Sequential parameter optimization for algorithm-based design generation using data from multiphysics simulations

Abstract

The implementation of algorithmic modelling in CAD technologies is an opportunity to reduce manual design work in repetitive design tasks. This increases the importance of design automation and digital workflows. This technology can process the results from computational fluid dynamics (CFD) and finite element analysis (FEA) automatically and can optimize designed geometries sequentially. Nevertheless, this design process often sets high computational requirements. The aim of this paper is to present a design automation workflow that reduces the computational time of a design process. The computational resource requirements of the system are reduced by using knowledge-based engineering techniques to obtain information from previous successful designs, decomposing the design into sub-parts according to their functions and optimizing each sub-part individually. Furthermore, through algorithmic modelling, the different input geometries required for the physical description of each simulation are made separately. This allows different design simplifications to be made for each simulation domain. Once the output of the simulations is obtained, the design is evaluated in CAD to optimize the geometry. After each sub-part has been optimized, the sub-parts are composed to obtain the final design. The case study of a reactor for methanol synthesis supports the results of this paper.