Article(id=1172525475827167706, tenantId=1146029695717560320, journalId=1152916057816748034, issueId=1172525471628673796, articleNumber=null, orderNo=null, doi=10.3969/j.issn.2095‒1469.2025.04.17, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1716134400000, receivedDateStr=2024-05-20, revisedDate=1719417600000, revisedDateStr=2024-06-27, acceptedDate=null, acceptedDateStr=null, onlineDate=1757481457906, onlineDateStr=2025-09-10, pubDate=1752940800000, pubDateStr=2025-07-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1757481457906, onlineIssueDateStr=2025-09-10, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1757481457906, creator=13701087609, updateTime=1757481457906, updator=13701087609, issue=Issue{id=1172525471628673796, tenantId=1146029695717560320, journalId=1152916057816748034, year='2025', volume='15', issue='4', pageStart='427', pageEnd='619', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1757481456904, creator=13701087609, updateTime=1757489711911, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1172560095704662894, tenantId=1146029695717560320, journalId=1152916057816748034, issueId=1172525471628673796, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1172560095704662895, tenantId=1146029695717560320, journalId=1152916057816748034, issueId=1172525471628673796, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=611, endPage=619, ext={EN=ArticleExt(id=1172525476103991771, articleId=1172525475827167706, tenantId=1146029695717560320, journalId=1152916057816748034, language=EN, title=Stability Analysis of Acoustic Materials Sound Absorption and Insulation Based on Evidence Theory, columnId=1153756966938731297, journalTitle=Chinese Journal of Automotive Engineering, columnName=Other Technologies, runingTitle=null, highlight=null, articleAbstract=

The strong uncertainty of acoustic material parameters leads to the poor stability of its sound absorption and insulation performance. In this paper, a method based on evidence theory is proposed to analyze the stability of acoustic materials. The uncertainty of acoustic material parameters is described by evidence theory, and the focal element interval and the basic confidence assignment of each parameter are determined. Using the interval perturbation method, the upper and lower bounds of sound absorption and insulation performance corresponding to all focal element interval combinations are calculated. The reliability and plausibility of uncertainty problems are calculated according to the identification framework. Reliability and plausibility are taken as optimization objectives, and particle swarm optimization is used to improve the stability of acoustic material's sound absorption and insulation performance. Taking an inner front wall as an example, the stability analysis and optimization of the acoustic insulation performance of the acoustic material were carried out. After optimization, the quality of the part was reduced by 18%, and the stability of the acoustic insulation performance was greatly improved in the whole frequency band, especially in the middle and low frequency band.

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声学材料参数存在较强的不确定性,导致其吸隔声性能的稳定性较差。提出一种基于证据理论的声学材料吸隔声性能稳定性分析方法。采用证据理论对声学材料参数的不确定性进行描述,确定每个参数的焦元区间及基本可信度分配;使用区间摄动方法计算所有焦元区间组合对应的吸隔声性能上下界;根据辨识框架计算不确定性问题的可信度和似真度;以可信度、似真度作为优化目标,使用粒子群算法进行优化,提升声学材料吸隔声性能的稳定性。以某汽车声学包内前围为例进行了声学材料隔声性能稳定性分析和优化。优化后,零件的质量降低了18%,同时隔声性能的稳定性在全频段内大幅度提升,在中、低频段尤为明显。

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郝耀东(1988-),男,天津市人,博士,高级工程师,主要研究方向为汽车NVH。

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郝耀东(1988-),男,天津市人,博士,高级工程师,主要研究方向为汽车NVH。

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郝耀东(1988-),男,天津市人,博士,高级工程师,主要研究方向为汽车NVH。

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参数 中心值
EVA材料 厚度/mm 2
密度/(kg/m3 1 500
PU发泡材料 厚度/mm 23
密度/(kg/m3 25
泊松比 0.38
弹性模量/Pa 55 000
流阻/(Nm/s-4 5 600
孔隙率 0.97
曲折因子 1.28
黏性特征长度/m 0.000 12
热特征长度/m 0.000 35
), ArticleFig(id=1175522297990497187, tenantId=1146029695717560320, journalId=1152916057816748034, articleId=1172525475827167706, language=CN, label=表1, caption=

内前围材料参数

, figureFileSmall=null, figureFileBig=null, tableContent=
参数 中心值
EVA材料 厚度/mm 2
密度/(kg/m3 1 500
PU发泡材料 厚度/mm 23
密度/(kg/m3 25
泊松比 0.38
弹性模量/Pa 55 000
流阻/(Nm/s-4 5 600
孔隙率 0.97
曲折因子 1.28
黏性特征长度/m 0.000 12
热特征长度/m 0.000 35
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参数 焦元区间 BPA
EVA材料 厚度/ mm [1.9,2.0] 0.5
[2.0,2.1] 0.5
密度/(kg/m3 [14 001,500] 0.5
[15 001,600] 0.5
PU发泡材料 厚度/mm [20,22] 0.158 5
[22,24] 0.683 0
[24,26] 0.158 5
密度/(kg/m3 [23,25] 0.5
[25,27] 0.5
弹性模量/Pa

[50 000,

520 000]

0.158 5

[520 000,

550 000]

0.683 0

[550 000,

600 000]

0.158 5
流阻/(Nm/s-4 [50 005,400] 0.2
[5 400,5 800] 0.6
[5 800,6 200] 0.2
孔隙率 [0.95,0.97] 0.4
[0.97,0.98] 0.6
曲折因子 [1.25,1.28] 0.5
[1.28,1.31] 0.5
), ArticleFig(id=1175522298107937701, tenantId=1146029695717560320, journalId=1152916057816748034, articleId=1172525475827167706, language=CN, label=表2, caption=

不确定性变量的基本可信度分配

, figureFileSmall=null, figureFileBig=null, tableContent=
参数 焦元区间 BPA
EVA材料 厚度/ mm [1.9,2.0] 0.5
[2.0,2.1] 0.5
密度/(kg/m3 [14 001,500] 0.5
[15 001,600] 0.5
PU发泡材料 厚度/mm [20,22] 0.158 5
[22,24] 0.683 0
[24,26] 0.158 5
密度/(kg/m3 [23,25] 0.5
[25,27] 0.5
弹性模量/Pa

[50 000,

520 000]

0.158 5

[520 000,

550 000]

0.683 0

[550 000,

600 000]

0.158 5
流阻/(Nm/s-4 [50 005,400] 0.2
[5 400,5 800] 0.6
[5 800,6 200] 0.2
孔隙率 [0.95,0.97] 0.4
[0.97,0.98] 0.6
曲折因子 [1.25,1.28] 0.5
[1.28,1.31] 0.5
), ArticleFig(id=1175522298187629478, tenantId=1146029695717560320, journalId=1152916057816748034, articleId=1172525475827167706, language=EN, label=null, caption=null, figureFileSmall=null, figureFileBig=null, tableContent=
参数 优化前 优化后
EVA材料 厚度/mm 2 1.8
密度/(kg/m3 1 500 1 600
PU发泡材料 厚度/mm 23 25
密度/(kg/m3 25 18
流阻/(Nm/s-4 5 600 6 418
弹性模量/Pa 55 000 52 458
曲折因子 1.28 1.11
零件单位面积质量/(kg/m2 3.575 2.925
), ArticleFig(id=1175522298296681383, tenantId=1146029695717560320, journalId=1152916057816748034, articleId=1172525475827167706, language=CN, label=表3, caption=

优化前后内前围材料参数和质量变换

, figureFileSmall=null, figureFileBig=null, tableContent=
参数 优化前 优化后
EVA材料 厚度/mm 2 1.8
密度/(kg/m3 1 500 1 600
PU发泡材料 厚度/mm 23 25
密度/(kg/m3 25 18
流阻/(Nm/s-4 5 600 6 418
弹性模量/Pa 55 000 52 458
曲折因子 1.28 1.11
零件单位面积质量/(kg/m2 3.575 2.925
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基于证据理论的声学材料吸隔声稳定性分析
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汪旭 1 , 郝耀东 2 , 苏桂丽 3 , 李林 2 , 曹永志 2
汽车工程学报 | 其他 2025,15(4): 611-619
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汽车工程学报 | 其他 2025, 15(4): 611-619
基于证据理论的声学材料吸隔声稳定性分析
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汪旭1, 郝耀东2 , 苏桂丽3, 李林2, 曹永志2
作者信息
  • 1 广西汽车集团有限公司,广西,柳州 545007
  • 2 中汽研(天津)汽车工程研究院有限公司,天津 300399
  • 3 中汽研汽车工业工程(天津)有限公司,天津 300399
  • 郝耀东(1988-),男,天津市人,博士,高级工程师,主要研究方向为汽车NVH。

