Digital Twin vs Simulation

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Engineers, product designers, and operational teams often look for innovative ways to improve development cycles, reduce costs, and validate ideas. Digital representations of physical assets and processes represent a critical strategy for accomplishing these goals. Professionals now have a broader range of solutions that support early-stage design, real-time testing, predictive analysis, and integration with legacy systems. These solutions address pain points such as delays in product launches, high prototyping expenses, and uncertainty in large-scale rollouts. Certain methods focus on highly detailed, always-updated models, while others concentrate on targeted representations meant for performance forecasting. Accelerated time to market becomes feasible when decision makers choose the right modeling approach that aligns with business objectives, production schedules, and expected returns.

Organizations seeking better resource utilization also benefit from integrated feedback loops between the physical world and its digital representation. That alignment fosters improved visibility into operations. Some approaches incorporate deep analytics, while others rely on a simpler set of variables that still provide valuable insights. Both hold potential to support big-picture strategy, but the best fit depends on factors such as scalability, required fidelity, and investment scope. Stakeholders often ask about digital twin vs simulation solutions, and clarifying these methods can lead to stronger buy-in from executive sponsors and technical teams.

What is a Digital Twin?


Many professionals define a digital twin as a living virtual model that mirrors a physical entity or system across its entire lifecycle. The concept relies on continuous data streams and sensor feedback to maintain real-time updates. It commonly features high-fidelity representations and advanced analytics that adapt to changes in hardware and processes. This level of detail supports tasks like anomaly detection, scenario testing, and advanced forecasting.

Organizations often adopt digital twins when they need to capture data throughout an asset’s operational life, from design and engineering to maintenance and end-of-life decisions. Communication between the twin and the physical asset ensures the model remains current, allowing teams to track everything from performance metrics to potential wear and tear. That two-way link reduces guesswork in complex projects and creates opportunities to adjust designs on the fly, which can accelerate project timelines and improve financial returns.

 

“Digital representations of physical assets and processes represent a critical strategy for accomplishing these goals.”

Applications of Digital Twins Across Industries


Digital twins have found a place in many sectors due to their capacity to maintain continuous synchronization with actual operations. These uses range from large-scale energy grids to precision manufacturing lines.

  • Power generation optimization: Real-time models of turbines, generators, and distribution systems provide insight into maintenance schedules, load balancing, and the most cost-effective strategies for peak periods.
  • Automotive prototyping and testing: Simulated versions of new vehicle designs or e-mobility components reveal problem areas before they appear on production lines. That approach reduces physical testing expenses and enhances time to market.
  • Aerospace system monitoring: Integrated sensor data from propulsion, navigation, and avionics systems feed into detailed twins, minimizing the risk of mission-critical failures. This method also streamlines inspection planning.
  • Facility management: Digital twins of warehouses, office buildings, and manufacturing plants help owners track occupancy, power usage, and heating/cooling performance. Adjustments are made promptly to keep costs down and optimize occupant comfort.
  • Healthcare device lifecycle support: Complex equipment such as MRI machines, robotic surgical systems, or infusion pumps rely on digital twins to evaluate performance in near real time, enhancing patient safety and reliability.
  • Infrastructure and construction: Detailed 3D replicas of bridges, tunnels, and city utilities allow planners to analyze structural integrity over time. Scheduling for inspections and repairs becomes more precise.

Engineers gain a strategic advantage when investing in digital twins for mission-critical operations. That approach supports better system uptime, which in turn can boost revenues and protect brand reputation. It also allows organizations to uncover untapped business potential by exploring hypothetical “what if” scenarios without risking downtime or incurring excessive trial-and-error expenses.

What is a Simulation?


A simulation typically involves a virtual model replicating certain processes, behaviors, or interactions using mathematical formulas, physics-based models, or other computational methods. The primary objective is to test hypotheses or predict outcomes under various conditions. Simulations may range from simple 2D or 3D prototypes to advanced real-time testing with high levels of fidelity. Organizations often rely on these models for tasks that require validation and analysis without exposing real equipment to potential damage.

