Article(id=1209871063517893628, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1209871062716781562, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20230506, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1766385339859, onlineDateStr=2025-12-22, pubDate=1727107200000, pubDateStr=2024-09-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1766385339859, onlineIssueDateStr=2025-12-22, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1766385339859, creator=13701087609, updateTime=1766385339859, updator=13701087609, issue=Issue{id=1209871062716781562, tenantId=1146029695717560320, journalId=1189621681917173762, year='2024', volume='', issue='9', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1766385339669, creator=13701087609, updateTime=1766392185924, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1209899778067010242, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1209871062716781562, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1209899778067010243, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1209871062716781562, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=57, endPage=62, ext={EN=ArticleExt(id=1209871063761163263, articleId=1209871063517893628, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Uncertainty Optimization of Automotive Acoustic Package Parts Based on Interval Analysis, columnId=null, journalTitle=Automobile Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In order to solve the problem of the robustness of the performance of automotive acoustic package parts in mass production, an uncertainty optimization method based on interval analysis is proposed. The BIOT theory and the transfer matrix method are used to simulate the sound absorption and insulation performance of the acoustic package parts, the Interval perturbation theory is used to analyze the uncertainty of acoustic performance, and the interval uncertainty optimization method is introduced to optimize the material selection and structural design parameters of the parts. The results show that the method is used to analyze and design the inner front wall parts of a certain model, the quality of the parts decreases by 12.8%, and the robustness of the system is greatly improved, and the maximum fluctuation of insertion loss decreases from 8 dB before optimization to 5 dB after optimization.

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为解决汽车声学包零件大批量生产时零件性能稳健性的问题,提出了一种基于区间分析的不确定性优化方法。该方法采用比奥(BIOT)理论和传递矩阵方法对声学包零件的吸隔声性能进行仿真,采用区间摄动理论对零件声学性能的不确定性进行分析,引入区间不确定性优化方法对零件的材料选择与结构设计参数进行优化设计。应用该方法对某车型内前围零件进行了分析与设计,结果表明,零件质量下降12.8%,同时系统的稳健性大幅度提升,插入损失最大波动由8 dB下降至5 dB。

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李宏庚(1981—),学士,高级工程师,主要研究方向为汽车振动噪声控制集成,
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材料名称 参数名称 中心值 区间半径
EVA材料 厚度/mm 2
密度/kg·m-3 1 400 140
PU发泡材料 厚度/mm 20
密度/kg·m-3 22 2.2
泊松比 0.4 0.04
弹性模量/Pa 46 500 4 650
流阻/N·m·s-4 5 000 500
孔隙率 0.96 0.096
曲折因子 1.24 0.124
粘性特征长度/m 0.000 105 0.000 010 5
热特征长度/m 0.000 34 0.000 034
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内前围材料参数

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材料名称 参数名称 中心值 区间半径
EVA材料 厚度/mm 2
密度/kg·m-3 1 400 140
PU发泡材料 厚度/mm 20
密度/kg·m-3 22 2.2
泊松比 0.4 0.04
弹性模量/Pa 46 500 4 650
流阻/N·m·s-4 5 000 500
孔隙率 0.96 0.096
曲折因子 1.24 0.124
粘性特征长度/m 0.000 105 0.000 010 5
热特征长度/m 0.000 34 0.000 034
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材料名称 参数名称 最小值 最大值
EVA材料 厚度/mm 1.5 2.5
密度/kg·m-3 1 000 1 800
PU发泡材料 厚度/mm 15 22
密度/kg·m-3 10 24
流阻/N·m·s-4 4 000 6 000
孔隙率 0.9 0.099
曲折因子 1.1 1.3
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内前围优化模型设计变量

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材料名称 参数名称 最小值 最大值
EVA材料 厚度/mm 1.5 2.5
密度/kg·m-3 1 000 1 800
PU发泡材料 厚度/mm 15 22
密度/kg·m-3 10 24
流阻/N·m·s-4 4 000 6 000
孔隙率 0.9 0.099
曲折因子 1.1 1.3
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材料名称 参数名称 优化前 优化后
EVA材料 厚度/mm 2 1.8
密度/kg·m-3 1 400 1 350
PU发泡材料 厚度/mm 20 22
密度/kg·m-3 22 18
流阻/N·m·s-4 5 000 6 418
孔隙率 0.96 0.98
曲折因子 1.24 1.11
零件总面密度/kg·m-2 3.24 2.826
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优化前后设计变量和质量变化

