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February 16, 20260 citationsOpen Access

A Vortex-Induced Correction Method for Pressure Loss Prediction in Fluid Network Theory

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YLYihan LiLLLiqiang LiangQSQingsong Song

Key Points

  • To develop a correction method for more accurate pressure loss predictions in fluid network theory.
  • Conducted three-dimensional simulations on a multidirectional parallel pipe bundle.
  • Analyzed effects of fluid properties and structural parameters on pressure loss.
  • Introduced a vortex-induced loss coefficient to modify pressure-loss formulations.
  • Higher viscosity, larger branch diameter, and favorable manifold arrangements improve prediction accuracy.
  • Counter-flow arrangements increase prediction deviations due to intensified vortex effects.
  • Correction method reduces prediction errors to within 5%.

Abstract

Traditional fluid network theory often underestimates pressure losses in complex pipe-bundle systems operating under vortex-dominated flow conditions, with deviations exceeding 20% in many cases. To address this limitation, this study proposes a vortex-based correction method. Three-dimensional simulations were performed on a multidirectional parallel pipe bundle to analyze vortex formation and to quantify the effects of fluid properties (viscosity and inlet velocity) and structural parameters (branch diameter, manifold cross-sectional ratio, and manifold arrangement) on pressure loss. To account for vortex-induced energy dissipation that is overlooked by conventional one-dimensional network models, an additional vortex-induced loss coefficient, α, is introduced to modify the pressure-loss formulation. Results indicate that higher viscosity, larger branch diameter, a higher manifold cross-sectional ratio, and a co-flow arrangement improve flow uniformity and prediction accuracy. Conversely, higher inlet velocities and counter-flow arrangements intensify vortex effects and increase prediction deviations. Least-squares fitting indicates that α ranges from 1.15 to 1.37. Implementation of the proposed correction reduces pressure-loss prediction errors to within 5%, demonstrating the method’s effectiveness and extending the applicability of fluid network theory to vortex-dominated flows.

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Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/69926503eb1f82dc367a0ca3https://doi.org/10.3390/fluids11020052
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