Problem Definition: Predicting low-concentration pollutant emissions is computationally expensive due to the large number of chemical species involved. Methods to reduce the cost of detailed chemistry simulations are needed. Uncertainty Quantification (UQ) and optimization are increasingly used in chemical mechanism development. A key challenge in optimization is estimating uncertainty ranges for kinetic parameters derived from ab initio calculations, which involve electronic structure and reaction rate constant estimations.
Research Objectives:
- Efficient optimization to develop highly reduced chemical mechanisms, enabling high-fidelity simulations with realistic chemistry instead of simplified global mechanisms
- An innovative optimization approach applying a genetic algorithm to the constrained space of the feasible reaction parameters
- This approach will deliver an affordable mechanism size for CFD applications, while retaining the accuracy required for fine prediction of polluting species
Expected Results:
- Development of a reduction technique allowing affordable chemical scheme to compute pollutants in CFD.
- Integration of the scheme in CFD solver with validation against detailed chemical scheme
Secondments:
- Institut supérieur de l'aéronautique et de l'espace (ISAE, Toulouse, France, ca. 3 months): integration of schemes in CFD solver
- Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR, Göttingen, Germany, ca. 3 months): comparison to high-fidelity simulations
Main Supervisor:
Co-Supervisors:
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