FORUM TERATEC 2024

Presentation of 4 research projects


Project #1: NEASQC Project - 10mn

Hard optimisation problems for smart-charging of electric vehicles 

We present a case study of quantum optimization for smart-charging of electric vehicles. Tailored implementations of the Quantum Approximate Optimization Algorithm (QAOA) have been developed for this problem, and tested numerically with classical resources either by emulation of Pasqal's Rydberg atom based quantum device or using Atos Quantum Learning Machine. In some cases, quantum algorithms exhibit the same approximation ratios than conventional approximation algorithms, or improve them. 

By Joseph Mikael, Head of the Quantum Computing and Quantum Information Project, EDF
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Project #2: As part of FF4EuroHPC-10m

Quantitative studies for energy transition and high-performance computing 

Artelys is a company specialising in optimisation and decision support. The company assists, in particular, major public (ministries, regulators, cities, etc.) and private (energy companies, industrial associations, etc.) players in Europe and around the world with energy strategy and planning issues. These missions require the simulation and optimisation of energy production and transport networks (electricity, gas, heat, hydrogen) on a very large scale (e.g. the whole of Europe). Moreover, when one wishes to project into the future to build a robust production strategy or plan, one must also take into account random dimensions such as outages, weather or price scenarios (e.g., gas). The integration of these hazards requires the running of many complex and very large simulations which cannot be achieved without significant computational resources. During the presentation, we will explain how we have recently been able to unlock technological barriers for these calculations thanks to HPC, as well as the business and technological perspectives that these new capabilities open up for the future. 

 By Michaël Gabay, Director Artelys
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Project #3 - 10mn 

 Simulations of energy storage and conversion nanodevices

Over the past decade, nanoporous carbons have become established in a number of technologies, ranging from electricity storage in supercapacitors to seawater desalination. These materials are also being studied for blue energy concepts, obtained by the difference in salinity between sea water and fresh water. To understand the mechanisms by which these devices work requires probing matter at the nanoscale, which is currently beyond the reach of experiments. Our team has therefore developed a molecular simulation software, MetalWalls, dedicated to the study of electrode-electrolyte interfaces in electrochemistry. Thanks to the use of supercomputers, we have been able to study systems of previously inaccessible dimensions, with a much greater precision in the consideration of intermolecular interactions. Our work has thus allowed us to understand the mechanisms of adsorption of ions in supercapacitors, as well as their charge dynamics. In a second step, we were able to predict the performance of desalination systems, work that ultimately allowed us to parameterise continuous models used to size experimental installations. We are now trying to extend the models used to the more complex case of batteries (Li-ion or future generations), which requires the use of machine-learning type potentials for better accuracy, within the framework of the PEPR Batteries project started in 2023. 

 By Mathieu Salanne, director of the ISCD - Institut des sciences du calcul et des données Sorbonne Université
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Project #4: As part of CoEC - 10mn 

 Wind turbine modelling with the Lattice Boltzmann Method (LBM)

The production of green, low-carbon electricity from wind power is one of the key elements in the fight against climate change. As part of the wind farm design process, reliable prediction of the energy output of a wind farm is essential and is often achieved by large current simulations. However, traditional solvers based on the Navier-Stokes equations are often too expensive in terms of computational performance. More recently, the Boltzmann lattice method (LBM) has emerged as a suitable alternative to these solvers due to its outstanding computational performance on massively parallel and heterogeneous computer systems. This presentation will discuss how the LBM method can be coupled with wind turbine models and eventually allow large-scale simulations of entire wind farms. 

 By Helen Schottenhamml, PhD researcher, IFPEN
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