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Article(id=1239211865490911849, tenantId=1146029695717560320, journalId=1238823019242635269, issueId=1239211861397270994, articleNumber=null, orderNo=null, doi=10.12465/j.issn.0253-4339.2025.01.140, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1698336000000, receivedDateStr=2023-10-27, revisedDate=1702742400000, revisedDateStr=2023-12-17, acceptedDate=1705334400000, acceptedDateStr=2024-01-16, onlineDate=1773380732175, onlineDateStr=2026-03-13, pubDate=1739635200000, pubDateStr=2025-02-16, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773380732175, onlineIssueDateStr=2026-03-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773380732175, creator=13701087609, updateTime=1773380732175, updator=13701087609, issue=Issue{id=1239211861397270994, tenantId=1146029695717560320, journalId=1238823019242635269, year='2025', volume='46', issue='1', pageStart='1', pageEnd='166', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=0, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773380731200, creator=13701087609, updateTime=1773384112372, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1239226043106652319, tenantId=1146029695717560320, journalId=1238823019242635269, issueId=1239211861397270994, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1239226043106652320, tenantId=1146029695717560320, journalId=1238823019242635269, issueId=1239211861397270994, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=140, endPage=149, ext={EN=ArticleExt(id=1239211865771930238, articleId=1239211865490911849, tenantId=1146029695717560320, journalId=1238823019242635269, language=EN, title=Vortex Identification in the Flow Field of Opposed Jet-Vents in Occupied Enclosure, columnId=null, journalTitle=Journal of Refrigeration, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In an occupied enclosure space formed by multiple opposing jet outlets, understanding vortex structures is crucial in controlling the spread of viruses and pollutants. In this study, a scaled model of an occupied enclosed space with opposing jet flows was constructed by incorporating a heated floor as a heat source to create thermal plumes under cooling conditions. A particle image velocimetry (PIV) system was employed to measure the flow field under isothermal and cooling conditions. The identification performance of different vortex identification algorithms was compared by studying the turbulent characteristics of the flow field from the perspective of the vortex. The Liutex vortex identification method was applied to analyze the vortex motion within one oscillation period, revealing distinct strengths in counterclockwise and clockwise directions, with maximum intensities of 50 s-1 and 110 s-1, respectively. The study concluded that the motion, merging, and annihilation of vortices influenced the flow field structure with a jet oscillation period of approximately 3.67 s. Owing to the trapping effect of vortices on pollutants, areas of pollutant accumulation can be represented using relative frenquency distribution maps of vortex cores. Despite the unsteady flow field, the vortex distribution remained relatively stable. Specifically, on the left side of the CS4 cross-section, the vortex core appeared at the same point up to 21 times. Under cooling conditions, pollutants are confined to smaller regions, which aids in containing the spread of pollutants.

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Wang Congcong, male, lecturer, School of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, 86-18222202285, E-mail: . Research fields: indoor environmental quality and green energy in buildings.
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在由多个对置射流风口所构成的封闭人居环境内，对涡旋结构的认识对遏制病毒和污染物传播起到至关重要的作用。搭建了对置射流封闭人居空间的缩比模型，使用PIV（particle image velocimetry）系统对等温条件和冷却条件下的流场进行测量。在涡的视角下对流场的湍流特性进行研究，对比了不同的涡识别算法的识别效果。筛选出Liutex涡识别方法对一个摆动周期内的涡旋运动进行分析，发现逆时针和顺时针方向涡旋强度不同，最大强度分别为50 s-1和110 s-1，得出涡旋的运动、合并、湮灭会对流场结构产生影响，射流摆动的周期约为3.67 s。由于涡对污染物具有锁定作用，通过涡心相对频率分布图表示污染物聚集的区域，分析得出虽然流场是非定常的，但涡的分布却相对稳定，其中CS4截面左侧涡心在同一点出现的次数高达21次。在冷却条件下污染物被锁定在更小的区域，这有助于遏制污染物的传播。

