Article(id=1228805277844177203, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1228805274362904818, articleNumber=null, orderNo=null, doi=10.16385/j.cnki.issn.1004-4523.2025.05.012, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1684080000000, receivedDateStr=2023-05-15, revisedDate=1689868800000, revisedDateStr=2023-07-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1770899608336, onlineDateStr=2026-02-12, pubDate=1746806400000, pubDateStr=2025-05-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1770899608336, onlineIssueDateStr=2026-02-12, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1770899608336, creator=13701087609, updateTime=1770899608336, updator=13701087609, issue=Issue{id=1228805274362904818, tenantId=1146029695717560320, journalId=1225147924628267009, year='2025', volume='38', issue='5', pageStart='889', pageEnd='1132', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1770899607506, creator=13701087609, updateTime=1770901500406, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1228813213828051801, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1228805274362904818, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1228813213828051802, tenantId=1146029695717560320, journalId=1225147924628267009, issueId=1228805274362904818, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1002, endPage=1015, ext={EN=ArticleExt(id=1228805278246830399, articleId=1228805277844177203, tenantId=1146029695717560320, journalId=1225147924628267009, language=EN, title=Power capture characteristics of rail-double acoustic black hole piezoelectric beam based on Gaussian expansion method, columnId=null, journalTitle=Journal of Vibration Engineering, columnName=null, runingTitle=null, highlight=null, articleAbstract=

The acoustic black hole (ABH) effect, which decelerates the propagation of elastic waves and suppresses boundary reflections, presents a novel mechanism for vibration energy harvesting. A dual-ABH piezoelectric beam energy harvester has been designed for application in railway track systems. A semi-analytical electromechanical coupling model was developed using the energy functional variational principle and Gaussian expansion method. Validation was conducted through finite element simulations. Under train-induced loading, energy harvesting behavior was investigated with respect to ABH geometric parameters and terminal mass. Four principal energy harvesting bands were identified within the 0~1500 Hz range, yielding a peak output voltage of 4.83 V and a maximum efficiency of 2.23%. Optimal energy conversion was achieved when the piezoelectric patch length equaled half the bending wave wavelength of the host structure. Efficiency was further improved by strengthening the ABH effect or through appropriate tuning of the terminal mass.

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声学黑洞效应通过减缓结构中弹性波的传播并抑制边界反射,为振动能量回收提供了思路。设计了一种用于铁路轨道系统的双声学黑洞压电梁俘能装置,基于能量泛函变分原理与高斯展开法构建了钢轨-俘能装置的力-电耦合半解析模型,并经有限元模拟验证。结合列车荷载条件,系统分析了能量回收特性及其关键影响因素,包括黑洞几何参数和端部附加质量。结果表明:在0~1500 Hz频率范围内形成四个主要能量回收频带,最大输出电压为4.83 V,回收效率达2.23%。当压电片长度与结构弯曲波半波长匹配时回收性能最优,增强黑洞效应或合理配置附加质量可显著提升能量回收效率。

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冯青松(1978—),男,博士,教授。E-mail:
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figureFileBig=8Cqo2ejmGResLR+mArG8bg==, tableContent=null), ArticleFig(id=1228805289101688843, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805277844177203, language=CN, label=图18, caption=声学黑洞梁左端压电片内归一化振幅随频率的变化 (附加质量的影响), figureFileSmall=k9DnHjPyera8otuacf4Fcg==, figureFileBig=8Cqo2ejmGResLR+mArG8bg==, tableContent=null), ArticleFig(id=1228805289193963540, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805277844177203, language=EN, label=Tab. 1, caption=

Geometrical parameters and material parameters of the rail, double acoustic black hole beam and piezoelectric layer

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钢轨ABH压电层
参数取值参数取值参数取值
Er210 GPaEa210 GPacp127.21 GPa
Ar7.74×10−3 m2hu0.008 mhp0.0004 m
Ir3.21×10−5 m4b0.1 mb0.1 m
ρr7850 kg/m3ρa7850 kg/m3ρp7500 kg/m3
κr0.4ra0.1667 mezx−6.62281 C/m2
Gr77 GPah00.0008 mεzz1.2693×10−8 F/m
kfv60×106 N/mLa0.5 mLp0.03 m
kfh25×106 N/mηa0.01kt1012 N/m2
ηr0.01m2kr1012 N/rad
ηf0.25kl1012 N/m2
Lr0.6 m
), ArticleFig(id=1228805289286238230, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805277844177203, language=CN, label=表1, caption=

钢轨、双声学黑洞梁及压电层的几何和材料参数

, figureFileSmall=null, figureFileBig=null, tableContent=
钢轨ABH压电层
参数取值参数取值参数取值
Er210 GPaEa210 GPacp127.21 GPa
Ar7.74×10−3 m2hu0.008 mhp0.0004 m
Ir3.21×10−5 m4b0.1 mb0.1 m
ρr7850 kg/m3ρa7850 kg/m3ρp7500 kg/m3
κr0.4ra0.1667 mezx−6.62281 C/m2
Gr77 GPah00.0008 mεzz1.2693×10−8 F/m
kfv60×106 N/mLa0.5 mLp0.03 m
kfh25×106 N/mηa0.01kt1012 N/m2
ηr0.01m2kr1012 N/rad
ηf0.25kl1012 N/m2
Lr0.6 m
), ArticleFig(id=1228805289365930011, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805277844177203, language=EN, label=Tab. 2, caption=

Calculation parameters of subway A-type vehicle[34]

