
Surrogate Modeling of Fluid Dynamics with a Multigrid Inspired Neural Network Architecture
Algebraic or geometric multigrid methods are commonly used in numerical ...
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Deeplearningbased coupled flowgeomechanics surrogate model for CO_2 sequestration
A deeplearningbased surrogate model capable of predicting flow and geo...
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Predicting the flow field in a Ubend with deep neural networks
This paper describes a study based on computational fluid dynamics (CFD)...
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Modelling pressureHessian from local velocity gradients information in an incompressible turbulent flow field using deep neural networks
The understanding of the dynamics of the velocity gradients in turbulent...
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CFDNet: a deep learningbased accelerator for fluid simulations
CFD is widely used in physical system design and optimization, where it ...
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Constraining subglacial processes from surface velocity observations using surrogatebased Bayesian inference
Basal motion is the primary mechanism for ice flux outside Antarctica, y...
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Deep Learning for RealTime Aerodynamic Evaluations of Arbitrary Vehicle Shapes
The aerodynamic optimization process of cars requires multiple iteration...
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UNetBased Surrogate Model For Evaluation of Microfluidic Channels
Microfluidics have shown great promise in multiple applications, especially in biomedical diagnostics and separations. While the flow properties of these microfluidic devices can be solved by numerical methods such as computational fluid dynamics (CFD), the process of mesh generation and setting up a numerical solver requires some domain familiarity, while more intuitive commercial programs such as Fluent and StarCCM can be expensive. Hence, in this work, we demonstrated the use of a UNet convolutional neural network as a surrogate model for predicting the velocity and pressure fields that would result for a particular set of microfluidic filter designs. The surrogate model is fast, easy to setup and can be used to predict and assess the flow velocity and pressure fields across the domain for new designs of interest via the input of a geometryencoding matrix. In addition, we demonstrate that the same methodology can also be used to train a network to predict pressure based on velocity data, and propose that this can be an alternative to numerical algorithms for calculating pressure based on velocity measurements from particleimage velocimetry measurements. Critically, in both applications, we demonstrate prediction test errors of less than 1 indeed a viable method.
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