RT info:eu-repo/semantics/article
T1 Model-free short-term fluid dynamics estimator with a deep 3D-convolutional neural network
A1 López Martín, Manuel
A1 Le Clainche, Soledad
A1 Carro Martínez, Belén
K1 Computational fluid dynamics
K1 Prediction
K1 Deep learning
K1 Convolutional neural network
K1 33 Ciencias Tecnológicas
K1 3325 Tecnología de las Telecomunicaciones
AB Deep learning models are not yet fully applied to fluid dynamics predictions, while they are the state-of-the-art solution in many other areas i.e. video and language processing, finance, robotics . Prediction problems on high-dimensional, complex dynamical systems require deep learning models devised to avoid overfitting while maintaining the required model complexity. In this work we present a deep learning prediction model based on a combination of 3D convolutional layers and a low-dimensional intermediate representation that is specifically designed to forecast the future states of this type of dynamical systems. The model predicts p future velocity-field time-slices (samples) based on k past samples from a training dataset consisting of a synthetic jet in transitional regime. The complexity of this flow is characterized by two topology patterns that are periodically changing, making this flow as a suitable example to test the performance of deep learning models to predict time states in complex flows. Moreover, the wide number of applications of synthetic jets (i.e.: fluid mixing, heat transfer enhancement, flow control), points out this example as a reference for future applications, where modeling synthetic jet flows with a reduced computational effort is needed. This work additionally opens up research opportunities for other areas that also operate with complex and high-dimensional time-series data: future frame video prediction, network traffic forecasting, network intrusion detection .The proposed model is presented in detail. A comprehensive analysis of the results is provided. The results are based on a strict validation strategy to ensure its generalization. The model offers an average symmetric mean absolute error (sMAPE) and a relative root mean square error (RRMSE) of 1.068 and 0.026 respectively (one order of magnitude improvement over low-rank approximation tools), using 10 past samples and predicting 6 future samples of a two-dimensional velocity field on a 70x50 point matrix associated to a synthetic jet dataset.
PB Elsevier
SN 0957-4174
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/54207
UL https://uvadoc.uva.es/handle/10324/54207
LA eng
NO Expert Systems with Applications, 2021, vol. 177, p. 114924
NO Producción Científica
DS UVaDOC
RD 19-oct-2024