The Effect of Oxidizer Particle Size Distribution on the Steady and Nonsteady Combustion of Composite Propellants.

Condon, J.A.; Osborn, J. R.

doi:10.21236/ada056892

Cited by 23 publications

(5 citation statements)

References 14 publications

(20 reference statements)

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“…However, for AP particles, the size distribution has significant physical importance [4,27]. Bimodal distributions of AP are common, and tri-and quad-modal are not unheardof [9].…”

Section: Featurizationmentioning

confidence: 99%

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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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“…However, for AP particles, the size distribution has significant physical importance [4,27]. Bimodal distributions of AP are common, and tri-and quad-modal are not unheardof [9].…”

Section: Featurizationmentioning

confidence: 99%

“…To address this challenge, many different groups developed models to predict the burning rates of various classes of solid rocket propellants [1][2][3][4][5][6][7]. These models make a variety of assumptions regarding either spatial properties of the reactions (inhomogeneities, edge effects, etc.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Solid Propellant Burning Rate Characteristics Using Machine Learning Techniques

Klinger¹,

Casey

Manship

et al. 2023

Propellants Explo Pyrotec

View full text Add to dashboard Cite

show abstract

“…The method produces the same qualitative behavior as the Cohen model. Further calculations by Condon and Osborn 28 show that the width of the size distribution for each modal size has a significant effect upon the appearance of the response function curve for the aggregate propellant.…”

Section: Perturbed Bdp Modelmentioning

confidence: 98%

“…Some calculations of the velocity-coupled response function based upon application of the Cohen postulates appear in Ref. 28. A more extensive discussion of velocity coupling is beyond the scope of this paper.…”

Section: Perturbed Bdp Modelmentioning

confidence: 99%

Response Function Theories That Account for Size Distribution Effects-A Review

Cohen

1981

AIAA Journal

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NomenclatureA,A',B,B',C = constants appearing in response function expressions c g = specific heat of gas c s = specific heat of solid propellant DJ =/th particle diameter in a multimodal distribution D r D 4 = constants appearing in Zeldovich response function expression E r E 4 = constants appearing in Cohen-Bowyer response function expression E g = activation energy of gas-phase reactions E s = activation energy of surface decomposition / = frequency of oscillations // =peak response frequency, or preferred frequency, for the /th particle size m,m r =mean and fluctuating components of propellant mass flux n = burning rate pressure exponent p = pressure p,p' = mean and fluctuating components of pressure R = universal gas constant R b = pressure-coupled response function r -propellant burning rate T F = flame temperature T 0 = initial propellant temperature T w = propellant surface temperature oL i = weight fraction of /th particle size K = propellant thermal diffusivity X, X' = complex variables defined in text Q = dimensionless frequency of oscillations OP = burning rate temperature sensitivity

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“…This can balance [NCO] groups from the propellant and achieve a stoichiometric design to prevent propellant interface softening. Adding an oxidizer to the liner bonding agent, or a substance with a bonding group, can effectively improve the bond strength of the liner [8]. Disturbances arising during bonding may lead to the curing of the propellant, which makes it soften easily.…”

Section: Introductionmentioning

confidence: 99%

Improving Bond Strength at Propellant–Liner Interfaces

Liao

Lee

2015

Journal of Propulsion and Power

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In solid rocket motors, a strong bond at the propellant-liner interface ensures that the propellant functions as designed. If the interface degums, the propellant burn surface area will change and the performance of internal ballistics will be adversely affected. In this study, two cases of motor failure are considered to investigate methods for improving the bond strength between the propellant grain and the liner interface, with the aim of achieving a tensile strength exceeding the 8 kg∕cm 2 requirement. Hydroxyl-terminated polybutadiene was used as the liner material. This hydroxyl-terminated-polybutadiene-based material was conditioned and processed under the conditions of 5-20 torr pressure and 60-70°C temperature. Further, toluene diisocyanate was considered as the hardener and its contents were increased in the liner by 0 (i.e., original content), 2, 5, 10, and 15% to obtain diisocyanate NCO/OH equivalence ratios of 1.035, 1.057, 1.087, 1.144, and 1.187, respectively. Thereafter, bond strength tests and analyses were conducted. The tensile strength gradually increased with increasing toluene diisocyanate content in the liner. The bond strength was found to increase after improving the liner, performing hydroxyl-terminated polybutadiene heating and vacuum extraction pretreatments, and increasing the toluene diisocyanate content by 10%.

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The Effect of Oxidizer Particle Size Distribution on the Steady and Nonsteady Combustion of Composite Propellants.

Cited by 23 publications

References 14 publications

Prediction of Solid Propellant Burning Rate Characteristics Using Machine Learning Techniques

Prediction of Solid Propellant Burning Rate Characteristics Using Machine Learning Techniques

Response Function Theories That Account for Size Distribution Effects-A Review

Improving Bond Strength at Propellant–Liner Interfaces

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