Prediction of head loss in bore pipe using neural network modelling

Muhamad Nasir, Nurul Najihah (2018) Prediction of head loss in bore pipe using neural network modelling. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

The research of this study is to quantify friction head loss in pipe by doing the experiment,calculated using equation and predicted the output by using Neural Network Modelling (NNM).In engineering practice,it is frequently necessary to estimate the head loss incurred by a fluid as it flows through a pipeline and can cause friction force along the pipe wall that created against the fluid. The method used in this experiment is by applying the Neural Network Modelling in the fluid friction head loss experiment using Matlab Software.Neural Network modelling is a computer programs designed to gather the knowledge by detecting the patterns and relationships in data and learn (or trained) through experience.Based on the result and analysis obtained,it is suggested best to use Tansig transfer function in Neural Network Modelling for prediction output as it give the closest and nearest value to the calculation data.Besides that,an error calculation also were done to determine the best performance of prediction output from the modelling.As conclusion,it is proven that this is one of alternatives that can be used to save money and time.

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