A numerical determination of complex solid gun propellant burn rates through closed bomb simulation

Christopher Houthuysen, Nicholaus Parziale
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Abstract

Closed bomb testing is a prominent means of characterizing the combustion behavior of solid gun propellants. This sub‐scale test allows the propellant to burn in a constant volume environment, where the resulting pressure‐time trace can be collected via a pressure transducer. Historically, numerical procedures have been developed to determine the burn rates of the gun propellants from these pressure‐time traces; however, no standardized procedure exists to determine the burn rates of grains with variable surface thermochemistry and ignition criteria. To address this capability gap, a non‐linearly constrained, multivariate optimization algorithm has been developed to decouple propellant grain surfaces and determine surface‐specific burn rates [1]. In this work, the optimization algorithm as well as the legacy Excel‐based Closed Bomb (XLCB) program [2] were used to determine the burn rates of homogeneous, deterred, and layered propellants from experimental data. Closed bomb simulations using these burn rates were then conducted with the two‐phase, multidimensional, interior ballistics solver, iBallistix [3]. The maximum mean error between the simulated and experimental pressure‐time curves was 6.8 % for the optimization algorithm and 23.8 % for XLCB, showing a marked improvement with our new approach. Furthermore, the approach discussed herein improves burn rate predictions of complex solid gun propellants when compared with legacy closed bomb data reduction analysis programs.
通过闭弹模拟对复杂固体炮推进剂燃烧速率进行数值测定
封闭式炸弹试验是表征固体喷枪推进剂燃烧行为的一种重要手段。这种次规模试验允许推进剂在恒定容积环境中燃烧,并通过压力传感器收集由此产生的压力-时间轨迹。从历史上看,人们已经开发了一些数值程序来根据这些压力-时间轨迹确定喷枪推进剂的燃烧速率;但是,目前还没有标准化程序来确定具有不同表面热化学特性和点火标准的颗粒的燃烧速率。为了弥补这一能力上的差距,我们开发了一种非线性约束的多元优化算法,以解耦推进剂晶粒表面并确定特定表面的燃烧率[1]。在这项工作中,优化算法和传统的基于 Excel 的封闭弹(XLCB)程序[2]被用来根据实验数据确定均质、去污和分层推进剂的燃烧率。然后使用两相、多维、内部弹道求解器 iBallistix [3],利用这些燃烧率进行封闭式炸弹模拟。优化算法的模拟压力-时间曲线与实验压力-时间曲线之间的最大平均误差为 6.8%,而 XLCB 的误差为 23.8%,这表明我们的新方法有了明显改善。此外,与传统的封闭式炸弹数据还原分析程序相比,本文讨论的方法改进了复杂固体炮用推进剂的燃烧率预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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