Fast pressure prediction along the NACA airfoil using the convolution neural network

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43957400" target="_blank" >RIV/49777513:23520/19:43957400 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Fast pressure prediction along the NACA airfoil using the convolution neural network
Original language description
The knowledge of the pressure coefficient distribution around the airfoil body is the most important airfoil characteristic. Using the pressure distribution, lift and drag coefficients could be established. When a new profile is being designed, some of the optimisation techniques are usually used, for example, to have a maximal lift. This requires the calculation of the pressure field for many different variations of the geometry, which is very time-consuming. This contribution aims to utilize a convolution neural network (CNN) for the fast prediction of the pressure distribution around NACA airfoil. The CNN input contains a mesh point coordinates. At the CNN output the flow field, which includes density, velocity and pressure, is generated. The CNN was created using opensource software Keras and TensorFlow through the python interface. The CNN was trained on the set of 785 random profiles generated using an opensource CFD solver FlowPro.
Czech name
—
Czech description
—

Project
<a href="/en/project/TE01020068" target="_blank" >TE01020068: Centre of research and experimental development of reliable energy production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)