Practical trainings, student assistants and theses

Storing energy is a promising solution to address the intermittent nature of renewables sources and to increase their share in the energy mix. Indeed, during periods of surplus production, energy can be stored as heat and later released to a power cycle when demand peaks. Since the involved temperatures are high to store a maximum of energy, power cycles with supercritical CO2 (sCO2) show higher efficiency than any traditional power cycle. Hence, each component of the system must be carefully optimized, with this work focusing specifically on the heat exchangers.
Printed Circuit Heat Exchangers (PCHEs) have drawn attention as potential heat exchangers for sCO2 power cycles for the past 40 years, due to the compact design and high thermal efficiency. The channels have a characteristic cross-flow section in the order of 1 mm2 and they exhibit a large variety of shapes, ranging from straight channels to more complex shapes like airfoils fins. The optimization of such heat exchangers is a promising topic to improve processes within the energy system. However, most optimization algorithms are based on Nusselt number and friction factor correlations, which limited to simple designs and are not suitable for the complex geometry.
For this reason, developing a Computational Fluid Dynamics (CFD)-aided optimization algorithm is essential to maximize the heat transfer performance, while minimizing pressure drop, especially when no established correlation exists. The first step will involve the creation of a Python or MATLAB script to automatically generate and mesh the geometries in Ansys. Next, the model will be validated by an objective function or experimental data from the literature. Ideally, the algorithm would be extend to handle more complex geometries.

• Academic studies in the field of process engineering, chemical engineering, mechanical engineering or comparable fields of study
• Knowledge of thermodynamics, heat and mass transfer phenomena
• Knowledge of Python or MATLAB
• Knowledge of Ansys Package

• Duration: 6 months
• Funding: Remuneration according to HZDR internal regulations
• Start Date: As soon as possible