Next Article in Journal
Hydropower Plants Frequency Regulation Depending on Upper Reservoir Water Level
Next Article in Special Issue
A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings
Previous Article in Journal
History Matching and Forecast of Shale Gas Production Considering Hydraulic Fracture Closure
Previous Article in Special Issue
Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

Prediction of the Optimal Vortex in Synthetic Jets

E.T.S.I. Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Pza. Cardenal Cisneros 3, 28040 Madrid, Spain
Energies 2019, 12(9), 1635;
Received: 5 April 2019 / Revised: 23 April 2019 / Accepted: 23 April 2019 / Published: 29 April 2019
(This article belongs to the Special Issue Data Science and Big Data in Energy Forecasting with Applications)
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. View Full-Text
Keywords: reduced order model; prediction; data forecasting; HODMD; synthetic jets; soft computing reduced order model; prediction; data forecasting; HODMD; synthetic jets; soft computing
Show Figures

Figure 1

MDPI and ACS Style

Le Clainche, S. Prediction of the Optimal Vortex in Synthetic Jets. Energies 2019, 12, 1635.

AMA Style

Le Clainche S. Prediction of the Optimal Vortex in Synthetic Jets. Energies. 2019; 12(9):1635.

Chicago/Turabian Style

Le Clainche, Soledad. 2019. "Prediction of the Optimal Vortex in Synthetic Jets" Energies 12, no. 9: 1635.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop