Data-driven closure model selection for multiphase CFD via matrix completion

The rapidly expanding closure model catalogue for multiphase Computational Fluid Dynamics (CFD) presents an increasingly pressing challenge: the selection of optimal closure models for new simulation scenarios. Conventional approaches to this model selection dilemma rely heavily on extensive domain expertise, on expensive trial-and-error model evaluations, and ongoing research efforts to develop universally applicable model sets. This paper introduces an alternative, collaborative paradigm for model selection: by constructing validation databases and storing the CFD performance of case–model interactions we aim to preserve the CFD experience and embed it within a data-driven recommender system framework. In this paper we present a first-of-its-kind performance matrix that enables the novel application of recommender system algorithms to the closure model selection problem. We showcase the power of matrix completion methods to predict optimal closure models from extremely sparse performance data, highlighting a promising pathway towards more efficient and intelligent CFD model selection. • Systematic preservation of CFD closure model experience within a performance database. • Novel application of recommender algorithms to model selection in multiphase CFD. • Recommendations based on matrix completion outperform a conventional baseline model.