Some teams use simulations to investigate design alternatives and forecast future events without building physical prototypes. Engineers, product developers, and research departments see value in verifying performance aspects like throughput, safety thresholds, or potential design flaws. Simulation-based methods also facilitate stakeholder alignment, since the entire group can visualize scenarios and work toward data-driven decisions.

Simulation tools have historically covered an array of industries, from large-scale manufacturing to precision electronics, by offering cost-effective risk assessment before major investments. Many solutions focus on exploring system behavior and supporting measured outcomes. The focus might be on discrete events, fluid dynamics, thermal distributions, or multi-physics phenomena. Scalable computational resources allow these models to capture complex details without requiring a fully connected digital representation of the asset. Hardware-in-the-loop solutions from OPAL-RT complement these virtual models by enabling near-real-time replication of operational conditions for both software and hardware testing.


Applications of Simulations Across Industries

 

Simulations appear in numerous fields, offering ways to validate designs, refine operational decisions, and test feasibility. These models let teams address issues proactively.

  • Automotive safety testing: Scenario-based exploration of crash events, traction under different weather conditions, and occupant protection.
  • Aerospace flight dynamics: Replicating aerodynamic loads and propulsion demands under different altitude and velocity profiles.
  • Electronics thermal performance: Identifying hotspots in circuit boards or processors to improve cooling mechanisms.
  • Production line throughput: Modeling workflows in manufacturing plants to analyze bottlenecks and optimize cycle times.
  • Resource allocation: Examining supply chain efficiency and capacity requirements for distribution networks.
  • Control system validation: Using specialized real-time solutions to test software and hardware interactions before physical deployment.

High-fidelity simulations frequently speed up the path to market, as organizations can test multiple designs without full-scale prototypes. System refinement becomes more efficient when data from each scenario iteration can be evaluated quickly. That process supports cost containment by detecting potential design flaws or operational misalignments before building or integrating equipment.

 

“A simulation typically involves a virtual model replicating certain processes, behaviors, or interactions using mathematical formulas, physics-based models, or other computational methods.”

Differences Between Digital Twins vs Simulations



The main difference between digital twins and simulations lies in their data flow and lifecycle scope. A digital twin is a persistent representation of a physical entity, receiving continual updates from sensors and operational feedback. A simulation may be confined to specific use cases, running for a set period or under certain conditions without constant real-time connections.

Systems that rely on digital twins focus on mirroring ongoing operations and adjusting to actual conditions as they change. That approach lets decision makers respond to issues promptly, making it particularly valuable for large-scale infrastructure, high-stakes manufacturing, or safety-critical applications. Simulations, on the other hand, often address targeted research questions or design verifications. The depth of detail in a simulation depends on the purpose, and some might only require partial data sets.

Here is a straightforward comparison table illustrating major distinctions:

Aspect

Digital Twin

Simulation

Definition

Ongoing virtual model synced to a physical asset

Model representing a scenario or process for analysis

Data Flow

Continuous updates from sensors and operations

Data input often preset; limited real-time feedback

Lifecycle Scope

Spans entire lifecycle with evolving conditions

Confined to discrete phases or targeted experiments

Complexity

High fidelity and multi-layer analytics

Ranges from basic to advanced, depending on goals

Primary Benefit

Immediate insights and predictive maintenance

Cost-effective risk assessment and design validation

Advanced teams sometimes bridge these approaches. An example scenario could involve a simulation model that eventually feeds data into a digital twin for ongoing monitoring. That hybrid style can maximize value when project budgets support continuous refinement, but simpler use cases might only require one or the other.

Choosing Between Digital Twins and Simulations


Practical requirements such as real-time awareness, upfront development budgets, and desired outcome metrics strongly influence the choice. A digital twin suits scenarios where physical assets must be traced or managed continuously. That might include integrated systems where an outage disrupts core business operations or introduces safety hazards. Digital twins generally require robust sensor networks, reliable connectivity, and enough computing resources to process near-real-time data.