, figureFileSmall=null, figureFileBig=null, tableContent=
部件参数取值
车体一半车体质量19250 kg
点头惯量2.5×106 kg·m2
转向架一半转向架质量1490 kg
点头惯量3.6×103 kg·m2
相邻车厢转向架之间的距离L23.3 m
车轮单轮质量675 kg
轴距L12 m
二系悬挂垂向刚度2.5×106 N/m
垂向阻尼2.0×105 N·s/m
一系悬挂垂向刚度2.1×106 N/m
垂向阻尼4.9×104 N·s/m
), ArticleFig(id=1228805289462399010, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805277844177203, language=CN, label=表2, caption=

地铁列车A型车计算参数[34]

, figureFileSmall=null, figureFileBig=null, tableContent=
部件参数取值
车体一半车体质量19250 kg
点头惯量2.5×106 kg·m2
转向架一半转向架质量1490 kg
点头惯量3.6×103 kg·m2
相邻车厢转向架之间的距离L23.3 m
车轮单轮质量675 kg
轴距L12 m
二系悬挂垂向刚度2.5×106 N/m
垂向阻尼2.0×105 N·s/m
一系悬挂垂向刚度2.1×106 N/m
垂向阻尼4.9×104 N·s/m
), ArticleFig(id=1228805289563062312, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805277844177203, language=EN, label=Tab. 3, caption=

Effective operational frequency band of rail vibration energy harvesting

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有效工作频段/Hz最大输出电压/V输出电压均值 /V最大回收效率/%
Ⅰ:88~940.550.270.00246
Ⅱ:292~3523.090.910.65
Ⅲ:538~9844.831.022.23
Ⅳ:1223~15002.070.881.76
), ArticleFig(id=1228805289659531309, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805277844177203, language=CN, label=表3, caption=

钢轨振动能量回收有效工作频段

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有效工作频段/Hz最大输出电压/V输出电压均值 /V最大回收效率/%
Ⅰ:88~940.550.270.00246
Ⅱ:292~3523.090.910.65
Ⅲ:538~9844.831.022.23
Ⅳ:1223~15002.070.881.76
), ArticleFig(id=1228805289764388913, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805277844177203, language=EN, label=Tab. 4, caption=

Effective operational frequency band of rail vibration energy harvesting corresponding to different additional mass

, figureFileSmall=null, figureFileBig=null, tableContent=
附加质量块尺寸有效工作频段/Hz最大输出电压/V输出电压均值/V最大回收效率/%
lm=huⅠ:76~830.750.360.00316
Ⅱ:278~3472.760.780.92
Ⅲ:529~9908.121.682.93
Ⅳ:1206~15002.111.121.56
lm=2huⅠ:48~500.540.330.00184
Ⅱ:255~3411.610.410.53
Ⅲ:520~8264.911.412.75
Ⅳ:1183~15002.511.191.21
), ArticleFig(id=1228805289856663607, tenantId=1146029695717560320, journalId=1225147924628267009, articleId=1228805277844177203, language=CN, label=表4, caption=

不同附加质量对应的钢轨振动能量回收有效工作频段

, figureFileSmall=null, figureFileBig=null, tableContent=
附加质量块尺寸有效工作频段/Hz最大输出电压/V输出电压均值/V最大回收效率/%
lm=huⅠ:76~830.750.360.00316
Ⅱ:278~3472.760.780.92
Ⅲ:529~9908.121.682.93
Ⅳ:1206~15002.111.121.56
lm=2huⅠ:48~500.540.330.00184
Ⅱ:255~3411.610.410.53
Ⅲ:520~8264.911.412.75
Ⅳ:1183~15002.511.191.21
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基于高斯展开法的钢轨-双声学黑洞压电梁俘能特性研究
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杨舟 1, 2 , 冯青松 1 , 邓杰 3 , 张凌 1 , 贺辉 2
振动工程学报 | 2025,38(5): 1002-1015
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振动工程学报 | 2025, 38(5): 1002-1015
基于高斯展开法的钢轨-双声学黑洞压电梁俘能特性研究
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杨舟1, 2, 冯青松1 , 邓杰3, 张凌1, 贺辉2
作者信息
  • 1.华东交通大学山区土木工程安全与韧性全国重点实验室,江西 南昌 330013
  • 2.湖南工学院土木与建筑工程学院,湖南 衡阳 421002
  • 3.西北工业大学海洋声学信息感知重点实验室,陕西 西安 710129

通讯作者:

冯青松(1978—),男,博士,教授。E-mail:
Power capture characteristics of rail-double acoustic black hole piezoelectric beam based on Gaussian expansion method
Zhou YANG1, 2, Qingsong FENG1 , Jie DENG3, Ling ZHANG1, Hui HE2
Affiliations
  • 1.State Key Laboratory of Safety and Resilience of Civil Engineering in Mountain Area,East China Jiaotong University,Nanchang 330013,China
  • 2.School of Civil and Architectural Engineering,Hunan Institute of Technology, Hengyang 421002,China
  • 3.Key Laboratory of Ocean Acoustic and Sensing,Northwestern Polytechnical University,Xi’ an 710129,China
出版时间: 2025-05-10 doi: 10.16385/j.cnki.issn.1004-4523.2025.05.012
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声学黑洞效应通过减缓结构中弹性波的传播并抑制边界反射,为振动能量回收提供了思路。设计了一种用于铁路轨道系统的双声学黑洞压电梁俘能装置,基于能量泛函变分原理与高斯展开法构建了钢轨-俘能装置的力-电耦合半解析模型,并经有限元模拟验证。结合列车荷载条件,系统分析了能量回收特性及其关键影响因素,包括黑洞几何参数和端部附加质量。结果表明:在0~1500 Hz频率范围内形成四个主要能量回收频带,最大输出电压为4.83 V,回收效率达2.23%。当压电片长度与结构弯曲波半波长匹配时回收性能最优,增强黑洞效应或合理配置附加质量可显著提升能量回收效率。

