In the aerospace industry, MBD plays a crucial role in the design and development of aircraft, spacecraft and complex aerospace systems
Model-based design (MBD) is an engineering methodology used to develop and design highly complex systems where all requirements, interfaces and processes are defined by a model. These models of the system under design are created and used as a central part of the development process. The models range from simple diagrams to more complex mathematical representations and they help engineers and designers understand the system, simulate it and test it before it is physically built or prototyped.
In the aerospace industry, MBD plays a crucial role in the design and development of aircraft, spacecraft and myriad critical and complex aerospace systems contained within them. There are a number of aspects to what MBD is and how it can be applied, including:
System understanding: MBD helps engineers develop a thorough understanding of the entire system being designed by creating models that test and verify different aspects, such as aerodynamics, propulsion, avionics and control systems.
Simulation and analysis: Engineers use the models created through MBD to predict the behavior of the aerospace system under different operating conditions and scenarios. For example, engineers can simulate the flight performance of an aircraft under various weather conditions, loads and maneuvers and forecast how they will perform over the life of the aircraft.
Verification and Validation: MBD enables engineers to verify and validate the design of aerospace systems before physical prototypes are built. By simulating the system behavior using models, engineers can identify and rectify potential issues early in the design process, reducing the need for costly design changes after the first run of parts.
Integration and Collaboration: MBD facilitates integration and collaboration among multidisciplinary teams involved in aerospace system development. Different teams work on developing models for their respective subsystems, which can then be integrated to create a comprehensive model of the entire system.
Iterative Design Process: Iterative design is a crucial process in aerospace engineering, where engineers continuously refine and improve the design of aerospace systems based on simulation results and feedback from stakeholders.
Drawbacks and risks of implementing MBD
While model-based design (MBD) is commonly used in the aerospace sector, there are some drawbacks associated with its implementation that need to be considered. And the biggest is how complex the process can be. Developing and maintaining models—especially for large and highly integrated systems—is no easy task. If done incorrectly, non-optimum results could be “baked-in” to the model without engineers realizing.
Creating accurate models that represent all aspects of the system requires significant time and effort. Ensuring that models accurately represent the real-world system can be challenging because models often involve simplifications and abstractions of the real-world system, which may lead to inaccuracies or cause designers to overlook important details, impacting the reliability and accuracy of simulation results.
Implementing MBD often requires engineers to learn new modeling languages, tools and methodologies, which can have a steep learning curve that could cause a temporary dip in productivity as all the users become proficient in the process. Integrating the models developed by different teams within your company or close supply chain partners, or using different modeling languages, can be challenging. Incompatibilities between models may arise, requiring additional effort to resolve.
The cost of implementing MBD can be significant, especially for smaller organizations or projects with limited resources and scope. It requires investment in software, training and digital infrastructure.
Once the initial setup is complete, ongoing investment is required to maintain models and keep them up to date as the system, product or manufacturing process evolves. Running simulations of complex models is computationally intensive and limits to the computational resources within your organization can impact simulation runtime and scalability, especially when designing large-scale systems.
Another risk is the potential over-reliance on models, where decisions are based solely on simulation results without considering real-world factors or empirical data. This can lead to poor design decisions or unexpected behavior when the system is deployed. Models developed for specific projects may have limited reusability for other projects or applications and companies looking to trim some budget by reusing models across projects will need to perform careful abstraction and generalization, which may not always be feasible and could negatively impact the end results of all products derived from the model.
Check out this list of commonly used model-based design terms
