Autor: Starosta, Roman - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Minimization of critical flow velocity of aeroelastic energy harvester via delayed feedback control
Autorzy:: Sarbinowski, Filip
Starosta, Roman
Powiązania:: https://bibliotekanauki.pl/articles/2146571.pdf
Data publikacji:: 2020
Wydawca:: Politechnika Poznańska. Instytut Mechaniki Stosowanej
Tematy:: energy harvesting
delayed feedback control
flow-induced vibration
harvesting energii
sprzężenie zwrotne z opóźnieniem
drgania wzbudzone przepływem
Opis:: The paper describes the procedure of modelling and optimization of the aeroelastic energy harvester from the point of view of their operation at very low flow velocities. Using analytical solutions of models of different device variants, the relationships between their efficiency and flow velocity were presented. By way of analytical considerations, the conditions for high performance operation of the device have been demonstrated, indicating at the same time the difficulty in maintaining it at low operation velocities. As a solution to the problem, the application of external delayed feedback control was proposed and its effectiveness was demonstrated.
Źródło:: Vibrations in Physical Systems; 2020, 31, 2; art. no. 2020225
0860-6897
Pojawia się w:: Vibrations in Physical Systems
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: AI-based method of vortex core tracking as an alternative for Lambda2
Autorzy:: Majchrzak, Maciej
Jakubowski, Mateusz
Starosta, Roman
Powiązania:: https://bibliotekanauki.pl/articles/2146608.pdf
Data publikacji:: 2020
Wydawca:: Politechnika Poznańska. Instytut Mechaniki Stosowanej
Tematy:: vortex core tracking
Lambda2
AI
neural network
rdzeń wiru
sieć neuronowa
Opis:: The paper presents a new method of vortex core detection developed for use in CFD simulation result analysis. Apart from the conventional approach involving vector algebra, mainly the Lambda2 method, it focuses on the identification of certain features in a graphic representation of the velocity field. It is done by generating a series of slices of the said field in the postprocessing software and training a Convolutional Neural Network (AI) to recognize vortex cores. The neural network can be integrated into a simple python program and used to quickly identify vortex cores on a large number of images and translate their locations to coordinates of a CFD model for visualisation.
Źródło:: Vibrations in Physical Systems; 2020, 31, 3; art. no. 2020319
0860-6897
Pojawia się w:: Vibrations in Physical Systems
Dostawca treści:: Biblioteka Nauki

Artykuł

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