Simulations might represent a more cost-effective solution if the project scope revolves around testing discrete events or hypothetical use cases with minimal requirements for live data. That approach works well for teams focused on design optimization, scenario planning, or verifying new features before production. The level of fidelity in these models can be scaled up or down. Developers often adjust mesh sizes, data parameters, or computational precision based on the project’s budget and timelines.

Senior engineers and financial decision makers typically ask about total cost of ownership when evaluating these approaches. Digital twins may require higher upfront investment in infrastructure, but the payoff can be substantial if they replace frequent physical inspections or reduce unplanned downtime. Simulations can shorten time to market by discovering design flaws early, but they may not continue delivering insights once the testing phase concludes.

Trends in Digital Twins and Simulations



Both digital twins and simulation models are seeing expanded adoption, especially with the rise of advanced analytics and cloud-based processing. More organizations plan to integrate these methods into enterprise resource planning systems, product lifecycle management tools, and industrial automation frameworks. This integration brings the promise of scaling to larger and more complex systems while maintaining user-friendly interfaces.

Specialized hardware platforms also allow engineers to run real-time simulations that replicate actual operating speeds with high accuracy. OPAL-RT provides powerful real-time solutions that streamline advanced test cycles for faster iterations, supporting rapid control prototyping and robust data analysis. Emerging technology such as machine learning often gets layered into these solutions, refining predictions based on historical data. Some industries leverage digital twins and simulation together for tasks like bridging reliability data with advanced scenario testing.

Stakeholders are beginning to prioritize measurable business outcomes, such as quantifiable improvements in energy efficiency or faster product iterations. Governments and regulatory bodies appreciate transparent modeling methods that show compliance with safety guidelines. Commercial demand for digital twins and simulation is expected to keep growing as more companies pursue risk mitigation and expansions into new markets.

Digital twins and simulation methods each offer valuable avenues to accelerate development and improve efficiency. One approach focuses on continuous, high-fidelity representation of physical systems, while the other uses targeted models to test specific conditions. Both can yield measurable business benefits, from cutting unnecessary prototypes to preventing operational downtime. Scalability and cost considerations often guide the final decision, along with the need for ongoing data feedback or specialized one-time experimentation.

Engineers and innovators globally rely on real-time simulation to accelerate development, mitigate risk, and break new ground in system design. At OPAL-RT, decades of expertise combine with a passion for innovation to provide open, scalable, and high-performance solutions that support both digital twins and simulation-based projects. Our flexible platforms equip you to design, test, and validate with confidence.

Frequently Asked Questions


Which industries see the most benefits from digital twins and simulations?

Many sectors, including automotive, aerospace, and energy, gain practical advantages when comparing digital twin vs simulation solutions. Continuous modeling supports ongoing insights, while targeted experiments deliver fast validation for product enhancements.

How do digital twins and simulation approaches help reduce product development time?

High-fidelity virtual models reveal design flaws and improvement opportunities long before physical prototypes are built. That advantage translates into lower production expenses, better use of resources, and faster progression from concept to release.

Is it expensive to maintain a digital twin vs a simulation setup?

Costs vary depending on complexity, sensor integrations, and software resources. A well-planned digital twin might require more upfront investment, but simulations can also grow in expense when higher fidelity and real-time updates are required.

Do digital twins or simulations need specialized hardware?

Some high-performance simulation tasks and real-time digital twins benefit from dedicated hardware platforms that handle intensive computations. Many organizations also adopt cloud solutions for scalable performance without heavy on-site infrastructure.

What is the difference between running a real-time digital twin vs a traditional simulation?

A real-time digital twin frequently connects to physical assets, feeding live operational data into ongoing analysis. Traditional simulations often run discrete scenarios, using preset conditions to highlight potential outcomes without continuous sensor feedback.

 

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What is Hardware-in-the-Loop?

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Hardware-in-the-loop testing is a direct method for predicting how physical equipment interacts with control software in real time. Engineers integrate actual hardware components with virtual models to fine-tune complex systems before large-scale manufacturing. This approach helps uncover design flaws early and avoids expensive rework. Project teams appreciate the precision and immediate feedback, which ultimately shorten development cycles.

