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

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

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

Projekt

<a href="/cs/project/GA21-31457S" target="_blank" >GA21-31457S: Použití neuronových sítí pro rychlou predikci proudového pole v úlohách interakce tekutiny s tělesem</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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