Simulation Data Management Takes New Twists and Turns

For all the talk about the democratization of simulation, here’s how much of the work still gets done today: A highly trained expert, often with an advanced degree, toils away on a local desktop, gathering data, building models, running simulations and then circulating a report that sums up his/her findings to colleagues throughout the design process.

Dashboard view of projects and studies in ESI’s cloud-based SDM application with quick status illustration required for projects and studies assigned to the engineer. Image courtesy of ESI Group.

The increasing complexity of products — more reliance on cyber/physical systems and especially software — calls for more pervasive use of simulation, performed regularly throughout the design cycle, not just at the back-end for validation. In the new model-based design approach, simulation data and results need to be widely and continuously shared with the rest of the engineering organization, even if the skilled analyst remains the epicenter of simulation work. The requirement is prompting renewed interest in simulation data management (SDM), not just as a separate enterprise hub for managing simulation data and processes, but as an integral part of the broader product development lifecycle.

“It gets to what is the strategic value of simulation as a whole in the product development process—that’s what’s changing,” says Don Tolle, director of the Simulation-Driven Systems Development practice at CIMdata. “People are seeing business cases that support the need to do simulation early and often. If you’re going to get value out of all this simulation work, you have to manage it. Otherwise it’s just chaos.”

While the market for SDM is still relatively small — CIMdata sizes it around $50 million — the category is becoming more popular and expanding from its roots in the automotive sector as a repository specifically used to manage car crash simulation data. Heightened interest in the Internet of Things (IoT) and the debut of the digital twin concept, which made a splash this year as a way to create a mirror image of a product that bridges the digital and physical worlds and captures behavior, is also encouraging more widespread use of simulation.

As a result, engineering organizations are increasingly open to finding new ways to manage simulation data and processes, not as an isolated silo, but rather as part of the overall development process and design platform, Tolle says. “The old design paradigm moving to a model-based paradigm is what is getting us over the hump,” he says. “It’s not quite a wave, but [adoption] is building.”

Knocking Down Barriers to SDM

For years, SDM has remained a fairly niche offering, especially when compared to other design and engineering-centric enterprise platforms like PLM (product lifecycle management) and PDM (product data management). One of the big barriers has been cultural in that the people in charge of simulation aren’t open to technology that centrally manages their data or moves it out of their control. “Simulation folks are very independent and are not used to having their data managed by Big Brother,” Tolle says, adding that most prefer to maintain models and simulation data in their own shared drives, which doesn’t support a very traceable or repeatable environment.

Also, because simulation work is iterative, involving a lot of tweaking of models and rerunning of simulations, analysts have balked at enterprise platforms they see in direct conflict with their longstanding workflows. “The idea of automatically archiving something the way you might do with CAD is not the way a lot of simulation work happens today,” Tolle adds. “That’s been a challenge that’s been recognized by vendors.”

What’s also been a challenge is SDM’s traditional emphasis on managing documents and static reports, which leaves a lot on the table, according to Dale Berry, senior technical director, SIMULIA Growth for Dassault Systèmes. As opposed to managing a specific document or model, the real benefit of SDM comes with sharing the knowledge, learning, and methods surrounding the simulation so that others in the organization can benefit.

“When a model resides on someone’s local machine, it’s not a corporate asset,” Berry explains. “The industry is at a tipping point where we are crossing over from thinking of SDM as document management to more like PLM. What’s changing is the recognition that it’s not the document that has value, it’s the knowledge. We need to digitize the conclusion that this expert came to and make it available to everyone downstream.”

Berry uses the example of an airline boarding pass in both PDF and mobile app form to illustrate his point. “The PDF is an electronic copy of the document and is not able to be updated or linked to other information or used in any way other than being a mirror image of a document,” he explains. “In contrast, a mobile boarding pass is a living thing that can change any time a gate or flight time is updated.”

To support this modern vision of SDM, Dassault, along with other SDM vendors, are making significant changes to their offerings. Dassault, for example, no longer maintains separate SDM and PLM offerings — it delivers simulation and SDM capabilities as an integrated part of the 3DEXPERIENCE platform. “When an expert sits down to build a simulation model on the 3DEXPERIENCE platform, everything they do is managed and digitized and becomes available for downstream use,” says Sumanth Kumar, vice president, SIMULIA Growth at Dassault Systèmes. “That way the IT guys don’t have to enforce it, it just happens naturally and is part of the paradigm shift taken in 3DEXPERIENCE.”

