An application of machine learning for geometric optimization of a dual-throat bent nozzle

IF 4 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Advances in Engineering Software Pub Date : 2025-02-03 DOI:10.1016/j.advengsoft.2025.103869

Homin Kim, Dong-Hun Han, Tae Hee Lee, Jung-Wuk Hong

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引用次数: 0

Abstract

The optimal thrust shape for a dual-throat bent nozzle (DTBN), designed as a hybrid thrust vectoring nozzle, is derived through machine learning. A compressible, steady-state numerical analysis using the

k - ω

SST model is employed for model construction. The main geometric parameters that determine the shape of the DTBN are selected as the convergence angle

θ_{c}

, divergence angle

θ_{d}

, and cavity length

l_{c}

. By varying these parameters, DTBN models with a total of 600 different geometries are generated, and the axial and normal forces at the nozzle exit are observed to derive the thrust magnitude and thrust vectoring angle. A model that can accurately predict the correlation between input and output parameters is built by comparing various machine learning algorithms. The model using the random forest regression algorithm shows the best performance. Based on this developed machine learning model, optimized shapes of the DTBN are presented. The optimally designed DTBNs are expected to contribute to the development of a new system with more convenient thrust control.

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来源期刊

Advances in Engineering Software 工程技术-计算机：跨学科应用

CiteScore

7.70

自引率

4.20%

发文量

169

审稿时长

37 days

期刊介绍： The objective of this journal is to communicate recent and projected advances in computer-based engineering techniques. The fields covered include mechanical, aerospace, civil and environmental engineering, with an emphasis on research and development leading to practical problem-solving. The scope of the journal includes: • Innovative computational strategies and numerical algorithms for large-scale engineering problems • Analysis and simulation techniques and systems • Model and mesh generation • Control of the accuracy, stability and efficiency of computational process • Exploitation of new computing environments (eg distributed hetergeneous and collaborative computing) • Advanced visualization techniques, virtual environments and prototyping • Applications of AI, knowledge-based systems, computational intelligence, including fuzzy logic, neural networks and evolutionary computations • Application of object-oriented technology to engineering problems • Intelligent human computer interfaces • Design automation, multidisciplinary design and optimization • CAD, CAE and integrated process and product development systems • Quality and reliability.