This presentation was made at the NAFEMS Americas Seminar "Model-Based Engineering: What is it & How Will It Impact Engineering Simulation" held on the 1st of October 2019 in Columbus Ohio

Resource Abstract

“Combining the modelling and simulation perspectives of both systems engineering and engineering simulation can improve communications and coordination across the product development lifecycle.” The authors firmly believe that this conference tag line is not only true but is achievable today with the right infrastructure, (existing) building blocks, and product development methodologies. If so, then why has this desirable goal been elusive?



The authors believe that many pervasive issues in the simulation landscape have prevented the seamless merging of systems engineering and simulation data, tools and processes. Here are some of the primary issues:



1. Silos of SME’s, tools, data and processes:

MBSE and multiple simulation silos within an organization have encouraged the separation of these domains, spawning the independent development of data models and tools. The data in these silos must be “integrated” manually and, often, in a serial manner by the engineers, with a severe impact on accuracy and efficiency, limiting the number of simulations that can be performed at the right mixed-fidelity level.



2. Independent development of taxonomies:

In the early decades, a “wild west” attitude existed amongst disparate software vendors, each developing their own taxonomies, data formats and APIs. Of late, while multiple related standards efforts have attempted to bring some order to the chaos, MBSE standards and simulation standards have evolved largely separately.



3. Independent development of tools:

The tool landscape has for decades been severely fragmented and highly competitive. While standards and consolidation have brought much order and, hence, a stable base for rapid progress in many other technological domains, the systems engineering and simulation domains (product lifecycle tools) have lagged. Each of the tools has its own data model, file formats and API’s, resulting in a veritable Product Development Tower of Babel.



4. Manual processes that are inefficient and effort-prone are a barrier to mixed-fidelity modelling:

Despite the tools requiring experts, the processes employed by them are highly manual and fraught with human error. Lack of integration of the tools, models and results into a broader enterprise engineering backbone results in the need to manually search for inputs, manually generate reports and manually insert key results into the enterprise platform. The added burden and difficulty of mixed-fidelity modelling – the manual process of bringing systems analysis and high-fidelity simulation models together into a single simulation model – has proven to be too time-consuming and error prone within the current environment.



Using a case study involving the multi-physics and multi-fidelity simulation of a complex laser system, the authors will present a solution that successfully overcame many of the issues described above. In this case study, the Air Force Research Laboratory wanted to simulate an early design that was being tested in the lab and was showing aberrant behavior. For computational efficiency, it was necessary to combine lumped-parameter systems models for most of the laser system with 3-D models of certain subsystems that required higher-fidelity, using co-simulation techniques within a unified simulation platform - the simulation is a mixed-fidelity, multi-physics simulation. The simulation process was automated, significantly increasing the efficiency of the transient co-simulation trade studies that were required. [1]



The authors will present some of the details of the solution including the unified data model and API, and a simulation automation platform that is robust across significant changes to the design. They will also present an open enterprise product innovation platform approach that is requirements-driven and systems-centric, while seamlessly integrating systems modelling with simulation at all mixed levels of model fidelity.



References:

[1] Model-Based Engineering for Laser Weapons Systems, Malcolm Panthaki, Steve Coy, SPIE Proceedings, August 2011.