| Title: | A novel data-driven surrogate approach for fast evaluation of the dynamics of soft ellipsoidal micro-particles in dilute viscous flow |
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| Authors: | ID Wedel, Jana (Author)
ID Horvat, Ivan Dominik (Author)
ID Vovk, Nejc (Author)
ID Hriberšek, Matjaž (Author)
ID Ravnik, Jure (Author)
ID Steinmann, Paul (Author) |
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| Files: |  1-s2.0-S0045782525007248-main.pdf (7,16 MB)
MD5: D1F390D1338128C4AA5B202B403B8A37
 https://www.sciencedirect.com/science/article/pii/S0045782525007248
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | We present a novel data-driven surrogate approach for fast evaluation of the deformation dynamics of soft particles, both initially spherical and ellipsoidal, suspended in external flows, specifically predicting the hydrodynamic tractions on the particle surface. The core of the approach relies on expressing the required force dyad as a linear combination of velocity gradient components, modulated by form coefficients. These coefficients scale shear, rotational, and extensional flow contributions to the velocity gradient. Two training strategies are proposed: one utilizing analytical data, which enables a computational speedup, and another based on data obtained with 3D direct numerical simulations (DNS) using the boundary element method (BEM), with the latter demonstrating the feasibility of this approach even in the absence of analytical solutions. Validation against established literature benchmarks confirms the model’s accuracy in three scenarios: (i) ellipsoidal particles in the quasi-rigid limit in pipe flow, (ii) initially spherical particles in shear flow, and (iii) initially ellipsoidal particles in shear flow. In all cases, the data-driven surrogate approach achieves excellent agreement with reference results. This work establishes a foundation for extending our data-driven approach to flow-induced deformations of soft particles of more complex particle shapes, such as superellipsoids and other non-ellipsoidal geometries, where no analytical traction expression is available. |
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| Keywords: | neural network, pseudo-rigid bodies, Barycenter and shape dynamics, Lagrangian particle tracking |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Publication date: | 30.10.2025 |
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| Publisher: | Elsevier |
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| Year of publishing: | 2026 |
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| Number of pages: | 28 str. |
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| Numbering: | Vol. 448, part B, [article no.] 118452 |
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| PID: | 20.500.12556/DKUM-96052  |
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| UDC: | 532:004.8 |
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| ISSN on article: | 1879-2138 |
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| COBISS.SI-ID: | 257466627 |
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| Publication date in DKUM: | 27.11.2025 |
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| Views: | 162 |
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| Downloads: | 7 |
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| Metadata: |    |
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| Categories: | Misc.
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