Mr. Hexi Baoyin Profile

Hexi Baoyin

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Proceedings Article | 1 April 2024 Paper

Aerodynamic characteristics analysis of the tailback shape of a flying car based on LBM

Hongfei Liu, Jingyang Li, Hexi Baoyin

Proceedings Volume 13082, 1308220 (2024) https://doi.org/10.1117/12.3026299

KEYWORDS: Aerodynamics, Shape analysis, Particles, 3D modeling, Resistance, Motion models, Numerical simulations, Lithium, Fluid dynamics, 3D applications

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The flying car combines the functions of the aircraft and the car, and its aerodynamic shape satisfies the aerodynamic characteristics of the car and the aircraft. The Catia three-dimensional modeling software is used to draw a ducted flying car shape, and the step-back, fast-back, and square-back type tailback models are established. The CFD software Xflow is used to perform grid-independent experiments on the model to determine the optimal simulation accuracy of the vehicle model. The aerodynamic analysis of the three tailback models is carried out by using the obtained optimal simulation accuracy. Through the analysis and comparison of the aerodynamic characteristics of the three tailback shapes, it is concluded that the aerodynamic parameters of the fast-back tailback are the best for improving aerodynamic performance. By comparing the inclination angle of the fast-back tailback, it is found that when the inclination angle is 30, the aerodynamic parameters improve the aerodynamic performance.

Proceedings Article | 9 January 2024 Paper

An improved dung beetle optimizer

Jinxiang Feng, Jingyang Li, Yufeng Zhang, Hexi Baoyin

Proceedings Volume 12969, 129692V (2024) https://doi.org/10.1117/12.3014472

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Dung Beetle Optimizer(DBO) is an effective metaheuristic algorithm proposed in 2022. But at the same time, DBO also suffers from a local-global imbalance in the exploration process, tends to fall into local optimization and exploitability needs to be further improved, etc. Therefore, we propose an improved DBO algorithm to address these shortcomings and named it CDBO. Firstly, Tent chaotic mapping can be used for the purpose of initializing the population, improving the quality of initial solutions, promoting the enhancement of population variety, and augmenting the global search capability of the algorithm. Secondly, introducing dynamic weighting factors enables the algorithm to fully search for local areas while also taking into account global exploration. To assess the effectiveness of CDBO, a total of 12 benchmark test functions were utilized to evaluate the performance of this algorithm, wherein CDBO was compared with other widely recognized metaheuristic algorithms. The results showed that CDBO had improved search accuracy and convergence speed. Finally, CDBO was applied to airfoil optimization problem, verifying the feasibility of applying CDBO to practical engineering problems.

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