Many engineering teams often ask what is hardware-in-the -oop and how it aligns with best practices for real-time simulation. HIL offers an advanced way to see how mechanical systems behave under varied operating conditions without constructing full-scale prototypes. Testing procedures become more streamlined and repeatable, helping you reduce cost. Integrating real sensors and actuators into a simulated test framework also ensures data accuracy for thorough analysis.

What Is Hardware-in-the-Loop (HIL) Testing?


Hardware-in the-loop (HIL) testing often prompts a straightforward explanation: it links physical hardware components to a virtual model running in a real-time simulator. This setup evaluates genuine system performance under controlled conditions, which is essential when verifying safety, efficiency, or reliability metrics. Traditional bench testing might reveal certain issues, yet HIL offers deeper visibility because it replicates dynamic events in a repeatable way. Developers use this type of testing to confirm that control signals and power flows are properly managed before field deployment.

The approach typically involves connecting sensors, actuators, or even entire subassemblies to a digital domain designed to mirror operational scenarios. While software simulations alone can guide early development, the presence of tangible hardware adds a layer of authenticity that purely virtual methods cannot match. HIL helps you gather data on the physical responses to varying loads, temperatures, or voltage levels without building a costly test bench. Engineers across many industries, including power electronics and automotive, value HIL for accelerating validation schedules.

How HIL Testing Validates Control Systems




Control systems often exhibit complex interactions between multiple components, making them prone to hidden faults if tested only in simplified conditions. HIL provides a structured domain for testing every control loop with genuine hardware signals, thus capturing real performance data in real time. This reduces ambiguity and offers clarity on how sensors respond and how controllers adjust outputs based on the input conditions. Accurate insights gained from HIL allow engineering teams to refine algorithms and calibrate hardware interfaces more effectively.

For instance, a powertrain control system in an electric vehicle benefits from HIL testing by allowing the battery management unit, drivetrain components, and other modules to work together as they would in normal operation. This integrated approach leads to better alignment between hardware and software, minimizing unexpected failures after mass production begins. Large-scale projects see HIL as an integral strategy because it sets a high standard for performance evaluation at each phase. The result is a stable, well-coordinated system that meets or exceeds compliance requirements.

Main Types of HIL Configurations




Organizations employ various HIL setups to match the specific demands of their development projects. Some solutions focus on micro-level component validation, while others handle entire assemblies or system-level interactions. Different configurations are chosen based on budget, testing frequency, or hardware availability. A well-planned HIL layout significantly boosts reliability and return on investment by ensuring that every part is evaluated under the right conditions.

  • Processor-in-the-Loop (PIL): This setup verifies the functionality of embedded processors by interfacing real processing units with simulated inputs and outputs. Engineers often rely on PIL to highlight timing constraints and confirm if the target processor can handle computational demands. It shows exactly how the application code behaves under real processor conditions.
  • Power Hardware-in-the-Loop (PHIL): This configuration adds actual power components to the loop, such as converters or power amplifiers, allowing teams to assess behavior under load. System stability and safety become clearer because current and voltage waveforms are subjected to genuine electrical effects. PHIL is especially common in microgrid and renewable energy projects that need accurate power flow representation.
  • Electric Motor HIL: This option involves connecting the motor drive hardware to a digital representation of mechanical loads, letting you measure torque responses and other performance metrics. Development teams rely on motor HIL to confirm if speed control algorithms function correctly across a broad range of conditions. This approach identifies mechanical stress points early, which reduces maintenance costs later.
  • Automotive ECU HIL: Automotive engineers often use HIL benches to test electronic control units in real-time conditions without the full vehicle. Signals for sensors like temperature, pressure, or speed are emulated, and the ECU responds as if it were in a running system. This method helps confirm compliance with stringent industry regulations by isolating faults before the final assembly.
  • Mechanical Subassembly HIL: Some organizations test specific mechanical subassemblies, like hydraulic actuators or gearboxes, by coupling them with simulated conditions. The hardware experiences forces and motion that mirror real operation, enabling precise optimization. This configuration highlights how physical wear and tear might develop over time, prompting earlier design modifications.

