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Added-mass and added-moment-of-inertia tensors of porous fractal flocs

Lucja Stawikowska1, Samuel Briney2, Xiao Yu3, and S. Balachandar1,3,*

  • 1Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida, USA
  • 2Corvid Technologies, Mooresville, North Carolina, USA
  • 3Engineering School of Sustainable Infrastructure and Environment, Gainesville, Florida, USA

  • *Contact author: bala1s@ufl.edu
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Phys. Rev. Fluids 11, 094303 – Published 10 September, 2026

DOI: https://doi.org/10.1103/58rw-pvt4

Abstract

Fractal flocs are highly porous aggregates whose hydrodynamic response differs fundamentally from that of solid particles of comparable size and shape. In particular, the fluid trapped within the porous structure alters the effective added inertia and rotational coupling experienced by the aggregate, thereby influencing its translational and rotational dynamics in unsteady flows. In this work we investigate the added-mass and added-moment-of-inertia tensors of fractal flocs generated over a broad range of floc sizes and fractal dimensions using a binary-superposition-based computational framework. Large statistical ensembles are used to characterize both the mean behavior and the morphology-induced variability of the inertia tensors. The results show that the principal added-mass and added-moment-of-inertia coefficients exhibit systematic scaling with floc size and fractal morphology while displaying substantial deviations from equivalent solid ellipsoids. Despite significant geometric irregularity at the level of individual particles, the variability among different realizations decreases with increasing aggregate size, revealing an emergent self-averaging behavior for large flocs. Due to their highly anisotropic and porous structure, flocs with smaller fractal dimension exhibit substantially larger added-moment-of-inertia coefficients, which could influence their orientation dynamics in accelerating or turbulent environments. Based on these observations, reduced-order stochastic representations are developed to model the tensor statistics using a small number of physically meaningful parameters. The statistical framework developed here provides a foundation for incorporating morphology-dependent added-inertia effects into large-scale simulations of particle-laden and multiphase flows, including applications involving sediment transport, marine aggregates, aerosols, and industrial particulate systems.

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Phys. Rev. Fluids 11, 094303– Published 10 September, 2026
  • Received 17 May 2026
  • Accepted 19 August 2026
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