Fast blade shape optimization based on a neural-network-predicted flow field

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43963410" target="_blank" >RIV/49777513:23520/21:43963410 - isvavai.cz</a>

Result on the web

<a href="http://hdl.handle.net/11025/45916" target="_blank" >http://hdl.handle.net/11025/45916</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Fast blade shape optimization based on a neural-network-predicted flow field

Original language description

The design of the blade shape is a computationally demanding task because the flow field needs to be evaluated many times for slightly modified geometries. Convolution neural networks can significantly improve the speed of the optimization process by predicting flow fields extremely quickly. The aim of this work is to develop a convolution neural network which is able to predict flow fields in a blade cascade while satisfying the periodic boundary condition. The developed convolution neural network architecture is applied for the optimization of a blade profile while minimizing the pressure loss.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA21-31457S" target="_blank" >GA21-31457S: Fast flow-field prediction using deep neural networks for solving fluid-structure interaction problems</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů