Dry-type transformer optimization using high performance cloud computing: Performance evaluation

Large-scale numerical simulations are more and more widely applied in product design and optimization. One example are simulations for dry-type transformers which have growing applications in the transformer market because this technology is non-flammable, safer and environmental friendly when compared to their liquid-immersed competitors. However, dry-type transformer units typically suffer from larger dimensions in order to have sufficient dielectric insulation and cooling performance. Therefore, one of the high priority tasks of a dry-type transformer manufacturer is to design dry-type transformers with smaller sizes resulting in lower material cost while still satisfying cooling requirements. In this context, the present paper applies cloud based HPC resources to conduct a parametric study of the core window clearance distance as an example to demonstrate how much high performance cloud computing can speed up compute-intensive product optimization. The performance of the cloud platform was then compared with the performance of the applications running on a powerful local workstation.