Data Assimilation in Combustion

Data Assimilation in Combustion

Internship Description

Data assimilation (DA) constrains numerical models with observations for improved
simulations and predictions. Recently, ensemble-based Bayesian data assimilation (EnDA)
methods gained in popularity, being proven efficient for estimation and uncertainty
quantification at reasonable computational requirements. The goal of this project is to explore
the feasibility of applying advanced EnDA methods to combustion simulations and assess
their efficiency in estimating state variables and/or reaction parameters. We will focus on
steady one-dimensional flames, as well as transient ignition simulations.

Faculty Name

Field of Study

Applied Mathematics, Mechanical Engineering