基于粒子群算法对纬编针织物Johnson-Champoux-Allard模型参数反分析研究

doi:10.13475/j.fzxb.20230805501

纺织学报 ›› 2024, Vol. 45 ›› Issue (10): 103-112.doi: 10.13475/j.fzxb.20230805501

基于粒子群算法对纬编针织物Johnson-Champoux-Allard模型参数反分析研究

韩炜¹, 邢晓梦¹^,², 张海宝³, 姜茜¹^,², 刘天威¹, 卢佳浩³, 闫志强³, 巩继贤¹, 吴利伟¹^,²^,³()

1.天津工业大学纺织科学与工程学院, 天津 300387
2.天津工业大学先进纺织复合材料教育部重点实验室, 天津 300387
3.青海省产品质量检验检测院, 青海西宁 810099

收稿日期:2023-08-25 修回日期:2024-05-24 出版日期:2024-10-15 发布日期:2024-10-22
通讯作者: 吴利伟(1984—),男,副教授。主要研究方向为功能性纺织品。E-mail: wuliwei@tiangong.edu.cn。
作者简介:韩炜(2000—),女,硕士生。主要研究方向为功能性纺织品。
基金资助:
浙江省纱线材料成形与复合加工技术研究重点实验室开放基金项目(MTC2021-02);天津市高等学校创新团队项目(TD13-5043)

Parametric inverse analysis of Johnson-Champoux-Allard acoustic model for weft knitted fabrics based on particle swarm algorithm

HAN Wei¹, XING Xiaomeng¹^,², ZHANG Haibao³, JIANG Qian¹^,², LIU Tianwei¹, LU Jiahao³, YAN Zhiqiang³, GONG Jixian¹, WU Liwei¹^,²^,³()

1. School of Textile Science and Engineering, Tiangong University, Tianjin 300387, China
2. Key Laboratory of Advanced Textile Composite Materials of Ministry of Education, Tiangong University, Tianjin 300387, China
3. Qinghai Provincial Institute for Product Quality Inspection and Testing, Xining, Qinghai 810099, China

Received:2023-08-25 Revised:2024-05-24 Published:2024-10-15 Online:2024-10-22

摘要/Abstract

摘要：

为快速获取纬编针织物JCA(Johnson-Champoux-Allard)模型参数,建立材料、结构与模型函数关系,实现吸声性能预估,提出迭代次数少、可快速达到全局最优解的粒子群算法对JCA模型进行反分析求解,并以吸声系数实验值和设定值差值平方和的最小值为适应度函数,设置约束,添加学习因子与惯性权重对反分析过程进行限制,多次迭代得到孔隙率、流阻率、曲折因子、黏性特征长度与热特征长度的数值;然后建立JCA模型参数(孔隙率与流阻率)与针织物结构参数(未充满系数)的函数关系,以达到快速获取不同结构针织物JCA模型参数,进而对其吸声情况进行预估的目的;最后,用有限元方法对快速获得的JCA模型参数准确性进行验证。结果表明:基于粒子群算法可精确获得针织物JCA模型的5种参数,迭代次数少于200;针对纬平针组织不同规格,未充满系数可直接换算结构参数(孔隙率与流阻率),结合已知材料参数(曲折因子、黏性特征长度与热特征长度)快速计算出纬平针组织和双反面组织的吸声系数曲线发现,吸声系数曲线拟合度较高,R²分别为0.809和0.852。

关键词: Johnson-Champoux-Allard模型, 粒子群算法, 反分析, 纬编针织物, 吸声性能

Abstract:

Objective Textiles are widely used in the field of acoustic absorption due to their porous texture, lightweight and formability. Due to the viscous inertia and thermal dissipation mechanisms in acoustic absorption of textile materials, the Johnson-Champoux-Allard(JCA) acoustic model is believed suitable for characterization their acoustic property. However, few studies were conducted on the acquisition of acoustic parameters in JCA model and the relations between acoustic parameters and fabric structure remains vague. This paper proposes a method to quickly acquire JCA acoustic model parameters and predict the acoustic absorption of weft-knitted knitted fabric. The particle swarm algorithm was chosen to obtain the JCA acoustic model parameters by inverse analysis. The relations between fabric structure and acoustic parameters were explored, and the sound absorption coefficients of knitted fabrics with different structures were predicted.

