Simulation of a High-Speed Train against a Turbulent Air Flow using Computational Fluid Mechanics Method and Multi-Layer Feed-Forward Neural Network Algorithm

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Hajipour Alireza; MIRABDOLAH LAVASANI ARASH; Eftekhari Yazdi Mohammad

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Modeling in Engineering Year:2022 | Volume:19 | Issue:67 Page(s): 119-134

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

Simulation of a High-Speed Train against a Turbulent Air Flow using Computational Fluid Mechanics Method and Multi-Layer Feed-Forward Neural Network Algorithm

Author(s)

Hajipour Alireza | MIRABDOLAH LAVASANI ARASH | Eftekhari Yazdi Mohammad | Issue Writer Certificate

Keywords

CFD

Aerodynamics

High-speed train

k-ω

(SST) Turbulence Model

Neural Network Algorithm

Abstract

In this study, the aerodynamic performance of a High-speed train against a turbulent air flow is examined numerically from two approaches. First, using computational fluid dynamics, the parameters of Aerodynamics and fluid flow are analyzed and then, using Multi-Layer Feed-Forward Neural Network (MLFFNN) Algorithm, a prediction and comparison with the obtained values from the CFD analysis are presented. To achieve this, using Reynolds-Averaged Navier-Stokes (RANS) method with 𝑘,-𝜔,(SST) turbulence model, an incompressible turbulent air flow around a High-speed train model by OpenFOAM CFD Software is simulated. In this research, some of the significant and key parameters of fluid flow and Aerodynamics as velocity, pressure, streamlines, flow structure, pressure coefficients, drag, lift and side forces for some yaw angles of wind movement and velocity changes are analyzed and compared. In the following, the Multi-Layer Feed-Forward Neural Network which is modified with various data is applied for prediction of the output of the problem. Accordingly, the aerodynamic drag, lift and side forces for the yaw angles of wind movement and velocity changes by this algorithm method are obtained and compared with the obtained results from CFD analysis. The comparisons indicate an appropriate similarity between the CFD data and the used MLFFNN one.

Cites

No record.

References

No record.

Cite

APA: Copy

Hajipour, Alireza, MIRABDOLAH LAVASANI, ARASH, & Eftekhari Yazdi, Mohammad. (2022). Simulation of a High-Speed Train against a Turbulent Air Flow using Computational Fluid Mechanics Method and Multi-Layer Feed-Forward Neural Network Algorithm. JOURNAL OF MODELING IN ENGINEERING, 19(67 ), 119-134. SID. https://sid.ir/paper/992993/en

Vancouver: Copy

Hajipour Alireza, MIRABDOLAH LAVASANI ARASH, Eftekhari Yazdi Mohammad. Simulation of a High-Speed Train against a Turbulent Air Flow using Computational Fluid Mechanics Method and Multi-Layer Feed-Forward Neural Network Algorithm. JOURNAL OF MODELING IN ENGINEERING[Internet]. 2022;19(67 ):119-134. Available from: https://sid.ir/paper/992993/en

IEEE: Copy

Alireza Hajipour, ARASH MIRABDOLAH LAVASANI, and Mohammad Eftekhari Yazdi, “Simulation of a High-Speed Train against a Turbulent Air Flow using Computational Fluid Mechanics Method and Multi-Layer Feed-Forward Neural Network Algorithm,” JOURNAL OF MODELING IN ENGINEERING, vol. 19, no. 67 , pp. 119–134, 2022, [Online]. Available: https://sid.ir/paper/992993/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops