Fluid Viscosity Prediction Leveraging Computer Vision and Robot Interaction

• URL: http://arxiv.org/abs/2308.02715v2
• Date: Sun, 3 Dec 2023 00:28:03 GMT
• Title: Fluid Viscosity Prediction Leveraging Computer Vision and Robot Interaction
• Authors: Jong Hoon Park, Gauri Pramod Dalwankar, Alison Bartsch, Abraham George, Amir Barati Farimani
• Abstract summary: This work explores the feasibility of predicting fluid viscosity by analyzing fluid oscillations captured in video data. The pipeline employs a 3D convolutional autoencoder pretrained in a self-supervised manner to extract and learn features from semantic segmentation masks of oscillating fluids. When the latent representations generated by the pretrained autoencoder are used for classification, the system achieves a 97.1% accuracy across a total of 4,140 test datapoints.
• Score: 9.312155153982982
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurately determining fluid viscosity is crucial for various industrial and scientific applications. Traditional methods of viscosity measurement, though reliable, often require manual intervention and cannot easily adapt to real-time monitoring. With advancements in machine learning and computer vision, this work explores the feasibility of predicting fluid viscosity by analyzing fluid oscillations captured in video data. The pipeline employs a 3D convolutional autoencoder pretrained in a self-supervised manner to extract and learn features from semantic segmentation masks of oscillating fluids. Then, the latent representations of the input data, produced from the pretrained autoencoder, is processed with a distinct inference head to infer either the fluid category (classification) or the fluid viscosity (regression) in a time-resolved manner. When the latent representations generated by the pretrained autoencoder are used for classification, the system achieves a 97.1% accuracy across a total of 4,140 test datapoints. Similarly, for regression tasks, employing an additional fully-connected network as a regression head allows the pipeline to achieve a mean absolute error of 0.258 over 4,416 test datapoints. This study represents an innovative contribution to both fluid characterization and the evolving landscape of Artificial Intelligence, demonstrating the potential of deep learning in achieving near real-time viscosity estimation and addressing practical challenges in fluid dynamics through the analysis of video data capturing oscillating fluid dynamics.

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)
• Coupling Machine Learning Local Predictions with a Computational Fluid Dynamics Solver to Accelerate Transient Buoyant Plume Simulations [0.0]
  This study presents a versatile and scalable hybrid methodology, combining CFD and machine learning. The objective was to leverage local features to predict the temporal changes in the pressure field in comparable scenarios. Pressure estimates were employed as initial values to accelerate the pressure-velocity coupling procedure.
  arXiv  Detail & Related papers  (2024-09-11T10:38:30Z)
• Inpainting Computational Fluid Dynamics with Deep Learning [8.397730500554047]
  An effective fluid data completion method reduces the required number of sensors in a fluid dynamics experiment. The ill-posed nature of the fluid data completion problem makes it prohibitively difficult to obtain a theoretical solution. We employ the vector quantization technique to map both complete and incomplete fluid data spaces onto discrete-valued lower-dimensional representations.
  arXiv  Detail & Related papers  (2024-02-27T03:44:55Z)
• FluidLab: A Differentiable Environment for Benchmarking Complex Fluid Manipulation [80.63838153351804]
  We introduce FluidLab, a simulation environment with a diverse set of manipulation tasks involving complex fluid dynamics. At the heart of our platform is a fully differentiable physics simulator, providing GPU-accelerated simulations and gradient calculations. We propose several domain-specific optimization schemes coupled with differentiable physics.
  arXiv  Detail & Related papers  (2023-03-04T07:24:22Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Information-Theoretic Odometry Learning [83.36195426897768]
  We propose a unified information theoretic framework for learning-motivated methods aimed at odometry estimation. The proposed framework provides an elegant tool for performance evaluation and understanding in information-theoretic language.
  arXiv  Detail & Related papers  (2022-03-11T02:37:35Z)
• Visual-tactile sensing for Real-time liquid Volume Estimation in Grasping [58.50342759993186]
  We propose a visuo-tactile model for realtime estimation of the liquid inside a deformable container. We fuse two sensory modalities, i.e., the raw visual inputs from the RGB camera and the tactile cues from our specific tactile sensor. The robotic system is well controlled and adjusted based on the estimation model in real time.
  arXiv  Detail & Related papers  (2022-02-23T13:38:31Z)
• Teaching the Incompressible Navier-Stokes Equations to Fast Neural Surrogate Models in 3D [4.981834139548193]
  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 propose an efficient architecture to cope with the high demands of 3D grids in terms of memory and computational complexity. Our method indicates strong improvements in terms of accuracy, speed and generalization capabilities over current 3D NN-based fluid models.
  arXiv  Detail & Related papers  (2020-12-22T09:21:40Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Learning Incompressible Fluid Dynamics from Scratch -- Towards Fast, Differentiable Fluid Models that Generalize [7.707887663337803]
  Recent deep learning based approaches promise vast speed-ups but do not generalize to new fluid domains. We propose a novel physics-constrained training approach that generalizes to new fluid domains. We present an interactive real-time demo to show the speed and generalization capabilities of our trained models.
  arXiv  Detail & Related papers  (2020-06-15T20:59:28Z)

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.