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Fundamental Research
News 31 March 2021

“Floating Car Data” to improve air quality

IFPEN’s teams have developed a tool that, using data provided by connected vehicles (Floating Car Data or FCD), makes it possible to evaluate the pollution emitted by characteristic driving behaviors and, ultimately, produce a detailed map of vehicle pollutant emissions for different sections of a region’s road network. 

Issue 46 of Science@ifpen - Earth Sciences and Environmental Technologies
News in brief

Geoheritage and geodiversity accessible to all thanks to digital technology

Emerging in the 1990s, the notions of geoheritage and geodiversity have been receiving growing attention from academic communities, international organizations and public authorities. (...) It was in this context that, in 2020, IFPEN signed a partnership agreement with UNESCO, one of the objectives of which is to share digital tools facilitating the promotion of geoheritage and geodiversity to the general public...
Issue 46 of Science@ifpen - Earth Sciences and Environmental Technologies
News in brief

Underground modeling: an essential step for the energy transition

To address the challenges of the energy transition, the subsurface has an important role to play, both in terms of providing resources and offering storage solutions. (...) Numerical models can help gain a better understanding of the subsurface with a view to its long-term management and optimal use. Developed for a number of years now at IFPEN, initially for the petroleum industry, such models cover scales ranging from the sedimentary basin to the reservoir...
Individual page

Delphine SINOQUET

Research engineer / project leader in optimization
PhD in applied mathematics
Master degree in numerical analysis (Paris 6 university) PhD in applied mathematics (Paris 13 university) : inversion problem of seismic tomography 2003-now : research engineer in applied mathematics
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Thomas LEROY

Project Manager Storage and Energy Systems for Sustainable Mobility
Thomas Leroy is graduated from ESSTIN and received the PhD degree in Control theory and Mathematics from the Ecole des Mines ParisTech, France, in 2010. He joined IFPEN as control research engineer on
Issue 45 of Science@ifpen
News in brief

Faster “flash” calculations thanks to deep learning

A large number of simulators, whether they relate to the design of reaction processes, the evolution of oil reservoirs or combustion systems, require access to thermodynamic properties. In order to provide these properties, IFPEN has been developing a library of calculation modules, called “Carnot”, named after the famous French thermodynamics expert. These calculations, in particular those concerning phase equilibrium (also known as flash calculations), generally require the use of substantial calculation resources due to the complexity of the systems considered, and represent in many cases the most time-consuming step in the simulation process.
Issue 45 of Science@ifpen
News in brief

Semantic segmentation through deep learning in materials sciences

Semantic segmentation conducted on microscopy images is a processing operation carried out to quantify a material’s porosity and its heterogeneity. It is aimed at classifying every pixel within the image (on the basis of degree of heterogeneity and porosity). However, for some materials (such as aluminas employed for catalysis), it is very difficult or even impossible using a traditional image processing approach, since porosity differences are characterized by small contrasts and complex textural variations. One way of overcoming this obstacle is to tackle semantic segmentation via deep learning, using a convolutional neural network.
Issue 45 of Science@ifpen
News in brief

Artificial Intelligence-assisted interpretation of geological images

Over the last decade, deep learning applied to image analysis has rapidly developed in scope to cover numerous fields. However, its potential remains underexploited in geology, despite the fact that it is a discipline that relies to a large extent on visual interpretation. To contribute to the digital transformation of industries related to the underground environment, researchers at IFPEN have implemented deep learning in three “profession-specific contexts”, each involving different types of geological images.
Issue 45 of Science@ifpen
News in brief

Digital Rock Physics at IFPEN

Today, characterization of geological reservoirs, a long-standing theme in petroleum exploration, becomes a base of interest for a variety of applications, such as CO2 and hydrogen storage as well as geothermal energy. In recent years, the combined use of 3D microtomography (or micro-CT ) imaging and advanced simulation techniques has allowed the emergence of a digital approach to computing the petrophysical properties of reservoir rocks (Digital Rock Physics). This represents a real complement - and in some cases an alternative - to traditional laboratory measurements.
Issue 45 of Science@ifpen
News in brief

Numerical design based on the analysis of multi-scale porous material microstructures

The design of high-quality porous materials is a major challenge for the energy efficiency of industrial processes in the fields of catalysis and biocatalysis and separation and purification operations. For such applications, these materials derive their properties of interest from their specific microstructure, incorporating a large quantity of empty spaces that are organized and connected on a nanometric scale. IFPEN and Saint Gobain Research Provence (SGRP) joined forces to acquire a tool that will ultimately facilitate the development of porous materials optimized for given usages.
Issue 45 of Science@ifpen
News in brief

Acceleration of chemical kinetics calculations through Machine Learning methods

Numerical simulations are now widely employed in the industrial world to help design systems and predict complex phenomena. Reactive flow simulation, for example, is important for numerous applications, such as vehicle and aircraft propulsion and processes in the chemicals industry.