Stability Analysis of Acoustic Materials Sound Absorption and Insulation Based on Evidence Theory
Xu WANG1, Yaodong HAO2 , Guili SU3, Lin LI2, Yongzhi CAO2
Affiliations
  • 1 Guangxi Automobile Group Co.,Ltd.,Liuzhou 545007,Guangxi,China
  • 2 CATARC(Tianjin)Automotive Engineering Research Institute Co.,Ltd.,Tianjin 300399,China
  • 3 CATARC Automotive Industry Engineering (Tianjin)Co.,Ltd.,Tianjin 300399,China
出版时间: 2025-07-20 doi: 10.3969/j.issn.2095‒1469.2025.04.17
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声学材料参数存在较强的不确定性,导致其吸隔声性能的稳定性较差。提出一种基于证据理论的声学材料吸隔声性能稳定性分析方法。采用证据理论对声学材料参数的不确定性进行描述,确定每个参数的焦元区间及基本可信度分配;使用区间摄动方法计算所有焦元区间组合对应的吸隔声性能上下界;根据辨识框架计算不确定性问题的可信度和似真度;以可信度、似真度作为优化目标,使用粒子群算法进行优化,提升声学材料吸隔声性能的稳定性。以某汽车声学包内前围为例进行了声学材料隔声性能稳定性分析和优化。优化后,零件的质量降低了18%,同时隔声性能的稳定性在全频段内大幅度提升,在中、低频段尤为明显。

声学材料  /  证据理论  /  吸声系数  /  传递损失  /  稳定性

The strong uncertainty of acoustic material parameters leads to the poor stability of its sound absorption and insulation performance. In this paper, a method based on evidence theory is proposed to analyze the stability of acoustic materials. The uncertainty of acoustic material parameters is described by evidence theory, and the focal element interval and the basic confidence assignment of each parameter are determined. Using the interval perturbation method, the upper and lower bounds of sound absorption and insulation performance corresponding to all focal element interval combinations are calculated. The reliability and plausibility of uncertainty problems are calculated according to the identification framework. Reliability and plausibility are taken as optimization objectives, and particle swarm optimization is used to improve the stability of acoustic material's sound absorption and insulation performance. Taking an inner front wall as an example, the stability analysis and optimization of the acoustic insulation performance of the acoustic material were carried out. After optimization, the quality of the part was reduced by 18%, and the stability of the acoustic insulation performance was greatly improved in the whole frequency band, especially in the middle and low frequency band.

