Reduced order modelling of combustion using convolutional neural network

It is well known that CFD simulations of a complex combustion system, such as Moderate or Intense Low-oxygen Dilution (MILD) combustion, requires consid- erable computational resources. This precludes various applications including the use of CFD in real time control systems. The idea of a reduced order model (ROM) was born from the desire to overcome this obstacle. A ROM, if properly instructed, returns the output of a requested CFD simulation in extremely short time. This one is an ideal mechanism with two basic gears: the input size reduction technique and the interpolation method. This project proposes a study on the applicability of convolutional neural network (CNN) as a dimensionality reduction technique. The code written for this purpose will be presented in detail, as well as pre and post processing. A sensibility analysis will be carry out to find out which parame- ters to adjust and how in order to achieve the optimum. Finally, the network will be compared in its peculiarity and its results with Principal Component Analysis (PCA), the technique used by the BURN group of Libre University of Bruxelles for the same purpose. Moreover with the desire to improve, we went further by trying to overcome the limits dictated by the rules of a legitimate comparation between PCA and CNN. Lastly, the author considers necessary to provide the theoretical basis in order to enrich and support what has just been described. Therefore, you will also find introductions / insights on MILD combustion, CFD of a combustion system, neural networks and the aspects related to them.