Siavash Hosseini

Siavash Hosseini
Lakehead University Thunder Bay Campus · Department of Electrical Engineering

Electrical & Computer Engineering

About

9
Publications
553
Reads
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96
Citations
Citations since 2017
9 Research Items
96 Citations
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Introduction
My research works are oriented toward Artificial Intelligence, Deep neural networks, Data Analysis, and Feature Engineering with demonstrated experience in Oil and Gas related industries. In my recent publications, I have focused on time-series data analysis to develop robust deep regressors to accurately predict the flow regimes, void fractions, and production levels of hydrocarbon products. More information about my research is available on my Google Scholar profile.

Publications

Publications (9)
Article
Full-text available
Two-phase flow is very important in many areas of science, engineering, and industry. Two-phase flow comprising gas and liquid phases is a common occurrence in oil and gas related industries. This study considers three flow regimes, including homogeneous, annular, and stratified regimes ranging from 5–90% of void fractions simulated via the Mont Ca...
Article
Full-text available
Measuring fluid characteristics is of high importance in various industries such as the polymer, petroleum, and petrochemical industries, etc. Flow regime classification and void fraction measurement are essential for predicting the performance of many systems. The efficiency of multiphase flow meters strongly depends on the flow parameters. In thi...
Article
Eight optimizer methods are combined with a perceptron neural network to achieve an optimal network and minimize errors for predicting the heat transfer rate of a ribbed triple-tube heat exchanger operating with the graphene nanoplatelets-based nanofluid. The optimization techniques consist of Harris Hawks Optimizer (HHO), Grey Wolf Optimizer (GWO)...
Article
Four nature-inspired optimizers are combined with a multilayer perceptron neural network for reaching an optimal structure aimed at predicting the overall heat transfer coefficient of a ribbed triple-tube heat exchanger in terms of the rib pitch, rib height, and nanoparticle concentration. The heat exchanger works with a hybrid nanofluid having gra...
Article
The current paper numerically predicts the convective heat transfer coefficient, pumping power, and total entropy generation of an ecofriendly-functionalized graphene nanoplatelets nanofluid inside the tubes enhanced with a novel rotary coaxial double-twisted tape, which rotates at various rotational speeds. The impacts of the nanoparticle concentr...
Article
Flow regime information can be used to enhance measurement accuracy of flowmeters. Void fraction measurement and regime identification of two-phase flows including, liquid and gas phases are crucial issues in oil and gas industries. In this study, three different regimes including annular, stratified and homogeneous in the range of 5%–90% void frac...
Conference Paper
Two-Phase Flows is Significant for Many Industries. in This Paper Three Different Regimes Including Annular, Stratified and Homogeneous in The Range of %5-90% Void Fraction, Were Simulated by Mont Carlo N-Particle(MCNP) Codes. in This Simulation, a Cesium 137 Source and Two Nal Detectors Were Used to Record Received Photons. Features of Signals in...

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Projects

Project (1)
Project
Signals Features Extraction in 2-Phase Flow Measurements