能量回收  /  高斯展开法  /  铁路轨道  /  双声学黑洞压电梁  /  动态轮轨力

The acoustic black hole (ABH) effect, which decelerates the propagation of elastic waves and suppresses boundary reflections, presents a novel mechanism for vibration energy harvesting. A dual-ABH piezoelectric beam energy harvester has been designed for application in railway track systems. A semi-analytical electromechanical coupling model was developed using the energy functional variational principle and Gaussian expansion method. Validation was conducted through finite element simulations. Under train-induced loading, energy harvesting behavior was investigated with respect to ABH geometric parameters and terminal mass. Four principal energy harvesting bands were identified within the 0~1500 Hz range, yielding a peak output voltage of 4.83 V and a maximum efficiency of 2.23%. Optimal energy conversion was achieved when the piezoelectric patch length equaled half the bending wave wavelength of the host structure. Efficiency was further improved by strengthening the ABH effect or through appropriate tuning of the terminal mass.

energy harvesting  /  Gaussian expansion method  /  railway tracks  /  double acoustic black hole piezoelectric beam  /  dynamic wheel-rail force
杨舟, 冯青松, 邓杰, 张凌, 贺辉. 基于高斯展开法的钢轨-双声学黑洞压电梁俘能特性研究. 振动工程学报, 2025 , 38 (5) : 1002 -1015 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.05.012
Zhou YANG, Qingsong FENG, Jie DENG, Ling ZHANG, Hui HE. Power capture characteristics of rail-double acoustic black hole piezoelectric beam based on Gaussian expansion method[J]. Journal of Vibration Engineering, 2025 , 38 (5) : 1002 -1015 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.05.012
随着轨道交通的迅速发展,各类铁路监测设备和传感器件的需求日益增加,与之匹配的电力供应需求也随之增大。目前铁路监测设备和传感器件的供电方式主要有有线和无线供电两种[1]。有线供电建设周期长、成本高,而无线供电主要通过外接移动电源的方式提供电力,电池能量耗尽后更换困难、维护成本高。因而探寻一种低成本、少维护的供能方式是未来轨道技术发展的必然趋势。而列车通过时引起的轨道振动作为一种可持续的能量源,若能实现高效回收和利用,可有效解决铁路监测设备的供能问题。
近年来,振动能量俘获技术因其在实现环保、节能以及实时监测系统自供电等方面的潜力,受到了研究人员的广泛关注[2-5]。在轨道系统振动能量回收技术方面,国内外学者也展开了相关研究,提出了滚珠丝杆式电磁能量采集器[6]、适用于铁路隧道结构的压电阵列俘能器[7]、适用于地铁低频振动能量收集的悬臂式MFC俘能装置[8]、基于钢轨吸振器的多模态压电电磁复合式俘能器[9]、适用于铁路减速顶的高效动能采集器[10]以及具有宽频调谐、高效俘能特性的新型压电悬臂梁结构等[11]。由于轨道随机振动具有宽频激励、瞬时及非周期等特点,因此找到一种具有放大振幅和振动周期相对固定的俘能材料载体更有利于轨道振动能量的回收[12]。具有能量聚集效应的声学黑洞结构为轨道振动能量回收提供了一种思路。声学黑洞效应是通过对结构厚度进行幂律剪裁或者对材料特性参数修改梯度的方式使得弯曲波速度急剧减小而无法发生反射,从而实现振动能量在尖端位置的聚集[13-15]。与传统的能量回收装置不同,利用声学黑洞进行能量回收改变了传统的设计理念和结构控制方法,通过对结构的优化设计等对能量高度集中和高效回收[16-20]
鉴于此,利用声学黑洞的能量聚集特性,设计了一种双声学黑洞压电梁俘能装置,用于铁路轨道的振动能量回收。基于能量泛函变分原理,采用高斯展开法建立了钢轨-双声学黑洞压电梁力-电耦合半解析模型,并利用有限元模拟对本文方法进行了验证。进一步考虑了列车荷载的作用,计算了多车轮作用下的动态轮轨力,分析了动态轮轨力作用下钢轨的振动能量回收特性以及双声学黑洞压电梁的工作原理。最后详细讨论了声学黑洞半径、残余厚度、黑洞阶次以及声学黑洞梁尖端处附加额外质量对输出电压和回收效率的影响。
设计的双声学黑洞压电梁结构如图1(a)所示,其中梁的两端体现声学黑洞特征,声学黑洞梁长度为La,声学黑洞半径为ra,残余厚度为h0,均质厚度为hu,将系统的坐标原点Oa 设在梁的中点, 左右两个黑洞厚度的变化规律为ha(x)=(huh0)ram(x±La/La2)m,其中m表示声学黑洞阶次。压电片安装在声学黑洞梁的两端,长度和厚度分别为Lp1Lp2hp1hp2,负载分别为R1R2
图1(b)所示,将轨道结构简化为单层弹性点支承梁模型。进一步考虑钢轨和双声学黑洞压电梁的耦合,双声学黑洞压电梁安装在相邻扣件间钢轨的跨中位置,定义三组线弹簧(垂向位移弹簧kt、纵向位移弹簧kl和转角弹簧kr)用于模拟钢轨与双声学黑洞压电梁之间的耦合边界条件,如图1(b)、(c)所示。图中,Lr表示相邻扣件间钢轨的长度,kf表示扣件垂向静刚度。在分析中,钢轨考虑为Timoshenko梁,扣件简化为弹簧单元,双声学黑洞压电梁考虑为复合Euler-Bernoulli梁。
基于能量泛函变分原理,本文采用高斯展开法建立钢轨-双声学黑洞压电梁力-电耦合半解析模型。钢轨和双声学黑洞压电梁位移场可以表示为基函数αi(x)、βi(x)和未知的权重系数aj,i(t);j=1,2,3,4,5的组合:
{wr(x,t)=iNa1,i(t)αi(x)=a1Tα=αTa1Θr(x,t)=iNa2,i(t)αi(x)=a2Tα=αTa2ur(x,t)=iNa3,i(t)αi(x)=a3Tα=αTa3wa(x,t)=iNa4,i(t)βi(x)=a4Tβ=βTa4ua(x,t)=iNa5,i(t)βi(x)=a5Tβ=βTa5 
式中,wr(x,t)、Θr(x,t)和ur(x,t)分别表示钢轨的垂向位移、截面转角和纵向位移函数;wa(x,t)和ua(x,t)分别表示声学黑洞梁的弯曲振动和纵向振动位移函数;N表示参与结构位移场表征的位移函数项数;a表示未知系数列向量;αβ分别表示用于表征钢轨和双声学黑洞压电梁位移场的基函数组成的列向量。
高斯展开法核心在于选取具有局域化特性的高斯小波函数为位移型函数,不同于传统的改进傅里叶级数[21]、切比雪夫级数[22-23]等(适用于描述连续性较好的结构),选取具有局域化特性的位移函数能够确保准确捕捉到声学黑洞结构厚度变化区域的特征,进而提高计算效率[24-25]。将式(1)中的基函数αi(x)和βi(x)定义为如下形式:
{αi(x)=2p/2exp[(2pxq)2/2]βi(x)=2k/2exp[(2kxs)2/2] 
式中,p、kq、s分别表示伸缩因子和平移因子。
关于如何正确选择式(2)中的伸缩因子和平移因子的特定细节,可以参考文献[26]。此外,为获取待定系数向量,需要建立耦合系统的拉格朗日量,并推导出钢轨-双ABH压电梁的运动方程。
首先考虑钢轨和ABH梁的耦合边界势能,根据图1(b)~(c),耦合边界弹性势能可表示为:
Uedge=12ktLa2+raLa2ra[wr(x)wa(x)]2dx+12krLa2+raLa2ra[Θr(x)wax]2dx+12klLa2+raLa2ra[ur(x)+12hrΘr(x)ua(x)+12ha(x)wax]2dx=12aTKedgea 
式中,aT = [a1Ta2Ta3Ta4Ta5T]Kedge表示耦合边界弹性势能刚度矩阵。
系统的动能包括钢轨、ABH梁及两部分压电层的动能,即K=Kr+Ka+Kp1+Kp2,具体各部分的动能可表示为:
{Kr=12Lr2Lr2ρrAr(w˙r2+u˙r2)w˙r2dx+12Lr2Lr2ρrIrΘ˙r2dx=12a˙TMra˙Ka=12La2La2ρabha(x)(w˙a2+u˙a2)dx=12a˙TMaa˙Kp1=12La2La2+Lp1ρpbhp1w˙a2dx=12a˙TMp1a˙Kp2=12La2Lp2La2ρpbhp2w˙a2dx=12a˙TMp2a˙ 
式中,Kr、KaKp1Kp2分别表示钢轨、ABH梁及两部分压电层的动能;ρr、ArIr分别表示钢轨的材料密度、截面面积和惯性矩;ρa、ρpb分别表示声学黑洞梁、压电层的材料密度和宽度;MrMaMp1Mp2分别表示钢轨、ABH梁及两部分压电层的质量矩阵;a˙T = [a˙1Ta˙2Ta˙3Ta˙4Ta˙5T]
同样地,系统的势能也包括四个部分,即U=Ur+Ua+Up1+Up2,其中钢轨和声学黑洞梁的势能可表示为:
{Ur=12Lr2Lr2ErIr(Θrx)2 + κrGrAr(wrxΘr)2 + ErAr(urx)2dx=12aTKraUa=12La2La2EaIa(x)(2wax2)2 + Eabha(x)(uax)2dx=12aTKaa 
