Prashant B. Godse, Harshal D. Akolekar, A. M. Pradeep
Surface roughness is a major contributor to performance degradation in gas turbine engines. The fan and the compressor, as the first components in the engine's air path, are especially vulnerable to the effects of surface roughness. Debris ingestion, accumulation of grime, dust, or insect remnants, typically at low atmospheric conditions, over several cycles of operation are some major causes of surface roughness over the blade surfaces. The flow in compressor rotors is inherently highly complex. From the perspective of the component designers, it is thus important to study the effect of surface roughness on the performance and flow physics, especially at near-stall conditions. In this study, we examine the effect of surface roughness on flow physics such as shock-boundary layer interactions, tip and hub flow separations, the formation and changes in the critical points, and tip leakage vortices amongst other phenomena. Steady and unsteady Reynolds Averaged Navier Stokes (RANS) calculations are conducted at near-stall conditions for smooth and rough NASA (National Aeronautics and Space Administration) rotor 67 blades. Surface streamlines, Q-criterion, and entropy contours aid in analyzing the flow physics qualitatively and quantitatively. It is observed that from the onset of stall, to fully stalled conditions, the blockage varies from 21.7% to 59.6% from 90% span to the tip in the smooth case, and from 40.5% to 75.2% in the rough case. This significant blockage, caused by vortex breakdown and chaotic flow structures, leads to the onset of full rotor stall.
{"title":"Surface roughness effects in a transonic axial flow compressor operating at near-stall conditions","authors":"Prashant B. Godse, Harshal D. Akolekar, A. M. Pradeep","doi":"arxiv-2409.07344","DOIUrl":"https://doi.org/arxiv-2409.07344","url":null,"abstract":"Surface roughness is a major contributor to performance degradation in gas\u0000turbine engines. The fan and the compressor, as the first components in the\u0000engine's air path, are especially vulnerable to the effects of surface\u0000roughness. Debris ingestion, accumulation of grime, dust, or insect remnants,\u0000typically at low atmospheric conditions, over several cycles of operation are\u0000some major causes of surface roughness over the blade surfaces. The flow in\u0000compressor rotors is inherently highly complex. From the perspective of the\u0000component designers, it is thus important to study the effect of surface\u0000roughness on the performance and flow physics, especially at near-stall\u0000conditions. In this study, we examine the effect of surface roughness on flow\u0000physics such as shock-boundary layer interactions, tip and hub flow\u0000separations, the formation and changes in the critical points, and tip leakage\u0000vortices amongst other phenomena. Steady and unsteady Reynolds Averaged Navier\u0000Stokes (RANS) calculations are conducted at near-stall conditions for smooth\u0000and rough NASA (National Aeronautics and Space Administration) rotor 67 blades.\u0000Surface streamlines, Q-criterion, and entropy contours aid in analyzing the\u0000flow physics qualitatively and quantitatively. It is observed that from the\u0000onset of stall, to fully stalled conditions, the blockage varies from 21.7% to\u000059.6% from 90% span to the tip in the smooth case, and from 40.5% to 75.2%\u0000in the rough case. This significant blockage, caused by vortex breakdown and\u0000chaotic flow structures, leads to the onset of full rotor stall.","PeriodicalId":501125,"journal":{"name":"arXiv - PHYS - Fluid Dynamics","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David Nieto Simavilla, Andrea Bonfanti, Imanol García de Beristain, Pep Español, Marco Ellero
We present a versatile framework that employs Physics-Informed Neural Networks (PINNs) to discover the entropic contribution that leads to the constitutive equation for the extra-stress in rheological models of polymer solutions. In this framework the training of the Neural Network is guided by an evolution equation for the conformation tensor which is GENERIC-compliant. We compare two training methodologies for the data-driven PINN constitutive models: one trained on data from the analytical solution of the Oldroyd-B model under steady-state rheometric flows (PINN-rheometric), and another trained on in-silico data generated from complex flow CFD simulations around a cylinder that use the Oldroyd-B model (PINN-complex). The capacity of the PINN models to provide good predictions are evaluated by comparison with CFD simulations using the underlying Oldroyd-B model as a reference. Both models are capable of predicting flow behavior in transient and complex conditions; however, the PINN-complex model, trained on a broader range of mixed flow data, outperforms the PINN-rheometric model in complex flow scenarios. The geometry agnostic character of our methodology allows us to apply the learned PINN models to flows with different topologies than the ones used for training.
