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ABSTRAK Muhammad Ridho Alhafiz
PUBLIC Irwan Sofiyan ![](https://digilib.itb.ac.id/assets/icon/pdf.gif)
BAB 1 Muhammad Ridho Alhafiz
PUBLIC Irwan Sofiyan ![](https://digilib.itb.ac.id/assets/icon/pdf.gif)
BAB 2 Muhammad Ridho Alhafiz
PUBLIC Irwan Sofiyan ![](https://digilib.itb.ac.id/assets/icon/pdf.gif)
BAB 3 Muhammad Ridho Alhafiz
PUBLIC Irwan Sofiyan ![](https://digilib.itb.ac.id/assets/icon/pdf.gif)
BAB 4 Muhammad Ridho Alhafiz
PUBLIC Irwan Sofiyan ![](https://digilib.itb.ac.id/assets/icon/pdf.gif)
BAB 5 Muhammad Ridho Alhafiz
PUBLIC Irwan Sofiyan ![](https://digilib.itb.ac.id/assets/icon/pdf.gif)
COVER Muhammad Ridho Alhafiz
PUBLIC Irwan Sofiyan ![](https://digilib.itb.ac.id/assets/icon/pdf.gif)
DAFTAR PUSTAKA Muhammad Ridho Alhafiz
PUBLIC Irwan Sofiyan
Reynolds-Averaged Navier Stokes (RANS) method is highly favoured for turbulent
flow simulation due to its efficiency and relatively lower computational
cost than other methods. This method requires an appropriate turbulence
model to obtain an accurate turbulent flow solution. In this work, we develop
three machine learning-based turbulence models and one of them is our proposed
Feedforward Neural Network (FFNN) architecture. All of these models
are used to calculate the Reynolds stress based on the flow properties from
RANS data. These models are tested in several flow configurations where the
RANS model as the conventional method struggles. This study demonstrates
that our proposed neural network model surpasses the benchmark performance
of the conventional k ? ? turbulence model and outperforms other machine
learning-based turbulence models. The results show that the Reynolds stress
field predicted by the proposed neural network closely resembles the Direct Numerical
Simulation and Large Eddy Simulation. In this work, we also studied
the effect of input feature selection on the model’s accuracy and performed feature
importance analysis using SHAPley Additive Explanation (SHAP) that
supports the result of our study of input feature effect.