20.500.12556/DKUM-98315 Generalization of a surrogate drag force model for particle laden flow We present a three-way validation of a data-driven interparticle force model against high-fidelity direct numerical simulations (DNS) and classical Faxén predictions, in the context of laminar particle-laden pipe flow. We present an Interaction-Decomposed Neural Network (IDNN) architecture, trained for unbounded flows, and perform finite-volume DNS that fully resolve the flow around stationary spherical particles placed in a circular pipe. This allows direct measurement of hydrodynamic forces for a range of particle configurations and flow conditions. Forces computed from the DNS are compared to the IDNN model evaluated on the same instantaneous geometric inputs and to analytical estimates from Faxén-type expansions. Although the IDNN was trained on unbounded flows, we show it remains predictive in pipe geometries provided the nearest walls are sufficiently distant compared with particle cloud size; in these cases the IDNN reproduces the DNS force components and trends with particle spacing and relative orientation with close quantitative agreement. The three-way comparison confirms that the IDNN can accurately reproduce resolved forces while avoiding the high cost of particle-resolved simulations and the complex Laplacian evaluations required by Faxén theory. These results inform the use of data-driven force models in Euler–Lagrange simulations and guide future model improvement for wall-influenced and strongly interacting particle systems. multiphase flow particle-laden flow particle-resolved simulation drag force surrogate models machine learning večfazni tok tok obremenjen z delci simulacija z ločljivostjo delcev sila upora nadomestni modeli strojno učenje true false true Elsevier Angleški jezik Slovenski jezik https://www.sciencedirect.com/science/article/pii/S0997754626000695?via%3Dihub Članek v reviji 2026-06-04 14:16:07 2026-06-04 14:16:07 2026-07-20 12:54:54 0000-00-00 00:00:00 2026 0 0 11 str. part B, [article no.] 204522 Vol. 119 Sept./Oct. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2026-01-08 2026-03-18 2026-04-23 532:519.6 279667971 10.1016/j.euromechflu.2026.204522 1879-2138 1-s2.0-S0997754626000695-main.pdf 1-s2.0-S0997754626000695-main.pdf 1 E14D8A851703B7DD4A5D356D50C85D75 bd029df1ef7f305227bb2f8b0d96f2ae6221d6aa4148d1dc85079f4b8a75ae74 764c4d8d-600f-11f1-86e5-00155d580e34 https://dk.um.si/Dokument.php?lang=slv&id=206744 https://www.sciencedirect.com/science/article/pii/S0997754626000695 1 2af0686f-600f-11f1-86e5-00155d580e34 https://dk.um.si/Dokument.php?lang=slv&id=206743 Fakulteta za strojništvo 0 0 0