Data Avalanche: Growing Need for New Storage Strategies

Larger, more complex models and simulations mean data storage and management are more important than ever.

Larger, more complex models and simulations mean data storage and management are more important than ever.

Siemens integrates PDM capabilities inside both of its CAD products: NX and Solid Edge. Image courtesy of Siemens Digital Industries Software.

Simulations are now conducted earlier in the design process and more frequently, and new tools have helped integrate different types of analysis into the daily workflows of designers. Engineers are also working with larger and more complex models and simulation, and firms are beginning to integration artificial intelligence, machine learning, and other data-intensive processes into their design frameworks. The amount of data has increased, which means the task of managing and storing that data effectively so that it can be shared, searched and reused in the future is even more challenging.

As a result, many firms have turned to cloud-based file storage solutions. In some cases these solutions are integrated with existing product data management or simulation data management tools; in others, firms have taken an ad hoc approach using general purpose cloud storage tools. However, the latter makes it difficult to leverage the data in a product data management (PDM) ecosystem.

Companies should take a more thoughtful approach to data storage that is integrated with their overall data management requirements. That’s because storing the data is only part of the problem. The data also must be accessible and searchable, particularly as companies strive to make better use of existing simulation results and other information for future design projects.

The Rise of Simulation Data Management

The market for simulation data management (SDM) tools is relatively small but growing, as it expands from its roots in the automotive sector.

In the past, organizations were resistant to SDM approaches because engineers were reluctant to switch to a centralized management structure, and because so much simulation is iterative. However, the SDM approach makes it easier to share 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,” says Dale Berry, Senior Director, SIMULIA Product Experience Technical, for Dassault Systèmes. “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.”

As manufacturers embrace digitization, traditional PDM systems are buckling under the weight of managing larger CAD models, high-fidelity simulations, electronic CAD files and digital twin data models. These larger, more complex systems models need to be managed in the context of linkages to multiple design, document and simulation applications; have intricate relationships and interdependencies; and require highly sophisticated search capabilities beyond what’s been widely available in conventional PDM and CAD management solutions.

To support this modern vision of SDM, Dassault and other SDM vendors are evolving 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. Likewise, Siemens’ Simcenter incorporated a shared data management foundation and analytics capabilities at its core to enable predictive engineering.

SDM can also be used for applications beyond simulation. ANSYS has outlined three use cases for its Minerva knowledge management 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. The cloud-based platform can 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.

ESTECO’s Simulation Data Management solution provides a way to access, organize and share simulation data, for example, while Altair offers SimData Manager to provide access to CAE data via a partnership with PDTec AG.

ESI Group offers the Vdot simulation data management software to help organize and provide access to data. The company’s VisualDSS solution also makes it easier to share models and resolve conflicts so that engineers can identify the optimal designs based on different physics and parameters. Users can build and maintain a bidirectional link between CAD data stored in PLM systems and simulation domains. Design and engineering changes can be propagated across the virtual tests, while maintaining data traceability.

Because many companies utilize a multiphysics approach that often involves tools from different vendors, there are other emerging solutions that can help streamline the storage issue. VCollab, for example, created a proprietary CAX format to manage a variety of different types of simulation data. It says its smart extraction and storage approach can reduce file sizes by as much as 99%.

Data Management Challenges

The challenges that companies need to address with SDM will vary by company. Some firms are interested in streamlining simulation processes to make it easier to retrieve analysis results and create reports. Model re-use is another issue—data management can help ensure that different groups analyzing different physics can start from the same CAD version or re-use existing meshes. This approach also helps reduce storage waste.

Data relevancy is also important when it comes to managing the large volume of simulation data now being generated. It may not be possible to move all simulation data into a central repository; in some cases, large analysis files may be discarded fairly quickly after they are generated.

SDM tools should help ensure that key results are added to the database, as well as helping to automate the process of sorting that data.

“There are mechanisms for doing this if you don’t want to store all the files forever,” says Sanjay Angadi, director of product management at ANSYS. “You can use a lightweight visualization that doesn’t keep the large files, but does keep the input deck and some of the important information. Some customers have a 90-day policy. In some fields, like aerospace, there might be a need to carry the data forward.”

“Depending on the type of simulations, the files can be huge,” says Ravi Shankar, simulation product marketing director at Siemens Digital Industries Software. “Companies need to make a decision where to store the data. For example, in Teamcenter, the storage can be online, nearline, and the data can be stored in external databases if need be.

“There are certain types of data that you might want to always access within the system, while other types could be archived,” Shankar says. “What would be stored within the system is a link or a subset of data extracted from the simulation. You can do quick comparisons when making design changes. The flexibility needs to be in the system so each company can address their specific needs.”

With a robust data management tool in place and an optimized approach to data storage, engineering firms can improve simulation processes and compliance, while lowering the cost of finding information and creating reports. This can ultimately improve designs and reduce redundancies with updated designs or similar designs that are being created.

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