Optimal Design of Divergent Part of an Over-expanded Nozzle Profile using Artificial Intelligence

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.

Bagherzadeh Seyed Amin; Emami Sobhan; Salehi Seyed Ahmadreza

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

Journal Paper

Paper Information

Journal: JOURNAL OF APPLIED AND COMPUTATIONAL SCIENCES IN MECHANICS Year:2023 | Volume:35 | Issue:1 Page(s): 19-36

Download Full-Text

Persian Verion

View:

Download:

Cites:

Information Journal Paper

Title

Optimal Design of Divergent Part of an Over-expanded Nozzle Profile using Artificial Intelligence

Author(s)

Bagherzadeh Seyed Amin | Emami Sobhan | Salehi Seyed Ahmadreza | Issue Writer Certificate

Keywords

convergent-divergent nozzle

Optimization

Artificial neural network

Genetic algorithm

Nozzle Profile

Flow Separation

Abstract

The present article deals with the optimal design of the contour or profile of the divergent part of an over-expanded supersonic nozzle in order to achieve the maximum possible thrust while maintaining the length and the exit to throat area ratio of the nozzle. To do so, a reliable and robust tool has been developed by combining computational fluid dynamics (CFD) and artificial intelligence. At first, the original profile is modeled by an innovative method using a third-order B-spline, and then by changing the breakpoints of the profile, a set of possible profiles is produced. This set of profiles has been analyzed by CFD. The geometry of the nozzle along with the thrust force obtained from the profiles have been used to train the artificial neural network. In the next step, the optimal profile was obtained by applying the genetic algorithm. Finally, the prediction of the artificial intelligence for the optimal nozzle thrust is compared with the value obtained from the CFD, which shows the validity of the present approach. The comparison between the original profile and the optimal profile for the nozzle pressure ratio of 14 shows a 36% increase in thrust and a 138% increase in the total pressure recovery factor. The comparison of nozzles for off-design conditions shows that the performance of the optimal nozzle is better than the original nozzle up to a pressure ratio of 30. If the nozzle pressure ratio exceeds 30, using the optimal nozzle instead of the original nozzle will not have priority.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans