Work

Polymer-Induced Turbulent Drag Reduction: Mechanisms, Governing Parameters, Numerical Modeling, and Emerging Machine Learning Approaches—A Comprehensive Review

Year: 2026

Type: article

Abstract: This comprehensive review systematically examines the fundamental and contemporary concepts underlying the Toms effect—the phenomenon of turbulent drag reduction (DR) induced by the addition of minute concentrations of high-molecular-weight linear polymers to turbulent flows. The evolution of scientific understanding is traced from the classical studies of the mid-twentieth century to contemporary machine-learning-based approaches. The influence of four key parameters is examined in detail: the dimensionless solvent viscosity ratio β, Reynolds number Re, conformational chain length Lc/MW, and macromolecular relaxation time λ. Polymer concentration C is treated as an independent control variable through which the values of these four parameters are partially determined. The principal physical mechanisms at the molecular and hydrodynamic levels are described. The capabilities of numerical modeling approaches (DNS, LES, and RANS) are critically reviewed, along with promising directions for the application of artificial intelligence. Finally, practical guidelines are proposed for validating and interpreting experimental and numerical drag-reduction data over a broad range of hydrodynamic conditions.

Source: Applied Sciences

DOI: 10.3390/app16178403

Authors: Uzak Zhapbasbayev, Timur Bekibayev, Gaukhar Ramazanova

Institution: Satbayev University

Language: English

Topic: Rheology and Fluid Dynamics Studies

Subfield: Fluid Flow and Transfer Processes

Field: Chemical Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: Ministry of Science and Higher Education of the Republic of Kazakhstan