固体发动机实际成型药柱燃面退移快速算法

刘舜; 卢洪义; 张维维; 章斌; 杨禹成; 桑豆豆

doi:10.13700/j.bh.1001-5965.2021.0795

固体发动机实际成型药柱燃面退移快速算法

doi: 10.13700/j.bh.1001-5965.2021.0795

1.
南昌航空大学飞行器工程学院，南昌 330063
2.
内蒙动力机械研究所，呼和浩特 010010

基金项目: 江西省自然科学基金(20201BBE51002)；江西省研究生创新专项资金项目(YC2021-S685)

详细信息

通讯作者:
E-mail：13964508115@163.com

中图分类号: V435⁺.12
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出版历程
- 收稿日期: 2021-12-31
- 录用日期: 2022-04-23
- 网络出版日期: 2022-05-10
- 整期出版日期: 2023-11-30

Fast algorithm for grain burnback of actually shaped grains of solid motor

1.
School of Aircraft Engineering，Nanchang Hangkong University，Nanchang 330063，China
2.
Inner Mongolia Institute of Power Machinery，Hohhot 010010，China

Funds: Natural Science Foundation of Jiangxi Province (20201BBE51002); Jiangxi Province Graduate Student Innovation Special Fund Project (YC2021-S685)

More Information

Corresponding author: E-mail：13964508115@163.com

摘要

摘要:
针对固体发动机电子计算机断层扫描(CT)图像数据具有伪影噪声的问题，和实际成型药柱燃面粗糙度大、退移计算难度大的问题，提出一种CT数据的快速最小距离函数(CT-FMDF)法。固体火箭发动机CT图像中存在伪影噪声，采用非均值滤波(NLM)算法对CT图像进行去噪处理，采用Scharr算子对去伪影后的图像进行Canny边缘检测，提取装药燃面。最大类间方差(OTSU)算法将装药分离，并建立三维装药体数据模型，对燃面数据建立多个并行K-d树，快速检索出装药到燃面的最小距离。实验结果证明：对于不同的装药结构，可以完成任意燃去厚度时的燃面位置，且CT-FMDF法运行时间更短。基于CT实测数据，对于带有初始燃面缺陷的装药，可计算出燃烧时缺陷对燃面的影响。
- 固体发动机 /
- 最小距离函数法 /
- CT图像 /
- 燃面退移 /
- K-d树
Abstract:
A fast minimum distance function method for CT data (CT-FMDF) is proposed to address the artifact noise in CT image data of solid rocket motors, the large combustion surface roughness of actually formed grains, and the difficulty in transition calculation. The non-local means (NLM) filtering algorithm is used to denoise the CT images. Then, the Canny edge detection improved by the Scharr operator is used to extract the initial burning surface of the grains from the de-artifacted image. The maximum between-class variance algorithm (OTSU) separates the grains, establishing a three-dimensional volume data model of grains, and multiple parallel K-dimension trees for the combustion surface data. The minimum distance from the grains to the combustion surface is quickly retrieved. The experimental results show that for different grains structures, the position of a burning surface at any thickness can be achieved and that the algorithm has a shorter running time. For the grains with initial burning surface defects, the effect of the defects on the burning surface during combustion can be calculated based on the actual CT data.
- solid motor /
- minimum distance function /
- CT image /
- grain burnback /
- K-d tree

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图 1 NLM算法

Figure 1. NLM algorithm

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图 2 非极大抑制

Figure 2. Non-maximum suppression

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图 3 K-d树分割空间

Figure 3. K-d tree partition space

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图 4 K-d树

Figure 4. K-dimension tree

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图 5 CT-FMDF算法流程

Figure 5. CT-FMDF algorithm flow chart

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图 6 滤波算法对比

Figure 6. Comparison of filtering algorithms

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图 7 Canny边缘检测

Figure 7. Canny edge detection

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图 8 初始燃面提取

Figure 8. Extraction of initial burning surface

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图 9 OTSU阈值分割

Figure 9. OTSU threshold segmentation

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图 10 CT数据的MDF法燃面退移

Figure 10. Grain burnback by MDF method based on CT data

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图 11 三维体数据场的建立

Figure 11. Establishment of 3D volume data field

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图 12 星形装药燃烧面积变化

Figure 12. Grain burnback area of star-shaped grain

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图 13 星形装药剖切图

Figure 13. Cutaway view of star-shaped grain

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图 14 圆柱形装药燃烧面积变化

Figure 14. Grain burnback area of cylindrical grain

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图 15 圆柱装药剖切图

Figure 15. Cutaway view of cylindrical grain

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表 1 K-d树的数据结构

Table 1. K-d tree data structure

域名	数据类型	描述
Node-data	数据矢量	数据集中某个数据点，是n维矢量（本文就是k维）
Range	空间矢量	该节点所代表的空间范围
split	整数	垂直于分割超平面的方向轴序号
Left	K-d树	由位于该节点分割超平面左子空间内所有数据点所构成的K-d树
Right	K-d树	由位于该节点分割超平面右子空间内所有数据点所构成的K-d树
Parent	K-d树	父节点

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表 2 去噪声图像质量分析

Table 2. Quality analysis of denoising image

去噪算法	MSE	PSNR	SSIM
NLM（星形）	1.62984	46.0094	0.999048
NLM（圆柱）	6.33753	40.1116	0.997223
BM3D（星形）	2.16834	44.7695	0.998738
BM3D（圆柱）	3.74647	42.3946	0.998386
双边滤波（星形）	5.76638	40.5218	0.996544
双边滤波（圆柱）	10.8642	37.7708	0.995227
高斯滤波（星形）	8.27121	38.9551	0.995228
高斯滤波（圆柱）	7.36916	39.4566	0.996813

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表 3 星形装药特征参数

Table 3. Characteristic parameters of star grain

肉厚 w/mm	星角数n	星边夹角θ/（°）	星尖角分数ε	星尖导圆半径f/mm	特征长度l/mm	星根尖倒圆半径r/mm
220	6	45	0.7	20	230	20

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