This thesis aims to develop a flexible and time-efficient framework for machine design optimization that considers driving cycles,... Show moreThis thesis aims to develop a flexible and time-efficient framework for machine design optimization that considers driving cycles, multiphysics domains and current design. The proposed development of the framework is based on the enhancement of three key aspects in the machine design process. A data mining algorithm – the X-means – is employed in the driving cycle analysis, to establish a trade-off between the optimization objectives and the computational intensity. A novel vibration surrogate model is proposed to evaluate the vibroacoustic behavior of the machine in an accurate and time-efficient way. In the identification process, the time effectiveness of the model is attained with a minimized number of finite element simulations. Furthermore, the principle of simultaneous coupled optimization is considered in the framework, where current design variables are included in the optimization environment to allow identifying design candidates with improved performance. Show less
