Get the Data Right for Effective Multidisciplinary SPDM – Making the Case for a Tool-Independent Unified Data Model

This paper was produced for the 2019 NAFEMS World Congress in Quebec Canada

Resource Abstract

Increasing complexities in product architectures is placing a new level of emphasis on some traditional SPDM challenges. The complexity of effectively conducting 3-D simulations for various physics scenarios has been compounded by the necessity to create and manage mixed-fidelity and multidisciplinary models, and to rapidly conduct large numbers of simulations in the overall systems engineering process, including on-board software which often dynamically controls system behavior.



Today, much of the data required to drive multi-physics, multi-fidelity simulations are specified in the disparate data formats of each of the underlying multi-vendor tools. These siloed data must be “integrated” manually by the engineers, with a severe impact on accuracy and efficiency, limiting the number of simulations that can be performed. Also, when multiple disciplines such as mechanical, electronics, and software must be considered to simulate system behavior, the data and processes are exponentially more complex.



The authors contend that a tool-agnostic, unified, requirements-driven, systems-centric data model is required to best capture these data for simulation and SPDM, and that this approach has many advantages over a federated approach to integrate data. The advantages are further emphasized when you consider the need for intelligent simulation automation that works across significant design changes and large numbers of product variants in a product family, and the need to manage a fully-associative Digital Thread across the entire product lifecycle with support for versioning and configuration management.



The authors will present two previously published case studies to illustrate the benefits of this approach for representing and managing the simulation data and automating complex multi-fidelity, multidisciplinary simulations that include dynamic controls software.