Dimensionality reduction of chemical kinetic mechanisms using data-driven clustering techniques for MILD Combustion by Pietro PAGANI

The urgent need to transition to cleaner energy sources is highlighted by record-breaking temperatures and increasingly frequent extreme weather. With fossil fuels currently meeting over two-thirds of global energy needs, developing fuel-flexible and environmentally friendly combustion technologies is essential. Among the proposed techniques, Moderate or Intense Low-oxygen Dilution (MILD) combustion stands out for its high efficiency and reduced pollutant emissions, particularly of nitrogen oxides (NOx) and soot.
This phD thesis explores the application of MILD combustion by enhancing the accuracy of computational fluid dynamics (CFD) simulations. The key focus is given on data-driven modelling, which has emerged as a powerful tool for developing locally reduced chemical models from high-fidelity data.

The present work advances the state-of-the-art by enhancing the Sample-Partitioning Adaptive Reduced Chemistry (SPARC) workflow, resulting in the optimized eSPARC framework. Such method couples adaptive chemistry and machine learning to build a library of skeletal mechanisms, associated to clusters of similar thermo-chemical states and identified in a training dataset. Moreover, the novel eSPARC approach integrates advanced reduction and clustering techniques (such as Computational Singular Perturbation (CSP) coupled with Tangential Stress Rate (TSR) analysis and physic-informed clustering) to optimize the balance between chemical mechanism accuracy and computational efficiency.
This enhanced workflow demonstrates improvements in computational speed of numerical simulations in MILD conditions: the application to RANS and LES of the Adelaide Jet-in-Hot-Coflow (AJHC) burner showed speedup between 2 and 4 on the CPU time with maintained accuracy, depending on the size of the kinetic mechanism

Jury members :

• Prof. Francesco Contino (UCLouvain, Belgium), supervisor
• Prof. A. Parente (ULB, Belgium), supervisor
• Prof. Aude Simar (UCLouvain, Belgium), chairperson
• Prof. Perrine Pepiot (Cornell University, US)
• Prof. Luc Vervish (INSA Rouen Normandie, France)
• Prof. Axel Coussement (ULB, Belgium)

Link Teams : https://teams.microsoft.com/l/meetup-join/19%3ameeting_NmYwOGNiYTAtNTVjYy00ODIyLWFiMGMtMGZmNjgyMTQwZjVl%40thread.v2/0?context=%7b%22Tid%22%3a%227ab090d4-fa2e-4ecf-bc7c-4127b4d582ec%22%2c%22Oid%22%3a%22a56189bc-3713-4b21-aad0-42e1380cd2c6%22%7d