Engineering Simulation Interoperability Review

The development of an engineering simulation capability is challenged by fragmented and siloed approaches to Modelling and Simulation (M&S). This lack of interoperability creates barriers, even within a single organisation, often necessitating manual interventions that are both costly and prone to error. Poor interoperability limits an organisation’s ability collaborate with external partners and can lead to restrictive vendor lock-in. 

To mitigate these challenges, several strategic approaches to interoperability are commonly used: 

• adoption of industry standards, 
• the use of neutral file formats, 
• using software that provides Application Programming Interfaces (APIs) allowing different tools to communicate directly or with the support of middleware, 
• file convertors which can convert proprietary simulation formats into more accessible ones, 
• the use of ontologies and semantic mapping to help maintain data meaning when transferring models and results between people, organisations and software tools, 
• following defined best practices. 

This report has investigated these challenges by defining thirteen distinct interoperability scenarios and evaluating how the resources currently available to the engineering simulation community address them. Due to the diverse nature of these scenarios, there is no single standard or approach that provides a universal solution. Instead, organisations must navigate a diverse ecosystem of documents, best practices, recommendations, and standards to build a cohesive capability. 

At the foundation of the interoperability ecosystem sit domain-specific standards like VMAP, CGNS, EXODUS II and STEP. These formats allow for the exchange of high-fidelity data. The FMI/SSP standard provides a bridge between physics-based simulation and system level simulation. As industry moves towards predicting the behaviour of increasingly complex products, the role of coupling libraries and interfaces like preCICE and MpCCI become important for connection disparate simulation codes. 

While neutral file formats help to preserve the numerical integrity of a simulation, they often fail to capture the underlying engineering intent. This is where Metadata Specifications like the ASSESS Engineering Simulation Metadata Specification and the INCOSE Model Characterization Pattern provide the necessary context. Without this metadata, simulation results remain locked, lacking the pedigree required to support high-consequence decisions. This risk is amplified as organisations adopt Machine Learning (ML) strategies. For ML surrogates to be trustworthy, they must be trained on data that is fully traceable. 

By leveraging VTK and OpenUSD organisations have a method of accessing high-performance visualisation capabilities. VTK and the omniverse toolkits have the capability to extend simulation from a niche activity into a collaborative communication tool, allowing non-technical stakeholders to interact with physics-based data in conjunction with its real-world environmental context. 

For long-term data archiving, ISO 10303 AP209 (STEP) remains a robust choice due to its international governance and twenty-year pedigree but it’s applicable to engineering simulation is limited to specific analysis types. 

While the technical barriers are significant, a wide array of standards and recommendations exists to address them. By using this report to map their specific operational scenarios to the appropriate standards, engineering organisations can start their journey to build an interoperable simulation environment that drives innovation and efficiency. 

Acknowledgements

The ASSESS Initiative would like to acknowledge the efforts of the reviewers and the simulation experts who provided valuable information on the different approaches and resources which support interoperability. 

Reviewers 

• Tom Deighan, UK Atomic Energy Authority 
• Priyanka Gulati, Fraunhofer SCAI 
• Alexander Karl, NAFEMS Council Member 
• Riccardo Testi , Piaggio 
• Hubertus Tummescheit, Model Based Innovation 
• Klaus Wolf, Fraunhofer SCAI 

Author 

• Ian Symington, NAFEMS