Siemens PLM Software’s vision for simulation and SDM also calls for the practice to be blended into the underlying data management of the PLM platform — not performed as a separate process. Its recently announced Simcenter simulation portfolio has a shared data management foundation and analytics capabilities at its core to set the stage for what officials are calling predictive engineering, a design approach that integrates simulation at every stage of the lifecycle to support the creation of a digital twin. “If companies want to take steps towards the digital twin vision, they have to consider tying together everything with an underlying data management system … so there’s full traceability as to what’s done and why,” says Ravi Shankar, Siemens PLM Software’s director for Simulation Product Marketing. “Data management is critical and SDM is absolutely going to be essential.”

At ESI Group, increased demand for SDM capabilities are directly tied to its users’ desire for automating simulation processes, according to Andrea Gittens, visual product marketing manager for VisualDSS.

VisualDSS, which ESI touts as a decision support system for CAE, collects the simulation content of virtual tests, facilitating the capture, storage and reuse of enterprise knowledge while also automating repetitive tasks in the simulation and virtual prototyping workflows, Gittens explains. The software, to be released later this year as a fully cloud-based platform, enables a seamless connection to CAD systems and PLM platforms so it functions as an integrated part of the design workflow, not as a separate silo, Gittens explains. “Our philosophy from the very beginning was not just SDM, but to deliver dedicated CAE applications through a cloud platform supporting engineering challenges like MDO, systems modeling, workflow management, and more,” she says.

Yet SDM’s applications can spread beyond simulation. ANSYS sees three use cases for its EKM (Engineering Knowledge Manager) platform: A core simulation data management function for searching, indexing, and mining CAE data; automating simulation workflows; and leveraging the tool to more efficiently submit and manage HPC (high-performance computing) jobs, according to Ray Miehm, ANSYS vice president, Enterprise Solutions and Cloud. The platform is now cloud-based, to support performance and scalability as use of simulation grows, and it’s also designed with an open API (application programming interface) approach to ensure it integrates with mainstream engineering platforms. “The name of the game is to ensure an SDM platform is open enough to connect to PLM or to future big data systems and even existing big data analytics platforms,” Miehm says. “If the SDM is open enough, there’s no risk of becoming another silo.”

Big Data Connections

SDM’s connection with Big Data analytics seems to be where the category is headed next. Most of the major players in the category — ANSYS, Siemens PLM Software and Dassault, among others — see their investments and partnerships with Big Data analytics tools playing some role in SDM, whether as a tool for reducing the huge CAE data sets for more manageable access or for translating captured simulation knowledge into actionable insights that can impact future product designs.

As simulation becomes the bridge between the physical and virtual worlds, ANSYS believes in the power of analytics to help bolster the performance of smart machines in the field and to make predictions about future performance. In one example, the firm is working with GE Power Engineering to leverage the Predix Industrial Internet platform to deliver a “simulation as a service” pilot that blends physics-based simulation with Big Data analytics to help manufacturers reduce risk, avoid unplanned downtime and accelerate product development.

Through a web-based interface, ANSYS Engineering Knowledge Manager (EKM) is designed to facilitate seamless data exchange between expert and non-expert simulation users, providing them bi-directional connectivity with PLM and other business process management systems. Image courtesy of ANSYS.

Siemens’ acquisition of Camstar and its Omneo platform also points to Big Data analytics playing some sort of role in simulation and SDM. As engineers get simulation data under control, the next step is to perform some level of data mining to leverage data and knowledge in more meaningful ways, Shankar says. As its newly announced predictive engineering strategy spells out, simulation data, benchmark data, physical test data and even usage data collected from sensors will come together with analytics capabilities to help guide engineering decisions about how to evolve the product.

“We’re looking at how to take all that data and use it in more meaningful ways to improve the next generation of products or in some cases, to improve the product as it exists in the field today,” Shankar says. “It’s a very exciting area, but we’re in the early stages.”

More Info

• ANSYS
• CIMdata
• Dassault Systèmes
• ESI Group
• Siemens PLM Software