30th International Symposium on Ballistics, Long Beach, California, USA, 11–15 September 2017

Woodley, Clive

doi:10.1016/s2214-9147(17)30116-2

Cited by 2 publications

(3 citation statements)

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“…Most interior ballistic models currently in use follow the approach defined by the American code IBHVG2 for which friction is modeled by a resisting force profile applying to the projectile during its course into the gun [1]. Resistance profile can be determined by studying wear effects on used guns via endoscopy [2] but the intensity of these friction forces is often calibrated by ballisticians using experimental firings [3]. In most lumped parameter models, friction is integrated in the projectile's equation of motion, it has then a direct impact on the volume available for the gas, its pressure, and so on the whole combustion process especially in the first steps of the ballistic cycle.…”

Section: Introductionmentioning

confidence: 99%

“…Interior ballistics can be described traditionally by a set of fluid dynamics equations and the propellant itself by its thermodynamics and reaction kinetics. However, a real experiment remains complex to model for several reasons that make the accuracy of physical models more or less insufficient [6]. The first one is the ontology of experimental descriptors themselves.…”

Section: Introductionmentioning

confidence: 99%

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Deep and machine learning architecture for high accuracy prediction of interior ballistics for modular charges

Brunet,

Fitas,

Cuvelier

et al. 2023

Propellants Explo Pyrotec

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Interior ballistics modelling intends to calculate physical quantities by solving equations requiring a set of input like thermodynamical properties linked to propellant or mechanical properties like friction coefficients, linked to the system, that are often difficult to assess experimentally. Using a consequent and high‐quality dataset of thousands of 155 mm modular charge experiments, we investigated how deep and machine learning approaches could build an efficient model for predicting muzzle velocity, pressure, and ignition time. After identifying the main variables influencing the physics and several potentially relevant to the machine learning and deep learning algorithms, a stacking ensemble learning model (SEL) was built. It is composed of eight machine learning algorithms and one deep learning algorithm, all as weak learner, reaching a consensus for predicting experimental outputs through a strong learner. The performance assessment, on a 900 experiments test data set that were not used in training, shows a prediction accuracy of 97 % at 1 % accuracy for muzzle velocity, 93 % at 3 % for pressure and 72 % at 5 ms for ignition time. The prediction ability for muzzle velocity within 2 m/s reaches 73 % that can be considered as sufficient. Furthermore, the SEL model even having zero knowledge about physics can discriminate gun barrel erosion effect at high and low pressure. This approach allows to imagine a way of modelling physical phenomena poorly described by physical equations or requiring unmeasurable figures.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep and machine learning architecture for high accuracy prediction of interior ballistics for modular charges

Brunet,

Fitas,

Cuvelier

et al. 2023

Propellants Explo Pyrotec

View full text Add to dashboard Cite

show abstract

“…Thermite compositions consisting of a metal fuel and an oxidant (e.g., WO 3 /Al, Bi 2 O 3 , and Al/CuO/Fe 3 O 4 ) and foamed nitrocellulose with the addition of titanium particles were tested as alternative ignition materials [35][36][37][38][39]. In the case of other ignition methods, there has been interest in electrothermal-chemical (ETC) ignition-where the factor directly affecting the propellant is hot plasma-laser ignition, and gas ignition (e.g., a methane/oxygen mixture) [40][41][42][43][44][45][46][47][48][49][50][51][52][53].…”

Section: Introductionmentioning

confidence: 99%

Influence of the Ignition Method on the Characteristics of Propellant Burning in the Context of the Geometric Burning Model

2023

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Mathematical models for the simulation of the operation of various projectile-throwing systems are continuously modified, simultaneously with the development of propellants. For these models, properly determined values of energetic ballistic characteristics of propellants are very important. This paper presents an original test and the results of closed vessel comparative investigations. The study analyses specific combustion characteristics of single-base propellants using conventional black powder ignition, plasma ignition, and gas ignition. The results of the study indicate the need to take into account—in the calculation of the burning rate—not only the dynamics of gas pressure changes but also the real (experimental) form function, which differs from the theoretical one resulting from the assumed geometric burning model. The obtained results allow for a critical analysis of the existing methods used for the determination of values of ballistic characteristics of propellants. Further, it will allow for creating pyrostatic test conditions that would reflect the condition of instantaneous ignition of propellant grains over their entire surface in the most realistic way.

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30th International Symposium on Ballistics, Long Beach, California, USA, 11–15 September 2017

Cited by 2 publications

References 0 publications

Deep and machine learning architecture for high accuracy prediction of interior ballistics for modular charges

Deep and machine learning architecture for high accuracy prediction of interior ballistics for modular charges

Influence of the Ignition Method on the Characteristics of Propellant Burning in the Context of the Geometric Burning Model

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