elements to the duplication/redundancy notion (see: the mirrored copy of the hard drive), and add some levels of complexity, interchangeability, and communications (see: the iron bird), the functioning of what we currently mean by ‘digital twin’ in the context of real-time simulation (or HIL) begins to be clearer.

If we were to break it down functionally by what it does, is able to do, and at what it conceptually excels in our current reading:

• The digital twin can read data to/from its physically operating counterpart and report on itself via its hardware/software surroundings to an overseeing entity—for maintenance, for logging, for reporting, for control, etc.
  • Meaning: the digital twin—itself virtual–has a bridge of connectivity with the ‘real-world’
• The digital twin has a profile of its own in the form of a dynamic model and related parameters, and it is  adjusting itself, in close comparison, with its physical counterpart in real-time or ‘near real-time’
  • Thus, it is self-adaptive
• The digital twin is able to describe its inner components’ interactions in much greater detail than its physical counterpart can, since the latter has a limited amount of sensoring
  • Thus, it can act as a one-step removed, in-depth observer
• The digital twin can respond to the same stimuli as its physical counterpart and help identify abnormal operations, malfunctions and identify the source of the problem
  • Thus, it is a situationally-aware and decision-supporting tool
• The digital twin can respond to synthetic stimuli and help in obtaining what-if scenario analysis that cannot be analysed at will through its physical counterpart due to practical concerns. In this way, it will differ from a simulation (or real-time simulation) as it is a validated dynamic replica of its physical counterpart
  • Thus, it is extremely powerful as a predictive tool, and can be used additionally as a design and planning tool

Digital Twins: The Current State of Affairs

As a real-time simulation afficionado, the potential uses should be becoming clear to you by this point, but to give some genericized examples of current uses:

• An electrical car manufacturer can accumulate vast volumes of data on its vehicles during their operation. The data is pivot-table ready to the extent that it can be logged, viewed, and analyzed by driver type profile, geographical location of car, and many other slants or viewpoints on the data that may unearth valuable and/or useful results. (One of ‘big data’s’ chief strengths is the myriad of angles from which it can be read, each new angle potentially offering new insight.) Through this, the manufacturer can send notifications to the local garage, or to the end user, when a Battery Management System has unexpected behaviour, for example, or for engine part replacement.
• An electric utility can learn about usage/consumption patterns through this combination of logging/reporting, AI, and vast amounts of data to automate what is now called Demand Response, through adjusting residential boilers and heaters to ensure sufficient spinning reserve or stable operation.
• In the near future, predictive maintenance where—before failure has even occurred in a consumer good—a potential future breakdown can be predicted, all the possible routes towards a positive outcome can be analyzed, and action can be taken, before the user is even aware anything untoward has happened.

Into the Great, Wide-Open Future

We’re on the cusp of this particular combination of present and future-looking enhancements that can be applied across many categories for the benefit of design, prototyping, regular use and maintenance/replacement. The clustering of various recent phenomena has made this approach and all of its implicated uses possible: big data, AI, 5G and faster networks, cloud computing, and the Internet of Things, among others.

How will ‘digital twins’ help in the next 50 years and onwards for power grid operation? They’ll help stabilize our power systems through diagnosis, monitoring, experience, and predict through ‘what if’ scenarios. They are, in a way, digital time travel: going back in time to examine histories of aggregated data; going forward to predict outcomes. They’ll help us learn to operate our power grids better and more safely; and they’ll provide many times more and better test coverage for upgrades and improvements.

As they do all of this quietly and reliably, they’ll also provide better, more numerous, and more detailed data sets, and thus improve the training and output of AI immeasurably. They’ll also allow us to explore edge/corner cases we might never have thought of even simulating, based on their physical twin’s data. They will help in finding countermeasures to prevent wide area failures and to decrease grid downtime. They’ll improve autonomous system operation, and they’ll help optimize our simulation scenario selection through model-trained AI.

‘Digital twin’ is a concept where the language has been used fairly loosely thus far–yet combinations of the advances made possible through this thinking promise exciting and inevitable advances for real-time simulation. It’s not a matter of if this concept and its associated wholesale improvements and nuances are leveraged, but when and how.

Learn more

Watch the recorded RT20 Panel Discussion on Digital Twin to Increase Resilience of Power Grid