“I enjoy computation that supports scientific research. I am also a big fan of LaTeX”
Role in Vademecom:
Combustion data sets span large dimensionality due to many chemical species and many reactions taking part in the combustion of even the simplest fuels such as methane. This poses a challenge for their successful interpretation. The goal of my research is to produce reduced-order models that could be effectively applied to simulate and predict combustion phenomena. To achieve this, I use dimensionality reduction methods such as Principal Component Analysis (PCA), combined with regression and physical reduction techniques. These techniques are applied on training data sets since principal components from simple systems (such as 0-D or 1-D reactors) can be carried to simulate more complex ones. My aim is to start with sensitivity analysis to observe how changes in the training basis affect the generated model and next, how different models perform when applied to various real systems. The produced models will be validated in Large Eddy Simulations where I will also assess the impact of sub-grid models.
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