{"title":"Hammering at the entropy: A GENERIC-guided approach to learning polymeric rheological constitutive equations using PINNs","authors":"David Nieto Simavilla, Andrea Bonfanti, Imanol García de Beristain, Pep Español, Marco Ellero","doi":"arxiv-2409.07545","DOIUrl":"https://doi.org/arxiv-2409.07545","url":null,"abstract":"We present a versatile framework that employs Physics-Informed Neural\u0000Networks (PINNs) to discover the entropic contribution that leads to the\u0000constitutive equation for the extra-stress in rheological models of polymer\u0000solutions. In this framework the training of the Neural Network is guided by an\u0000evolution equation for the conformation tensor which is GENERIC-compliant. We\u0000compare two training methodologies for the data-driven PINN constitutive\u0000models: one trained on data from the analytical solution of the Oldroyd-B model\u0000under steady-state rheometric flows (PINN-rheometric), and another trained on\u0000in-silico data generated from complex flow CFD simulations around a cylinder\u0000that use the Oldroyd-B model (PINN-complex). The capacity of the PINN models to\u0000provide good predictions are evaluated by comparison with CFD simulations using\u0000the underlying Oldroyd-B model as a reference. Both models are capable of\u0000predicting flow behavior in transient and complex conditions; however, the\u0000PINN-complex model, trained on a broader range of mixed flow data, outperforms\u0000the PINN-rheometric model in complex flow scenarios. The geometry agnostic\u0000character of our methodology allows us to apply the learned PINN models to\u0000flows with different topologies than the ones used for training.","PeriodicalId":501125,"journal":{"name":"arXiv - PHYS - Fluid Dynamics","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work we assess the impact of the limited availability of wall-embedded sensors on the full 3D estimation of the flow field in a turbulent channel with Re{tau} = 200. The estimation technique is based on a 3D generative adversarial network (3D-GAN). We recently demonstrated that 3D-GANs are capable of estimating fields with good accuracy by employing fully-resolved wall quantities (pressure and streamwise/spanwise wall shear stress on a grid with DNS resolution). However, the practical implementation in an experimental setting is challenging due to the large number of sensors required. In this work, we aim to estimate the flow fields with substantially fewer sensors. The impact of the reduction of the number of sensors on the quality of the flow reconstruction is assessed in terms of accuracy degradation and spectral length-scales involved. It is found that the accuracy degradation is mainly due to the spatial undersampling of scales, rather than the reduction of the number of sensors per se. We explore the performance of the estimator in case only one wall quantity is available. When a large number of sensors is available, pressure measurements provide more accurate flow field estimations. Conversely, the elongated patterns of the streamwise wall shear stress make this quantity the most suitable when only few sensors are available. As a further step towards a real application, the effect of sensor noise is also quantified. It is shown that configurations with fewer sensors are less sensitive to measurement noise.
