In the future, gas power plants have to endure increasing flexibility in operation to balance load fluctuations caused by renewable energies. In cooperation with MAN Energy Solutions SE, this research project aims at the improvement of the internal flows inside turbomachinery components with the help of Magnetic Resonance Imaging (MRI).

In the design of turbomachinery for conventional power plants, the focus has so far been on the economy of the overall system for mainly constant operation. In the near future, the energy transition will require greater operational flexibility of turbomachinery. Furthermore, they will increasingly work away from their optimal operating point. Under these conditions, long service life and thus cost-effectiveness only remain if the physical processes inside their different components are well known. In terms of analyzing the flow, numerical methods for flow simulation (Computational Fluid Dynamics, CFD) are commonly used. However, they cannot predict all flow phenomena with sufficient accuracy and, thus, have to be supported by experimental data.

Conventional experimental methods, such as PIV (Particle Image Velocimetry) or LDA (Laser Doppler Anemometry), take up a considerable expenditure of money and time. For this reason, they are only used as late as possible in the design process. However, the predictive accuracy of CFD is significantly improved if experimental data is available in an early stage of the design process. In many cases, MRI can fill this gap and thus make a significant contribution to a more efficient development process.

In this project, a modular flow system and a process chain will be designed and put into operation. The main focus in doing so is an efficient measurement cycle followed by a highly automated evaluation of the measurement data. The process includes every step starting with the design data from the industrial partner and ending with handing over the evaluated 3D flow data. Thus, procedures for manufacturing the flow model, the MRI measurement routine, and the data evaluation are specified. The aim is to carry out the entire process within one week.

Firstly, the process chain is defined, and limits and assumptions are specified. This step includes modeling and manufacturing, as well as the design of MRI routines and the subsequent data evaluation.

Besides, modifications to the MRI scanner and the flow supply have to be made to provide a modular test infrastructure for quick and easy change of flow models. MRI components specially designed for flow measurements will be used to enable the most productive measurement cycle possible.

However, a reliable quantitative comparison of the flow field from MRI with the results from CFD is only possible after aligning and interpolating the data sets on the same grid. Making this process time and cost-efficient, a high degree of automation is intended.

The defined process is first tested with simplified geometries and optimized in terms of time and data quality. After this validation, complex components of the cooling system and the combustion chamber from the latest design process are measured and compared with CFD-data.