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July 5, 2026Journal of Non-Newtonian Fluid MechanicsOpen Access

Bayesian model selection for complex flows of yield stress fluids

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Authors

ARAricia RinkensCVClemens V. VerhooselAAAlexandra Alicke

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Overview

Randomized trial assesses Bayesian model selection for yield stress fluids, indicating improved predictions under uncertainty.

Key Points

  • This work aims to develop a Bayesian framework for calibrating constitutive models of yield stress fluids in complex flows.
  • Proposed a Bayesian uncertainty quantification framework for model calibration and selection.
  • Applied framework to rheological measurements and squeeze flow experiments on Carbopol 980.
  • Compared various constitutive models, incorporating model bias and prior information.
  • Bayesian model selection provided robust probabilistic predictions with assessed model suitability.
  • Herschel–Bulkley and biviscous power law models showed good performance, but exhibited mismatches under squeeze flow conditions.
  • Expert-informed squeeze flow analysis yielded more accurate predictions than standard rheological measurements.

Cite This Study

Rinkens et al. (2026) studied this question.

synapsesocial.com/papers/6a49f36ff5d1d45b287ff97dhttps://doi.org/10.1016/j.jnnfm.2026.105639
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