Method Particle swarm algorithm was chosen to inversely analyze the acoustic absorption coefficient of weft-knitted fabrics, to obtain parameters of JCA acoustic model, including porosity, flow resistance, tortuosity, viscous characteristic length and thermal characteristic length. By adding inertia weights and learning factors, the inverse analysis process was restricted, thus reducing the number of iterations, avoiding the local optimal solutions, and improving the accuracy of the parameters obtained from the inverse analysis. Based on the results of the inverse analysis, the relations between the structural parameters of the weft-knitted fabric (unfilled coefficient) and the structural parameters of the JCA acoustic model (porosity, flow resistance) were established, and the JCA acoustic model parameters of knitted fabrics with different structures were obtained quickly for acoustic absorption coefficient calculation. The accuracy of the obtained JCA acoustic parameters of fabrics with different structures was verified by the finite element method.

Results The acoustic parameters such as porosity, flow resistance, tortuosity, viscous characteristic length and thermal characteristic length were inversely analyzed by the particle swarm algorithm. After 100 iterations, the iteration speed slowed down and gradually stabilized, reaching the globally optimal solution. The final iteration number was less than 200, with a minimum value of 0.19. Comparison of the numerically calculated sound absorption coefficient with the experimentally measured curves showed that the particle swarm algorithm was able to accurately inverse-analyze the JCA acoustic parameters in the range of 500-5 000 Hz. When the structure was changed, the material parameters, including tortuosity, viscous characteristic length and thermal characteristic length, were empirically obtained from the inverse analysis. Porosity was determined by the unfilled factor according to the global optimal solution. Flow resistance was obtained by fitting the porosity and the flow resistance using the exponential function in the least squares method with known inverse analytical parameters. The coefficient of determination R² was 0.994 6, indicating the effective fitting. The accuracy of JCA acoustic parameters obtained by above method was verified by finite element method. The sound absorption coefficient curves obtained from the finite element calculations for the weft flat-needle tissues fitted well with the inverse analysis and experiments. The coefficient of determination R² was 0.809. The sound absorption coefficient curves obtained from finite element calculations of the double inverse organization fitted the inverse analysis and experiments well. The coefficient of determination R² was 0.852. The work proves the accuracy and reliability of the fabric structure parameters deduced from the JCA acoustic model.

Conclusion The particle swarm method was optimized to inversely analyzing the sound absorption coefficient of weft knitted acoustic-absorption material, and the number of iterations is less than 200, achieving the rapid acquisition of the parameters in the JCA acoustic model. For different textile structures, by directly obtaining the porosity and flow resistance coefficients and combining them with known material parameters, the sound absorption coefficients at different frequencies can be calculated quickly and with less error. This method provides new ideas for the acquisition of acoustic parameters and the prediction of sound absorption performance of acoustic-absorbing material.

Key words: Johnson-Champoux-Allard model, particle swarm algorithm, parametric inverse analysis, weft knitted fabric, sound absorption

中图分类号:

TB332

韩炜, 邢晓梦, 张海宝, 姜茜, 刘天威, 卢佳浩, 闫志强, 巩继贤, 吴利伟. 基于粒子群算法对纬编针织物Johnson-Champoux-Allard模型参数反分析研究[J]. 纺织学报, 2024, 45(10): 103-112.

HAN Wei, XING Xiaomeng, ZHANG Haibao, JIANG Qian, LIU Tianwei, LU Jiahao, YAN Zhiqiang, GONG Jixian, WU Liwei. Parametric inverse analysis of Johnson-Champoux-Allard acoustic model for weft knitted fabrics based on particle swarm algorithm[J]. Journal of Textile Research, 2024, 45(10): 103-112.

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链接本文: http://www.fzxb.org.cn/CN/10.13475/j.fzxb.20230805501

http://www.fzxb.org.cn/CN/Y2024/V45/I10/103

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孔隙率/ %	反分析流阻率/ (Pa·s·m^-2)	孔隙率/ %	反分析流阻率/ (Pa·s·m^-2)
50	48 673	75	35 795
55	44 305	80	33 612
60	42 496	85	29 628
65	39 256	90	27 642
70	37 062	95	24 234

基于粒子群算法对纬编针织物Johnson-Champoux-Allard模型参数反分析研究

Parametric inverse analysis of Johnson-Champoux-Allard acoustic model for weft knitted fabrics based on particle swarm algorithm

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