Parallel computing in automation of decoupled fluid-thermostructural simulation approach

TL;DR

This paper presents a parallel computing method to significantly reduce the time required for fluid-thermal-structural decoupling simulations in complex geometries, enhancing efficiency and practicality.

Contribution

It introduces a parallelized approach combining CFD simulations, clustering, and optimization techniques to accelerate decoupling simulations in fluid-thermostructural analysis.

Findings

Computation time reduced from days to hours.

Parallel execution improves efficiency and usability.

Effective decoupling with fewer CFD simulations.

Abstract

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.