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王聪聪，男，讲师，北京建筑大学环境与能源工程学院，18222202285，E-mail：。研究方向：建筑室内环境与绿色能源。
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截面CS3CS3.5CS4CS4.5CS5
顶部送风口有无有无有
底部回风口无无无有有
z轴坐标/mm6107208309401 050
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截面CS3CS3.5CS4CS4.5CS5
顶部送风口有无有无有
底部回风口无无无有有
z轴坐标/mm6107208309401 050
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准则数缩比舱室原型相对误差
Re5 2405 92111.94%
Gr9.72×1098.70×10910.53%
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准则数缩比舱室原型相对误差
Re5 2405 92111.94%
Gr9.72×1098.70×10910.53%
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工况截面左侧右侧
x/mmy/mm次数/次x/mmy/mm次数/次
等温条件CS3-77-628155-1169
CS3.5-178-11615013912
CS4-186-312123-467
CS4.5-186-10114186-9321
CS5-186-9315186-8518
冷却条件CS3-46-708155-10810
CS3.5-186-15519013213
CS4-186-1322131-3910
CS4.5-170-7714186-9315
CS5-1865416186-10114
), ArticleFig(id=1239224527654286255, tenantId=1146029695717560320, journalId=1238823019242635269, articleId=1239211865490911849, language=CN, label=表3, caption=涡心分布最大次数及其位置, figureFileSmall=null, figureFileBig=null, tableContent=
工况截面左侧右侧
x/mmy/mm次数/次x/mmy/mm次数/次
等温条件CS3-77-628155-1169
CS3.5-178-11615013912
CS4-186-312123-467
CS4.5-186-10114186-9321
CS5-186-9315186-8518
冷却条件CS3-46-708155-10810
CS3.5-186-15519013213
CS4-186-1322131-3910
CS4.5-170-7714186-9315
CS5-1865416186-10114
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对置射流封闭人居空间内流场的涡识别研究
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卢浩阳 1 , 王聪聪 1 , 陈红兵 1 , 张岩 1 , 李春阳 1 , 刘宇航 1 , 赵立国 2
制冷学报 | 2025,46(1): 140-149
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制冷学报 | 2025, 46(1): 140-149
对置射流封闭人居空间内流场的涡识别研究
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卢浩阳1, 王聪聪1 , 陈红兵1, 张岩1, 李春阳1, 刘宇航1, 赵立国2
作者信息
  • 1北京建筑大学环境与能源工程学院　北京　102616
  • 2北京市高强混凝土有限责任公司　北京　100071

通讯作者:

王聪聪，男，讲师，北京建筑大学环境与能源工程学院，18222202285，E-mail：。研究方向：建筑室内环境与绿色能源。
Vortex Identification in the Flow Field of Opposed Jet-Vents in Occupied Enclosure
Haoyang Lu1, Congcong Wang1 , Hongbing Chen1, Yan Zhang1, Chunyang Li1, Yuhang Liu1, Liguo Zhao2
Affiliations
  • 1.School of Environment and Energy Engineering, Beijing University of Civil Engineering and Architecture, Beijing, 102616, China
  • 2.Beijing High Strength Concrete Co., Ltd., Beijing, 100071, China
出版时间: 2025-02-16 doi: 10.12465/j.issn.0253-4339.2025.01.140
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摘要
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在由多个对置射流风口所构成的封闭人居环境内，对涡旋结构的认识对遏制病毒和污染物传播起到至关重要的作用。搭建了对置射流封闭人居空间的缩比模型，使用PIV（particle image velocimetry）系统对等温条件和冷却条件下的流场进行测量。在涡的视角下对流场的湍流特性进行研究，对比了不同的涡识别算法的识别效果。筛选出Liutex涡识别方法对一个摆动周期内的涡旋运动进行分析，发现逆时针和顺时针方向涡旋强度不同，最大强度分别为50 s-1和110 s-1，得出涡旋的运动、合并、湮灭会对流场结构产生影响，射流摆动的周期约为3.67 s。由于涡对污染物具有锁定作用，通过涡心相对频率分布图表示污染物聚集的区域，分析得出虽然流场是非定常的，但涡的分布却相对稳定，其中CS4截面左侧涡心在同一点出现的次数高达21次。在冷却条件下污染物被锁定在更小的区域，这有助于遏制污染物的传播。

In an occupied enclosure space formed by multiple opposing jet outlets, understanding vortex structures is crucial in controlling the spread of viruses and pollutants. In this study, a scaled model of an occupied enclosed space with opposing jet flows was constructed by incorporating a heated floor as a heat source to create thermal plumes under cooling conditions. A particle image velocimetry (PIV) system was employed to measure the flow field under isothermal and cooling conditions. The identification performance of different vortex identification algorithms was compared by studying the turbulent characteristics of the flow field from the perspective of the vortex. The Liutex vortex identification method was applied to analyze the vortex motion within one oscillation period, revealing distinct strengths in counterclockwise and clockwise directions, with maximum intensities of 50 s-1 and 110 s-1, respectively. The study concluded that the motion, merging, and annihilation of vortices influenced the flow field structure with a jet oscillation period of approximately 3.67 s. Owing to the trapping effect of vortices on pollutants, areas of pollutant accumulation can be represented using relative frenquency distribution maps of vortex cores. Despite the unsteady flow field, the vortex distribution remained relatively stable. Specifically, on the left side of the CS4 cross-section, the vortex core appeared at the same point up to 21 times. Under cooling conditions, pollutants are confined to smaller regions, which aids in containing the spread of pollutants.

