Abstract
In this study, the behavior of Turbulent Jet flow was investigated using Dynamic Mode Decomposition, which is a data-driven, dimension reduction method. Jet flow, which is an important and popular research topic in Fluid Dynamics and engineering applications, was considered as the fluid flow. A Large Eddy Simulation (LES) was performed using the openFOAM software to model the Jet flow. 180 snapshots were generated with the simulation to create a Jet Flow dataset of approximately 150 gigabytes. Firstly, the dynamic modes of the jet flow were extracted from this dataset to reveal the characteristic features of the flow. Then, state estimation for reconstruction of the flow were made. This significantly reduced the CPU and RAM requirement for processing data set and saved lots of disk space for storage. Performance measurements were made for the reconstructed images obtained as a result of the analyses. Two metrics were used for the measurements, namely Root Mean Square Error and Structural Similarity Index.
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