  Decoupling approach presents a novel solution/alternative to the highly
time-consuming fluid-thermal-structural simulation procedures when thermal
effects and resultant displacements on machine tools are analyzed. Using high
dimensional Characteristic Diagrams (CDs) along with a Clustering Algorithm
that immensely reduces the data needed for training, a limited number of CFD
simulations can suffice in effectively decoupling fluid and thermal-structural
simulations. This approach becomes highly significant when complex geometries
or dynamic components are considered. However, there is still scope for
improvement in the reduction of time needed to train CDs. Parallel computation
can be effectively utilized in decoupling approach in simultaneous execution of
(i) CFD simulations and data export, and (ii) Clustering technique involving
Genetic Algorithm and Radial Basis Function interpolation, which clusters and
optimizes the training data for CDs. Parallelization reduces the entire
computation duration from several days to a few hours and thereby, improving
the efficiency and ease-of-use of decoupling simulation approach.