在这项工作中，我们评估了嵌入式传感器的有限可用性对 Re{tau} = 200 湍流通道中流场的全三维估计的影响。估计技术基于三维生成式对抗网络（3D-GAN）。我们最近证明，三维生成式对抗网络（3D-GANs）能够通过使用有效解析的壁面量（在 DNS 分辨率网格上的压力和流向/跨向壁面剪应力）来准确估计流场。然而，由于需要大量传感器，在实验环境中实际应用具有挑战性。在这项工作中，我们的目标是用更少的传感器来估算流场。我们从精度下降和涉及的频谱长度尺度两个方面评估了传感器数量减少对流量重建质量的影响。结果发现，精度下降的主要原因是空间尺度采样不足，而不是传感器数量减少本身。我们探讨了估计器在只有一个壁面量的情况下的性能。相反，流向壁面剪应力的细长模式使其成为仅有少量传感器时最合适的量。在实际应用中，我们还对传感器噪声的影响进行了量化。结果表明，传感器数量较少的配置对测量噪声的敏感度较低。
{"title":"Some effects of limited wall-sensor availability on flow estimation with 3D-GANs","authors":"Antonio Cuéllar, Andrea Ianiro, Stefano Discetti","doi":"arxiv-2409.07348","DOIUrl":"https://doi.org/arxiv-2409.07348","url":null,"abstract":"In this work we assess the impact of the limited availability of\u0000wall-embedded sensors on the full 3D estimation of the flow field in a\u0000turbulent channel with Re{tau} = 200. The estimation technique is based on a\u00003D generative adversarial network (3D-GAN). We recently demonstrated that\u00003D-GANs are capable of estimating fields with good accuracy by employing\u0000fully-resolved wall quantities (pressure and streamwise/spanwise wall shear\u0000stress on a grid with DNS resolution). However, the practical implementation in\u0000an experimental setting is challenging due to the large number of sensors\u0000required. In this work, we aim to estimate the flow fields with substantially\u0000fewer sensors. The impact of the reduction of the number of sensors on the\u0000quality of the flow reconstruction is assessed in terms of accuracy degradation\u0000and spectral length-scales involved. It is found that the accuracy degradation\u0000is mainly due to the spatial undersampling of scales, rather than the reduction\u0000of the number of sensors per se. We explore the performance of the estimator in\u0000case only one wall quantity is available. When a large number of sensors is\u0000available, pressure measurements provide more accurate flow field estimations.\u0000Conversely, the elongated patterns of the streamwise wall shear stress make\u0000this quantity the most suitable when only few sensors are available. As a\u0000further step towards a real application, the effect of sensor noise is also\u0000quantified. It is shown that configurations with fewer sensors are less\u0000sensitive to measurement noise.","PeriodicalId":501125,"journal":{"name":"arXiv - PHYS - Fluid Dynamics","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Data-driven methods demonstrate considerable potential for accelerating the inherently expensive computational fluid dynamics (CFD) solvers. Nevertheless, pure machine-learning surrogate models face challenges in ensuring physical consistency and scaling up to address real-world problems. This study presents a versatile and scalable hybrid methodology, combining CFD and machine learning, to accelerate long-term incompressible fluid flow simulations without compromising accuracy. A neural network was trained offline using simulated data of various two-dimensional transient buoyant plume flows. The objective was to leverage local features to predict the temporal changes in the pressure field in comparable scenarios. Due to cell-level predictions, the methodology was successfully applied to diverse geometries without additional training. Pressure estimates were employed as initial values to accelerate the pressure-velocity coupling procedure. The results demonstrated an average improvement of 94% in the initial guess for solving the Poisson equation. The first pressure corrector acceleration reached a mean factor of 3, depending on the iterative solver employed. Our work reveals that machine learning estimates at the cell level can enhance the efficiency of CFD iterative linear solvers while maintaining accuracy. Although the scalability of the methodology to more complex cases has yet to be demonstrated, this study underscores the prospective value of domain-specific hybrid solvers for CFD.
