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

Document Type : Original Article

Authors

• Seyed Amin Bagherzadeh ¹
• Sobhan Emami Koopaei ²
• Seyed Ahmadreza Salehi ¹

¹ Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.

² Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

10.22067/jacsm.2022.78011.1134

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.

Keywords

• Convergent-Divergent Nozzle
• Optimization
• Artificial Neural Network
• Genetic Algorithm
• Nozzle Profile
• Flow Separation

Main Subjects

• Aerospace engineering

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