Development of Machine Learning Models for Critical Heat Flux predictions H/F

Summary: Internship focused on developing machine learning models to predict Critical Heat Flux in nuclear reactors. The role involves analyzing experimental data, assessing existing models, and applying transfer learning and uncertainty quantification techniques within an international OECD/NEA project.

### Subject

Development of Machine Learning Models for Critical Heat Flux predictions

### Job description

The Critical Heat Flux (CHF) is a physical phenomenon that may cause the deterioration of the heat transfer in the core of nuclear reactors, potentially leading to core damage. Its accurate prediction is therefore a crucial issue in nuclear reactor safety. The internship deals with the prediction of the CHF through the development of machine learning techniques and the quantification of the associated uncertainty. It will be realized in the framework of an international OECD/NEA project on the development and application of artificial intelligence methodologies for nuclear engineering. Within this framework, the intern will:

• Perform a preliminary literature review on the topic and get familiar with the previous works performed at CEA, based on experimental data in tubes with uniform axial heat flux profiles
• Analyse the new experimental CHF database (e.g., tube with non-uniform heat flux, rod-bundle, rectangular channel, …) to understand the physical phenomenon and to verify the coherence and exploitability of the experimental data.
• Perform an initial assessment of previously developed machine learning models to the new database.
• Develop transfer learning to predict as accurately as possible the CHF for the new experiments.
• If time allows it, investigate the explainability issue and quantify the uncertainty of the models developed during the internship applying different state-of-the-art methodologies such as conformal prediction, hierarchical models and other coverage-driven techniques.
• Present and discuss the work during technical meetings and write a final report.

### Applicant Profile

The ideal candidate is a MSc. student (in French system: M2 level or Ecole d’ingénieur) who has a good knowledge of artificial intelligence and neural networks with an interest in fluid mechanics. Knowledge of the physics processes underlying the CHF phenomenon will be considered a plus. Good skills in Python coding and code management are required. We welcome previous experience with any major deep learning framework (PyTorch, JAX, Tensorflow) and with uncertainty quantification approaches. Given the international context of the OECD/NEA project, fluency in English is considered necessary (French will be considered a plus).