Digital Twin Simulation: Engineering Smarter, Faster, and More Reliable Products

March 21, 2025
Digital Twin Simulation

Why Digital Twins are Necessary and Important

Imagine being able to predict equipment failures before they happen, optimize system performance in real time, and reduce expensive physical testing. This is the power of digital twins. A digital twin is a virtual replica of a physical asset, enabling real-time monitoring, simulation, and optimization. 

With increasing system complexity and the demand for faster, more efficient product development, digital twin technology is revolutionizing engineering across industries. 

Virtual Model using Digital Twin

Example of a Digital Twin

How Digital Twins Help Address Major Challenges  

Downtime and Maintenance: Unplanned machine downtime can disrupt workflows, delay production schedules, and lead to significant revenue losses. Unexpected failures not only impact profitability but also strain resources and damage customer trust. Digital twins help predict failures and optimize maintenance schedules, ensuring uninterrupted operations. 

Fault Detection and Prediction: Late fault detection can result in system failures, escalating repair costs, and potential damage to other components. At the same time, excessive false alarms can interrupt production and reduce efficiency. Digital twins enable precise fault detection, balancing accuracy with minimal disruption. 

Controls Optimization: Misconfigured control systems can lead to production stoppages and hardware degradation. Control software developed in lab conditions may not perform reliably in real-world applications. Digital twins allow engineers to test and optimize control strategies in a virtual environment before deployment, improving safety margins and efficiency. 

What-if Scenarios: Physical testing of all possible operating conditions is expensive and time-consuming. Relying solely on sensor data and human intervention can introduce inconsistencies. Digital twins enable engineers to simulate countless real-world scenarios quickly, reducing reliance on costly prototype testing. 

How Simulation is Making an Impact  

GT-SUITE provides a comprehensive platform to develop digital twins, integrating robust multi-physics simulation with cutting-edge data science. By seamlessly connecting with a customer’s data collection system in a cloud-based environment, GT-SUITE enhances asset performance, minimizes downtime, and improves decision-making. Let’s explore how digital twins solve critical challenges and how you can build one to unlock the full potential of your engineering systems. 

Digital Twin Image

Conceptual Visualization of Digital Twin for Fleet Optimization

“How to Guide” for Building a Digital Twin  

A digital twin functions by continuously updating a virtual replica of a physical asset, system, or process with real-time data. This enables simulation, analysis, optimization, and monitoring of the physical counterpart.  

Digital Twin Orchestration Workflow

Digital Twin Orchestration Workflow

Here’s how to build one: 

Step 1: Data Collection; Data can be collected from sensors on the physical asset (e.g., an engine, machine, or compressor), measuring parameters such as temperature, pressure, vibration, speed and more. Additionally, historical test data and field data from previous operations can be leveraged.

Data collection

Data Collection

Step 2: Data Integration; All collected data—whether from sensors, past tests, or field operations—is aggregated, cleaned, and processed to ensure accuracy and usability for simulation and analysis.

Data cleaning

Data Cleaning

Step 3: Virtual Model Creation;

    • Physics-based modeling: GT-SUITE enables the creation of high-fidelity digital twins using physics-based models calibrated with real-world measured data, ensuring precise system behavior predictions.
  • Virtual model building

    Virtual Model Building

    • Data-driven machine learning models: GT-SUITE’s Machine Learning Assistant utilizes datasets from sensors, testing, field operations, and design of experiments to create fast-running mathematical models (metamodels). These models leverage real-time sensor data for enhanced predictive accuracy and can also integrate synthetic data from physics-based simulations, reducing reliance on physical testing while improving reliability.
Data Driven Machine Learning Model

Data Driven Machine Learning Model

Step 4: Real-Time Interaction; The virtual model continuously updates via live data streams and sensor feedback which enables real-time monitoring, fault detection, and predictive analysis. This ensures that engineering teams can proactively optimize system performance, diagnose failures, and improve reliability.

Real time integration

Real-Time Integration of Virtual Model and Physical Asset

Case Studies: Real-World Applications of Digital Twins 

Case Study 1: Fuel Cell Fault Simulation and Detection for On-Board Diagnostics Using Real-Time Digital Twins 

As electrified powertrains become more complex, developing reliable On-board diagnostic (OBD) systems is crucial for ensuring compliance with evolving regulations. However, the lack of prototype hardware makes traditional validation methods impractical. To address this, real-time digital twins enable virtual testing through model-in-the-loop (MiL), software-in-the-loop (SiL), and hardware-in-the-loop (HiL) simulations.

Three Step Procedure for Hardware-in-the-Loop Simulation

This approach involves creating high-fidelity models to identify and analyze failure modes in key fuel cell components such as the compressor, recirculation pump, humidifier, and cooling system. The process begins with fuel cell stack calibration using measured polarization curves and predictive loss models. This is followed by balance of plant modelling that integrates the stack with sub-systems like the anode recirculation loop, the cathode system including humidifier, intercoolers, compressor, and turbine, and motor controls as well as the cooling systems. To enhance efficiency, the model is optimized using 0D and map-based approaches, ensuring fast simulation without sacrificing accuracy. Fault scenarios are then defined, specifying key variables to be monitored by the control system. In the HiL phase, the fast-running fuel cell model developed in GT-SUITE is integrated with MATLAB Simulink for real-time execution. The model’s performance is assessed by running real-time simulations, comparing speed and accuracy, and introducing faults to evaluate system response alongside sensor data. By leveraging real-time digital twins, engineers can efficiently develop and validate OBD systems, ensuring robust fault detection while reducing reliance on physical prototypes. This accelerates regulatory compliance and enhances the reliability of fuel cell powertrains. 

Click here to access the complete webinar.

 Case Study 2: Enhancing Cabin Thermal Management with Digital Twin Technology 

A major automotive company leveraged digital twin technology to optimize cabin thermal management, improving energy efficiency and driver comfort in electric trucks. The challenge was to balance the battery thermal system and cabin climate control while maintaining efficient energy use. 

Cabin Comfort Model in GT-TAITherm

Cabin Comfort Model in GT-TAITherm

To achieve this, the company calibrated high-fidelity models, dividing the cabin into flow volumes and creating surface mesh models for precise simulations. A co-simulation was then conducted by integrating GT-SUITE’s fluid solver with GT-TAITherm, leveraging key parameters. The model was rigorously validated against multiple real-world test datasets, ensuring accuracy and reliability. 

Building on this foundation, the company aims to take its digital twin implementation to the next level by developing fast-running models for SIL and HIL applications and integrating internet of things (IoT) connectivity for real-time data exchange and enhanced predictive capabilities. These advancements will enable smarter automation, deeper system insights, and more responsive thermal management strategies, bringing them closer to a fully realized digital twin ecosystem. 

Click here to access more digital twin related presentations.

Learn More About our Digital Twin Solutions  

Digital twins are transforming engineering by enabling predictive maintenance, optimizing system performance, and accelerating product development. By combining physics-based modeling with data-driven machine learning, GT-SUITE provides a powerful platform for creating and deploying digital twins across industries. 

Whether you are working on fuel cell powertrains, vehicle thermal management, or other complex systems, leveraging digital twins can drive efficiency, reduce costs, and improve reliability.  

Ready to take your engineering processes to the next level? Start building your digital twin with GT-SUITE today! If you’d like to learn more about how GT-SUITE‘s capabilities, contact us!