The design process of turbomachinery is divided into several steps, all of which can be performed at TFD. On the one hand, a preplanning of the process chain, the design itself, detailed design, simulation and the subsequent construction up to the production of prototypes.

Field of Application

  • Automated improvement of fluid machinery
  • Incorporation of multiple (even opposing) optimization objectives
  • Investigation of complex interrelationships in machine design


In recent years, optimization methods have been increasingly used to optimally design or improve turbomachinery, taking into account as many influencing variables as possible. The creation (parameterization) and evaluation of a machine design are automated. The level of detail of the evaluation can be selected according to the requirements. The machine design can be evaluated with a simplified design model or with numerical simulations. Since numerical flow simulations are very time-consuming, surrogate models are used to minimize the number of simulations required without unacceptably limiting the number of machine designs investigated. Surrogate models interpolate the operating behavior of previously examined machine designs in order to make a preselection in the optimization process and to exclude nonsensical designs. This database is expanded during the optimization process with the current results and thus the substitute model is continuously improved.

The automatic evaluation of the machine design is based on previously defined optimization criteria (e.g. efficiency, pumping distance, maximum mechanical stresses). Depending on the selected optimization algorithm, new promising machine designs are generated based on previous results. The degrees of freedom in the variation of the machine designs are determined by the parameterization.

As a result of optimization with several optimization criteria, a Pareto front is formed. The Pareto optimum is a set of machine designs, each of which represents an optimal compromise between the optimization criteria. According to the weighting of the optimization criteria, an optimal machine design can be selected.

Picture - Design Process Chain Picture - Design Process Chain Picture - Design Process Chain
Design Process Chain
Picture - Design Picture - Design Picture - Design
Picture - Detaildesign, Simulation Picture - Detaildesign, Simulation Picture - Detaildesign, Simulation
Detaildesign, Simulation
Picture - Construction Picture - Construction Picture - Construction
Picture - Manufactured prototype Picture - Manufactured prototype Picture - Manufactured prototype
Manufactured prototype