Course Descriptions

Morning Sessions (please select from the following three courses)

Theoretical and Numerical Combustion (July 06~10, Monday~Friday)

Thierry Poinsot, Institut de Mécanique des Fluides de Toulouse, CNRS, France

Course Content: Theory and numerical simulations are essential elements of modern combustion science. This course describes the fundamental theories needed to understand combustion before presenting numerical tools to compute flames, from laminar cases to turbulent burners. RANS, LES, and DNS modeling are discussed as well as numerical methods adapted to these models. The course includes the theoretical basis of turbulent combustion models and explores multiple examples of applications: steady turbulent combustion, ignition, quenching, flame-wall interactions, pollutant formation, hydrogen flames and combustion instabilities in real combustors (gas turbines, rocket engines, piston engines).

A significant part of the course can be found in the "Theoretical and Numerical Combustion" textbook with Dr Denis Veynante:

- English edition:

www.amazon.fr/Theoretical-Numerical-Combustion-Thierry-POINSOT/dp/2746639904

- Chinese edition: http://product.dangdang.com/29571730.html

Spectroscopic Diagnostics for Combustion Chemistry (July 06 ~07, Monday~Tuesday)

Pascale Desgroux, University of Lille-CNRS, France

Course Content: Laser spectroscopy enables highly sensitive measurements of important chemical species such as radicals, atoms, species (stable or radicals) in very low concentration and of course certain major species. Obtaining the concentrations of highly reactive radical species can significantly improve chemical mechanisms. Furthermore, one of the main advantages of spectroscopic diagnostics lies in their non-intrusive nature and their ability to measure instantaneously two-dimensional concentration fields. Regardless of the application (laminar/turbulent flames, high-pressure reactors, plasmas, etc.), obtaining quantitative and even relative concentrations requires a thorough understanding of the interaction between light and matter.

The objective of the course is to provide these fundamentals concepts so that users can apply them in their own research. The course will introduce some basics of spectroscopy and of light/matter interaction. It will then focus on sensitive diagnostics such as absorption methods, including cavity ring-down spectroscopy (CRDS), and 1- or 2-photon laser-induced fluorescence (LIF). The methodology for obtaining quantitative concentrations will be particularly highlighted. Finally, the last part of the course will be devoted to optical methods for measuring soot particles concentrations.

Introduction to Plasma-assisted Combustion (July 08~10, Wednesday~Friday)

Deanna Lacoste, King Adbullah University of Science and Technology, Saudi Arabia

Course Content: This short course provides the basic information to allow any graduate student, researcher or professional in engineering fields to understand the fundamentals of plasma-assisted combustion (PAC), as well as the main challenges and opportunities associated with this exciting discipline. First, an introduction of the context states the boundary conditions for the whys and hows of PAC. Then a short history of the discipline showcases the most important results obtained since the 1970’s. The basics of both plasma and combustion are then summarized before providing a detailed explanation of their coupling. The course also introduces the methods, diagnostics and tools (both experimental and modeling) available to study PAC. Finally, the most recent results as well as the open questions are discussed.

Afternoon Sessions (please select from the following three courses)

Combustion Chemistry: From Fundamentals to Kinetic Modelling for Low-Carbon Technologies (July 06~10, Monday~Friday)

Alison Tomlin, University of Leeds, UK

Course Content: Accurately predicting chemical changes is fundamentally important for predicting combustion within a range of devices including engines, boilers, furnaces and gas turbines. As we move to low carbon energy production technologies, robust models to assist in design of devices will be required for a potentially wide range of fuel types. Chemical oxidation processes, particularly for complex fuels such as biofuels or ammonia, involve very large numbers of species and reactions, posing challenges for including detailed chemistry within models of practical devices. With this in mind, the course will take students on a journey from the fundamentals of reaction kinetics basics through to constructing chemical mechanisms for different fuel types, reducing them to facilitate their use in reactive flow models and finally to quantifying the impact of inherent uncertainties on their predictive quality. Topics will include: chemical mechanism structure; temperature and pressure dependence of rate coefficients; determination of rate constants via experimental and theoretical methods; basic thermodynamics; automatic generation of reaction mechanisms including the use of machine learning; model evaluation and optimization over different temperature regimes; pollutant formation and mitigation mechanisms; future fuels and challenges they pose for combustion systems; model uncertainties and sensitivity analysis; chemical model reduction methods.

Quantum Mechanics, Statistical Mechanics, and Machine Learning for Molecular Simulations (July 06~07, Monday~ Tuesday)

Alexandre Tkatchenko, University of Luxembourg, Germany

Course Content: This course offers an introduction into the new "standard model" for molecular simulations that emerged over the past decade based on a tight integration between the principles of quantum mechanics (QM), statistical mechanics (SM), and machine learning (ML). Many challenging applications are now being tackled by increasingly powerful QM/SM/ML methodologies. These include modeling covalent materials, molecules, molecular crystals, surfaces, and even whole proteins under physiological conditions. In this lecture, I attempt to provide a reality check on these recent advances and on the developments required to enable fully predictive dynamics of complex functional molecular and material systems. Multiple challenges are highlighted -- in particular transferability in chemical space and interatomic interactions -- that should enable this field to grow for the foreseeable future.

AI for Combustion (July 08~10, Wednesday~Friday)

Matthias Ihme, Stanford University, USA

Course Content: This lecture provides an introduction to Machine Learning (ML) and Artificial Intelligence (AI) applied to combustion and reacting flows. Beginning with a review of statistical methods, we will explore essential ML techniques, including supervised, unsupervised, and generative models. This will be followed by a discussion on how thermo-physico-chemical principles can be integrated into ML frameworks to ensure robustness, stability, conservation, and interpretability. Additionally, we will examine how data-driven methods – specifically sparse regression and reinforcement learning – can be used to discover physical principles and control combustion-dynamical systems. A key emphasis of this course is contextualizing these methods within combustion, chemical kinetics, and wildfire applications, equipping attendees with a thorough understanding of the potential, limitations, and future opportunities for ML/AI in the broader field of reacting flows.