English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

An overarching challenge in rheology is to develop constitutive models forcomplex fluids for which we lack accurate first principles theory. A furtherchallenge is that most experiments probing dynamical structure and rheology doso only in very simple flow fields that are not characteristic of the complexdeformation histories experienced by material in a processing application. Arecently-developed experimental methodology holds potential to overcome thischallenge by incorporating a fluidic four-roll mill (FFoRM) into scanningsmall-angle x-ray scattering instrumentation (sSAXS) [Corona, P. T. et al. Sci.Rep. 8, 15559 (2018); Corona, P. T. et al. Phys. Rev. Mater 6, 045603 (2022)]to rapidly generate large data sets of scattering intensity for complex fluidsalong diverse Lagrangian flow histories. To exploit this uniquely richFFoRM-sSAXS data, we propose a machine learning framework, Scattering-InformedMicrostructure Prediction under Lagrangian Evolution (SIMPLE), which usesFFoRM-sSAXS data to learn an evolution equation for the scattering intensityand an associated tensorial differential constitutive equation for the stress.The framework incorporates material frame indifference and invariance toarbitrary rotations by data preprocessing. We use autoencoders to find anefficient reduced order model for the scattering intensity and neural networkordinary differential equations to predict the time evolution of the modelcoordinates. The framework is validated on a synthetic FFoRM-sSAXS data set fora dilute rigid rod suspension. The model accurately predicts microstructuralevolution and rheology for flows that differ significantly from those used intraining. SIMPLE is compatible with but does not require material-specificconstraints or assumptions.

Scattering-Informed Microstructure Prediction during Lagrangian Evolution (SIMPLE) -- A data-driven framework for modeling complex fluids in flow