Nanopositioning systems (NPS) are used in various fields of technology, such as micro- and nanoelectronics, optics, and biotechnology, where demands for higher dynamic performance and sub-nanometer accuracy are continuously increasing. Thus, the determination and compensation of stress-induced negative impacts on the systems gain significance to ensure accurate positioning. Major contributors are temperature gradients. Hence, understanding and predicting temperature changes is crucial for improving such systems. This work focuses on a substructure of an NPS drive system consisting of coil assemblies. This substructure serves as a primary heat source due to the occurrence of ohmic losses, leading to an increase in temperature and therefore significantly influencing the thermal deformation. The aim of this paper is to compose a CFD model with reduced submodels of the coil assembly, which, in comparison to experimental validation data, predicts its temperature development with satisfactory accuracy. By simplification of the system through a number of sub-models, computational effort is significantly lowered. The reduced CFD model not only enables efficient thermal analysis of the coil assembly but also provides a practical approach for broader use in system design and optimization, where fast and reliable thermal predictions are essential.
Naujokat et al. (Fri,) studied this question.