{"title":"Coupling Machine Learning Local Predictions with a Computational Fluid Dynamics Solver to Accelerate Transient Buoyant Plume Simulations","authors":"Clément Caron, Philippe Lauret, Alain Bastide","doi":"arxiv-2409.07175","DOIUrl":"https://doi.org/arxiv-2409.07175","url":null,"abstract":"Data-driven methods demonstrate considerable potential for accelerating the\u0000inherently expensive computational fluid dynamics (CFD) solvers. Nevertheless,\u0000pure machine-learning surrogate models face challenges in ensuring physical\u0000consistency and scaling up to address real-world problems. This study presents\u0000a versatile and scalable hybrid methodology, combining CFD and machine\u0000learning, to accelerate long-term incompressible fluid flow simulations without\u0000compromising accuracy. A neural network was trained offline using simulated\u0000data of various two-dimensional transient buoyant plume flows. The objective\u0000was to leverage local features to predict the temporal changes in the pressure\u0000field in comparable scenarios. Due to cell-level predictions, the methodology\u0000was successfully applied to diverse geometries without additional training.\u0000Pressure estimates were employed as initial values to accelerate the\u0000pressure-velocity coupling procedure. The results demonstrated an average\u0000improvement of 94% in the initial guess for solving the Poisson equation. The\u0000first pressure corrector acceleration reached a mean factor of 3, depending on\u0000the iterative solver employed. Our work reveals that machine learning estimates\u0000at the cell level can enhance the efficiency of CFD iterative linear solvers\u0000while maintaining accuracy. Although the scalability of the methodology to more\u0000complex cases has yet to be demonstrated, this study underscores the\u0000prospective value of domain-specific hybrid solvers for CFD.","PeriodicalId":501125,"journal":{"name":"arXiv - PHYS - Fluid Dynamics","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Antonio Cuéllar, Alejandro Güemes, Andrea Ianiro, Óscar Flores, Ricardo Vinuesa, Stefano Discetti
Different types of neural networks have been used to solve the flow sensing problem in turbulent flows, namely to estimate velocity in wall-parallel planes from wall measurements. Generative adversarial networks (GANs) are among the most promising methodologies, due to their more accurate estimations and better perceptual quality. This work tackles this flow sensing problem in the vicinity of the wall, addressing for the first time the reconstruction of the entire three-dimensional (3-D) field with a single network, i.e. a 3-D GAN. With this methodology, a single training and prediction process overcomes the limitation presented by the former approaches based on the independent estimation of wall-parallel planes. The network is capable of estimating the 3-D flow field with a level of error at each wall-normal distance comparable to that reported from wall-parallel plane estimations and at a lower training cost in terms of computational resources. The direct full 3-D reconstruction also unveils a direct interpretation in terms of coherent structures. It is shown that the accuracy of the network depends directly on the wall footprint of each individual turbulent structure. It is observed that wall-attached structures are predicted more accurately than wall-detached ones, especially at larger distances from the wall. Among wall-attached structures, smaller sweeps are reconstructed better than small ejections, while large ejections are reconstructed better than large sweeps as a consequence of their more intense footprint.
{"title":"Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements","authors":"Antonio Cuéllar, Alejandro Güemes, Andrea Ianiro, Óscar Flores, Ricardo Vinuesa, Stefano Discetti","doi":"arxiv-2409.06548","DOIUrl":"https://doi.org/arxiv-2409.06548","url":null,"abstract":"Different types of neural networks have been used to solve the flow sensing\u0000problem in turbulent flows, namely to estimate velocity in wall-parallel planes\u0000from wall measurements. Generative adversarial networks (GANs) are among the\u0000most promising methodologies, due to their more accurate estimations and better\u0000perceptual quality. This work tackles this flow sensing problem in the vicinity\u0000of the wall, addressing for the