Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements

• URL: http://arxiv.org/abs/2310.11147v2
• Date: Wed, 18 Oct 2023 10:03:18 GMT
• Title: Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements
• Authors: Esther Lagemann, Steven L. Brunton and Christian Lagemann
• Abstract summary: Friction drag from a turbulent fluid moving past or inside an object plays a crucial role in domains as diverse as transportation, public utility infrastructure, energy technology, and human health. We present a holistic approach that derives velocity and wall-shear stress fields with impressive spatial and temporal resolution from flow measurements using a deep optical flow estimator with physical knowledge.
• Score: 2.736093604280113
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Friction drag from a turbulent fluid moving past or inside an object plays a crucial role in domains as diverse as transportation, public utility infrastructure, energy technology, and human health. As a direct measure of the shear-induced friction forces, an accurate prediction of the wall-shear stress can contribute to sustainability, conservation of resources, and carbon neutrality in civil aviation as well as enhanced medical treatment of vascular diseases and cancer. Despite such importance for our modern society, we still lack adequate experimental methods to capture the instantaneous wall-shear stress dynamics. In this contribution, we present a holistic approach that derives velocity and wall-shear stress fields with impressive spatial and temporal resolution from flow measurements using a deep optical flow estimator with physical knowledge. The validity and physical correctness of the derived flow quantities is demonstrated with synthetic and real-world experimental data covering a range of relevant fluid flows.

Related papers

• The Sound of Water: Inferring Physical Properties from Pouring Liquids [85.30865788636386]
  We study the connection between audio-visual observations and the underlying physics of pouring liquids. Our objective is to automatically infer physical properties such as the liquid level, the shape and size of the container, the pouring rate and the time to fill.
  arXiv  Detail & Related papers  (2024-11-18T01:19:37Z)
• Water quality polluted by total suspended solids classified within an Artificial Neural Network approach [0.0]
  Water pollution by suspended solids poses significant environmental and health risks. To address these challenges, we developed a model that leverages a comprehensive dataset of water quality from total suspended solids. A convolutional neural network was trained under a transfer learning approach using data corresponding to different total suspended solids concentrations.
  arXiv  Detail & Related papers  (2024-10-19T01:33:08Z)
• A deep learning approach to wall-shear stress quantification: From numerical training to zero-shot experimental application [2.2284889035802036]
  We introduce a deep learning architecture that ingests wall-parallel velocity fields from the logarithmic layer of turbulent wall-bounded flows. The presented framework lays the groundwork for extracting inaccessible experimental wall-shear stress information from readily available velocity measurements.
  arXiv  Detail & Related papers  (2024-09-05T22:59:23Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Prediction of flow and elastic stresses in a viscoelastic turbulent channel flow using convolutional neural networks [0.9217021281095907]
  We use neural-network models to predict the instantaneous flow close to the wall in a viscoelastic turbulent channel flow. The models exhibit enhanced accuracy in predicting quantities of interest during the hibernation intervals. This method could be used in flow control or when only wall information is available from experiments.
  arXiv  Detail & Related papers  (2024-04-22T12:19:00Z)
• Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution [49.45309818782329]
  Flow image super-resolution (FISR) aims at recovering high-resolution turbulent velocity fields from low-resolution flow images. Existing FISR methods mainly process the flow images in natural image patterns. We propose the first flow visual property-informed FISR algorithm.
  arXiv  Detail & Related papers  (2024-01-29T06:48:16Z)
• HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction [66.38369833561039]
  HelmFluid is an accurate and interpretable predictor for fluid. Inspired by Helmholtz theorem, we design a HelmDynamics block to learn Helmholtz dynamics. By embedding the HelmDynamics block into a Multiscale Multihead Integral Architecture, HelmFluid can integrate learned Helmholtz dynamics along temporal dimension in multiple spatial scales.
  arXiv  Detail & Related papers  (2023-10-16T16:38:32Z)
• On the characteristics of natural hydraulic dampers: An image-based approach to study the fluid flow behaviour inside the human meniscal tissue [0.0]
  The internal layer of the meniscus is softer and more deformable than the outer layers, thanks to interconnected collagen channels that guide fluid flow. We analyze fluid flow in the internal architecture of the human meniscus across a range of inlet velocities. Some channels exhibit Re values of 1400 at an inlet velocity of 1.6m/s, and a transition from Darcy's regime to a non-Darcian regime occurs around an inlet velocity of 0.02m/s.
  arXiv  Detail & Related papers  (2023-07-24T18:19:39Z)
• NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields [65.07940731309856]
  Deep learning has shown great potential for modeling the physical dynamics of complex particle systems such as fluids. In this paper, we consider a partially observable scenario known as fluid dynamics grounding. We propose a differentiable two-stage network named NeuroFluid. It is shown to reasonably estimate the underlying physics of fluids with different initial shapes, viscosity, and densities.
  arXiv  Detail & Related papers  (2022-03-03T15:13:29Z)
• Ultrafast viscosity measurement with ballistic optical tweezers [55.41644538483948]
  Noninvasive viscosity measurements require integration times of seconds. We demonstrate a four orders-of-magnitude improvement in speed, down to twenty microseconds. We achieve this using the instantaneous velocity of a trapped particle in an optical tweezer.
  arXiv  Detail & Related papers  (2020-06-29T00:09:40Z)
• Deep Learning Convective Flow Using Conditional Generative Adversarial Networks [0.0]
  FluidGAN is capable of learning and predicting time-dependent convective flow coupled with energy transport. Our framework helps understand deterministic multiphysics phenomena where the underlying physical model is complex or unknown.
  arXiv  Detail & Related papers  (2020-05-13T16:52:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.