ML-Driven CFD Optimization for Falling-Film Packing

IFP Energies nouvelles - Lyon; Solaize; On-site; EUR 7,800 - 11,000; Full time

Benefits offered by this job

Remunerated internship

Job summary

IFPEN is seeking a motivated intern to work on CFD and machine learning-based optimization of structured packing geometry for falling film flow. The internship involves using validated multiphase CFD results as training data to build ML surrogates predicting wetted area and mass transfer from geometry and fluid properties.

Responsibilities include data preprocessing, model benchmarking, and exploring packing geometry optimization.

Qualifications

• Strong foundation in machine learning.
• Solid understanding of fluid mechanics/physics.
• Knowledge of chemical engineering and CFD is a plus.
• Programming skills: Python (scikit-learn, pandas, TensorFlow/PyTorch).

Responsibilities

• Literature review, selection of data, and pre-processing of CFD results.
• ML surrogate model for film flow and mass transfer.
• Benchmark against classical film and mass transfer models and integral correlations.
• Use the model to explore/optimize packing geometry.

Skills

Machine learning

Fluid mechanics

CFD knowledge

Education

Master’s degree in Applied Mathematics / Engineering

Tools

OpenFOAM

Python (scikit-learn, pandas)

TensorFlow/PyTorch

Job description

IFPEN is seeking a motivated intern to work on CFD and machine learning-based optimization of structured packing geometry for falling film flow. The internship involves using validated multiphase CFD results as training data to build ML surrogates predicting wetted area and mass transfer from geometry and fluid properties.

Responsibilities include data preprocessing, model benchmarking, and exploring packing geometry optimization.