Managing CAE from Black Box to Full Fidelity

Product design is often pictured as a linear process from first idea to detailed manufacturing instructions. In reality, it’s a series of loops and false starts as the design team searches for and finds the best way forward.

One way that design teams are course-correcting is by using simulation, aka computer-aided engineering (CAE), during preliminary design. This helps discover good alternatives and sets the stage for targeted, detailed design. That’s using CAE for innovation as well as validation and risk mitigation, to gain a deep understanding of the product. You’ll learn how each design option influences the product’s operation and be able to make decisions early, before change becomes too expensive.

If you’re simulating that early and often, you’re likely generating vast amounts of data—models, loads, material options, outputs, visualizations and animations—that you somehow need to manage. Looked at one way, this becomes yet another pile of paper or bits to keep track of until the project ends, and then never referred to again. But looked at with a different lens, this is a huge intellectual property asset. The thought process behind each design and the analyses that led to decisions can help inform future designs. How can you make the most of that value?

In the early stages, simulations will have the lowest fidelity that can yield a meaningful result. You’ll likely use black boxes as placeholders until you have more info. As more details about the system are known, the simulation fidelity improves. Eventually, you’ll run component-level simulations to ensure each design element meets its requirements, as well as simulating the whole assembly. Keeping track of what was known and what was assumed at each stage isn’t easy, but it is crucial if the team needs to backtrack during a design, or wants to jumpstart the next version.

Mining for Gold

All of that information is incredibly valuable, but only if you can find it. Many teams just print out reports summarizing the results of the simulations for their files. That’s a time-honored approach, but imagine staring at a wall of filing cabinets, hoping to strike gold. Now consider having that information organized, with pointers to the gold nugget—a much more comfortable situation, no?

Others use some sort of digital filing system. Perhaps a shared drive for archiving—or a PDM/PLM system that can associate models, input and output decks, animations and other information. But there’s another choice. Simulation data management (SDM) tools are purpose-built to manage simulation-related processes, models, documents and outputs. Some can also manage test data for correlations.

SDM FYI

Purpose-built SDM tools often run in the background, capturing data associated with every step in the simulation process. This makes sure everything is done to procedure and that it can be repeated from one design iteration to the next. That traceability can be very important as team members collaborate on designs and need to refer to other versions of the same concept. Some SDMs also enable automation to make it easier to import models and assemblies, create input decks, post-process and create reports.

If you’re investing in simulation, consider using a digital solution to manage your workflow. There’s too much value in knowing what worked and what didn’t to file it away in paper form. Consider the metadata you use when you archive your projects, so that they can be searched and retrieved for future projects. Try to capture both the process and the inputs and outputs—you want to be sure that the next simulation is run the same way, so that the results are comparable. The correct setup will allow you to backtrack for internal traceability and regulatory compliance.

Adherence to procedures is important, but perhaps the biggest benefit of an SDM is that it jumpstarts future innovation. If you can refer back to other projects, you have access to their simulation plans and processes, what was tried and what succeeded. That enables you to make better and faster decisions because you’ll be able to quickly reject alternatives that are proven to be unworkable—as well as areas of further exploration.

You simulate your product’s performance to make sure it is reliable, safe and fit for purpose. Managing the data asset that you create along the way is just as important for your business.