Interval uncertain optimization for interior ballistics based on Chebyshev surrogate model and affine arithmetic

Xu, Fengjie; Yang, Guolai; Wang, Liqun; Sun, Quanzhao

doi:10.1080/0305215x.2020.1790551

Cited by 10 publications

(5 citation statements)

References 25 publications

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“…Therefore, in this interval optimization, the interval midpoint of muzzle velocity

{{(v}_{g}^{c}}

) is taken as one of the objective functions. From the perspective of ballistics consistency, in order to improve the firing accuracy of CTA, it is necessary to reduce the deviation of muzzle velocity as much as possible [35, 36]. Due to the fact that the interval radius of muzzle velocity (

{{v}_{g}^{\omega }}

) can be used to describe the muzzle velocity deviation, so the interval radius of muzzle velocity is also taken as one of the objective functions.…”

Section: Interval Optimization Of Cta Interior Ballisticsmentioning

confidence: 99%

Retracted: Interval uncertain optimization of cased telescoped ammunition interior ballistics considering tolerance design

2023

Propellants Explo Pyrotec

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An interval optimization method considering tolerance design is proposed to solve the uncertainty optimization problem of interior ballistics of cased telescoped ammunition. Firstly, the interval optimization model of the interior ballistics of the cased telescoped ammunition considering tolerance design is constructed. The interval optimization model of the interior ballistics of the cased telescoped ammunition considering tolerance design under uncertainty is presented. Then, considering the extremely low computational efficiency of the interval uncertain optimization, the back propagation neural network surrogate model is introduced. A series of pareto optimal solution is obtained by using non‐dominated sorting genetic algorithm II. Through the given design scheme, it provides a reference for the optimization of the interior ballistic performance of the cased telescoped ammunition under uncertain condition.

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“…Therefore, in this interval optimization, the interval midpoint of muzzle velocity

{{(v}_{g}^{c}}

{{v}_{g}^{\omega }}

) can be used to describe the muzzle velocity deviation, so the interval radius of muzzle velocity is also taken as one of the objective functions.…”

Section: Interval Optimization Of Cta Interior Ballisticsmentioning

confidence: 99%

Retracted: Interval uncertain optimization of cased telescoped ammunition interior ballistics considering tolerance design

2023

Propellants Explo Pyrotec

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“…Though such processing has high precision, it suffers from expensive computational cost. For the consideration of efficiency, various approaches have been introduced to decouple the nested optimization into a single-layer one, including the degree of interval constraint violation (DICV) [115], the Karush-Kuhn-Tucker (KKT) condition [116], lightning attachment procedure optimization (LAPO) [117], affine arithmetic [118], etc.…”

Section: Optimization-based Methodsmentioning

confidence: 99%

Recent Advances in Surrogate Modeling Methods for Uncertainty Quantification and Propagation

Wang

Qiang

et al. 2022

Symmetry

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Surrogate-model-assisted uncertainty treatment practices have been the subject of increasing attention and investigations in recent decades for many symmetrical engineering systems. This paper delivers a review of surrogate modeling methods in both uncertainty quantification and propagation scenarios. To this end, the mathematical models for uncertainty quantification are firstly reviewed, and theories and advances on probabilistic, non-probabilistic and hybrid ones are discussed. Subsequently, numerical methods for uncertainty propagation are broadly reviewed under different computational strategies. Thirdly, several popular single surrogate models and novel hybrid techniques are reviewed, together with some general criteria for accuracy evaluation. In addition, sample generation techniques to improve the accuracy of surrogate models are discussed for both static sampling and its adaptive version. Finally, closing remarks are provided and future prospects are suggested.

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“…Step 3: Obtain the solutions of the subspace by the intelligent optimizer, such as the generational particle swarm optimization (GPSO). Calculate the space distance D i by equation (18). Set the initial distance D 0 ¼ D for the first loop; otherwise, continue the subspace loop until D ¼ D 0 or the end condition is satisfied.…”

Section: Meco Methodsmentioning

confidence: 99%

“…In the optimization problem with projectile-barrel interaction, studies which have optimized the artillery launch performance only consider part of the launching process. In addition, Wang et al, 17 Xu and Yang 18 used the interval uncertainty optimization method to optimize the launching accuracy and stability of the launch system. So far, few studies have focused on the complete optimization of the different stages of the projectile-barrel interaction.…”

Section: Introductionmentioning

confidence: 99%

Modified enhanced collaborative optimization method for artillery launching dynamic model with sequential process

Xie

Yang

Sun

2022

Journal of Low Frequency Noise, Vibration and Active Control

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The artillery launch system directly influences the muzzle energy and launching accuracy, and therefore it is important to optimize the artillery launch system’s complete process to improve the launch performance. As the objective function of the artillery launch system is non-smooth with coupling parameters in sequential processes, conventional optimization methods are hard to converge for the muti-sequential process of the interaction between the projectile and the barrel. This paper develops a coupled dynamic model for artillery launching, which can predict the performance of the engraving process of the rotating band and the projectile motion in the barrel. The independent optimization problem of the artillery launch system is divided into two subspace problems, and a modified enhanced collaborative optimization (MECO) method with global search capability is proposed, in which the distance criterion and penalty design boundary method are implemented. Results show that the MECO is dedicated not only to satisfying compatibility between coupling parameters of the two sequential processes effectively but also to improving the projectile axial speed at the muzzle and launching accuracy. The MECO maintains a much stable level of convergence than the ECO when the original optimization problem is multimodal.

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Interval uncertain optimization for interior ballistics based on Chebyshev surrogate model and affine arithmetic

Cited by 10 publications

References 25 publications

Retracted: Interval uncertain optimization of cased telescoped ammunition interior ballistics considering tolerance design

Retracted: Interval uncertain optimization of cased telescoped ammunition interior ballistics considering tolerance design

Recent Advances in Surrogate Modeling Methods for Uncertainty Quantification and Propagation

Modified enhanced collaborative optimization method for artillery launching dynamic model with sequential process

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