Automated generation of multiphysics simulation models to support multidisciplinary design optimization. Issue 4 (October 2015) - Tian, Fengnian; Voskuijl, Mark
Abstract: To ensure a consistent design representation for serving multidisciplinary analysis, this research study proposes an intelligent modeling system to automatically generate multiphysics simulation models to support multidisciplinary design optimization processes by using a knowledge based engineering approach. A key element of this system is a multiphysics information model (MIM), which integrates the design and simulation knowledge from multiple engineering domains. The intelligent modeling system defines classes with attributes to represent various aspects of physical entities. Moreover, it uses functions to capture the non-physical information, such as control architecture, simulation test maneuvers and simulation procedures. The challenge of system coupling and the interactions among the disciplines are taken into account during the process of knowledge acquisition. Depending on the domain requirements, the intelligent modeling system extracts the required knowledge from the MIM and uses this first to instantiate submodels and second to construct the multiphysics simulation model by combining all submodels. The objective of this research is to reduce the time and effort for modeling complex systems and to provide a consistent and concurrent design environment to support multidisciplinary design optimization. The development of an unstable and unmanned aerial vehicle, a multirotor UAV, is selected as test case. The intelligent modeling system is demonstrated by modeling thirty-thousand multirotor UAV designs with different topologies and by ensuring the automatic development of a consistent control system dedicated for each individual design. Moreover, the resulting multiphysics simulation model of the multirotor UAV is validated by comparing with the flight data of an actual quadrotor UAV. The results show that the multiphysics simulation model matches test data well and indicate that high fidelity models can be generated with the automatic model generation process.