Selecting the right configuration depends on the nature of your project and the extent of physical component integration required. Some teams combine multiple forms of HIL when working on large systems that span several domains, such as power distribution and vehicle control. Tailoring the approach ensures a balanced combination of scope and detail, yielding meaningful insights that drive better performance. Engineers who recognize these configurations can balance cost and testing depth, accelerating design cycles and production readiness.

Steps to Implement HIL




Effective HIL implementation hinges on a methodical process that aligns real hardware and software models in a stable test domain. Each step addresses potential sources of error and ensures that you gather accurate data for advanced system tuning. Teams reduce cost overruns by mapping out a clear plan before integrating all components. The following core stages help you achieve consistency and thorough validation:

Step 1: Define System Requirements

Clear objectives guide every successful HIL project. Engineers identify the control variables, performance constraints, and hardware specifications upfront. This approach helps you avoid confusion about the signals, data rates, and measurement ranges used during the tests. A structured list of requirements keeps the project focused and lowers the risk of scope creep.

Step 2: Develop Accurate Models

Functional models of the system or subsystem are created in real-time simulation tools, ensuring that the virtual elements mirror the physical domain. Engineers calibrate these models based on known performance benchmarks, verifying that each parameter, such as voltage level or fluid pressure, reflects real-life values. Detailed modeling reduces guesswork in subsequent steps. Verification at this stage lays the groundwork for integrating hardware seamlessly.

Step 3: Integrate Hardware Interfaces

Physical components such as sensors, actuators, or embedded controllers must connect smoothly to the simulator’s I/O channels. Proper cabling, signal conditioning, and data synchronization prevent faulty readings or missed events. This integration process often includes robust checklists to confirm accurate pin assignments and voltage references. Meticulous attention here guarantees that subsequent testing data remains trustworthy.

Step 4: Conduct Preliminary Validation

Initial trials confirm whether the combined hardware and simulation setup behaves as intended under controlled conditions. Engineers might run static load tests or simple operational scenarios to verify signal timing and data acquisition. These smaller evaluations help you fine-tune parameters before running high-fidelity scenarios. Addressing minor issues now can save significant effort once the system is fully operational.

Step 5: Iterate and Optimize

Ongoing refinement is essential after the first validation cycle. Teams examine logs and performance metrics to make incremental improvements, focusing on control algorithms or hardware response times. This iterative approach enhances system reliability by catching subtle design issues early. Each refinement cycle moves the project closer to a validated, production-ready solution.

Challenges in HIL


Implementing HIL may reveal complexities that require technical expertise, careful budgeting, or strong collaboration among multiple departments. These challenges can slow progress if not addressed systematically, yet foresight helps you reduce friction in the process. Some difficulties arise from hardware limitations, while others relate to organizational factors. Identifying these pitfalls early can substantially improve test outcomes.

  • Real-time synchronization difficulties: Maintaining precise timing between hardware signals and the simulator is vital, and any mismatch can compromise data integrity. Engineers often use dedicated hardware interfaces and high-speed protocols to handle this, but setup can be intricate.
  • Limited hardware availability: Some critical components might be scarce or costly, forcing test engineers to share resources with other teams. Efficient scheduling and resource management become necessary to keep the project on track.
  • Model fidelity concerns: High-fidelity simulations require detailed representations of mechanical, electrical, or thermal processes, which can be time-consuming to develop. Oversimplifying these models leads to inaccurate results.
  • Complexity in data interpretation: Large volumes of test data can overwhelm teams if they lack systematic tools for analysis. Well-chosen software solutions and robust data logging practices help you transform raw output into actionable insights.
  • Organizational communication gaps: Coordination between control engineers, hardware specialists, and project managers is crucial for timely decisions. Clear roles and responsibilities reduce misaligned efforts and missed milestones.

Addressing each challenge often involves a blend of technology upgrades, process improvements, and stakeholder alignment. Even advanced teams can encounter setbacks when new components or updated specifications emerge unexpectedly. Practical contingency plans and a willingness to refine initial assumptions keep the program on course. Ultimately, resilience in handling these hurdles benefits the entire development lifecycle.

