Surface Tension and Contact Angle Modelling in Multiphase Lagrangian Differencing Dynamics
Bibliographic record
Abstract
Surface tension, wetting, and contact line dynamics are critical to understanding flows involving interactions between different phases, such as liquid-liquid, liquid-gas, and liquid-solid interfaces.Accurately capturing the effects of surface tension and contact angle hysteresis is essential for enhancing simulation fidelity.This paper presents a methodology that integrates surface tension and contact angle force models within the Multiphase Lagrangian Differencing Dynamics (MP-LDD) framework.The pressure jump due to surface tension and the mobility of the contact angle are implicitly incorporated into the pressure equation using the Young-Laplace equation, yielding a good initial guess in the pressure calculation to improve the stability and convergence.Simultaneously, the corresponding volumetric force is integrated into the velocity equation, providing a comprehensive and accurate representation of interfacial dynamics.The MP-LDD framework focuses on the immediate vicinity of the interface, enabling sharper and more precise calculations of surface interactions without relying on ghost particles or complex extrapolations.The approach achieves faster computations by leveraging the dynamic contact angle (DCA) model without curvature calculations and eliminates instabilities caused by abrupt curvature changes.Additionally, the second-order consistency of MP-LDD enhances predictive accuracy.The direct operation on surface meshes allows precise identification of solid boundaries and accurate application of forces at the triple point.Validation against benchmark cases demonstrates the robustness and effectiveness of the proposed methodology in simulating complex multiphase flow scenarios, establishing it as a reliable and efficient tool for interfacial flow simulations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".