Single-phase fluid flow classification via learning models

Abstract

This paper applies learning models, such as support vector machines (SVM), neural networks, and mixed-integer programming kernel classifiers (MIPKC) to classify the flow pattern of a non-Newtonian fluid in an annulus/pipe. Classification of flow patterns is characterized by six attributes that represent the parameters that determine the fluid flow in the annulus/pipe. The SVM and MIPKC learning models construct a separating hyperplane in the feature space. The weights of the hyperplane represent a scaled level of importance for each of the parameters. Preliminary results show that the most efficient model with respect to computation time favours the SVM model with a polynomial kernel of degree 2. However, with respect to low error rates and sparseness of solution, one of the MIPKC models with a polynomial kernel of degree 2 outperforms the other methods.

Keywords:

• support vector machines
• neural networks
• mixed-integer programming kernel classifiers
• fluid flow pattern classification

Acknowledgements

This work was supported by the National Science Foundation Grant EIA-0205628.

Notes

^†Present address: IBM Business Analytics and Optimization, 71 S Wacker Drive, Chicago, IL 60606, USA.

1. Email: [email protected]

Additional information

Notes on contributors

Olutayo O. Oladunni

1 †