The re-kindled fascination in machine learning (ML), observed over the last few decades,
has also percolated into natural sciences and engineering. ML algorithms are now used in …

The identification of the two-phase flow regime is the most fundamental and crucial target of
fluid mechanics in the nuclear thermal-hydraulic analysis since the accuracy of fluid and …

Gas–liquid two-phase flows through long pipelines are one of the most common cases
found in chemical, oil, and gas industries. In contrast to the gas/Newtonian liquid systems …

Deep neural network is a powerful technique in discovering the hidden physics behind the
transport phenomena through big-data training. In this study, the application of physic …

Direct numerical simulations of bubbly multiphase flows are used to find closure terms for a
simple model of the average flow, using Neural Networks (NNs). The flow considered …

Liquid-gas flows in pipelines occur frequently in the mining, nuclear, and oil industry. One of
the non-contact techniques useful for studying such flows is the gamma ray absorption …

Swirling flow is one of the commonly-recognized techniques to control working processes in
various engineering fields. A fundamental understanding of the swirling flow pattern is …

The problem of predicting the regime of a two-phase flow is considered. An approach is
proposed that classifies the flow regime using Deep Neural Networks (DNNs) operating on …

Instantaneous readouts of an electrical resistivity probe are taken in an upward vertical air–
water mixture. The signals are further processed to render the statistical moments and the …

Multiphase flow measurement is a very challenging issue in process industry. There are
several techniques to estimate multiphase flow parameters. However, these techniques …