Automation of conceptual design and modification of aircraft type unmanned aerial vehicles using multidisciplinary optimization and evolutionary algorithms. Part 1: Methods and models

This paper proposes a method for selecting rational parameters for large-size aircraft-type unmanned aerial vehicles at the initial design stages using an optimization algorithm of differential evolution and numerical mathematical modeling of aerodynamic problems. The method assumes implementation of weight and aerodynamic balance in the main flight modes, it can consider aircraft-type unmanned aerial vehicles with one or two lifting surfaces, applies parallel calculations, and automatically generates a three-dimensional geometric model of the aircraft appearance based on the optimization results. A method for accelerating by more than three times the process of solving the problem of optimizing aircraft takeoff weight parameters by introducing the target function into the set of design variables is proposed and demonstrated. The results of assessing the reliability of the mathematical models used for aerodynamics and the correct calculation of the target function are presented, taking into account various constraints. A comprehensive check of the operability and effectiveness of the method were considered by solving demonstration problems by optimizing more than ten main design parameters of the appearance of two existing heavy-class unmanned aerial vehicles with known characteristics from open sources. Examples of using the optimization results to modify prototypes are provided.

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Publication Details

Journal
VESTNIK of Samara University Aerospace and Mechanical Engineering
Published
2024-10-23
DOI
https://doi.org/10.18287/2541-7533-2024-23-3-42-57
Citations
1
Primary Topic
Advanced Multi-Objective Optimization Algorithms
Type
article
Field-Weighted Citation Impact
0.36

Funders

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Automation of conceptual design and modification of aircraft type unmanned aerial vehicles using multidisciplinary optimization and evolutionary algorithms. Part 1: Methods and models

Van Hung Hoang, Evgenii Kurkin, В. А. Комаров, Oleg Lukyanov
1 citations
VESTNIK of Samara University Aerospace and Mechanical Engineering
Advanced Multi-Objective Optimization Algorithms
0.36
article

Automation of conceptual design and modification of aircraft type unmanned aerial vehicles using multidisciplinary optimization and evolutionary algorithms. Part 1: Methods and models

Van Hung Hoang, Evgenii Kurkin, В. А. Комаров, Oleg Lukyanov
article en
1 citations

Abstract

This paper proposes a method for selecting rational parameters for large-size aircraft-type unmanned aerial vehicles at the initial design stages using an optimization algorithm of differential evolution and numerical mathematical modeling of aerodynamic problems. The method assumes implementation of weight and aerodynamic balance in the main flight modes, it can consider aircraft-type unmanned aerial vehicles with one or two lifting surfaces, applies parallel calculations, and automatically generates a three-dimensional geometric model of the aircraft appearance based on the optimization results. A method for accelerating by more than three times the process of solving the problem of optimizing aircraft takeoff weight parameters by introducing the target function into the set of design variables is proposed and demonstrated. The results of assessing the reliability of the mathematical models used for aerodynamics and the correct calculation of the target function are presented, taking into account various constraints. A comprehensive check of the operability and effectiveness of the method were considered by solving demonstration problems by optimizing more than ten main design parameters of the appearance of two existing heavy-class unmanned aerial vehicles with known characteristics from open sources. Examples of using the optimization results to modify prototypes are provided.

VESTNIK of Samara University Aerospace and Mechanical EngineeringVol. 23(3)
Samara National Research University (RU)
Samara University
Openalex Percentile: Top 35%
Advanced Multi-Objective Optimization Algorithms
0.36
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