This dataset is designed to test Machine-Learning techniques on Computational Fluid Dynamics (CFD) data. It contains two-dimensional RANS simulations of the turbulent flow around NACA 4-digits airfoils, at fixed angle of attack (10 degrees) and at a fixed Reynolds number (3x10^6). The whole NACA family is spawned. The present dataset contains 425 geometries, 2600 further geometries are published in accompanying repository (10.5281/zenodo.4106752). For further information refer to: Schillaci, A., Quadrio, M., Pipolo, C., Restelli, M., Boracchi, G. "Inferring Functional Properties from Fluid Dynamics Features" 2020 25th International Conference on Pattern Recognition (ICPR) Milan, Italy, Jan 10-15, 202
A good mesh is a prerequisite for achieving reliable results from Computational Fluid Dynamics (CFD)...
In recent years, many data-driven approaches which leverage high-fidelity reference data have been d...
This paper aims to explore the advantages offered by machine learning (ML) for dimensionality reduct...
This dataset is designed to test Machine-Learning techniques on Computational Fluid Dynamics (CFD) d...
Computational Fluid Dynamics (CFD) is a tool utilized in industry and academia to help provide a bet...
Arfoil CFD simulation dataset. Each example has the flow at the back of the airfoil. The ground trut...
International audienceSurrogate models are necessary to optimize meaningful quantities in physical d...
As early as at the end of the 19th century, shortly after mathematical rules describing fluid flow—s...
Numerical efforts to estimate turbulence in fluid flows are focused on developing turbulence models,...
This study is to investigate the aerodynamic characteristic of an airfoil in low Reynolds number flo...
A study to analyze the efficacy of two novel, state-of-the-art deep learning methods used in flow-fi...
Many applications use symmetric or asymmetric airfoils, such as aircraft design, wind turbines, and ...
In this study, we propose an encoder–decoder convolutional neural network-based approach for estimat...
A good mesh is a prerequisite for achieving reliable results from Computational Fluid Dynamics (CFD)...
This paper presented a computational fluid dynamics (CFD) simulation of air flow past a 2D model NAC...
A good mesh is a prerequisite for achieving reliable results from Computational Fluid Dynamics (CFD)...
In recent years, many data-driven approaches which leverage high-fidelity reference data have been d...
This paper aims to explore the advantages offered by machine learning (ML) for dimensionality reduct...
This dataset is designed to test Machine-Learning techniques on Computational Fluid Dynamics (CFD) d...
Computational Fluid Dynamics (CFD) is a tool utilized in industry and academia to help provide a bet...
Arfoil CFD simulation dataset. Each example has the flow at the back of the airfoil. The ground trut...
International audienceSurrogate models are necessary to optimize meaningful quantities in physical d...
As early as at the end of the 19th century, shortly after mathematical rules describing fluid flow—s...
Numerical efforts to estimate turbulence in fluid flows are focused on developing turbulence models,...
This study is to investigate the aerodynamic characteristic of an airfoil in low Reynolds number flo...
A study to analyze the efficacy of two novel, state-of-the-art deep learning methods used in flow-fi...
Many applications use symmetric or asymmetric airfoils, such as aircraft design, wind turbines, and ...
In this study, we propose an encoder–decoder convolutional neural network-based approach for estimat...
A good mesh is a prerequisite for achieving reliable results from Computational Fluid Dynamics (CFD)...
This paper presented a computational fluid dynamics (CFD) simulation of air flow past a 2D model NAC...
A good mesh is a prerequisite for achieving reliable results from Computational Fluid Dynamics (CFD)...
In recent years, many data-driven approaches which leverage high-fidelity reference data have been d...
This paper aims to explore the advantages offered by machine learning (ML) for dimensionality reduct...