Towards digital design of gas turbines

Abstract

This paper shows the current research to move towards the full digital design of a gas turbine. In the last years new manufacturing technologies, such as additive manufacturing, become more common for gas turbine applications, allowing greater flexibility in the design space. There is a need to fully exploit this flexibility and to design and validate in a digital environment new solutions. This work shows how optimization methods, mainly based on topology optimization strategies, requires more accurate estimator for critical applications, such as high temperature components of high pressure stages. For this reason a comparison of recent Gene Expression Programming and Neural Networks in topology optimization are shown. In particular it is shown how a RANS estimator in fluid topology optimization is capable of obtaining predictions compatible to high fidelity DES.