Prediction of the Optimal Vortex in Synthetic Jets

Le Clainche Martinez, Soledad (2019). Prediction of the Optimal Vortex in Synthetic Jets. "Energies", v. 12 (n. 9); pp. 9-26. ISSN 1996-1073.


Title: Prediction of the Optimal Vortex in Synthetic Jets
  • Le Clainche Martinez, Soledad
Item Type: Article
Título de Revista/Publicación: Energies
Date: 2019
ISSN: 1996-1073
Volume: 12
Freetext Keywords: Reduced order model; prediction; data forecasting; HODMD; synthetic jets; soft computing
Faculty: E.T.S. de Ingeniería Aeronáutica y del Espacio (UPM)
Department: Matemática Aplicada a la Ingeniería Aeroespacial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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This article presents three different low-order models to predict the main flow patterns in synthetic jets. The first model provides a simple theoretical approach based on experimental solutions explaining how to artificially generate the optimal vortex, which maximizes the production of thrust and system efficiency. The second model is a data-driven method that uses higher-order dynamic mode decomposition (HODMD). To construct this model, (i) Navier–Stokes equations are solved for a very short period of time providing a transient solution, (ii) a group of spatio-temporal data are collected containing the information of the transitory of the numerical simulations, and finally (iii) HODMD decomposes the solution as a Fourier-like expansion of modes that are extrapolated in time, providing accurate predictions of the large size structures describing the general flow dynamics, with a speed-up factor of 8.3 in the numerical solver. The third model is an extension of the second model, which combines HODMD with a low-rank approximation of the spatial domain, which is based on singular value decomposition (SVD). This novel approach reduces the memory requirements by 70% and reduces the computational time to generate the low-order model by 3, maintaining the speed-up factor to 8.3. This technique is suitable to predict the temporal flow patterns in a synthetic jet, showing that the general dynamics is driven by small amplitude variations along the streamwise direction. This new and efficient tool could also be potentially used for data forecasting or flow pattern identification in any type of big database.

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Item ID: 67856
DC Identifier:
OAI Identifier:
DOI: 10.3390/en12091635
Official URL:
Deposited by: Memoria Investigacion
Deposited on: 16 Nov 2021 07:35
Last Modified: 16 Nov 2021 07:35
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