acoustic materials  /  evidence theory  /  sound absorption coefficient  /  transmission loss  /  stability
汪旭, 郝耀东, 苏桂丽, 李林, 曹永志. 基于证据理论的声学材料吸隔声稳定性分析. 汽车工程学报, 2025 , 15 (4) : 611 -619 . DOI: 10.3969/j.issn.2095‒1469.2025.04.17
Xu WANG, Yaodong HAO, Guili SU, Lin LI, Yongzhi CAO. Stability Analysis of Acoustic Materials Sound Absorption and Insulation Based on Evidence Theory[J]. Chinese Journal of Automotive Engineering, 2025 , 15 (4) : 611 -619 . DOI: 10.3969/j.issn.2095‒1469.2025.04.17
随着新能源技术的发展,车内高频噪声失去了发动机噪声的掩盖,在汽车总体噪声中所占的比重逐渐提高[1-2]。声学包是汽车高频噪声控制的主要手段[3-4],通过吸收和阻隔噪声的方式降低车内高频噪声。
国内外对声学包的研究大多经历了试验验证为主、仿真与试验相结合、正向化开发等3个阶段[5]。伍先俊等[6]使用有限元和统计能量法对典型结构板和梁进行了动态响应分析,结果验证了统计能量法在解决高频问题时的有效性。HUANG Liangyu等[7]利用统计能量分析(Statistical Energy Analysis,SEA)模型优化声学包,降低了车内噪声,为声学包的研究指明了方向。FREEMAN等[8]提出了声学包的基本开发方法,并论证了声学包对于车内噪声控制的重要性。郝耀东等[2]建立电动汽车的整车SEA模型,将声载荷测试结果加载至整车模型中,实现了车内高频噪声的计算,可以较好地进行电动汽车车内高频噪声分析。崔聪聪等[9]应用统计能量分析原理建立整车通用概念SEA模型,以子系统声学包吸隔声性能为设计变量,以车内噪声目标为约束条件,以整体吸隔声变化量为目标函数,利用多岛遗传算法进行优化,获得目标分解结果。张延杰[10]建立某7座SUV车型的整车声学包SEA模型,利用SEA方法准确预测整车的高频噪音,验证SEA可有效应用于优化声学包设计方案。
以上研究基于确定性分析展开,即认为声学包零件材料和结构的参数不会发生变化。但实际上,由于制造误差(声学包零件的厚度、密度等)、装配误差和操作条件等因素,声学包零件参数的实际值与设计值往往存在较大差异,所以批量生产的车辆车内高频噪声存在较大的不一致性[11-12]。为了描述这种不一致性,需要引入不确定性方法。
描述系统不确定性的方法包括随机理论[13]、区间理论[14]、证据理论[15]等。其中,随机理论需要获取不确定性参数精确的概率密度函数,需要进行大量参数获取试验,由于耗费过量资源而难以实现;区间理论只需要获取不确定参数的上下界即可进行分析,但分析结果过于保守,过量估计了不确定性的影响。与之相比,证据理论能综合现有资料,将参数的不确定性描述为若干个焦元区间及可信度,并能综合不同描述方式的影响,对于描述声学材料的不确定性具有较强的适用性。
本文提出了一种基于证据理论的声学材料吸隔声性能稳定性分析方法。采用证据理论对声学材料参数的不确定性进行描述,通过确定每个参数的焦元区间及基本可信度分配,实现对不确定性的量化;采用区间摄动方法计算所有焦元区间组合对应的吸隔声性能的上下界,根据辨识框架计算不确定性问题的可信度和似真度;以可信度、似真度为优化目标,使用粒子群算法进行优化,实现对声学材料吸隔声性能稳定性的提升。
多孔材料是声学材料中最重要的类型,材料中细小的敞开孔道可以使声波衰减,从而实现吸声效果,和钢板、乙烯-醋酸乙烯酯共聚物(EVA)等弹性固体材料配合使用时,也可以实现良好的隔声效果。多孔材料的吸隔声性能仿真主要基于BIOT理论。
多孔材料中既存在空气组成的流体相,又存在材料组成的固体相。固体相、流体相的体积模量 K b K f可以分别表示为:
K b = 2 N ( μ + 1 ) 3 ( 1 - 2 μ )
K f = γ P 0 / [ γ - ( γ - 1 ) ( 1 + ϕ η j ω q 0 ' ρ 0 B 2 G ' ( ω ) ) - 1 ]
式中: N μ ϕ分别为材料的剪切模量、泊松比、孔隙率; P 0 γ η q 0 ' ρ 0 B 2分别为大气压力、空气绝热系数、黏滞系数、密度、热渗透率、普朗克常数; G ' ( ω )为频率的函数,可以通过式(3)进行计算。
