Abstract

CFD simulations are complex and thus demanding in practice: engineers often aim for high resolution while reducing computing time. Integrating CFD into computer-aided product development in the cloud meets these requirements and allows handling extremely large simulations. The challenge, however, is to reliably map the complete workflow of the analysis engineer in the cloud and to fully automate it. The fully automated workflow for an optimal aerodynamics simulation designed by GNS Systems increases the quality of the simulation results through a high scalability of the computing capacities in the cloud. In addition, improved job management reduces the complexity of computationally intensive CFD simulations for analysis engineers. They benefit from a tool that has already proven itself successfully in customer use and efficiently supports the coordinated processing of recurring process tasks, even in heterogeneous IT environments. The conception of the fully automated workflow in the cloud based on an aircraft model made of coloured plastic clamping bricks also includes practical tips for its integration into the often highly complex IT environments for CFD simulations. Based on realistic results from various experiments with procedures and methods of pre- and post-processing on lift, drag and rotational speed, a continuous workflow for computationally intensive simulations was created. The simulation of the complex model down to the smallest detail also enables detailed turbulence calculations, very small time steps and high spatial resolution. Behind the automated workflow is the use of a selected CFD toolset in the cloud, which is already being used successfully in practice in this way. For example, a tool for simplified job submission combines existing workflows with standardized containers, virtual machines, job scheduling and cluster management. The improved job management significantly reduces simulation job response time by determining resource requirements in the cloud based on the number and size of jobs in the waiting queue. Unnecessary nodes were shut down to optimize costs. In combination with other toolboxes, it succeeds in mapping the workflow of an optimal aerodynamics simulation in the cloud with just a few steps and increases productivity in simulation processes by minimizing the effort for job submission. Even for compute-intensive CFD simulations which use a very large number of cores, the automated workflow delivers reliable, error-free and high-quality results.