<![CDATA[Backward Facing Step(BFS) has been widely recognized for its application to turbulence fields in deep flow. The flow separation occurs due to a sudden change in geometry. To know the phenomenon of flow in BFS can be done with a numerical approach. In some cases, numerical studies have a weakness in the computational time aspect. This study focuses on the prediction of Cp and Cf on BFS flow using Machine Learning. It begins with a meshing sensitivity approach with the number of elements as much as 22188 cels in a numerical simulation with a step height of 12.7 mm. This numerical study will be carried out using Reynolds number in the turbulent region of Re 36000. The turbulent k-omega shear stress transport model was used to perform numerical simulations in the open-source software package OpenFOAM®. Simulation data in the form of speed and pressure at each node that represents the form of turbulence is used as a dataset in Machine Learning. Three Machine Learning models, namely Multi-Layer Perceptron, RandomForrest, and Multiple Linear Regression are used to predict Cp and Cf. The effectiveness of each of these models is -101.5% for Multi-Layer Perceptron, 96% for RandomForrest, and 99% for Multiple Linear Regression. With the best effectiveness value, the Machine Learning Multiple Linear Regression model is used to get the predicted Cp and Cf values from variations in step height of 9.525 mm, 6.35 mm, and 3.175 mm. With these results, it shows that the Machine Learning model can be used to predict the BFS turbulence flow obtained from the results of the OpenFoam® numerical approach.]]>
Copyrights © 2021