Key Benefits of Hardware-in-the-Loop




Project leads appreciate the consistent outcomes and measurable gains that HIL testing offers. Speed to market is often boosted by early detection of issues, and budgets are better managed due to fewer last-minute surprises. The flexibility of adding or substituting hardware components enables real-time diagnostics and iterative improvements. A closer look at these benefits highlights why HIL stands out as a practical approach.

  • Enhanced safety testing: Putting actual hardware in controlled test loops avoids risky on-site evaluations. Major hazards or malfunctions are discovered in a safe setting.
  • Reduced development cycles: Iterative feedback from real hardware shortens each testing phase, shrinking the timeline to launch. This efficiency helps you respond more effectively to design changes.
  • Lower overall costs: Early identification of design flaws prevents late-stage rework, which can consume significant resources. Eliminating excessive physical prototypes also conserves budget.
  • Greater confidence in final products: HIL reveals detailed performance data, enabling robust validations of control algorithms and mechanical behaviors. Stakeholders trust outcomes supported by real hardware interactions.
  • Improved collaboration among teams: Engineers, operators, and even financial managers can align on test results, thanks to transparent insights delivered by HIL setups. This alignment drives more coordinated project outcomes.

Organizations that invest in HIL often view it as a strategic asset rather than an isolated testing tool. The capacity to link hardware and software under precise conditions fosters deeper learning about every subsystem. Collaboration around shared data speeds up decisions while ensuring compliance with industry standards. Over time, these benefits compound, resulting in more effective growth.

Trends in HIL for Emerging Technologies


New developments in autonomous systems and renewable energy have placed HIL at the center of advanced product development. Engineers are integrating machine learning algorithms into the simulation loop, allowing predictive insights based on real sensor feedback. This shift elevates test coverage and helps you detect anomalies before they escalate into major failures. The growing need for zero-emission transport solutions also aligns with HIL to refine battery, motor, and charging systems at scale.

Cloud-based platforms now offer remote collaboration features, where distributed teams run large sets of HIL simulations concurrently. This technology accommodates broader test scenarios and speeds up your time to market. Enhanced synergy between hardware and AI-driven analytics refines control system calibration for better efficiency. Many companies view these HIL advancements as opportunities to tap into new revenue streams while minimizing overall risk.

Hardware-in-the-loop fosters robust system development across multiple sectors that demand high reliability and peak performance. The process connects real and virtual elements in a test bed that quickly flags potential issues and paves the way for cost-effective fixes. Engineers and project stakeholders rely on its accurate results to guide essential decisions for product deployment. When executed with a clear plan and scalable approach, HIL stands out as a key driver for quality and efficiency.

Engineers and innovators across industries are turning to real-time simulation to accelerate development, reduce risk, and push the boundaries of what’s possible. At OPAL-RT, we bring decades of expertise and a passion for innovation to deliver the most open, scalable, and high-performance simulation solutions in the industry. From Hardware-in-the-Loop testing to AI-based cloud simulation, our platforms allow you to design, test, and validate with confidence. 

Common Questions About HIL Testing


HIL stands for Hardware-in-the-Loop. It is a technique that integrates physical hardware components into a simulated test framework to ensure accurate testing of complex systems.




Software-in-the-loop (SIL) testing focuses only on virtual models, while HIL adds actual hardware components for deeper insights. The presence of real hardware in HIL captures physical behavior and unique performance factors that SIL alone may overlook.

Budgets remain more stable because defects are discovered early, avoiding last-minute design revisions. Fewer prototypes and rework cycles also lead to substantial savings over the project’s timeline.

Yes. Many platforms support higher power ratings, specialized I/O boards, and dedicated amplifiers to handle industrial demands while maintaining real-time performance.

Engineers want a reliable way to verify electronic control units, powertrains, and safety functions before physical vehicle assembly. HIL exposes software and hardware to real sensor inputs, revealing potential issues under realistic conditions.






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The Founding & Expansion of a Remote Learning Lab from a Long-Trusted Educational Partner

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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.

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