Current approaches in computational design synthesis (CDS) enable the human designer to explore large solution spaces for engineering design problems. To extend this to support designers in embodiment and detail design, not only the generation of solution spaces is needed but also the automated evaluation of engineering performance. Here, simulation methods can be used effectively to predict the behavior of a product. This paper builds on a general approach to automatically generate solution spaces for energy and signal-based engineering design tasks using first-order logic and Boolean satisfiability. The generated concept model graphs (CMGs) are now in this paper automatically transformed into corresponding bond-graph-based simulation models. To do this, guidelines for creating partial simulation models for the available synthesis building blocks are presented. The guidelines ensure valid causality in the final simulation model. Considering the connections in the concept model graphs, the simulation models are automatically generated and simulated. The simulation results are then used to calculate different objectives, constraints, and performance metrics. The method is validated using automotive powertrains as a case study. One hundred and sixty-two different powertrain concepts are generated and evaluated, showing the advantages of electric powertrains with respect to CO2 emissions and the importance of considering intelligent control strategies in the future for hybrid ones.
Simulation-Based Computational Design Synthesis Using Automated Generation of Simulation Models From Concept Model Graphs
Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received July 7, 2016; final manuscript received April 1, 2017; published online May 12, 2017. Assoc. Editor: Andy Dong.
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Muenzer, C., and Shea, K. (May 12, 2017). "Simulation-Based Computational Design Synthesis Using Automated Generation of Simulation Models From Concept Model Graphs." ASME. J. Mech. Des. July 2017; 139(7): 071101. https://doi.org/10.1115/1.4036567
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