Imperial College London
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ProfessorSylvainLaizet

Faculty of Engineering, Department of Aeronautics

Professor in Computational Fluid Mechanics
 
 
 
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Contact

 

+44 (0)20 7594 5045s.laizet Website

 
 
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Location

 

339City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Özbay:2022,
author = {Özbay, AG and Laizet, S},
title = {UNSTEADY TWO-DIMENSIONAL FLOW RECONSTRUCTION AND FORCE COEFFICIENT ESTIMATION AROUND ARBITRARY SHAPES VIA CONFORMAL MAPPING AIDED DEEP NEURAL NETWORKS},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - In many practical fluid dynamics experiments, measuring variables such as velocity and pressure is possible only at a limited number of sensor locations. However, knowledge of the full fields is necessary to understand the dynamics of many flows. Deep learning reconstruction of full flow fields from sparse measurements as a way of overcoming this limitation has recently garnered significant research interest, referred to as the flow reconstruction (FR) task. We extend existing FR models by enabling such models to make predictions on flows around arbitrary 2D geometries without the need for re-training. This geometry flexibility is achieved through an innovative mapping approach, whereby multiple fluid domains are mapped to an annulus. Using this mapping approach, we explore the performance of a novel FR model trained on 64 geometries and tested on a further 16 different geometries. We demonstrate that the model trained using the mapping approach reconstructs the flow fields well even on geometries not present in the training data.
AU  - Özbay,AG
AU  - Laizet,S
PY  - 2022///
TI  - UNSTEADY TWO-DIMENSIONAL FLOW RECONSTRUCTION AND FORCE COEFFICIENT ESTIMATION AROUND ARBITRARY SHAPES VIA CONFORMAL MAPPING AIDED DEEP NEURAL NETWORKS
ER  -
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