式中,Er、κrGr分别表示钢轨的弹性模量、剪切系数和剪切刚度;Ia(x)=bha3(x)/12为声学黑洞梁的局域化惯性矩;Ea表示声学黑洞梁的弹性模量;KrKa分别表示钢轨和ABH梁的刚度矩阵。
在计算中,钢轨与压电片自身阻尼的影响以复化弹性模量的形式考虑,而扣件阻尼的影响以复化扣件刚度的形式考虑,即Er=Er(1+iηr),Ea=Ea(1+iηa),kf=kf(1+iηf);其中,ηr表示钢轨的损耗因子,ηa表示压电片的损耗因子,ηf表示扣件的损耗因子。
对于压电层的势能,需要考虑薄梁的线性电弹性本构[20]
[σpD]=[cpEezxezxεzzs][γpE] 
式中,σp、γp、DE分别表示压电层的应力、应变、电位移和电场强度;cpEezxεzzs分别表示压电层的弹性刚度、压电常数和介电常数。
根据式(6),声学黑洞梁左端压电层的势能可表示为:
Up1=12Ωp1σpγpdΩ=12Ωp1(cpEγpezxE)γpdΩ=12La2La2+Lp1cpEIp1(x)(2wax2)2dx12Vp1La2La2+Lp1ezxb[ha(x)+hp1]22wax2dx=12aTKp1a12aTΘ1Vp1 
式中,Ip1(x)=112bhp13+hp1[ha(x)2+hp12]2表示声学黑洞梁左端压电层的局域化惯性矩;Kp1表示左端压电层的刚度矩阵;Θ1表示力-电耦合向量;Vp1表示左端压电层产生的电压;Ω表示积分的空间域,包含xyz三轴坐标,即dxdydzΩp1表示在左端压电层的三维空间域进行积分。
同理,可以得到声学黑洞梁右端压电层的势能:
Up2=12aTKp2a12aTΘ2Vp2 
式中,Kp2表示右端压电层的刚度矩阵;Θ2表示力-电耦合向量;Vp2表示右端压电层产生的电压。
进一步地,考虑压电片内储存的电能,根据式(6),声学黑洞梁左端压电层内储存的电能可表示为:
Wp1=12Ωp1EDdΩ=12Ωp1(ezxγp+εzzsE)EdΩ=12Vp1La2La2+Lp1ezxb[ha(x)+hp1]2(2wax2)2dx+12(εzzsbLp1hp1)Vp12=12aTΘ1Vp1+12Cp1Vp12 
式中,Cp1表示左端压电层的电容。
同理,可以得到声学黑洞梁右端压电层内储存的电能:
Wp2=12aTΘ2Vp2+12Cp2Vp22 
式中,Cp2表示右端压电层的电容。
此外,外力f(t) 作用在钢轨上输入的机械功和电荷q1q2 做的电力功的总和为:
Wf=f(t)wr(0)+Vp1q1+Vp2q2=aTf+Vp1q1+Vp2q2 
式中,f表示外荷载列向量。
最后考虑扣件的弹性势能,可表示为:
Uf=12kfv[wr2(Lr/2)+wr2(Lr/2)]+12kfh[ur2(Lr/2)+ur2(Lr/2)]=12aTKfa 
式中,kfvkfh分别表示扣件的垂向和纵向静刚度;Kf表示扣件弹簧的刚度矩阵。
综上,钢轨-双声学黑洞压电梁耦合系统的拉格朗日量可表示为:
L=EUUedgeUf+Wp1+Wp2+Wf=12a˙TMa˙12aTKa+aTΘ1Vp1+aTΘ2Vp2+12Cp1Vp12+12Cp2Vp22+aTf+Vp1q1+Vp2q2 
式中,L表示拉格朗日算子;总质量矩阵M=Mr+Ma+Mp1+Mp2;总刚度矩阵K=Kr+Ka+Kp1+Kp2+Kedge+Kf。进一步定义f=Feiωt,a=Aeiωt,Vp1=Vp1eiωt,Vp2=Vp2eiωt,其中,ω表示圆频率,A表示未知系数幅值列向量,变量上方加“ ”表示幅值,并结合Euler-Lagrange方程[20]
t(L[a˙T,V˙p1,V˙p2])L[aT,Vp1,Vp2]=0 
将式(13)处理为:
{(Kω2M)AΘ1Vp1Θ2Vp2=FiωΘ1T+(iωCp1+1R1)Vp1=0iωΘ2T+(iωCp2+1R2)Vp2=0 
将式(15)进一步处理为矩阵形式:
[Kω2MΘ1Θ2iωΘ1TiωCp1+1R10iωΘ2T0iωCp2+1R2][AVp1Vp2]=[F00] 
通过求解式(16)即可得到两端压电片的输出电压幅值Vp1Vp2以及待定系数向量A,将待定系数向量A代入式(1)中能进一步得到钢轨和双声学黑洞压电梁的振动位移响应。
钢轨振动由列车荷载激发,在分析钢轨的振动能量回收特性时需要考虑动态轮轨力的作用。在轨道交通中,车辆运行速度远小于钢轨中振动波的传播速度,故在频域内计算轮轨相互作用力时可采用移动不平顺模型[27-29],轮轨相互作用力可由下式计算[30]
Fw=Rαw+αc+αr 
式中,αwαcαr分别表示车轮、轮轨接触弹簧和钢轨的动柔度;αc=1/1khkh,其中kh表示线性轮轨接触刚度;R表示轨道不平顺幅值。可以看出,求解动态轮轨相互作用力的关键是获取式(17)中的三个动柔度。
进一步建立频域内车辆-轨道-双声学黑洞压电梁垂向耦合动力分析模型,如图2所示。图2中,k1c1分别表示车辆一系悬挂的刚度和阻尼,k2c2分别表示车辆二系悬挂的刚度和阻尼,McMbMw分别表示车体、转向架和车轮的轴重。其中,单节车辆模型考虑为含车体沉浮和点头(zc,θc)、转向架的沉浮和点头(zt1,θt1,zt2,θt2)以及四个车轮沉浮(zw1,zw2,zw3,zw4)共10个自由度的纵向半车模型。通过逐一对各刚体运用D’Alembert原理,可得车辆系统的动力学方程为:
MvZ¨v+CvZ˙v+KvZv=Fveiωt 
式中,MvCvKv分别表示车辆系统的质量、阻尼和刚度矩阵,具体形式可参考文献[31],Fv表示车辆系统的外荷载矩阵。定义Zv=Zveiωt,代入式(18)求解即能得到车轮的动柔度,表示为:
αw=(iωCv + Kvω2Mv)1Fv 
在多车轮作用下计算钢轨动柔度时,基于叠加原理,采用主动轮与被动轮的分类方法加以考虑[27-29]。当轮轨相互作用点距离动柔度考察点大于10 m 时,可忽略该轮轨相互作用点处的轮轨作用力对考察点处动柔度的影响[27-29]。因此,本文仅考虑相邻车厢间距离较近的4 个轮对之间的相互影响,即图2中序号1~4对应的4个车轮。
在本文利用高斯展开法建立的钢轨-双黑洞压电梁模型中,扣件反力的影响已经考虑为弹簧弹性势能附加到耦合系统的能量泛函中,因而在后续计算钢轨动柔度时只需要考虑车轮的叠加效应。以车轮2为主动轮为例,根据叠加原理,2号车轮处的钢轨动柔度αr2可表示为:
αr2=α(x2,x2)+Pw1α(x2,x1)+Pw3α(x2,x3)+Pw4α(x2,x4) 
式中,α(xi,xj)表示力作用在xj时,xi处的动柔度,可通过1.1节中的方法计算得到;Pwj=kwur(xj)表示被动轮处的轮轨接触力,其中,ur(xj)表示第j个被动轮处的钢轨位移,kw=1/(αw+αc)表示轮轨接触刚度。
参照式(20)的形式,可以写出其余三个被动轮处的钢轨位移,整理为矩阵形式:
[1kwα(x1,x1)0kwα(x1,x3)kwα(x1,x4)kwα(x2,x1)1kwα(x2,x3)kwα(x2,x4)kwα(x3,x1)01kwα(x3,x3)kwα(x3,x4)kwα(x4,x1)0kwα(x4,x3)1kwα(x4,x4)][ur(x1)αr2ur(x3)ur(x4)]=[α(x1,x2)α(x2,x2)α(x3,x2)α(x4,x2)]
通过求解式(21)能得到主动轮2处的钢轨动柔度αr2,结合式(19)计算得到的车轮动柔度αw以及轮轨接触弹簧动柔度αc=1/kh,一并代入式(17)中就能计算出车轮2处的动态轮轨力。同理,可以计算出车轮1、3、4为主动轮时,3个轮子的动态轮轨力。
此外, 为了模拟无限长轨道结构,尽可能消除在钢轨两端边界处反射波的影响,引入完美匹配层(PML)[32-33]。在图2中,于钢轨两侧扩展了2个额外的PML区域。在本文中,通过修改复杨氏模量的损耗因子来实现PML的效果。在PML区域中,考虑Er*= Er[1+i(ηrpml)],其中ηpml表示局部损耗因子,具体表示为:
ηpml=ηmax(djLc)c 
式中,dj表示PML层内任意一点到钢轨边界的距离;Lc表示PML的长度,并且在d=Lc时取到最大损耗因子ηmaxc表示平滑系数。
本节将验证第1节中钢轨-双声学黑洞压电梁力-电耦合模型及动态轮轨力计算模型的准确性。
利用有限元软件Comsol Multiphysics建立图1(b)所示的钢轨-双声学黑洞压电梁有限元模型,如图3所示。使用三维固体力学模块和静电模块计算钢轨-双声学黑洞压电梁的变形和应变引起的电荷。在电路模块考虑电阻的能量采集。所有模块都由Comsol Multiphysics的多物理场-压电效应进行耦合驱动。将有限元模型划分为有限个通过节点连接的单元,单元类型为Langrage-quadratic,网格类型为自由四面体网格,为满足计算精度需求,将其划分为813677个域单元、177440个边界单元以及12101个边单元,求解总自由度为4986384,相对容差设置为0.001。钢轨、双声学黑洞梁及压电层的几何和材料参数如表1所示。定义回收功率:
P=Vp1Vp1R12+Vp2Vp2R22 
式中,上标“*”表示共轭算子。
图4对比了利用高斯展开法和有限元法计算得到的钢轨-双声学黑洞压电梁的回收效率。此外,为了进一步证明本文方法的准确性,利用高斯展开法和有限元法对比了几个频率下的双声学黑洞压电梁的受迫振型,如图5所示。
图4可以看出,由于建模维度的差异(理论为一维,有限元建模为三维),两种方法的计算结果存在少量差异,但整体吻合度较好。此外,图5中两种方法计算得到的受迫振型也基本一致。图45的结果也表明了本文方法具有很好的准确性。
上文中提到,声学黑洞结构的一个重要特征是通过幂率剪裁厚度来实现能量集中。因此本节将会对声学黑洞的能量集中效应进行分析验证,并证明双声学黑洞压电梁对于能量回收的增强作用。
定义能量比Γ=10 lg(u˙abh2/u˙uni2),其中,u˙abh2表示双声学黑洞梁的厚度突变部分的均方速度,u˙uni2表示厚度均匀部分的均方速度,能量比越大,表示能量集中效应越明显。通过对比一根均质梁与双声学黑洞梁的Γ值,可以表征双声学黑洞梁的能量集中效应,如图6(a)所示。
图6(a)可以看出,与均匀梁相比,双声学黑洞梁的能量比在四个频段都有明显提升,这表明其对能量回收具有增强作用。进一步地,对比了两者的能量回收效率,如图6(b)所示,可以发现,与常规均匀梁相比,双声学黑洞梁对能量回收具有明显的促进作用。
为进一步探讨在钢轨上附加双声学黑洞压电梁对其工作状态的影响,计算分析了单位谐荷载作用下(作用于图2的钢轨跨中位置),有/无附加双ABH压电梁的钢轨动柔度对比结果如图7所示。在对比分析中,钢轨长度为50个扣件间距。
