Bayesian inference of the model parameters of a microfluidic acoustic flow

Revision as of 14:29, 4 March 2025

Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 955923MARIE SKŁODOWSKA-CURIE ACTIONS Innovative Training Networks (ITN) Call: H2020-MSCA-ITN-2020

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-== Abstract ==
+<pdf>Media:Draft_Lorente_Macias_227532749_2057_ECCOMAS_LorenteMacias_Juniper.pdf</pdf>
-Drop-on-demand inkjet printing is one of the most widespread applications of microflu
-idics. A typical inkjet printhead is composed of several microchannels and nozzles. Piezo
-electric actuators placed at the microchannel walls are used to force ink droplets through
-the nozzle. After droplet injection, acoustic waves travel through the microchannels until
-they are damped by viscous dissipation. These reverberations must be cancelled before
-jetting the next droplet so that all droplets are uniform. Open-loop control of the actuator
-has been designed to damp these reverberations faster and increase the jetting frequency
-[1, 2]. However, the pressure waves generated can also deform the microchannel walls [3].
-This deformation produces pressure waves in the adjacent microchannels. The amount
-of acoustic energy transmitted mainly depends on the structural properties of the shared
-boundaries. This phenomenon makes it challenging to control the reverberations effectively in the whole printhead. In this study, we formulate a Bayesian inverse problem to infer the mechanical properties of the compliant microchannel walls. The experiments are
-performed by selectively deforming one actuator while leaving the rest passive. The am
-plitude of the deformation is small enough to avoid droplet jetting. High-speed cameras
-capture the velocity of the meniscus attached to the nozzle outlets. We solve the data
-assimilation problem using a gradient-based optimization algorithm accelerated with the
-adjoint method for gradient computation. As a result, we obtain the wall properties that
-most likely fit the experimental data.
 == Acknowledgements ==
 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 955923MARIE SKŁODOWSKA-CURIE ACTIONS Innovative Training Networks (ITN) Call: H2020-MSCA-ITN-2020

Revision as of 14:29, 4 March 2025

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