first time the reconstruction of the entire\u0000three-dimensional (3-D) field with a single network, i.e. a 3-D GAN. With this\u0000methodology, a single training and prediction process overcomes the limitation\u0000presented by the former approaches based on the independent estimation of\u0000wall-parallel planes. The network is capable of estimating the 3-D flow field\u0000with a level of error at each wall-normal distance comparable to that reported\u0000from wall-parallel plane estimations and at a lower training cost in terms of\u0000computational resources. The direct full 3-D reconstruction also unveils a\u0000direct interpretation in terms of coherent structures. It is shown that the\u0000accuracy of the network depends directly on the wall footprint of each\u0000individual turbulent structure. It is observed that wall-attached structures\u0000are predicted more accurately than wall-detached ones, especially at larger\u0000distances from the wall. Among wall-attached structures, smaller sweeps are\u0000reconstructed better than small ejections, while large ejections are\u0000reconstructed better than large sweeps as a consequence of their more intense\u0000footprint.","PeriodicalId":501125,"journal":{"name":"arXiv - PHYS - Fluid Dynamics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stefan Kerkemeier, Christos E. Frouzakis, Ananias G. Tomboulides, Paul Fischer, Mathis Bode
Exascale computing enables high-fidelity simulations of chemically reactive flows in practical geometries and conditions, and paves the way for valuable insights that can optimize combustion processes, ultimately reducing emissions and improving fuel combustion efficiency. However, this requires software that can fully leverage the capabilities of current high performance computing systems. The paper introduces nekCRF, a high-order reactive low Mach flow solver specifically designed for this purpose. Its capabilities and efficiency are showcased on the pre-exascale system JUWELS Booster, a GPU-based supercomputer at the J"{u}lich Supercomputing Centre including a validation across diverse cases of varying complexity.
{"title":"nekCRF: A next generation high-order reactive low Mach flow solver for direct numerical simulations","authors":"Stefan Kerkemeier, Christos E. Frouzakis, Ananias G. Tomboulides, Paul Fischer, Mathis Bode","doi":"arxiv-2409.06404","DOIUrl":"https://doi.org/arxiv-2409.06404","url":null,"abstract":"Exascale computing enables high-fidelity simulations of chemically reactive\u0000flows in practical geometries and conditions, and paves the way for valuable\u0000insights that can optimize combustion processes, ultimately reducing emissions\u0000and improving fuel combustion efficiency. However, this requires software that\u0000can fully leverage the capabilities of current high performance computing\u0000systems. The paper introduces nekCRF, a high-order reactive low Mach flow\u0000solver specifically designed for this purpose. Its capabilities and efficiency\u0000are showcased on the pre-exascale system JUWELS Booster, a GPU-based\u0000supercomputer at the J\"{u}lich Supercomputing Centre including a validation\u0000across diverse cases of varying complexity.","PeriodicalId":501125,"journal":{"name":"arXiv - PHYS - Fluid Dynamics","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate the influence of elastic turbulence on mixing {of a scalar concentration field} within a viscoelastic fluid in a two-dimensional Taylor-Couette geometry using numerical solutions of the Oldroyd-B model. The flow state is determined through the secondary-flow order parameter indicating that the transition at the critical Weissenberg number $text{Wi}_c$ is subcritical. When {starting in the turbulent state and subsequently} lowering the Weissenberg number, a weakly-chaotic flow occurs below $text{Wi}_c$. Advection in both {the turbulent and weakly-chaotic} flow states induces mixing, which we illustrate by the time evolution of the standard deviation of the solute concentration from the uniform distribution. In particular, in the elastic turbulent state mixing is strong and we quantify it by the mixing rate, the mixing time, and the mixing efficiency. All three quantities follow scaling laws. Importantly, we show that the order parameter is strongly correlated to the mixing rate and hence is also a good indication of mixing within the fluid.
