Dataset of numerically-generated interfaces of Newtonian jets in CIJ regime

Maîtrejean, Guillaume; Samson, Adeline Leclercq; Roux, Denis

doi:10.1016/j.dib.2022.108215

Cited by 2 publications

(4 citation statements)

References 7 publications

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“…To assess the feasibility of the above approach, a large dataset 7,8 of numerically-generated jets of Newtonian fluids is used in the present article. First, in section II, a brief presentation of the generation of the dataset of fluid jets is made while machine learning methods and associated datasets are described in section III.…”

Section: Fig 1: Common Cij and Visualization Setupmentioning

confidence: 99%

“…The dataset of numerically-generated interfaces of Newtonian jets in CIJ regime used in this work can be found at 8 under Creative Commons Attribution license. Data contained in the dataset are thoroughly described in 7 and the reader is invited to refer to this article for more detailed information about the dataset itself. In this section we briefly recall the main features of this numerically-generated dataset that have been exclusively generated using the open-source libraries provided by the Basilisk 9 platform.…”

Section: Dataset Of Newtonian Fluid Jets Interfacesmentioning

confidence: 99%

“…The problem is addressed in dimensionless form and the numerical domain is a square of dimension 512. The boundary conditions (BC) are described in details in 7 and the inlet BC are depicted in Fig. 4.…”

Section: A Governing Equations and Boundary Conditionsmentioning

confidence: 99%

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Rheological identification of jetted fluid using machine learning

et al. 2022

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The understanding of flowing properties of fluids and the knowledge of the related rheological properties are crucial from both a research and industrial point of view. To determine the complex rheological properties of fluids many devices have thus been developed, the so-called rheometers.The main objective of the present proposal is to develop a new kind of rheometer usingboth large datasets and the Continuous Inkjet Printing (CIJ) process.When ejecting a fluid, the CIJ ejection process competes between several forces: inertial, viscous, surface tension and elasticity, which affect the morphology of the resulting jet.Also, under certain conditions, the morphology of the jet is unique and directly related to the rheological properties of the fluid.We want to use this uniqueness to identify the fluid among a large dataset of known fluid jet morphologies to be compared with, to obtain its rheological properties.Using a large numerically-generated dataset of Newtonian fluid jets, we show in this article that the identification of the viscosity using Neural Network (NN) is not only feasible, but proves to be very accurate with an average error of less than 1% for a large range of viscosities.

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Section: Fig 1: Common Cij and Visualization Setupmentioning

confidence: 99%

Section: Dataset Of Newtonian Fluid Jets Interfacesmentioning

confidence: 99%

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Rheological identification of jetted fluid using machine learning

et al. 2022

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“…In contrast, the present dataset distinguishes itself by offering a thorough and systematic approach to the study of Newtonian fluids in CIJ printing. Notably, the only publicly available dataset on that topic is numerically generated [1] and, by construction, does not provide the comprehensive, real-world insights offered by the present dataset. The present dataset stands out as an invaluable resource in the field of CIJ research due to its meticulous attention to completeness and systematic coverage of Newtonian fluids and disturbance amplitudes.…”

Section: Introductionmentioning

confidence: 99%