Inferring Functional Properties from Fluid Dynamics Features

Schillaci, Andrea; Quadrio, Maurizio; Pipolo, Carlotta; Restelli, Marcello; Boracchi, Giacomo

doi:10.1109/icpr48806.2021.9412157

Cited by 2 publications

(3 citation statements)

References 14 publications

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“…All the anatomies considered in the present work originate from a reference CAD-based simplified model of the human nasal cavities, already introduced by Schillaci et al (2021), which lends itself to a simple geometrical parametrization. The use of simplified model geometries for the study of the flow in the human nasal cavities is not new: for example, Liu et al (2009) employed a model obtained by averaging together the CT scans of 30 patients.…”

Section: The Anatomiesmentioning

confidence: 99%

“…The ML in fluid mechanics has recently seen a huge activity and recorded significant progresses (see e.g. the review by Vinuesa & Brunton 2022); however, very little information is available in the literature regarding the combined use of ML and CFD in rhinology, if exception is made for our own preliminary study (Schillaci et al 2021). The use of artificial intelligence and machine learning techniques in rhinology has been limited so far to classification of images derived from CT scans (Crowson et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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Comparing flow-based and anatomy-based features in the data-driven study of nasal pathologies

Schillaci,

Hasegawa,

Pipolo

et al. 2024

Flow

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In several problems involving fluid flows, computational fluid dynamics (CFD) provides detailed quantitative information and allows the designer to successfully optimize the system by minimizing a cost function. Sometimes, however, one cannot improve the system with CFD alone, because a suitable cost function is not readily available; one notable example is diagnosis in medicine. The application considered here belongs to the field of rhinology; a correct air flow is key for the functioning of the human nose, yet the notion of a functionally normal nose is not available and a cost function cannot be written. An alternative and attractive pathway to diagnosis and surgery planning is offered by data-driven methods. In this work, we consider the machine learning study of nasal impairment caused by anatomic malformations, with the aim of understanding whether fluid dynamic features, available after a CFD analysis, are more effective than purely geometric features at the training of a neural network for regression. Our experiments are carried out on an extremely simplified anatomic model and a correspondingly simple CFD approach; nevertheless, they show that flow-based features perform better than geometry-based ones and allow the training of a neural network with fewer inputs, a crucial advantage in fields like medicine.

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Section: The Anatomiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparing flow-based and anatomy-based features in the data-driven study of nasal pathologies

Schillaci,

Hasegawa,

Pipolo

et al. 2024

Flow

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“…The dataset that we generate to train our GNN model is similar in terms of the geometries, meshing and CFD pipeline to the work of Thuerey et al (2020), the main differences being the chosen RANS model and the post-processing (graph parsing). Other datasets found in the literature focus only on the NACA family of airfoils (Schillaci et al, 2021).…”

Section: Related Workmentioning

confidence: 99%

Graph Neural Networks for Aerodynamic Flow Reconstruction from Sparse Sensing

Duthé¹,

Abdallah²,

Barber³

et al. 2023

Preprint

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Sensing the fluid flow around an arbitrary geometry entails extrapolating from the physical quantities perceived at its surface in order to reconstruct the features of the surrounding fluid. This is a challenging inverse problem, yet one that if solved could have a significant impact on many engineering applications. The exploitation of such an inverse logic has gained interest in recent years with the advent of widely available cheap but capable MEMS-based sensors. When combined with novel data-driven methods, these sensors may allow for flow reconstruction around immersed structures, benefiting applications such as unmanned airborne/underwater vehicle path planning or control and structural health monitoring of wind turbine blades. In this work, we train deep reversible Graph Neural Networks (GNNs) to perform flow sensing (flow reconstruction) around two-dimensional aerodynamic shapes: airfoils. Motivated by recent work, which has shown that GNNs can be powerful alternatives to mesh-based forward physics simulators, we implement a Message-Passing Neural Network to simultaneously reconstruct both the pressure and velocity fields surrounding simulated airfoils based on their surface pressure distributions, whilst additionally gathering useful farfield properties in the form of context vectors. We generate a unique dataset of Computational Fluid Dynamics simulations by simulating random, yet meaningful combinations of input boundary conditions and airfoil shapes. We show that despite the challenges associated with reconstructing the flow around arbitrary airfoil geometries in high Reynolds turbulent inflow conditions, our framework is able to generalize well to unseen cases.

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Inferring Functional Properties from Fluid Dynamics Features

Cited by 2 publications

References 14 publications

Comparing flow-based and anatomy-based features in the data-driven study of nasal pathologies

Comparing flow-based and anatomy-based features in the data-driven study of nasal pathologies

Graph Neural Networks for Aerodynamic Flow Reconstruction from Sparse Sensing

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