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Design optimization of a fluidic thrust vectoring system based on coanda effect using meta-models

  • Emre Kara ORCID logo EMAIL logo and Dilek Funda Kurtuluş
Published/Copyright: December 30, 2024
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Abstract

In this study, parametric optimization is employed to identify optimal output parameters for physical components of HOMER nozzle type fluidic thrust vectoring (FTV) system. Optimization study is conducted on seven output parameters: moment around upper Coanda surface, MA; performance parameter, PP; thrust vectoring angle, θ T; total thrust, T; thrust vectoring efficiency, η; axial thrust, Tx; transverse thrust, Ty. 440 case studies in design-of-experiment stage are employed, utilizing a range of meta-models. Genetic-Aggregation meta-model exhibited highest coefficient of determination (R2) among tested meta-models. In response surface optimization stage, six objectives are enforced through use of Multi-Objective-Genetic-Algorithm technique. Subsequently, three optimization candidate points are identified for filtering of output parameters deemed to be of particular importance. Among them, candidate point with geometry-4 and parameter-1 = 39.23 m/s, exhibited the highest values for PP, θ T, η, and Ty, with 4.78 %, 4.48 %, 7.97 %, and 7.94 % higher values, respectively, compared to the baseline geometry.


Corresponding author: Emre Kara, Aerospace Engineering Department, Gaziantep University, Gaziantep, 27310, Türkiye, E-mail:

Funding source: Gaziantep University

Award Identifier / Grant number: HUBF.GAP.21.02

Funding source: National Center for High Performance Computing of Turkey (UHeM)

Award Identifier / Grant number: 1013062022

  1. Research ethics: Not applicable.

  2. Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.

  3. Author contributions: E.K. is responsible for the conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, supervision, visualization, writing – original draft of the paper. D.F.K. is responsible for conceptualization, investigation and writing – review & editing of the paper. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: Deepl.com is used to improve language.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: This work was financially supported by the Scientific Research Projects (BAP) unit of Gaziantep University, Turkey under the grant number of HUBF.GAP.21.02. Computing resources used in this work were provided by the National Center for High Performance Computing of Turkey (UHeM) under grant number 1013062022.

  7. Data availability: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/tjj-2024-0090).


Received: 2024-10-12
Accepted: 2024-12-14
Published Online: 2024-12-30
Published in Print: 2025-08-26

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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