G ' ( ω ) = [ 1 + ( 2 q 0 ' ϕ Λ ' ) 2 j ω v ' ] 1 2
式中: Λ '为材料的热特征长度; j为虚数单位。
多孔材料固体相等效密度 ρ b、流体相等效密度 ρ f和耦合等效密度 ρ c可以分别表示为:
ρ b = ρ 1 + ρ a - j σ ϕ 2 G ( ω ) ω
ρ f = ϕ ρ 0 + ρ a - j σ ϕ 2 G ( ω ) ω
ρ c = - ρ a + j σ ϕ 2 G ( ω ) ω
式中: ρ 1为材料密度; σ为流阻; ρ a为惯性耦合项; G ( ω )为圆频率 ω的函数。
并且有:
ρ a = ϕ ρ 0 ( α - 1 )
G ( ω ) = [ 1 + ( 2 α q 0 ϕ Λ ) 2 j ω ρ 0 η ] 1 2
式中: α 为材料曲折因子; q 0 = η / σ为黏性渗透率; Λ为材料的黏性特征长度。
当声音在多孔材料中传播时,弹性压缩波、弹性剪切波在固体相中传播;声波在流体相中传播。3种波的波数可以表示为:
δ 1 = ω 2 2 ( P × R - Q 2 ) ( P ρ f + R ρ b - 2 Q ρ c - Δ )
δ 2 = ω 2 2 ( P × R - Q 2 ) ( P ρ f + R ρ b - 2 Q ρ c + Δ )
δ 3 = ω 2 N ( ρ f ρ b - ρ c 2 ρ 22 )
式中: δ 1为弹性压缩波波数、 δ 2为弹性剪切波波数、 δ 3为声波波数。
P Q R Δ则可以采用式(12)~(15)进行计算。
P = 4 3 N + K b + 1 - ϕ 2 ϕ K f
Q = K f ( 1 - ϕ )
R = ϕ K f
Δ = ( P ρ f + R ρ b - 2 Q ρ c ) 2 - 4 ( P R - Q 2 ) ( ρ f ρ b - ρ c 2 )
通过波数即可计算材料的传递矩阵,例如,对于毛毡、吸音棉等柔性多孔材料,其传递矩阵可以表示为:
T = c o s δ 2 d c o s θ j Z c c o s θ s i n δ 2 d c o s θ j c o s θ Z c s i n δ 2 d c o s θ c o s δ 2 d c o s θ
式中: θ为入射声波角度; d为材料厚度。
在工程中,不同种类的多孔材料、弹性固体材料叠加在一起组成声学零件,共同起到吸隔声的作用。通过传递矩阵方法,可以进行声学零件的吸声系数和传递损失性能的仿真。
声波入射到1个包括n种声学材料的声学零件中,每种声学材料的特性都可以通过其传递矩阵表示,声波在不同材料之间的传播特性则可以通过两种材料的耦合矩阵表示,如图1所示。
声学零件中的声传播过程可以表示为:
D V = 0
式中: V为速度向量; D为集成传递矩阵和耦合矩阵后的零件声学特性矩阵。
当声学零件背衬刚性墙时,其特性矩阵 D可以通过式(18)表示;当声学零件背衬空气时,其特性矩阵可以通过式(19)表示。
D = I f 1 J f 1 T ( 1 ) 0 0 0 0 I 12 J 12 T ( 2 ) 0 0 0 0 0 J ( n - 2 ) ( n ) T ( n   - 1 ) 0 0 0 0 I ( n - 1 ) ( n   ) J ( n - 1 ) ( n   ) T ( n   ) 0 0 0 0 Y p
D = I f 1 J f 1 T ( 1 ) 0 0 0 0 I 12 J 12 T ( 2 ) 0 0 0 0 0 I ( n - 1 ) ( n ) J ( n - 1 ) ( n ) T ( n   ) 0 0 0 I ( n ) f J ( n ) f 0 0 0 0 - 1 Z B / c o s   θ
式中: I f 1 J f 1为空气与不同材料之间的耦合矩阵; Y p为不同材料与刚性墙之间的关系矩阵; Z B为空气特征阻抗。
当声波以 θ的入射角传递到声学零件表面时,零件的吸声系数可以表示为:
Z s = - d e t D 1 d e t D 2
R = Z s c o s θ - Z 0 Z s c o s θ + Z 0
α θ = 1 - R 2
式中: D 1为去掉 D第1列后的矩阵; D 2为去掉 D第2列后的矩阵。
当声波以 θ的入射角传递到声学零件表面时,零件的传递损失 T L可以表示为:
T = - 1 + R d e t D n + 1 d e t D 1
τ θ = T 2 θ
T L = - 10 l o g τ θ
式中: τ θ为传递系数; D n + 1为去掉 Dn+1列后的矩阵。