由于图2中仅在一个扣件间距内的钢轨布置双声学黑洞压电梁,而双声学黑洞压电梁的质量ma+mp=ρabLa2La2ha(x)dx+ρpbLphp1.9kg,而钢轨的质量为mr=ρrAr50Lr1824 kg,两者相比双声学黑洞压电梁的质量几乎可以忽略不计,因而其对钢轨工作状态的影响也很小。这一结论在图7中得到了体现:在0~1500 Hz范围内,不附加ABH的钢轨垂向振动产生两个峰值(S1对应钢轨-扣件系统的一阶共振频率,S2对应钢轨的一阶“pinned-pinned”共振频率);而附加ABH后,对两个峰值的位置和大小几乎没有影响,两条曲线也基本重合。这也进一步表明了在钢轨上安装双声学黑洞压电梁不会影响钢轨的刚度和工作状态。
第1.2节中提到,求解动态轮轨相互作用力的关键是获取式(17)中的三个动柔度,而其中钢轨动柔度的计算占主导地位。为了证明本文轮轨力计算模型的准确性,与文献[29]中钢轨动柔度计算结果进行了对比,如图8所示。
图8可以看出,本文计算得到的考虑多轮叠加效应后的钢轨动柔度与文献[29]一致,证明了本文轮轨力计算模型的准确性。进一步计算了图2中主动轮1~4处的动态轮轨力,如图9(a)所示。此外,为更好地对动态轮轨力幅值曲线进行说明,图9(b)给出了四个车轮接触点位置钢轨动柔度、车轮动柔度以及轮轨接触弹簧动柔度曲线。在计算中,车辆为地铁列车A型车,计算参数如表2所示。车速为70 km/h;轨道不平顺采用美国五级谱;钢轨长度为50×Lr=30 m;线性轮轨接触刚度kh=1.4×109 N/m;PML层的相关参数为:Lc=4 m,ηmax=10,c=2。
图9(b)可以看出,在0~100 Hz范围内,三个动柔度曲线中由车轮动柔度占主导,且四个车轮接触点处钢轨动柔度曲线基本一致,因此在该频率范围内四个车轮的动态轮轨力幅值曲线基本一致;而在250 Hz后,钢轨动柔度占主导,且四个车轮接触点处钢轨动柔度曲线产生明显差异,因此对应的轮轨力幅值产生明显差别。
考虑四个车轮同时作用,式(11)中外荷载做功调整为:
Wf=Vp1q1+Vp2q2+i=14aTFiα(xi) 
式中,xi表示图2中车轮1~4的位置坐标;Fi表示车轮1~4处的动态轮轨力幅值。
定义回收效率:
ηH=P/PPfPf,Pf=i=14ωFwr(xi) 
式中,Pf表示输入钢轨的轮轨力做功。
计算得到两部分压电层的输出电压和钢轨振动能量回收效率,如图10所示。将输出电压大于0.1 V对应的频率点视为能量回收的有效工作频率,表3列出了0~1500 Hz范围内钢轨振动能量回收有效工作频段。
结合图10表3可以发现,在动态轮轨力作用下,0~1500 Hz范围内共产生了4个振动能量回收的有效工作频段。且随着分析频率的增大,振动能量的回收效果也逐步增强,在有效工作频段Ⅲ内振动回收效果达到最佳,最大输出电压为4.83 V,回收效率为2.23%。分析频率继续增大,振动能量的回收效率呈现弱化的效果。为了揭示此现象的机理,计算并绘制了声学黑洞梁左端压电层的振幅随频率的变化关系图,如图11所示。为方便比较,对振幅数据进行了归一化处理。
当弯曲波传递到压电片区域时,弯曲应力使得压电片上下两面内产生匀强电场。随着频率的增大,压电片的变形越剧烈,上下两端的电势差整体趋势在增大,输出的电压值也逐步增大。当频率达到767 Hz时,压电片的长度刚好等于一个半波长(图11中红色曲线),压电片上下两端的电势差达到最大,输出电压达到最大值(对应图10(a)中的有效工作频段Ⅲ的峰值)。当频率继续增大,由图11可以看出,压电片内除了正向弯曲还逐渐产生背向弯曲,由于正向弯曲产生的电场与背向弯曲产生的电场方向相反,能量回收效率受到抑制。因而,相较于第三阶有效工作频段,第四阶的能量回收效果有所弱化。
为获得更好的能量回收效果,本节详细分析了黑洞半径、残余厚度和黑洞阶次对输出电压和回收效率的影响。首先考虑黑洞半径ra的影响,对比了不同声学黑洞半径下钢轨振动能量的回收特性,如图12所示。
随着声学黑洞半径的增大,黑洞效应逐步增强,能量集中效果也越明显。因而,从图12中可以看到,黑洞半径越大,共振峰值频率越多,能量回收的有效工作频段数也越多。因此,在实际应用中应当适当采取较大的黑洞半径,以获得更好的振动能量回收效果。
进一步考虑声学黑洞阶次m对能量回收特性的影响。黑洞阶次控制着厚度变化的速率,m的取值范围一般为[2, 3],本文中考虑了m=4的情况,如图13所示。由于过大的阶次将使得黑洞边缘处的光滑性下降,影响黑洞效应在低频的表现。因而从图13中可以看到,随着m的增大,在300 Hz的范围内,无论是输出电压还是回收效率,都有明显的下降。为了获得更好的能量回收效果,阶次m 的值应适当减小。
最后考虑声学黑洞残余厚度h0对能量回收特性的影响,如图14所示。随着残余厚度h0逐步减小,声学黑洞梁尖端处的能量聚集效果越显著。因此,从图14中可以看出,当残余厚度h0hu/5减小到hu/10时,输出电压幅值和回收效率都有明显的增大。由于压电片本身具有一定刚度,当黑洞尖端的刚度值小于压电片的刚度时,再继续减小残余厚度h0将不会带来任何可预测的有益效果,因为冲击到压电片边界的弯曲波将会被大幅反射回去。因此,在图14中,当残余厚度h0hu/10继续减小到hu/20时,输出电压幅值和回收效率没有出现明显的增强效果,甚至产生了抑制作用。