{"title":"Mixing in viscoelastic fluids using elastic turbulence","authors":"Reinier van Buel, Holger Stark","doi":"arxiv-2409.06391","DOIUrl":"https://doi.org/arxiv-2409.06391","url":null,"abstract":"We investigate the influence of elastic turbulence on mixing {of a scalar\u0000concentration field} within a viscoelastic fluid in a two-dimensional\u0000Taylor-Couette geometry using numerical solutions of the Oldroyd-B model. The\u0000flow state is determined through the secondary-flow order parameter indicating\u0000that the transition at the critical Weissenberg number $text{Wi}_c$ is\u0000subcritical. When {starting in the turbulent state and subsequently} lowering\u0000the Weissenberg number, a weakly-chaotic flow occurs below $text{Wi}_c$.\u0000Advection in both {the turbulent and weakly-chaotic} flow states induces\u0000mixing, which we illustrate by the time evolution of the standard deviation of\u0000the solute concentration from the uniform distribution. In particular, in the\u0000elastic turbulent state mixing is strong and we quantify it by the mixing rate,\u0000the mixing time, and the mixing efficiency. All three quantities follow scaling\u0000laws. Importantly, we show that the order parameter is strongly correlated to\u0000the mixing rate and hence is also a good indication of mixing within the fluid.","PeriodicalId":501125,"journal":{"name":"arXiv - PHYS - Fluid Dynamics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jack-William Barotta, Giuseppe Pucci, Eli Silver, Alireza Hooshanginejad, Daniel M. Harris
When a millimetric body is placed atop a vibrating liquid bath, the relative motion between the object and interface generates outward propagating waves with an associated momentum flux. Prior work has shown that isolated chiral objects, referred to as spinners, can thus rotate steadily in response to their self-generated wavefield. Here, we consider the case of two co-chiral spinners held at a fixed spacing from one another but otherwise free to interact hydrodynamically through their shared fluid substrate. Two identical spinners are able to synchronize their rotation, with their equilibrium phase difference sensitive to their spacing and initial conditions, and even cease to rotate when the coupling becomes sufficiently strong. Non-identical spinners can also find synchrony provided their intrinsic differences are not too disparate. A hydrodynamic wave model of the spinner interaction is proposed, recovering all salient features of the experiment. In all cases, the spatially periodic nature of the capillary wave coupling is directly reflected in the emergent equilibrium behaviors.
{"title":"Synchronization of wave-propelled capillary spinners","authors":"Jack-William Barotta, Giuseppe Pucci, Eli Silver, Alireza Hooshanginejad, Daniel M. Harris","doi":"arxiv-2409.06652","DOIUrl":"https://doi.org/arxiv-2409.06652","url":null,"abstract":"When a millimetric body is placed atop a vibrating liquid bath, the relative\u0000motion between the object and interface generates outward propagating waves\u0000with an associated momentum flux. Prior work has shown that isolated chiral\u0000objects, referred to as spinners, can thus rotate steadily in response to their\u0000self-generated wavefield. Here, we consider the case of two co-chiral spinners\u0000held at a fixed spacing from one another but otherwise free to interact\u0000hydrodynamically through their shared fluid substrate. Two identical spinners\u0000are able to synchronize their rotation, with their equilibrium phase difference\u0000sensitive to their spacing and initial conditions, and even cease to rotate\u0000when the coupling becomes sufficiently strong. Non-identical spinners can also\u0000find synchrony provided their intrinsic differences are not too disparate. A\u0000hydrodynamic wave model of the spinner interaction is proposed, recovering all\u0000salient features of the experiment. In all cases, the spatially periodic nature\u0000of the capillary wave coupling is directly reflected in the emergent\u0000equilibrium behaviors.","PeriodicalId":501125,"journal":{"name":"arXiv - PHYS - Fluid Dynamics","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhiqi Li, Duowen Chen, Candong Lin, Jinyuan Liu, Bo Zhu
We propose a novel framework for simulating ink as a particle-laden flow using particle flow maps. Our method addresses the limitations of existing flow-map techniques, which struggle with dissipative forces like viscosity and drag, thereby extending the application scope from solving the Euler equations to solving the Navier-Stokes equations with accurate viscosity and laden-particle treatment. Our key contribution lies in a coupling mechanism for two particle systems, coupling physical sediment particles and virtual flow-map particles on a background grid by solving a Poisson system. We implemented a novel path integral formula to incorporate viscosity and drag forces into the particle flow map process. Our approach enables state-of-the-art simulation of various particle-laden flow phenomena, exemplified by the bulging and breakup of suspension drop tails, torus formation, torus disintegration, and the coalescence of sedimenting drops. In particular, our method delivered high-fidelity ink diffusion simulations by accurately capturing vortex bulbs, viscous tails, fractal branching, and hierarchical structures.
