Introduction
In traditional product development, testing only happened once a design was “frozen” and a physical part was manufactured. This led to a “fix-on-failure” mentality. Using modeling and simulation to test designs flips this logic: it allows engineers to validate the behavior and logic of a system while the design is still fluid and purely digital.
By using high-level models to represent the system architecture, engineers can simulate interactions between components long before the detailed CAD models are finished. This approach, deeply rooted in Model-Based Systems Engineering (MBSE), ensures that the fundamental architecture of the product is sound, preventing “architectural debt” that can derail a project in its later stages.
The Core Process: How to Test Designs with Models
Testing a design through modeling and simulation involves a structured transition from abstraction to reality:
1. Functional Modeling (The Logic Test)
Before defining materials or dimensions, engineers create block diagrams or state machines to test the logic.
- Example: Testing the logic of an autonomous braking system. Does the “Apply Brakes” signal fire when the “Obstacle Detected” input is active?
- Tooling: Systems Modeling Language (SysML) and functional block diagrams.
2. Multi-Physics Simulation (The Physical Test)
Once the logic is sound, the design is tested against physical laws. This involves simulating the interaction of multiple domains—mechanical, electrical, and thermal—simultaneously.
- Objective: Ensure that the heat from the motor doesn’t interfere with the logic of the nearby electronic sensor.
3. Model-in-the-Loop (MiL) and Software-in-the-Loop (SiL)
These methodologies allow for the automated testing of control algorithms within a virtual model of the environment.
- MiL: The algorithm and the plant (physical system) are both mathematical models.
- SiL: The actual production code is tested against the virtual plant model.
Why Testing Designs via Simulation is Non-Negotiable
| Advantage | Engineering Reality |
| “Fail Fast, Fail Cheap” | Discovering a logic error in a model costs cents; discovering it in a physical crash test costs millions. |
| Optimized Design Space | Simulation allows for “Design of Experiments” (DoE), testing 1,000 variations of a design to find the most efficient one. |
| Safety in Extreme Conditions | Testing “What-if” scenarios that are too dangerous for human testers (e.g., battery thermal runaway). |
| Interdisciplinary Alignment | Models serve as a common language between software, mechanical, and electrical engineers. |
The Challenge: The Model-Requirement Gap
The most significant risk in using modeling to test designs is the loss of context. If the simulation model isn’t directly linked to the requirement it is supposed to verify, the “Proof of Compliance” is broken. A model that passes a test against the wrong requirement version is a liability, not an asset.
How Visure Solutions Integrates Modeling into the Testing Lifecycle
Visure Requirements ALM Platform acts as the glue between the abstract model and the concrete requirement:
- MBSE & SysML Integration: Visure integrates with modeling tools (like Cameo, Sparx Systems, or MATLAB/Simulink). This allows requirements in Visure to be mapped directly to model elements, ensuring the model always reflects the latest “intent.”
- Simulation-to-Requirement Traceability: When a simulation run is completed, the results are automatically linked back to the specific requirement in Visure. This creates an automated “Verification Evidence” trail.
- Parameter Synchronization: Visure can manage the technical parameters (e.g., max voltage, target weight) that feed into the simulation models, ensuring that the “virtual test” uses the most up-to-date engineering constraints.
- Suspect Link Logic: If a requirement changes in Visure, the system flags the associated model and simulation test case as “Suspect,” forcing a re-validation and preventing the use of obsolete models.
Conclusion
Using modeling and simulation to test designs is the difference between guessing and knowing. It allows organizations to move from a reactive quality control model to a proactive “Quality-by-Design” approach. By simulating early and often, engineering teams can explore more innovative concepts with the certainty that they will work in the real world.
For this methodology to succeed, it must be anchored in a robust requirements management framework. By using Visure to maintain the digital thread between requirements and models, companies ensure that their virtual testing is not just a technical exercise, but a verified step toward a safe and successful product launch.
Check out the 14-day free trial at Visure and experience how AI-driven change control can help you manage changes faster, safer, and with full audit readiness.
