Methods for the efficient utilisation of dynamic CAE solvers to derive appropriate optimisation loadings for complex problems - RYAN THORNHILL (2024)
Computer Aided Engineering (CAE) within the automotive sector is a constantly evolving area, with automotive manufacturers continuously seeking new techniques that will allow for an improved output within a shorter time frame, allowing them to keep up with the rapidly changing automotive market. This challenge is more evident than ever with the automotive industry facing one of the biggest changes in its history, as it begins to move away from conventional fossil fuel internal combustion engines (ICE) to battery electric vehicles (BEV). This change is leading to a much wider range of design options and new components to be considered, therefore the ability to asses new models rapidly and accurately is key to obtaining the optimal overall vehicle package. Therefore, this thesis details a number of investigations that have been carried out researching various options to allow for reducing simulation time, whilst simultaneously improving the quality of data that can be obtained and utilised in the design and lightweighting process of future vehicle components. Key areas of focus included the assement and optimisation of a planetary gear carrier, investigations into the load extraction from various sources to aid static part optimisations. Synopsis The aim is to develop methods for the eﬃcient utilisation of dynamic CAE solvers to derive appropriate topology loadings for complex problems. Typically, load conditions are known and for topology optimisation this then leads to the solution of a complex linear elastic problem. In realty however, loading is uncertain and varying, e.g. torque ripples in a drive train due to motor design and drive conditions, structural loading due to gust loading on for instance automotive car doors; This requires a mapping of the load space for these varied events onto the components of interest. For the ﬁrst case mentioned above this leads to a very high number of simulations. The objective is to reduce the computational work for deriving the correct loading conditions in an eﬃcient and robust manner.