Developing a framework to reconstruct bubble size distributions from local measurements

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• l’apport de solutions aux défis sociétaux de l’énergie et du climat, en favorisant la transition vers une mobilité durable et l’émergence d’un mix énergétique plus diversifié ;
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Partie intégrante d’IFPEN, l’école d’ingénieurs IFP School prépare les générations futures à relever ces défis.

Developing a framework to reconstruct bubble size distributions from local measurements

The bubble diameter is a critical parameter influencing both hydrodynamics and mass transfer in large-scale bubble column reactors. Doppler probes have been designed to detect the presence of gas and the velocity of the gas–liquid interface. However, these probes only measure the component of the translational velocity along the fiber axis. As a result, they do not provide the actual mean bubble diameter but instead yield an average chord length.

Description

Depending on the flow conditions, as well as the bubble shape and inclination, deconvolving the chord length distribution to compute the bubble diameter distribution, is highly challenging–if not impossible. Numerical methods offer a promising means to address this challenge by extracting features directly from the dataset.

This internship will focus on developing a methodology to establish a link between the information provided by the Doppler probe and the bubble diameter. For this purpose, experimental data obtained from a 40 cm bubble column, combined with numerical results produced with an in-house code, will be used to generate datasets.

The work will be divided in several stages:

• Conduct a literature review to select the best methodology to address the issue
• Acquire a large data set representative of realistic operating conditions
• Develop a numerical tool with the methodology previously chosen
• Propose relations between the cord distribution and the bubble size distribution from what has been learned on the datasets

Required profile

Master 2 in mathematics / computing sciences. 

• Experience in python scripting, programming skills, interested in developing machine-learning tools.

Additional information

Duration of the internship: 6 months
Workplace: IFPEN Lyon, Rond-point de l'échangeur de Solaize, 69360 Solaize
Transport: public transportation / personal vehicle
Paid internship: around 1130€/month (gross)