ANALYSIS OF THE DYNAMICS OF MULTIPHASE STRATIFIED FLOWS BASED ON MATHEMATICAL MODELING AND BIG DATA TECHNOLOGIES.
Keywords:
Multiphase flows, stratified flow dynamics, mathematical modeling, Big Data technologies, machine learning, Richardson number, neural networks, hydrodynamics.Abstract
This paper investigates the mathematical modeling and Big Data-driven analysis of multiphase stratified flow dynamics. Multiphase stratified flows are characterized by complex interactions between components with different densities, viscosities, and temperatures, as well as their temporal and spatial variations. The study employs modified Navier–Stokes equations, interphase mass exchange models, and conservation laws of mass and momentum to describe flow behavior. Special emphasis is placed on the integration of mathematical modeling techniques with modern intelligent technologies, including machine learning and neural networks. The application of Big Data technologies enables real-time processing of large-scale monitoring data, improving the accuracy of phase fraction estimation and stratification stability assessment. The Richardson number is used as a key criterion for evaluating stratification stability based on streaming data analytics. The proposed approach allows early prediction of transitions between stable and turbulent flow regimes. The results demonstrate significant scientific and practical relevance for oil and gas engineering, mining industries, water flow hydrodynamics, and environmental monitoring systems.
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