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材料名称 参数名称 优化前 优化后
EVA材料 厚度/mm 2 1.8
密度/kg·m-3 1 400 1 350
PU发泡材料 厚度/mm 20 22
密度/kg·m-3 22 18
流阻/N·m·s-4 5 000 6 418
孔隙率 0.96 0.98
曲折因子 1.24 1.11
零件总面密度/kg·m-2 3.24 2.826
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基于区间分析的汽车声学包零件不确定性优化*
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赵红飞 1 , 李宏庚 2
汽车技术 | 2024,(9): 57-62
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汽车技术 | 2024, (9): 57-62
基于区间分析的汽车声学包零件不确定性优化*
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赵红飞1, 李宏庚2
作者信息
  • 1 柳州工学院,柳州市测控智能化研究重点实验室,柳州 545616
  • 2 上汽通用五菱汽车股份有限公司,柳州 545007

通讯作者:

李宏庚(1981—),学士,高级工程师,主要研究方向为汽车振动噪声控制集成,
Uncertainty Optimization of Automotive Acoustic Package Parts Based on Interval Analysis
Hongfei Zhao1, Honggeng Li2
Affiliations
  • 1 Liuzhou Institute of Technology, Liuzhou Key Laboratory of Test and Control Intelligence, Liuzhou 545616;
  • 2 SAIC-GM-Wuling Automotive Co., Ltd., Liuzhou 545007
出版时间: 2024-09-24 doi: 10.19620/j.cnki.1000-3703.20230506
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为解决汽车声学包零件大批量生产时零件性能稳健性的问题,提出了一种基于区间分析的不确定性优化方法。该方法采用比奥(BIOT)理论和传递矩阵方法对声学包零件的吸隔声性能进行仿真,采用区间摄动理论对零件声学性能的不确定性进行分析,引入区间不确定性优化方法对零件的材料选择与结构设计参数进行优化设计。应用该方法对某车型内前围零件进行了分析与设计,结果表明,零件质量下降12.8%,同时系统的稳健性大幅度提升,插入损失最大波动由8 dB下降至5 dB。

汽车声学包  /  区间模型  /  不确定性优化

In order to solve the problem of the robustness of the performance of automotive acoustic package parts in mass production, an uncertainty optimization method based on interval analysis is proposed. The BIOT theory and the transfer matrix method are used to simulate the sound absorption and insulation performance of the acoustic package parts, the Interval perturbation theory is used to analyze the uncertainty of acoustic performance, and the interval uncertainty optimization method is introduced to optimize the material selection and structural design parameters of the parts. The results show that the method is used to analyze and design the inner front wall parts of a certain model, the quality of the parts decreases by 12.8%, and the robustness of the system is greatly improved, and the maximum fluctuation of insertion loss decreases from 8 dB before optimization to 5 dB after optimization.