本节分析了压电片厚度和电阻值对输出电压和回收效率的影响,结果分别如图1516所示。由图1516可以看出,减小压电片的厚度或增大电阻值都对输出电压有明显的增益效果;在对回收效率的影响方面,减小压电片厚度更有利于低频振动能量回收,对高频振动能量回收会起到抑制作用,而增大电阻值对于低频和高频振动能量回收都能起到一定的促进作用。
本节将进一步探讨在双声学黑洞梁的左右两侧尖端位置附加质量块对能量回收特性的影响。得益于本文方法的优势,质量块可以以额外动能项的形式附加到耦合系统的拉格朗日能量泛函中,质量块对应的动能项可表示为:
Km=12ρmlm3[w˙a2(La/2)+w˙a2(La/2)]=12a˙TMmassa˙ 
式中,ρmlm分别表示质量块的密度和边长;Mmass表示尖端质量块对应的质量矩阵。
将式(26)附加到式(13)中,即可考虑质量块的影响。比较不同附加质量对能量回收特性的影响,结果如图17所示。表4给出了不同附加质量对应的钢轨振动能量回收有效工作频段。在分析中,质量块的密度与声学黑洞梁密度相同。
当附加质量块边长为lm=hu时(质量约0.004 kg),有效工作频段Ⅰ~Ⅲ内的能量回收能力都有所增强,其中有效工作频段Ⅲ的增强效果最为明显,最大输出电压由4.83 V增大到8.12 V,回收效率由2.23%增大到2.93%。可以发现,附加质量对振动能量回收有着一定的促进作用。此外,由于增加了系统质量,系统的各阶固有频率都向低频移动,这有利于低频振动能量回收。同时,从图17表4中可以发现,当附加质量继续增大时,能量回收效果明显减弱。为了更好地说明这一规律,绘制了两种情况下左端压电层的归一化振幅随频率变化关系图,如图18所示。
随着附加质量的增大,压电片内的半波长在更低的频率出现。因而与图18(a)相比,图18(b)中的压电片在更低的频率产生反向弯曲,能量回收效率受到了抑制。这就导致当附加质量块边长lm=2hu时(质量约0.032 kg),表4中有效工作频段Ⅱ和Ⅲ内能量回收效果反而产生明显减弱的现象。
(1)与有限元法的对比结果表明,本文方法具有很好的准确性。得益于能量法具有将微分方程边值问题转化为泛函极值问题的优点,本文方法可以进一步拓展到结构形式更为复杂的组合结构的力-电耦合分析之中。
(2)在0~1500 Hz范围内的动态轮轨力作用下,产生了88~94 Hz、292~352 Hz、538~984 Hz及1223~1500 Hz四个主要的能量回收频带,其中第三阶频带内能量回收效果最佳,在负载100 Ω的情况下,最大输出电压为4.83 V,最高回收效率为2.23%。此外,当压电片长度与结构中传播的弯曲波半波长匹配时,能量回收能力达到最大。
(3)通过增大声学黑洞半径、减小黑洞阶次和残余厚度来增强黑洞效应,能产生更明显的能量集中效果,进而在一定程度上提升振动能量回收能力;在声学黑洞梁尖端处附加额外质量对促进能量回收有一定的效果,但过大的附加质量反而会降低能量回收能力。
(4) 本文针对轨道结构的振动能量回收在研究方法和俘能装置的结构型式上进行了拓展,可为铁路轨道的振动能量回收提供一种设计和分析思路。
  • 国家自然科学基金委员会-中国国家铁路集团有限公司铁路基础研究联合基金项目(U2468226)
  • 国家自然科学基金资助项目(52468063)
  • 国家自然科学基金资助项目(52178423)
  • 国家自然科学基金资助项目(52408327)
  • 湖南省自然科学基金资助项目(2024JJ6198)
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2025年第38卷第5期
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doi: 10.16385/j.cnki.issn.1004-4523.2025.05.012
  • 接收时间:2023-05-15
  • 首发时间:2026-02-12
  • 出版时间:2025-05-10
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  • 收稿日期:2023-05-15
  • 修回日期:2023-07-21
基金
国家自然科学基金委员会-中国国家铁路集团有限公司铁路基础研究联合基金项目(U2468226)
国家自然科学基金资助项目(52468063)
国家自然科学基金资助项目(52178423)
国家自然科学基金资助项目(52408327)
湖南省自然科学基金资助项目(2024JJ6198)
作者信息
    1.华东交通大学山区土木工程安全与韧性全国重点实验室,江西 南昌 330013
    2.湖南工学院土木与建筑工程学院,湖南 衡阳 421002
    3.西北工业大学海洋声学信息感知重点实验室,陕西 西安 710129

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冯青松(1978—),男,博士,教授。E-mail:
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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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