Neural Networks in Friction Factor Analysis of Smooth Pipe Bends

Published: 19 December 2022| Version 1 | DOI: 10.17632/sjvbwh5ckg.1


PROGRAM SUMMARY No. of lines in distributed program, including test data, etc.: 481 No. of bytes in distributed program, including test data, etc.: 14540.8 Distribution format: .py, .csv Programming language: Python Computer: Any workstation or laptop computer running TensorFlow, Google Colab, Anaconda, Jupyter, pandas, NumPy, Microsoft Azure and Alteryx. Operating system: Windows and Mac OS, Linux. Nature of problem: Navier-Stokes equations are solved numerically in ANSYS Fluent using Reynolds stress model for turbulence. The simulated values of friction factor are validated with theoretical and experimental data obtained from literature. Artificial neural networks are then used for a prediction-based augmentation of friction factor. The capabilities of the neural networks is discussed, in regard to computational cost and domain limitations. Solution method: The simulation data is obtained through Reynolds stress modelling of fluid flow through pipe. This data is augmented using the artificial neural network model that predicts within and without data domain. Restrictions: The code used in this research is limited to smooth pipe bends, in which friction factor is analysed using a steady state incompressible fluid flow. Runtime: The artificial neural network produces results within a span of 20 seconds for three-dimensional geometry, using the allocated free computational resources of Google Colaboratory cloud-based computing system.


Steps to reproduce

1. Load the program '' 2. Copy the datafile 'ANN_Friction_TrainSetRaw.csv' into working folder 3. Trained model will be saved as 'Vasa_ANN_model.h5' 4. Prediction data will be saved as 'ANN_Prediction.csv' 5. Trained model may be used for further analysis without the need to execute entire code again.


National Institute of Technology Rourkela


Machine Learning, Pipeline, Skin Friction Drag, Deep Learning, Neural Network