Automotive Acoustic Package  /  Interval model  /  Uncertainty optimization
赵红飞, 李宏庚. 基于区间分析的汽车声学包零件不确定性优化*. 汽车技术, 2024 , (9) : 57 -62 . DOI: 10.19620/j.cnki.1000-3703.20230506
Hongfei Zhao, Honggeng Li. Uncertainty Optimization of Automotive Acoustic Package Parts Based on Interval Analysis[J]. Automobile Technology, 2024 , (9) : 57 -62 . DOI: 10.19620/j.cnki.1000-3703.20230506
随着新能源技术的发展,高频噪声在汽车总体噪声中所占比重逐渐提高[1],声学包装是控制汽车高频噪声的主要手段[2-3]
吸声系数和传递损失是声学包零件声学性能的具体表征,分别反映了零件的吸声性能和隔声性能[4]。在保证质量、空间、成本要求的前提下,尽可能提高零件的吸声系数和传递损失,是声学包零件设计的主要方向。在相关研究中,吴宪等[5]建立了计算汽车前围板传递损失的统计能量分析模型,采用最优拉丁超立方法生成了声学包方案的试验点,采用NSGA-Ⅱ算法进行以声学包隔声量及质量为目标的多目标优化;徐雪莹[6]采用经典声学理论计算了前围隔声垫的整体隔声量,并进行了轻量化研究;唐中华等[7]以声学包零件各层材料厚度为设计变量,以驾驶员头部声腔总声压级和声学包总质量为目标,采用多目标遗传算法对声学包材料厚度进行优化。
但由于制造工艺等原因,声学包零件的流致、孔隙率等关键参数具有较高的不确定性[8],采用传统确定性设计与优化方法无法保证大批量零件的性能稳健性。
基于此,本文提出了一种基于区间分析的汽车声学包零件不确定性优化方法,将区间不确定性理论[9]引入声学包零件设计中,首先采用比奥(BIOT)理论[10]和传递矩阵方法[11]实现声学包零件吸隔声性能仿真;其次采用区间摄动理论进行零件声学性能的不确定性分析;最后通过区间不确定性优化方法对零件的材料和结构参数进行优化设计,实现了声学包零件性能的稳健性最优化设计。
现阶段汽车声学包零件主要由柔性多孔材料(如毛毡、吸音棉、聚氨基甲酸酯发泡等)和弹性多孔材料(如乙烯-醋酸乙烯酯、乙烯-丙烯-非共轭二烯烃三元共聚物等)组成。多孔材料的吸隔声性能预测主要基于BIOT理论。
对于聚氨基甲酸酯(Polyurethane,PU)发泡等弹性多孔材料,其介质中同时存在3种波,弹性压缩波、弹性剪切波和声波。其中,弹性压缩波、弹性剪切波在材料固体相中进行传播,声波则在材料流体相中进行传播。对于毛毡、吸音棉等柔性多孔材料,其固体相刚度近似为0,材料中只存在声波,不存在弹性压缩波和剪切波。
汽车声学包零件通常由几种不同的材料组成,对于多层材料的声学性能,通常采用传递矩阵方法进行计算,每层材料均采用一个传递矩阵进行描述。
对于柔性多孔材料,其传递矩阵 T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, j为虚数单位, δ 2为声波波数, Z c为材料特征阻抗。
对于弹性多孔材料,其传递矩阵可表示为:
T = ω k t c 13 - j ω k t s 13 ω k t c 23 - j ω k t s 23 j ω k 33 s 33 - ω k 33 c 33 - j ω k 13 s 13 ω k 13 c 13 - j ω k 23 s 23 ω k 23 c 23 ω k t c 33 - j ω k t s 33 - j ω k 13 u 1 s 13 ω k 13 u 1 c 13 - j ω k 23 u 2 s 23 ω k 23 u 2 c 23 ω k t u 3 c 33 - j ω k t u 3 s 33 - D 1 c 13 j D 1 s 13 - D 2 c 23 j D 2 s 23 2 j N k 33 k t s 33 - 2 N k 33 k t c 33 2 j N k 13 k t s 13 - 2 N k 13 k t c 13 2 j N k 23 k t s 23 - 2 N k 23 k t c 23 N ( k 33 2 - k t 2 ) c 33 - j N ( k 33 2 - k t 2 ) s 33 - E 1 c 13 j E 1 s 13 - E 2 c 23 j E 2 s 23 0 0
式中: k 13 = δ 1 2 - k t 2 1 2 k 23 = δ 2 2 - k t 2 1 2     k 33 = δ 3 2 - k t 2 1 2分别为3种声波波数在材料厚度方向的分量, k t为声波波数在主方向的分量, δ 1为弹性压缩波波数, δ 3为弹性剪切波波数, ω为圆频率, N为材料的剪切模量, u ii=1, 2, 3)为3种声波液相和固相的速度比, D i E i c i 3 s i 3i=1, 2, 3)的计算公式为:
D i = ( P + Q u i ) ( k t 2 + k i 3 2 ) - 2 N k t 2 E i = ( R u i + Q ) ( k t 2 + k i 3 2 ) c i 3 = c o s ( k i 3 d ) s i 3 = s i n ( k i 3 d )
式中: P Q为过程物理量。 P Q的计算方式为:
