A data-driven multiscale model for reactive wetting simulations

• Published in: Computers & fluids Vol. 276; no. C; p. 106259
• Main Authors: Ray, Jaideep, Horner, Jeffrey S., Winter, Ian, Kemmenoe, David J., Arata, Edward R., Chandross, Michael, Roberts, Scott A., Grillet, Anne M.
• Format: Journal Article
• Language: English
• Published: United Kingdom Elsevier Ltd 30.05.2024; Elsevier
• Subjects: Brazing; Continuum modeling; Markov process; Molecular dynamics; Reactive wetting; Sessile drops; Markov process; Molecular dynamics; Brazing; Continuum modeling; Sessile drops; Reactive wetting
• ISSN: 0045-7930; 1879-0747
• DOI: 10.1016/j.compfluid.2024.106259

We describe a data-driven, multiscale technique to model reactive wetting of a silver–aluminum alloy on a Kovar™ (Fe-Ni-Co alloy) surface. We employ molecular dynamics simulations to elucidate the dependence of surface tension and wetting angle on the drop’s composition and temperature. A design of computational experiments is used to efficiently generate training data of surface tension and wetting angle from a limited number of molecular dynamics simulations. The simulation results are used to parameterize models of the material’s wetting properties and compute the uncertainty in the models due to limited data. The data-driven models are incorporated into an engineering-scale (continuum) model of a silver–aluminum sessile drop on a Kovar™ substrate. Model predictions of the wetting angle are compared with experiments of pure silver spreading on Kovar™ to quantify the model-form errors introduced by the limited training data versus the simplifications inherent in the molecular dynamics simulations. The paper presents innovations in the determination of “convergence” of noisy MD simulations before they are used to extract the wetting angle and surface tension, and the construction of their models which approximate physio-chemical processes that are left unresolved by the engineering-scale model. Together, these constitute a multiscale approach that integrates molecular-scale information into continuum scale models. •Constructed a data-driven wetting model from molecular dynamics simulations.•Studied wetting of molten silver-aluminum alloy on a Kovar™ substrate for brazing.•Performed molecular dynamics simulation to predict surface tension & wetting angle.•Calibrated probabilistic, data-driven surrogate model using limited training data.•Integrated wetting model into engineering-scale finite element brazing model.•Compared model to experiment, identifying missing physics in simulations.