سیویلیکا را در شبکه های اجتماعی دنبال نمایید.

Intelligent Prediction of Separated Flow Dynamics using Machine Learning

Publish Year: 1404
Type: Journal paper
Language: English
View: 49

This Paper With 20 Page And PDF Format Ready To Download

Export:

Link to this Paper:

Document National Code:

JR_JAFM-18-2_008

Index date: 11 December 2024

Intelligent Prediction of Separated Flow Dynamics using Machine Learning abstract

Understanding separated flow dynamics is crucial for implementing effective flow control techniques. These techniques help mitigate adverse effects on vehicle performance and environmental pollution. This research aims to improve flow control strategies by predicting separated flow dynamics solely through wall pressure measurements using artificial intelligence and numerical data. Initially, we identify numerical models that accurately replicate separated flow dynamics. Notably, the Detached Eddy Simulation (DES) model strongly agrees with experimental data, particularly in the turbulent regime at Reh= 89100, downstream of backward facing steps (BFS). Subsequently we conducted a correlational analysis that revealed a significant relationship between various wall pressure points and the velocity field, leading to the adoption of deep learning techniques such as Recurrent Neural Networks with Long Short-Term Memory (LSTM). These neural networks, tailored for time-dependent data, demonstrate high accuracy of low MSE of 13.48% using ten wall pressure points in predicting velocity magnitude contour over (BFS). To enhance predictions, Proper Orthogonal Decomposition (POD) is utilized to reduce system complexity while retaining essential dynamics, resulting in a lower MSE of 5.07%. Additionally, we identify the ideal wall pressure measurement region that accurately captures the entire dynamic behavior, achieving an acceptable MSE of 23.48% for predicting low order vorticity, with only three wall pressure points. This research aids in developing efficient flow control strategies with limited pressure data and offers valuable insights for closed-loop flow control applications.

Intelligent Prediction of Separated Flow Dynamics using Machine Learning Keywords:

Intelligent Prediction of Separated Flow Dynamics using Machine Learning authors

S. Kouah

Research laboratory of applied and fundamental physic /Blida ۱ University BP ۲۷۰ Route Soumâa, Blida, Algeria

F. Fadla

Research Laboratory of energetic, flow and transfers /AMC BP ۴۸ Cherchell terre ۴۲۰۰۶, Tipaza, Algeria

M. Roudane

Research laboratory of applied and fundamental physic /Blida ۱ University BP ۲۷۰ Route Soumâa, Blida, Algeria

مراجع و منابع این Paper:

لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :
Almohammadi, K. M. (۲۰۲۰). Assessment of reattachment length using turbulence ...
Altché, F., & Fortelle, A. D. L. (۲۰۱۷). An LSTM ...
Antonio, V., & Lacerda De Brederode, S. (n.d.). Three-dimensional effects ...
Bengio, Y. (۲۰۰۹). Learning deep Architectures for AI. Foundations and ...
Brown, B., Yu, X., & Garverick, S. (۲۰۰۴). Mixed-mode analog ...
Carrio, A., Sampedro, C., Rodriguez-Ramos, A., & Campoy, P. (۲۰۱۷). ...
Chen, T. B., & Soo, V. W. (۱۹۹۶). A comparative ...
Chovet, C., Lippert, M., Foucaut, J. M., & Keirsbulck, L. ...
Chovet, C., Lippert, M., Keirsbulck, L., & Foucaut, J. M. ...
Duriez, T., Brunton, S. L., & Noack, B. R. (n.d.). ...
Elman, J. L. (۱۹۹۰). Finding structure in time. Cognitive Science, ...
Fadla, F., Alizard, F., Keirsbulck, L., Robinet, J. C., Laval, ...
Fadla, F., Graziani, A., Kerherve, F., Mathis, R., Lippert, M., ...
Fernández, S., Graves, A., & Schmidhuber, J. (۲۰۰۷). An application ...
Fukushima, K. (۱۹۸۰). Neocognitron: A self-organizing neural network model for ...
Giannopoulos, A., & Aider, J. L. (۲۰۲۰). Prediction of the ...
Gallagher, J. C., Boddhu, S. K., & Vigraham, S. (۲۰۰۵). ...
Guo, Y., Liu, Y., Georgiou, T., & Lew, M. S. ...
He, T., & Droppo, J. (۲۰۱۶). Exploiting LSTM structure in ...
Hsu, W. N., Zhang, Y., Lee, A., & Glass, J. ...
Ivakhnenko, A. G., & Lapa, V. G. (۱۹۶۵). Cybernetic predicting ...
Ivakhnenko, A. G. (۱۹۷۱). Polynomial theory of complex systems. IEEE ...
Jordan, M. (۱۹۸۶). Attractor dynamics and parallelism in a connectionist ...
Khan, S., & Yairi, T. (۲۰۱۸). A review on the ...
Kumar, K. R., & Selvaraj, M. (۲۰۲۳). Novel deep learning ...
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (۱۹۹۸). ...
Luo, D. (۲۰۱۹). Numerical simulation of turbulent flow over a ...
Mallinar, N., &Rosset, C. (۲۰۱۸). Deep canonically correlated LSTMs. https://doi.org/۱۰.۴۸۵۵۰/arXiv.۱۸۰۱.۰۵۴۰ ...
Mehrez, Z., Bouterra, M., Cafsi, A. El, Belghith, A., & ...
Ötügen, M. V. (۱۹۹۱). Ex rimeas m l mds expansion ...
Palangi, H., Deng, L., Shen, Y., Gao, J., He, X., ...
Probst, A., Radespiel, R., Wolf, C., Knopp, T., & Schwamborn, ...
Pearlmutter, B. A. (۱۹۸۹). Learning state space trajectories in recurrent ...
Qu, Z., Haghani, P., Weinstein, E., & Moreno, P. (۲۰۱۷). ...
Rajabi, E., & Kavianpour, M. R. (۲۰۱۲). Intelligent prediction of ...
Ranzato, M. A., Szlam, A., Bruna, J., Mathieu, M., Collobert, ...
Rawat, W., &Wang, Z. (۲۰۱۷). Deep convolutional neural Networks for ...
Robinson, A. J., & Fallside, F. (۱۹۸۷). The utility driven ...
Sak, H. I., Senior, A., & Beaufays, F. O. (۲۰۱۴). ...
Šarić, S., Jakirlić, S., & Tropea, C. (۲۰۰۵). A periodically ...
Sharma, P., & Singh, A. (۲۰۱۷). Era of deep neural ...
Singh, A. P., Medida, S., & Duraisamy, K. (۲۰۱۷). Machine-learning-augmented ...
Smirnov, E. M., Smirnovsky, A. A., Schur, N. A., Zaitsev, ...
Sohankar, A., Khodadadi, M., Rangraz, E., & Alam, M. M. ...
Šter, B. (۲۰۱۳). Selective recurrent neural network. Neural Processing Letters, ...
Sujar Garrido, P., Moreau, É., Bonnet, J.-P., Benard, N., & ...
Talele, V., Mathew, V. K., Sonawane, N., Sanap, S., Chandak, ...
Werbos, P. J. (۱۹۸۸). Generalization of backpropagation with application to ...
Weng, J. J., Ahuja, N., & Huang, T. S. (۱۹۹۳, ...
Williams, R. J. (۱۹۸۹). Complexity of exact gradient computation algorithms ...
Yu, Y., Si, X., Hu, C., & Zhang, J. (۲۰۱۹). ...
نمایش کامل مراجع