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 )
式中: Δ R为过程物理量, K b K f分别为材料流体相体积模量和固体相体积模量, ρ b ρ f ρ c分别为材料固体相有效密度、流体相有效密度和耦合有效密度, ϕ为材料孔隙率。
当声波入射至多层材料表面时,如图1所示,同层材料的声传递通过传递矩阵表达,不同层材料的声传递通过耦合矩阵表达。若相邻材料为同种材料,则二者之间的耦合矩阵为单位矩阵。若相邻材料种类不同,则第 i层材料和第( i + 1)层材料的耦合矩阵 I i ( i + 1 ) J i ( i + 1 )具有如下关系:
I i ( i + 1 ) V ( i ) + J i ( i + 1 ) V ( i + 1 ) = 0
式中: V ( i ) V ( i + 1 )为两层连接处的速度。
多层材料整体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多层材料特性矩阵。
当计算材料的吸声性能时,声学材料背衬刚性墙,此时多层材料特性矩阵 D可以表示为:
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
式中: Y p为弹性多孔材料与刚性墙之间的关系矩阵。
当计算材料的吸声性能时,声学材料背衬空气层,此时多层材料特性矩阵 D可以表示为:
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为空气与多孔材料之间的耦合矩阵, Z B为空气特征阻抗。
以入射角为 θ的平面波入射时,材料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 B Z s c o s θ + Z B
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, Z S为材料表面阻抗,R为反射系数。
扩散声场下的材料吸声系数可以表示为:
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
式中: α d为扩散声场下的吸声系数; θ m a x θ m i n分别为扩散声场中声波的最大、最小入射角,通常在0°~90°之间。
入射角为 θ的平面波入射时,材料的传递损失TL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: T为零件的吸隔声性能矩阵, τ θ为传递系数, D n + 1为去掉 D第(n+1)列后的矩阵, T L为传递损失。
扩散声场下的材料传递损失可以表示为:
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
在设计、制造、装配过程中,汽车声学包零件材料的不确定性无可避免,其孔隙率、流阻等声学参数以及厚度等结构参数不可能完全相同,不确定性必然存在。随机模型和区间模型是描述参数不确定性最常用的2种方法,其中随机模型需要获取不确定性参数的概率密度函数,这意味着需进行大量的试验测试,需要耗费不菲的时间和人力成本。而区间模型只需要获取不确定性参数可能的最小值和最大值,因此,引入区间模型来描述声学包零件参数的不确定性。
在区间模型中,所有不确定参数 b j j = 1,2 , , n n为区间不确定参数数量)组成不确定参数向量 b,且有
b b _ , b -
式中: b _为不确定参数的下界向量, b -为不确定参数的上界向量。
对于声学包装零件,其吸声系数 α d和传递损失 T L d均可以视为不确定参数向量 b的函数,即
α d = f 1 ( b ) T L d = f 2 ( b )
将吸声系数 α d和传递损失 T L d进行一阶泰勒级数展开,令 b C为不确定参数向量的中心值, Δ b表示不确定参数向量的摄动区间, Δ b为区间半径,则有
α d = f 1 ( b ) b = b C + j = 1 n f 1 ( b ) b j b = b C Δ b j T L d = f 2 ( b ) b = b C + j = 1 n f 2 ( b ) b j b = b C Δ b j
α d C T L d C分别表示区间向量 b取其中心值 b C时的输入功率向量和损耗因子矩阵, Δ α d Δ T L d表示输入功率向量和损耗因子矩阵的摄动区间,则有:
α d C = f 1 ( b ) b = b C T L d C = f 2 ( b ) b = b C Δ α d = j = 1 n f 1 ( b ) b j b = b C Δ b j Δ T L d = j = 1 n f 2 ( b ) b j b = b C Δ b j
在获得吸声系数、传递损失的中心值和区间半径后,零件吸声系数和传递损失的上界 α - d T ¯ L d和下界 α _ d T _ L d可以表示为:
α - d = α d C + Δ α d α _ d = α d C - Δ α d T ¯ L d = T L d C + Δ T L d T _ L d = T L d C - Δ T L d
为了获得更轻的声学包零件质量,同时保证零件性能的稳健性,可以采用区间不确定性优化的方法对材料BIOT参数和结构参数进行优化。以声学包零件BIOT参数和结构参数向量作为设计变量 x,存在不确定参数向量 b,以零件吸隔声性能向量 T作为约束条件,以声学包零件质量构建目标函数 F,建立的优化模型为:
m i n b           F ( x ) s . t . P r ( T ( x , b ) T 0 ) = λ λ P b _ b b ¯ x _ x x -
式中: P r为区间数比较的可能度,其值越大,表示可能性越高; λ为不确定约束条件的区间可能度(Reliability-based Possibility Degree of Interval,RPDI)水平; λ P为不确定约束条件的预设RPDI水平,反映了对约束的限制程度; T 0为传递损失约束向量; x _ x -分别为设计变量向量的下界和上界。
采用多岛遗传算法即可对式(22)进行优化,在优化模型每一步的迭代中,都需要对不确定约束条件的RPDI水平进行计算,这样的优化模型是一个双层嵌套问题,外层为设计变量的寻优,内层为计算约束函数响应边界。
采用本文提出的方法对某内前围零件进行优化设计,该零件主要由厚度为2 mm的乙烯-醋酸乙烯酯(Ethylene Vinyl Acetate,EVA)材料和厚度为20 mm的PU发泡材料组成。由于制造等原因,材料参数存在不确定性,采用区间模型对不确定性进行描述。其中,材料的密度和BIOT参数的区间半径取中心值的10%,材料厚度在制造过程中控制精度较高,忽略其不确定性造成的影响。材料参数如表1所示。
将零件裁剪为合适的尺寸(0.67 m×0.67 m),采用混响-半消声室方法测试其插入损失性能,如图2所示,测试原理如图3所示。图中Li表示混响室声压级,It表示消声室声强级。
采用BIOT理论和传递矩阵方法计算内前围零件的插入损失性能,并将仿真结果和测试结果进行对比,如图4所示。由对比结果可知,在400~3 150 Hz内仿真与测试结果对比误差小于1 dB,在3 150 Hz~8 000 Hz内仿真与测试结果对比误差小于2.5 dB,体现了较高的一致性,表明采用BIOT理论和传递矩阵方法能够获得较高精度的声学包零件隔声性能仿真结果。
采用区间摄动方法对内外围隔声性能的不确定性进行分析,分析结果如图5所示。由图5可知,在材料密度和BIOT参数存在不确定性的条件下,内前围零件的插入损失存在较大波动,最大高达8 dB,稳健性有待提高。
采用区间不确定性优化方法对内前围质量和插入损失性能进行优化,以材料结构和部分BIOT参数为设计变量,如表2所示,以插入损失向量为约束条件,零件质量为目标函数,建立优化模型。
调用多岛遗传算法对模型进行优化,共迭代12 000次,优化后的设计变量和质量变化如表3所示,优化后的插入损失曲线和插入损失波动上、下界分别如图6图7所示。
表3可知,优化后,零件面密度由3.24 kg/m2下降至2.826 kg/m2,下降幅度达到12.8%。同时由图6可知,优化后内前围零件的插入损失在5 000 Hz以下基本保持不变,5 000 Hz以上则有所提升,提升幅度高达2 dB。由图7可知,优化后系统的稳健性大幅度提升,插入损失最大波动由优化前的8 dB下降至优化后的5 dB。
本文提出了一种基于区间分析的汽车声学包零件不确定性优化方法,该方法采用BIOT理论和传递矩阵方法进行声学包零件吸隔声性能的仿真,采用区间摄动理论进行了零件声学性能的不确定性分析,引入区间不确定性优化方法对零件的材料和结构参数进行了优化设计。
应用该方法对某车型内前围零件进行了分析与设计。采用BIOT理论和传递矩阵方法计算内前围零件的插入损失性能,并将仿真结果和测试结果进行对比,仿真结果与试验测试结果具有较高的一致性;采用区间不确定性优化方法对内前围质量和插入损失性能进行优化,优化后零件质量减轻12.8%,同时系统的稳健性大幅度提升,插入损失最大波动由优化前的8 dB下降至5 dB。
  • *广西高校中青年教师科研基础能力提升项目(2023KY1782)
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2024年第卷第9期
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doi: 10.19620/j.cnki.1000-3703.20230506
  • 首发时间:2025-12-22
  • 出版时间:2024-09-24
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*广西高校中青年教师科研基础能力提升项目(2023KY1782)
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    1 柳州工学院,柳州市测控智能化研究重点实验室,柳州 545616
    2 上汽通用五菱汽车股份有限公司,柳州 545007

通讯作者:

李宏庚(1981—),学士,高级工程师,主要研究方向为汽车振动噪声控制集成,
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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
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Genus
种数
Number of
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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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