Data-driven sparse modeling and decomposition for superspreading-wetting dynamics of a droplet
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| Format: | Preprint |
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2026
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| _version_ | 1866918272118554624 |
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| author | Fukami, Kai Shoji, Eita |
| author_facet | Fukami, Kai Shoji, Eita |
| contents | Superspreading wetting is traditionally attributed to surfactant-driven mechanisms. However, recent observations of superspreading in surfactant-free nanofluids defy standard theoretical explanations. This study considers a data-driven approach to model droplet dynamics with the thickness of liquid films on the nanometer-micrometer scale in a compact form of a partial differential equation. We examine spatiotemporal film-thickness profiles resolved at the nanometer scale via phase-shifting imaging ellipsometry. For a pure solvent, the present governing equation recovers the classical lubrication physics driven by disjoining pressure and evaporation. In contrast, the nanofluid dynamics necessitates a unique transport term scaling with the gradient of the inverse film thickness. Theoretical analysis suggests this term represents a nanoparticle-induced bias flux, consistent with a hypothesized capillary wicking mechanism within the precursor film. The identification of the current nanofluid-specific term underscores the efficacy of integrating high-precision experimental measurements with data-driven modeling to unravel complex wetting dynamics. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_01776 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data-driven sparse modeling and decomposition for superspreading-wetting dynamics of a droplet Fukami, Kai Shoji, Eita Fluid Dynamics Computational Physics Superspreading wetting is traditionally attributed to surfactant-driven mechanisms. However, recent observations of superspreading in surfactant-free nanofluids defy standard theoretical explanations. This study considers a data-driven approach to model droplet dynamics with the thickness of liquid films on the nanometer-micrometer scale in a compact form of a partial differential equation. We examine spatiotemporal film-thickness profiles resolved at the nanometer scale via phase-shifting imaging ellipsometry. For a pure solvent, the present governing equation recovers the classical lubrication physics driven by disjoining pressure and evaporation. In contrast, the nanofluid dynamics necessitates a unique transport term scaling with the gradient of the inverse film thickness. Theoretical analysis suggests this term represents a nanoparticle-induced bias flux, consistent with a hypothesized capillary wicking mechanism within the precursor film. The identification of the current nanofluid-specific term underscores the efficacy of integrating high-precision experimental measurements with data-driven modeling to unravel complex wetting dynamics. |
| title | Data-driven sparse modeling and decomposition for superspreading-wetting dynamics of a droplet |
| topic | Fluid Dynamics Computational Physics |
| url | https://arxiv.org/abs/2601.01776 |