{"title":"Particle-Laden Fluid on Flow Maps","authors":"Zhiqi Li, Duowen Chen, Candong Lin, Jinyuan Liu, Bo Zhu","doi":"arxiv-2409.06246","DOIUrl":"https://doi.org/arxiv-2409.06246","url":null,"abstract":"We propose a novel framework for simulating ink as a particle-laden flow\u0000using particle flow maps. Our method addresses the limitations of existing\u0000flow-map techniques, which struggle with dissipative forces like viscosity and\u0000drag, thereby extending the application scope from solving the Euler equations\u0000to solving the Navier-Stokes equations with accurate viscosity and\u0000laden-particle treatment. Our key contribution lies in a coupling mechanism for\u0000two particle systems, coupling physical sediment particles and virtual flow-map\u0000particles on a background grid by solving a Poisson system. We implemented a\u0000novel path integral formula to incorporate viscosity and drag forces into the\u0000particle flow map process. Our approach enables state-of-the-art simulation of\u0000various particle-laden flow phenomena, exemplified by the bulging and breakup\u0000of suspension drop tails, torus formation, torus disintegration, and the\u0000coalescence of sedimenting drops. In particular, our method delivered\u0000high-fidelity ink diffusion simulations by accurately capturing vortex bulbs,\u0000viscous tails, fractal branching, and hierarchical structures.","PeriodicalId":501125,"journal":{"name":"arXiv - PHYS - Fluid Dynamics","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The non-linear dynamics of driven oscillations in the size of a spherical bubble are mapped to the dynamics of a Newtonian particle in a potential within the incompressible liquid regime. The compressible liquid regime, which is important during the bubble's sonic collapse, is approached adiabatically. This new framework naturally distinguishes between the two time scales involved in the non-linear oscillations of a bubble. It also explains the experimentally observed sharp rebound of the bubble upon collapse. Guided by this new vantage point, we develop analytical approximations for several key aspects of bubble motion. First, we formulate a tensile strength law that integrates the bubble's ideal gas behavior with a general polytropic index. Next, we derive an acoustic energy dissipation formula for the bubble's sonic collapse, dependent solely on the bubble's collapse radii and velocity. Finally, we establish a straightforward physical criterion for Bjerknes force reversal, governed by the driving pressure, ambient pressure and tensile strength.
{"title":"Mapping Driven Oscillations in the Size of a Bubble to the Dynamics of a Newtonian Particle in a Potential","authors":"Uri Shimon, Ady Stern","doi":"arxiv-2409.05961","DOIUrl":"https://doi.org/arxiv-2409.05961","url":null,"abstract":"The non-linear dynamics of driven oscillations in the size of a spherical\u0000bubble are mapped to the dynamics of a Newtonian particle in a potential within\u0000the incompressible liquid regime. The compressible liquid regime, which is\u0000important during the bubble's sonic collapse, is approached adiabatically. This\u0000new framework naturally distinguishes between the two time scales involved in\u0000the non-linear oscillations of a bubble. It also explains the experimentally\u0000observed sharp rebound of the bubble upon collapse. Guided by this new vantage\u0000point, we develop analytical approximations for several key aspects of bubble\u0000motion. First, we formulate a tensile strength law that integrates the bubble's\u0000ideal gas behavior with a general polytropic index. Next, we derive an acoustic\u0000energy dissipation formula for the bubble's sonic collapse, dependent solely on\u0000the bubble's collapse radii and velocity. Finally, we establish a\u0000straightforward physical criterion for Bjerknes force reversal, governed by the\u0000driving pressure, ambient pressure and tensile strength.","PeriodicalId":501125,"journal":{"name":"arXiv - PHYS - Fluid Dynamics","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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