Reports to (Job Title):
R&D Manager – Codes & Methods
To collaborate to newcleo’s experimental campaign of thermal-hydraulic experiments to support the design activities for new LFR units.
A compilation of the available empirical correlations from literature in a single and comprehensive library is currently on-going to characterize heat transfer and thermal-hydraulic phenomena with liquid metals. This library will also collect the results from the future experiments. This internship position focuses on the literature review about the existing correlations and supports the development of this library.
Several empirical correlations are available in literature to describe heat transfer, thermalhydraulics and pressure drop phenomena occurring in presence of liquid metals, which are used as coolant for nuclear fast reactors. Every physical correlation comes with its validity range given in terms of dimensionless numbers, flow conditions, and for particular geometrical configurations. The use of a correlation outside the validity range does not guarantee accurate predictions of the quantity of interest. So a careful check about the correlation’s appropriateness
must be done before use.
Some correlations yield directly dimensionless numbers, expressing the importance of a given phenomenon with respect to other competing ones, like in the case of the Nusselt number. Heat transfer and pressure drop correlations (Darcy factor for instance) are fundamental for the design of heat exchangers, which constitutes a large part of the forthcoming experimental program planned by newcleo. Newcleo is implementing a Python package as unique and standardized entry point for evaluating all the empirical correlations needed for general LMFR applications. Moreover, this package will be used to support the next experimental campaign, including possible implementation of the new correlations arising from it.
During this internship the student will participate to the development of the Python package lmth23 (Liquid-Metal Thermal-Hydraulics). In particular, the correlations for the friction factor and for the Nusselt number in LMFR fuel bundles and once-through heat exchangers with cross-flow will be considered. The first release of the package will provide the desired quantity given the characteristic geometric parameters and coolant velocity at input to calculate the Reynolds number appropriately. In addition to reviewing and implementing the physical correlations, the student will focus on implementation techniques and high quality standards for code development. This will make the package easier to maintain and extend in the future, serving both uses in industry and research. Therefore, the student will develop/consolidate skills in the following aspects of software engineering in Python:
• Object-Oriented programming based on SOLID principles;
• Python Dynamic programming;
• design patterns (template and factory patterns in particular);
• CI/CD pipelines (including regression tests);
• write well-documented code for scientific applications.
The internship will be supervised by an engineer from R&D team of newcleo.
Master student in Nuclear, Mechanical or Aerospace Engineering; Applied Physics; Informatics
and Computational Sciences.
Fluent Italian and English
Experience / Professional requirements
- Fundamentals in thermal-hydraulics and heat transfer
- • Basic knowledge of software programming (previous experience in Python is very appreciated)
- • Basic knowledge of Linux operative system
- Experience using git, basic knowledge on liquid metal-cooled fast reactors.