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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Thermal greases, often used as thermal interface materials, are complexpaste-like mixtures composed of a base polymer in which dense metallic (orceramic) filler particles are dispersed to improve the heat transfer propertiesof the material. They have complex rheological properties that impact theperformance of the thermal interface material over its lifetime. We performrheological experiments on thermal greases and observe both stress relaxationand stress buildup regimes. This time-dependent rheological behavior of suchcomplex fluid-like materials is not captured by steady shear-thinning modelsoften used to describe these materials. We find that thixo-elasto-visco-plastic(TEVP) and nonlinear-elasto-visco-plastic (NEVP) constitutive modelscharacterize the observed stress relaxation and buildup regimes respectively.Specifically, we use the models within a data-driven approach based onphysics-informed neural networks (PINNs). PINNs are used to solve the inverseproblem of determining the rheological model parameters from the dynamicresponse in experiments. This training data is generated by startup flowexperiments at different (constant) shear rates using a shear rheometer. Wevalidate the ``learned'' models by comparing their predicted shear stressevolution to experiments under shear rates not used in the training datasets.We further validate the learned TEVP model by solving a forward problemnumerically to determine the shear stress evolution for an input step-strainprofile. Meanwhile, the NEVP model is further validated by comparison to asteady Herschel--Bulkley fit of the material's flow curve.

Data-driven rheological characterization of stress buildup and relaxation in thermal greases