Swimming efficiency in a shear-thinning fluid

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   Sthavishtha R. Bhopalam, Ruben Juanes, Hector Gomez · 2025 · Fluid Dynamics · AI summary available

   Abstract

   The displacement of a more viscous fluid by a less viscous immiscible fluid in confined geometries is a fundamental problem in multiphase flows. Recent experiments have shown that such fluid-fluid displacement in micro-capillary tubes can lead to interfacial instabilities and, eventually, bubble pinch-off. A critical yet often overlooked aspect of this system is the effect of the tube's deformability on the onset of interfacial instability and bubble pinch-off. Here, we present a computational fluid-structure interaction model and an algorithm to simulate this fluid-fluid displacement problem in a soft capillary tube. We use a phase-field model for the fluids and a nonlinear hyperelastic mod…

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   Neil Ashton, Johannes Brandstetter, Siddhartha Mishra · 2025 · Fluid Dynamics · AI summary available

   Abstract

   Driven by the advancement of GPUs and AI, the field of Computational Fluid Dynamics (CFD) is undergoing significant transformations. This paper bridges the gap between the machine learning and CFD communities by deconstructing industrial-scale CFD simulations into their core components. Our main contribution is to propose the first scaling law that incorporates CFD inputs for both data generation and model training to outline the unique challenges of developing and deploying these next-generation AI models for complex fluid dynamics problems. Using our new scaling law, we establish quantitative estimates for the large-scale limit, distinguishing between regimes where the cost of data generat…

3. Related Not relevant

   Teaching the Incompressible Navier-Stokes Equations to Fast Neural Surrogate Models in 3D

   Nils Wandel, Michael Weinmann, Reinhard Klein · 2020 · Fluid Dynamics · Not summarized yet

   Abstract

   Physically plausible fluid simulations play an important role in modern computer graphics and engineering. However, in order to achieve real-time performance, computational speed needs to be traded-off with physical accuracy. Surrogate fluid models based on neural networks have the potential to achieve both, fast fluid simulations and high physical accuracy. However, these approaches rely on massive amounts of training data, require complex pipelines for training and inference or do not generalize to new fluid domains. In this work, we present significant extensions to a recently proposed deep learning framework, which addresses the aforementioned challenges in 2D. We go from 2D to 3D and pr…

4. Related Not relevant

   Single-bubble dynamics in histotripsy and high-amplitude ultrasound: Modeling and validation

   Lauren Mancia, Mauro Rodriguez, Jonathan Sukovich et al. · 2020 · Fluid Dynamics · AI summary available

   Abstract

   A variety of approaches have been used to model the dynamics of a single, isolated bubble nucleated by a microsecond length high-amplitude ultrasound pulse (e.g., a histotripsy pulse). Until recently, the lack of single--bubble experimental radius vs. time data for bubble dynamics under a well-characterized driving pressure has limited model validation efforts. This study uses radius vs. time measurements of single, spherical histotripsy-nucleated bubbles in water [Wilson et al., Phys. Rev. E, 2019, 99, 043103] to quantitatively compare and validate a variety of bubble dynamics modeling approaches, including compressible and incompressible models as well as different thermal models. A strate…

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   Mixing by stirring: optimizing shapes and strategies

   Maximilian F. Eggl, Peter J. Schmid · 2021 · Fluid Dynamics · AI summary available

   Abstract

   The mixing of binary fluids by stirrers is a commonplace procedure in many industrial and natural settings, and mixing efficiency directly translates into more homogeneous final products, more enriched compounds, and often substantial economic savings in energy and input ingredients. Enhancements in mixing efficiency can be accomplished by unorthodox stirring protocols as well as modified stirrer shapes that utilize unsteady hydrodynamics and vortex-shedding features to instigate the formation of fluid filaments which ultimately succumb to diffusion and produce a homogeneous mixture. We propose a PDE-constrained optimization approach to address the problem of mixing enhancement for binary fl…

6. Related Not relevant

   Effects of microplastics and surfactants on surface roughness of water waves

   Yukun Sun, Christopher Ruf, Thomas Bakker et al. · 2021 · Fluid Dynamics · Not summarized yet

   Abstract

   In this paper, we study the flow physics underlying the recently developed remote sensing capability of detecting oceanic microplastics, which is based on the measurable surface roughness reduction induced by the presence of microplastics on the ocean surface. In particular, we are interested in whether this roughness reduction is caused by the microplastics as floating particles, or by the surfactants which follow similar transport paths as microplastics. For this purpose, we experimentally test the effects of floating particles and surfactants on surface roughness, quantified by the mean square slope (MSS), with waves generated by a mechanical wave maker or by wind. For microplastics, we f…

7. Related Not relevant

   Physics-informed neural networks (PINNs) for fluid mechanics: A review

   Shengze Cai, Zhiping Mao, Zhicheng Wang et al. · 2021 · Fluid Dynamics · AI summary available

   Abstract

   Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier-Stokes equations (NSE), we still cannot incorporate seamlessly noisy data into existing algorithms, mesh-generation is complex, and we cannot tackle high-dimensional problems governed by parametrized NSE. Moreover, solving inverse flow problems is often prohibitively expensive and requires complex and expensive formulations and new computer codes. Here, we review flow physics-informed learning, integrating seamlessly data and mathematical models, and implementing them using physics-informed neural networks (PINNs). We demonstrate the effectiveness of PINNs for inve…

8. Related Not relevant

   State-space aerodynamic model reveals high force control authority and predictability in flapping flight

   Yagiz E. Bayiz, Bo Cheng · 2021 · Journal of Royal Society Interface, Volume 18, Issue 181 (2021) · AI summary available

   Abstract

   Flying animals resort to fast, large-degree-of-freedom motion of flapping wings, a key feature that distinguishes them from rotary or fixed-winged robotic fliers with limited motion of aerodynamic surfaces. However, flapping-wing aerodynamics are characterised by highly unsteady and three-dimensional flows difficult to model or control, and accurate aerodynamic force predictions often rely on expensive computational or experimental methods. Here, we developed a computationally efficient and data-driven state-space model to dynamically map wing kinematics to aerodynamic forces/moments. This model was trained and tested with a total of 548 different flapping-wing motions and surpassed the accu…