扩散声场下的吸声系数 α d和传递损失 T L d可以通过不同入射角的声波进行叠加之后得到:
α d = θ m i n θ m a x α ( θ ) c o s θ s i n θ d θ θ m i n θ m a x c o s θ s i n θ d θ
T L d = - 10 l o g θ m i n θ m a x τ ( θ ) c o s θ s i n θ d θ θ m i n θ m a x c o s θ s i n θ d θ
式中: θ m a x θ m i n为扩散声场中声波的最大最小入射角,一般在0°~90°之间。
声学材料的吸隔声性能由其厚度、密度、弹性模量、泊松比、流阻等参数所决定,受到制造环境、制造工艺、制造精度、使用环境等影响,声学材料的参数取值必然存在不确定性,即声学材料参数的实际值与其设计值之间必然存在差异,这种差异可能会造成声学材料吸隔声性能不稳定,在大批量制造中出现性能不符合要求的产品。
为了描述参数变化对声学材料性能造成的影响,需要引入不确定性模型对不确定性参数进行描述,常用的不确定性模型包括随机模型、区间模型、证据模型等。其中,随机模型需要获得各不确定性参数变化的概率分布,所需信息量较大,工程实现困难;区间模型仅需要不确定性参数变化的上、下界,所需信息量少,但得出的结果偏于保守。相比之下,证据模型的理论框架灵活,可以充分利用现有的信息,适用于各种情况下的工程实际问题。
证据理论构造了一个由可信度和似真度组成的概率区间,包括辨识框架(Frame of Diseernment,FD)、基本可信度分配(Basic Probability Assignment,BPA)、信任函数(Belief Function,Bel)、似真函数(Plausibility Function,Pl)等关键概念。
辨识框架表示不确定问题中所有可能的结果的集合,辨识框架中包含有限个元素,每一个元素均表示一个可能结果的样本。令 Θ表示某一辨识框架,取 Θ中任一子集B Θ中任一元素属于B的概率mB)即表示B的基本可信度分配。B的信任函数和似真函数可以表示为:
B e l ( B ) = C B m ( C ) , B Θ  
P l ( B ) = C B m ( C ) , B Θ
式中:CB的任意子集
信任函数的值又被称作B的可信度,似真函数的值又被称作B的似真度。可信度表示完全支持B的基本可信度之和,即基本可信度的上边界;似真度表示完全或部分支持B的基本可信度之和,即基本可信度的下边界。
对于声学材料性能稳定性问题 F b), b表示不确定性参数向量, F表示吸隔声性能指标向量,即各频率下声学材料吸声系数与传递损失数值组成的向量。相比计算量的大幅度增加,高阶泰勒技术摄动项对于计算精度的改善较小,因此只保留一阶泰勒级数项。将 F b)进行一阶泰勒级数展开,可得:
F ( b ) = F ( b ) b = b C + j = 1 n F ( b ) b j Δ b j = F C + Δ F
将每一个不确定性变量划分为若干个焦元区间,并根据数据库、经验、专家建议等赋予每一个区间一个概率。令 b k表示各不确定参数的任一个区间组合。对于每一种组合,计算该组合下的性能指标的中心值F C和摄动半径 Δ F k
F k C = F ( b k ) b k = b k C
Δ F k = j = 1 n F ( b k ) b j , k Δ b j , k
通过中心值和摄动半径可以计算子区间组合 b k下性能指标向量的上、下界:
F ¯ k = F k C + Δ F k
F ̲ k = F k C - Δ F k
通过每个子区间组合的概率和上下界,就可以通过式(28)、(29)计算各个性能指标取不同值时的可信度和似真度。可信度和似真度越高,表示性能达到某一水平的可能程度越高;可信度和似真度越接近,表示问题的稳定性越高。
内前围是汽车声学包中最重要的零件之一,负责隔绝动力舱向驾驶舱的声音传递。内前围一般包括软层和硬层两部分,二者共同起到隔声作用。对某SUV车型的内前围进行隔声稳定性分析,该零件硬层为2 mm厚的EVA材料,软层为23 mm厚的PU发泡材料,材料的BIOT参数见表1
采用混响室-半消声室方法测试该零件的插入损失性能,如图2所示。将样件布置在隔声良好的隔声墙上,隔声墙一侧为混响室,另一侧为半消声室。在混响室放置声源,并测试声压级;在半消声室用声腔探头测试透射声的声强,通过计算即可得到零件的插入损失。将测试结果与采用BIOT理论-传递损失方法仿真得到的结果进行对比,如图3所示。
图3可知,采用BIOT理论-传递矩阵方法计算声学包零件的隔声性能具有较高的精度,仿真结果在400~8 000 Hz频率范围内与测试结果保持较高的一致性。
