Rheological Identification of Fluids Based on Jet Morphology

Objectives

Almost all industrial production processes involve, at some stage, the transformation of materials in a fluid state. Understanding fluid behavior—particularly that of fluids with complex responses—is therefore essential in an industrial context. Traditional rheometers are designed to measure properties such as viscosity, elasticity, and yield stress, collectively referred to as rheological properties. However, measuring these properties is a lengthy process that requires specialized expertise and often costly equipment (the global rheometry market is estimated at several billion dollars annually).

We propose an innovative approach based on a paradigm shift: instead of directly measuring fluids, we identify them through unique signatures of their rheological properties, revealed in the shapes of droplets generated using precise jetting techniques that exploit the Rayleigh–Plateau instability. Widely used in continuous inkjet (CIJ) technology, this instability provides a stable and reliable way to generate droplets from fluid jets. It subjects fluids to conditions that activate the full spectrum of their rheological properties, resulting in droplet morphologies that are directly linked to their intrinsic characteristics.

Results

By injecting various fluids with known rheological properties and building a database of the corresponding droplet shapes, we can identify an unknown fluid (from a rheological standpoint) by comparing the morphology of its droplets to this database. As demonstrated in our previous work—first using numerically generated datasets and more recently with experimental data—the identification process has proven to be remarkably accurate, with a margin of error below 0.5%. The identification method, based on a simple injection device and a camera, is cost-effective, reliable, robust, fast, and requires no specialized technical expertise. This approach has already been patented.

References

Maîtrejean, G., Samson, A., Roux, D. C., & El-Kissi, N. (2022). Rheological identification of jetted fluid using machine learning. Physics of Fluids, 34(9).

Maîtrejean, Guillaume, Adeline Samson, and Denis CD Roux. "Dataset of numerically-generated interfaces of Newtonian jets in CIJ regime." Data in Brief 42 (2022): 108215.

Maîtrejean, Guillaume, et al. "Comprehensive experimental dataset on large-amplitude Rayleigh-Plateau instability in continuous InkJet printing regime." Data in Brief 52 (2024): 109941.

Guillaume Maîtrejean, Denis Roux, Method For Determining The Rheological Parameters Of A Fluid, UGA-CNRS-INPG Wo Fr Fr3112856a1, Priority 2020-07-27, Filed 2020-07-27, Published 2022-01-28