Energies, Vol. 18, Pages 5935: A Similarity-Based Scaling Methodology for the Thermal-Hydraulic Design of Dual Fluid Reactor Demonstrators

Energies, Vol. 18, Pages 5935: A Similarity-Based Scaling Methodology for the Thermal-Hydraulic Design of Dual Fluid Reactor Demonstrators

Energies doi: 10.3390/en18225935

Authors:
Michał Spirzewski
Mateusz Marek Nowak

The Dual Fluid Reactor (DFR) is a Generation IV concept that relies on a phased development pathway using a low-temperature microdemonstrator (μDEMO) and a high-temperature minidemonstrator (mDEMO). A rigorous methodology is required to scale experimental data between these facilities to ensure the reliable design of the final reactor. This paper establishes such a methodology grounded in Similarity Theory. The Cathare-2 system code was used to perform a parametric study on a simplified model of the demonstrators, which use lead–bismuth eutectic and pure liquid lead, respectively. This study focused on identifying the specific operating conditions required to match key “defining” dimensionless numbers—the Reynolds number (Re) for dynamic similarity and the Peclet number (Peh) for thermal similarity. The analysis successfully identified and presented the distinct operating ranges of fluid velocity and mass flow required to achieve either state. Results show that matching the Reynolds number allows for the dimensionless pressure drop to be scaled with a deviation below 0.2%, while matching the Peclet number allows for the dimensionless temperature profile to be scaled with a deviation under 2.5%. The central finding is that dynamic and thermal similarity cannot be achieved simultaneously due to the different working fluids and temperatures of the demonstrators. This forces a strategic choice in experimental design, where an experiment must be tailored to investigate either fluid dynamics or heat transfer. This work provides the foundational “rulebook” for designing these crucial experiments, ensuring that data from the DFR demonstrator program is both reliable and scalable.