The fluid dynamics of a viscoelastic fluid dripping onto a substrate.

Extensional flows of complex fluids play an important role in many industrial applications, such as spraying and atomisation, as well as microfluidic-based drop deposition. The dripping-on-substrate (DoS) technique is a conceptually-simple, but dynamically-complex, probe of the extensional rheology of low-viscosity, non-Newtonian fluids. It incorporates the capillary-driven thinning of a liquid bridge, produced by a single drop as it is slowly dispensed from a syringe pump onto a solid partially-wetting substrate.

Intercostal Lung Hernias Presenting After Minimally Invasive Cardiac Surgery.

Introduction: With the introduction of minimally invasive cardiac surgery, more commonly cases of lung herniation are starting to appear. Acquired lung hernias are classified as postoperative, traumatic, pathologic, and spontaneous. Up to 83% of lung hernias are intercostal.

Determining interfacial tension and critical micelle concentrations of surfactants from atomistic molecular simulations.

Hypothesis Atomistically-detailed models of surfactants provide quantitative information on the molecular interactions and spatial distributions at fluid interfaces. Hence, it should be possible to extract from this information, macroscopical thermophysical properties such as interfacial tension, critical micelle concentrations and the relationship between these properties and the bulk fluid surfactant concentrations. Simulations and Experiments Molecular-scale interfacial of systems containing n-dodecyl β-glucoside (APG) are simulated using classical molecular dynamics.

Liquid-Liquid Dispersion Performance Prediction and Uncertainty Quantification Using Recurrent Neural Networks.

We demonstrate the application of a recurrent neural network (RNN) to perform multistep and multivariate time-series performance predictions for stirred and static mixers as exemplars of complex multiphase systems. We employ two network architectures in this study, fitted with either long short-term memory and gated recurrent unit cells, which are trained on high-fidelity, three-dimensional, computational fluid dynamics simulations of the mixer performance, in the presence and absence of surfactants, in terms of drop size distributions and interfacial areas as a function of system parameters; these include physicochemical properties, mixer geometry, and operating conditions. Our results demonstrate that while it is possible to train RNNs with a single fully connected layer more efficiently than with an encoder-decoder structure, the latter is shown to be more capable of learning long-term dynamics underlying dispersion metrics.