Published: 2 December 2023
Contributors: Amanda McGrath, Alexandra Jonker
Model-based systems engineering (MBSE) is a methodology that uses models to support the entire lifecycle of a system, from conception and design to verification and validation activities, through to decommissioning.
Unlike traditional engineering methods that rely on text-based documents and manual processes, MBSE uses digital modeling and simulation to design systems. These models provide a visual and interactive way to represent system components and the connections between them. This approach is especially helpful for complex systems and interfaces.
Digital models are also easier to update. Not only does this make MBSE more efficient than traditional methods, but it can add clarity, reduce the risk of error and improve communication and decision-making among engineering teams and other stakeholders. This enables information consistency throughout all lifecycle phases of the project.
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The model-based systems engineering process has several advantages over traditional engineering approaches when designing complex systems and subsystems:
The visual and interactive nature of models helps diverse stakeholders better understand and contribute to the development process.
MBSE reduces the risk of errors and inconsistencies, which are common in document-centric systems, by allowing greater traceability. Models provide a single source of truth, ensuring that all stakeholders are working with the same information and can identify system-level issues quickly.
By enabling early detection and correction of issues, MBSE can significantly reduce development time and costs. It allows quick analysis through simulations and rapid iteration, and can facilitate automation.
MBSE is helpful when working with a “system of systems,” or any project that pools the resources of several systems to create a more complex one. It is because MBSE allows for the clear, open representation of intricate relationships and dependencies within a system.
MBSE can be adapted to projects of various sizes and complexities, making it a versatile approach suitable for a wide range of applications. It can also help support product development through the entire product lifecycle.
There are three primary components of MBSE: The first is the systems architecture model (SAM), which serves as a single source of truth for the project. Second is the engineering simulation software that determines whether what is in the SAM matches the requirements and whether it will operate as planned.
Finally, a centralized computation center, which can be cloud-based or physical, performs all functions and stores results. Together, these parts comprise the digital thread, which ensures that when updates are made to one model, they are subsequently updated across all other models in the system.
The process of model-based systems engineering is a series of interconnected phases that guide system development from concept to completion.
First, stakeholders define the system’s objectives and the context in which it will operate. Those needs are then translated into specific, measurable system requirements. MBSE tools and data modeling allow for the creation of a detailed model that captures these requirements, so that they are consistent and aligned with the overall objectives. Through system architecture design, the overall structure of the system is developed.
Once the architecture is in place, more detailed design of individual components begins. MBSE facilitates the simulation of system behavior, allowing engineers to test and refine their designs in a virtual environment. This helps in identifying and resolving problems before physical prototypes are built.
From here, the system is built and assembled, using the model as a reference to ensure consistency. Verification, testing and validation processes follow to make sure the system works as intended. Once deployed, MBSE models can be used to support maintenance activities and any future upgrades, ensuring the system continues to meet changing needs over its lifecycle.
MBSE approaches are used in many industries, especially software development and software engineering. Some key examples include:
NASA is a prominent example of an organization that employs MBSE for complex projects like spacecraft and satellite systems. MBSE helps to handle the intricate interactions between different systems and to simulate various space mission scenarios. This approach significantly reduces the risks associated with space exploration.
Major automotive companies like Ford and BMW have incorporated MBSE in designing their vehicles. This approach helps in managing the increasing complexity of modern cars, which include advanced electronics and software.
The U.S. Department of Defense (DoD) uses MBSE for the development of defense systems. This includes everything from weapon systems to surveillance and communication networks.
Companies in the telecommunications sector use MBSE to design and manage complex network systems. This includes the development of infrastructure for emerging technologies like 5G, where MBSE helps in optimizing network layouts and ensuring reliable, high-speed communications.
As a systems engineering approach, MBSE relies on various tools and languages, ranging from simple diagramming applications, spreadsheets, modeling tools and workflow platforms to sophisticated software. Some examples include:
- SysML (Systems Modeling Language): SysML is a graphical modeling language that extends UML (Unified Modeling Language) and is tailored for systems engineering. It provides a standardized way to represent systems with various diagrams that help in visualizing system components and behaviors.
- MBSE software tools: Software tools specifically designed for MBSE offer environments for creating, editing and managing SysML models. They facilitate collaboration, version control and integration with other engineering tools.
- Simulation and analysis tools: MBSE often uses simulation tools for testing and system analysis. Digital twins, or virtual representations of objects or systems that span their lifecycle, serve as a dynamic copy for testing to see how a system behaves under different conditions; this helps identify potential issues and optimize performance.
- Requirements management tools: Keeping track of system requirements is paramount in complex projects, so tools are used to ensure they are all captured, tracked and compatible with the system design.
MBSE is particularly beneficial for large, complex projects due to its ability to manage complexity and facilitate communication among large teams. But it can be adapted to smaller projects as well, resulting in improved communication and error reduction. MBSE can also be implemented incrementally, so that it is easy to track and organize projects as they expand. It can be valuable across digital engineering and other engineering disciplines.
By enabling more efficient resource allocation, reducing waste and facilitating the design of more energy-efficient systems, MBSE can help aid sustainable development goals. Its emphasis on holistic and integrated systems thinking helps in developing solutions that are not only technically sound but also environmentally responsible.
Through MBSE, engineers can model and simulate the environmental impact of their designs before they are built. This helps in identifying and mitigating potential environmental risks early in the development process. It also facilitates the integration of renewable energy sources and sustainable materials into system designs, promoting environmentally friendly solutions.
MBSE optimizes design and operational efficiencies, helping limit the environmental footprint of new projects. MBSE ensures that these projects consume less energy and resources, contributing to a reduction in carbon emissions and other environmental impacts.
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Three-tier architecture separates applications into three logical and physical computing tiers.
Data modeling is the process of creating a visual representation of either a whole information system or parts of it to communicate connections between data points and structures.
Automation is the use of technology to perform tasks with minimal human input.