在零件大批量量产过程中,材料参数的实际值和设计值之间必然存在一定差距,采用证据理论对这种参数的不确定性进行描述,设置各不确定性参数的焦元区间,并对每个区间进行基本可信度分配,分配情况见表2,各不确定变量的焦元区间共有864种组合。
基于证据理论计算可以得到内前围零件在不同频率下插入损失的可信度以及似真的累积概率密度函数(CDF)曲线,如图4所示。
可信度和似真度用于评价不确定性条件下系统稳定性的表现,可信度和似真度越接近,表示系统的稳定性越优秀。由图4可知,400 Hz时,插入损失为6.1 dB所对应的可信度和似真度分别为18.5%和85.7%;2 500 Hz时,插入损失为35.6 dB所对应的可信度和似真度分别为48.5%和80.6%;8 000 Hz时,插入损失为49.5 dB所对应的可信度和似真度分别为8.9%和59.6%。对于400 Hz频率,插入损失6.1 dB所对应的可信度为18.5%,对应的似真度为85.7%,表示插入损失不小于6.1 dB的最小概率为18.5%,最大概率为85.7%。3个频段上插入损失的可信度和似真度差别均较大,说明内前围隔声性能的稳定性亟需提升;其中相对于中频段,低频段、高频段的稳定性尤其较弱。
为了提升内前围零件的隔声性能稳定性,建立优化模型,以EVA、PU发泡材料的参数设计值为设计变量,内前围零件的质量为约束条件,400、2 500、8 000 Hz三个频段的似真度、可信度差值为目标函数,建立优化模型。其中,400 Hz的吸声系数和传递损失用于评价零件的低频吸隔声性能;2 500 Hz则用于评价零件的中频吸隔声性能;8 000 Hz则用于评价零件的高频吸隔声性能。采用粒子群算法调用优化模型进行优化,共迭代9 876次,设计变量优化前后的取值见表3
优化前后,内前围总成的插入损失变化如图5所示,400、2 500、8 000 Hz位置的插入损失可信度、似真度CDF曲线如图6所示。
表3图5可知,优化后,零件质量降低18%,同时零件的隔声性能在400~8 000 Hz的频率范围内基本没有变化,只在高频段略有降低。由图6可知,优化后,在400、2 500、8 000 Hz三个频段内,似真度与可信度之间的差别大范围收窄。400 Hz时,插入损失6.1 dB对应的可信度与似真度之间的差别由67.2%大幅度降低为15.3%;2 500 Hz时,插入损失35.6 dB所对应的可信度和似真度之间的差距由39.1%降低为12.3%;8 000 Hz时,插入损失49.5 dB所对应的可信度和似真度差别由50.3%降低为33.8%。系统隔声性能的稳定性在全频段范围内显著提升,在中、低频尤为明显。
本文提出了一种基于证据理论的声学材料吸隔声性能稳定性分析方法。首先,采用证据理论对声学材料参数的不确定性进行描述,确定每个参数的焦元区间及基本可信度分配;其次,采用区间摄动方法计算所有焦元区间组合所对应的吸隔声性能上、下界;再次,根据辨识框架计算不确定性问题的可信度和似真度,评估系统的稳定性优劣;最后,建立优化模型,以性能的似真度、可信度差值为目标函数,重新设计声学材料参数,保证声学材料零件的性能稳定性。
以某内前围为例进行了隔声性能稳定性分析和优化,优化后,零件质量降低18%,零件的隔声性能在400~8 000 Hz的频率范围内基本没有变化,隔声性能的稳定性在全频段内均大幅度提升,在中、低频段尤为明显。
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2025年第15卷第4期
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doi: 10.3969/j.issn.2095‒1469.2025.04.17
  • 接收时间:2024-05-20
  • 首发时间:2025-09-10
  • 出版时间:2025-07-20
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  • 收稿日期:2024-05-20
  • 修回日期:2024-06-27
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    1 广西汽车集团有限公司,广西,柳州 545007
    2 中汽研(天津)汽车工程研究院有限公司,天津 300399
    3 中汽研汽车工业工程(天津)有限公司,天津 300399
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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