Data-Driven Aerodynamic Optimization of Coupled Finstabilizer and Boat-Tail in Achieving Desired Stability Management through an Automated CFD Workflow

In this study, a data-driven aerodynamic optimization has been carried out to address the static stability management of a slender body air vehicle configuration. The coupled effect of the fin-stabilizer and boat-tail on the stability margin has been investigated by establishing an automated CFD workflow and implementing state-of-the-art machine learning techniques. The study brought out recommendation of a single, flight-agnostic ‘optimized’ aerodynamic configuration design catering to different phases of flight, having different static stability constraints. The paper discusses the advantages of using response surface model(RSM) based optimization technique for exhaustive exploration of the design space and evolve at an optimal aerodynamic configuration in much lesser turn-around time and without over-reliance on real CFD data

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Publication Details

Journal
Journal of Aerospace Sciences and Technologies
Published
2025-08-07
DOI
https://doi.org/10.61653/joast.v77i3.2025.1089
Primary Topic
Computational Fluid Dynamics and Aerodynamics
Type
article
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article

Data-Driven Aerodynamic Optimization of Coupled Finstabilizer and Boat-Tail in Achieving Desired Stability Management through an Automated CFD Workflow

S. Kalyana Rama, Shiladitya Bhowmick, T Sri Venkateshwaran
Journal of Aerospace Sciences and Technologies
Computational Fluid Dynamics and Aerodynamics
article

Data-Driven Aerodynamic Optimization of Coupled Finstabilizer and Boat-Tail in Achieving Desired Stability Management through an Automated CFD Workflow

S. Kalyana Rama, Shiladitya Bhowmick, T Sri Venkateshwaran
article en

Abstract

In this study, a data-driven aerodynamic optimization has been carried out to address the static stability management of a slender body air vehicle configuration. The coupled effect of the fin-stabilizer and boat-tail on the stability margin has been investigated by establishing an automated CFD workflow and implementing state-of-the-art machine learning techniques. The study brought out recommendation of a single, flight-agnostic ‘optimized’ aerodynamic configuration design catering to different phases of flight, having different static stability constraints. The paper discusses the advantages of using response surface model(RSM) based optimization technique for exhaustive exploration of the design space and evolve at an optimal aerodynamic configuration in much lesser turn-around time and without over-reliance on real CFD data

Journal of Aerospace Sciences and Technologies
JDA Software (United States) (US), Defence Research and Development Laboratory (IN)
Openalex Percentile: Top 77%
Computational Fluid Dynamics and Aerodynamics
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