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Abstract The utilization of drag-reducing polymers has long been hindered by their irritancy, corrosiveness, and toxicity across various domains. In this investigation, we explored sodium alginate, a natural drag reducer, for its efficacy in reducing drag and its resilience to shear in millimeter-scale pipelines. Initially, an experimental setup was devised to assess the drag reduction capabilities of sodium alginate at varying concentrations and flow rates using Response Surface Methodology (RSM). The relationship between drag reduction (DR), concentration (C), and flow rate (Q) was established by analyzing the experimental data. Subsequently, variance analysis was employed to validate the data accuracy, with a comparison between predicted and experimental DR values revealing an error margin within ± 20%. Analysis of cyclic shear testing of sodium alginate solution in tubes demonstrated its effectiveness as a shear flow drag reducer. Furthermore, results from laser particle size analysis indicated minimal molecular breakage of sodium alginate during cyclic shear.

Abstract In this study, we investigated the drag reduction property of a premixed slurry drag reducer in a millimeter-scale pipe. The aim of this study is to establish the correlation between Darcy friction factor and drag reducer concentration (C) and volume flow (Q). First, the experimental plan was designed by using the response surface method (RSM), and then the experimental data were processed to establish the quadratic correlation between the response variable and the description variable. After that, ANOVA analysis of variance was used to verify the accuracy of the experimental data and the correlation. Finally, the prediction model is extended to a larger concentration and volume flow range, and it is found that the accuracy between the predicted value of friction coefficient and the experimental value is ± 30%, thus verifying that the correlation is suitable for the small-scale fully turbulent region. Compared with traditional experimental design and correlation methods, the implementation of Response Surface Methodology (RSM) in this study not only reduces the experimental time but also yields a more robust correlation for predicting the friction coefficient.