MBSE transforms and simplifies planning for complex systems

Modeling the effects of downstream systemic change

NASA used a systems modeling language (SysML) approach, using MBSE methodologies, to create a digital twin of the electrical system on the Orion spacecraft used on the Artemis 1 mission, an unmanned flight to lunar orbit that will take place in late 2022. Orion’s chief engineer was previously dissatisfied with the project’s technical expertise, Hill explains. Such highly complex systems of systems are prime candidates for MBSE.

“The model captures all the diagrams you would expect in an electrical schematic, but with all the system data, channel information, and connectivity behind it,” Hill says. “You can run simulations in real time and connect the model to analytics tools. It’s really about how we manage system information. Instead of managing siloed documents, we manage integrated models, data systems, and tools.”

Recently, Hill says, MBSE workflows helped shorten the initial design review process from six weeks to three.

The models created within MBSE use SysML, designed specifically for systems engineering applications. Hill describes these models as a “back-end database with a graphical front-end” that allows project stakeholders to immediately see the effects of any changes made to the modeled system.

“If the system is set up correctly, you change a variable, like weight, and you pass that down through the product lifecycle management tool, all the way to the engineers in charge, who know they need to redesign the part,” he says.

Collaborate on models in real time

Allan Dianic, director of software engineering in the Systems Engineering and Architecture Division in the Office of the Under Secretary of Defense (Research and Engineering), says MBSE is being used in some areas of the federal government, and “those communities are growing.”

“We’re starting to bring groups together to develop and refine best practices,” he says, adding that digital models lead to better decisions early in the design process.

“You can see if a set of requirements is unachievable and what changes can be made,” says Dianic. “With MBSE, you can think better and deeper earlier in the process, which saves a lot of time later.”

By using a model that simulates systems before they are built, he says, teams can identify errors and inconsistencies when fixing them is relatively easy and inexpensive.

Teams across the division use a variety of graphics and modeling tools to support software and data engineering work, including Microsoft Visio and detailed data modeling programs such as erwin.

“As we move into the next phase of integrated AI-driven engineering, we intend to transition to structured data tools that integrate with DevSecOps processes and support AI,” said Tim Gorman, spokesman for the Office of the Secretary of Defense.

In addition to preventing errors, the model-based approach enables co-authoring and collaboration in real time. “Multiple groups can review and modify a model at the same time, rather than one person opening the file,” says Dianic.

Technology has become an increasingly important part of virtually all Defense Department projects, he says, noting that some have described the F-35 stealth fighter as a “supercomputer with wings.”

“A lot of our platforms are software-defined in many ways, and that’s powerful,” Dianic says. “At the same time, it’s important to be able to model that.”

MBSE supports 5G innovations

As the Navy began designing its 5G smart warehouse at Naval Base Coronado, engineers adopted MBSE practices to integrate the work of different technology vendors and promote visibility into all the complex systems embedded in the facility. As a result, the Navy streamlined approval processes, helping to more quickly unlock the benefits of the 5G warehouse.

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“One of the successes of this project was the accreditation process for the risk management framework,” says David Nacario, a model-based systems engineer at Naval Information Warfare Center (NIWC) Pacific and the MBSE lead for the 5G smart warehouse. “We were able to use artifacts directly from the model to submit all of our data for accreditation. This streamlined our accreditation process to weeks instead of months.”

Having a robust digital engineering model also sped up the interim test authorization process, said Andrew Leidy, principal systems engineer, corporate communications and networks, NIWC Pacific.

“We got our IATT pretty quickly, thanks in large part to the really good documentation we had with the MBSE tools,” he says. “I think the project would have been delayed a lot more if we hadn’t had MBSE.”

The 5G Smart Warehouse has a full mesh of radio frequency identification that helps track inventory in real time and prevent spills (loss due to lost parts or theft). The facility is also equipped with automated vehicles that can drive to where a part is located, retrieve containers from high shelves and identify parts with a laser pointer.

Over time, this model will simplify facility maintenance and prevent situations where upgrades are delayed because engineers can’t find the data they need, Nacario says.

“We won’t have a situation where someone leaves and then someone else needs their records and we don’t know where they are,” Nacario says. “With what’s in the model, anyone can access the information they need at any time.”

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