Computational Design for Optimality and Robustness in multi-physics and multi-criteria problems

Design of the interior of a guide vane, forskningsprojekt om gasturbiner
Design of the interior of a guide vane for optimal cooling. Left: Design domain with boundary conditions. Right: An optimized design.

Background and industrial relevance

Mechanical components in for example gas turbines and jet engines are subjected to extreme mechanical and thermal loads. When designing such components it is therefore necessary to simultaneously take into account mechanical design criteria such as weight, stiffness, strength and fatigue; control maximum temperatures and temperature gradients; and to enable efficient internal (cooling media) and external (hot gas) fluid flow. Adding to that, manufacturability and robust performance for a wide range of operating scenarios must also be ensured. Design of, say, gas turbine components is thus clearly a very challenging multi-physics and multi-criteria optimization problem which requires using computer-based methods for simulation and optimization to obtain the best possible designs. Prompted by this need, the proposed project aims at developing computational design methods for optimality and robustness in multi-physics and multi-criteria problems.

The project is a collaboration with Siemens Energy AB who are interested in developing and using computational design methods for design of gas turbine components. As an important test case we consider design of so-called guide vanes (wing-like structures situated inside a gas turbine to guide the flow of hot gas), leading to challenging, coupled three-dimensional thermal-fluid-solid optimization problems.

The collaboration with Siemens Energy means that we will be able to print optimized designs using state-of-the-art metal AM machines. The printing and subsequent testing of such design will serve as an important guide for the project work, depending on the outcomes steering it towards for example refined physics-models or modification of the optimization problem formulations to facilitate translation to CAD models and manufacturing. 

Facts: funding, partner and project time

  • Funded by Centrum för Industriell Informationsteknologi - CENIIT
  • Project start January 2021.

OrganisationShow/Hide content