Comprehensive Study of AP Particle Size and Loading Effects on the Burning Rates of Composite AP/HTPB Propellants

Thomas, James C.; Morrow, Gordon R.; Dillier, Catherine A.; Petersen, Eric L.

doi:10.2514/6.2018-4874

Cited by 7 publications

(1 citation statement)

References 22 publications

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“…These models can be evaluated against a range of published experiments on propellants under various conditions and with different material constituents [9][10][11][12]. Given the availability of data and the interest in computationally inexpensive models, machine learning (ML) techniques have been applied to predicting propellant burning rates in recent years [13][14][15][16]. Physics-agnostic ML approaches are often very data-hungry, requiring thousands or even millions of data entries to perform well [17,18].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Solid Propellant Burning Rate Characteristics Using Machine Learning Techniques

Klinger¹,

Casey

Manship

et al. 2023

Propellants Explo Pyrotec

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When formulating a new solid propellant, one of the most important aspects of its performance is the burning rate's response to a change in pressure. For this reason, it is useful to be able to predict the burning rate response of a given propellant before the propellant formulation is created such that experimental trade studies are minimized or reduced in scale. While many theoretical and phenomenological models have been proposed to explain various aspects of energetic material combustion, little work has been made publicly available in the application of machine learning models to predicting solid propellant burning rates. To facilitate model creation, the material formulation and burning rate parameters for over 600 publicly available propellant formulations have been collected into a coherent data set. This work utilizes the large amount of publicly available data to inform a random forest machine learning (ML) model in the prediction of solid propellant burning rate parameters. This ML model operates over a large parameter space including ammonium perchlorate composite, plastic bonded high explosive, and double-base propellants. The model's accuracy, adaptability, and prediction capabilities are presented and discussed. The effects of different materials on a propellant's expected burning rate are examined.

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