卢浩阳, 王聪聪, 陈红兵, 张岩, 李春阳, 刘宇航, 赵立国. 对置射流封闭人居空间内流场的涡识别研究. 制冷学报, 2025 , 46 (1) : 140 -149 . DOI: 10.12465/j.issn.0253-4339.2025.01.140
Haoyang Lu, Congcong Wang, Hongbing Chen, Yan Zhang, Chunyang Li, Yuhang Liu, Liguo Zhao. Vortex Identification in the Flow Field of Opposed Jet-Vents in Occupied Enclosure[J]. Journal of Refrigeration, 2025 , 46 (1) : 140 -149 . DOI: 10.12465/j.issn.0253-4339.2025.01.140
正文
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研究表明，大部分人90%以上的时间是在室内和交通工具等封闭空间内度过的[1]。近年来，由于新冠病毒爆发，人员在封闭人居空间被感染的报道屡见不鲜，而交通工具中的气流组织主导了病毒的分布和传播[2-3]。由于对置撞击射流气流组织形式具有排热效率高的优点，目前在公交、地铁、高铁、客机及航天站等封闭人居空间中较多采用[4]。因此，大量学者对封闭人居环境中的对置射流流场特性产生了极大兴趣。
封闭人居空间内对置射流的流场是典型的湍流，而涡是湍流形成和维持的基本机制之一[5-6]。许多学者从涡的视角分析对置射流，J. H. Thysen等[7]搭建了对置射流的缩比模型，使用涡心识别算法识别出射流周围存在不对称的涡列，分析了这些涡列运动和成因，但未分析冷却条件下热羽流对流场结构的影响。而王昕等[8]认为热源形成的热羽流会干扰环境流场，导致污染物不受控。为研究热羽流对流场结构的影响，Cao Xiaodong等[9]采用PIV (particle image velocimetry)测量方法对机舱模型中的对置射流的气流分布进行表征，从涡量场中分析得出射流下方的自由剪切区产生的涡会卷吸周围的空气使射流体积流量增大，但涡量并不能很好地识别出涡结构。为了更准确地识别涡结构，Li Jiayu等[10]在全尺寸的机舱中采用PIV系统测量了对置射流平面内射流区和碰撞区的流场结构。使用Q准则和λci准则对涡结构进行识别，但识别效果不理想。上述文献仅分析了单个对置射流平面内的涡分布，实际上封闭人居环境的气流组织是由多组对置射流营造的，纵向气流也能够影响射流平面内的气流分布。Han Yifei等[11]使用Ω涡识别方法对机舱中全局流场中涡结构进行识别，分析得出这些涡结构可以增强气流的能量传递，但也增加了污染物聚集的风险，未分析热羽流对全局流场涡分布的影响。
综上所述，之前的文章主要关注等温条件下单个对置射流平面内的涡特性，而对封闭人居空间中的全局流场非等温条件下涡影响的研究较少。因此，本文采用PIV系统在等温条件下和冷却条件下对封闭人居空间内多对置射流的全局流场进行了实验研究，使用不同的涡识别方法对瞬态流场中的涡结构进行识别并对涡的瞬态特征进行分析。使用涡心识别算法绘制出涡心相对频率分布图以刻画污染物的锁定区域。
1 实验设置与研究方法
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1.1 实验设置
本文搭建了一个缩比实验腔室，缩比模型的几何尺寸为400 mm×400 mm×1 440 mm，而全尺寸封闭人居空间的尺寸为2 000 mm×2 000 mm×7 200 mm，二者的尺寸比例为1∶5。每个送风口和回风口的尺寸分别为100 mm×20 mm、168 mm×20 mm，如图1所示。实验在等温条件和冷却条件下进行，为了消除腔外温度对实验的影响，实验模型放置在温度可控的恒温室内。腔室上部为通风控制系统，下部为封闭舱室和PIV测试系统，具体尺寸如图2所示。两侧对置送风口的静压差在2 Pa以内，可以认为各个送风口的送风量基本相同。在模型的地板均匀铺设电阻丝作为热源，并控制其温度为44.4 ℃。等温条件下底部加热板关闭，冷却条件下底部加热板开启以模拟热羽流对流场结构的影响。采用精度为±1.5 ℃的红外热像仪(VarioCAM hr)和精度为±0.02 m/s的热球风速仪(Air Dist Sys 5000)测量温度和速度边界条件。测得等温条件下的边界条件为：送风温度和壁面温度均为23.6 ℃，送风速度平均为0.79 m/s，湍流强度平均为9.20%，雷诺数Re平均为5 240。冷却条件下的边界条件为：送风温度、顶棚、左右壁面、前壁面、后壁面、地板的温度分别为23.6、27.2、27.5、27.7、26.8、44.4 ℃，速度边界条件和等温条件下相同。
PIV系统如图2(a)所示，5个测量截面CS3、CS3.5、CS4、CS4.5、CS5的位置和几何特征如图2(b)和表1所示。涵盖送风口的CS3、CS4、CS5截面分别位于入口3和3′、4和4′、5和5′的中心截面，CS3.5截面位于CS3和CS4之间，CS4.5截面位于回风口的中心。采用直径为1 μm的癸二酸二异辛酯气溶胶(Di-Ethyl-Hexyl-Sebacat，DEHS)作为示踪粒子。通过使用Laskin粒子发生器来保持长时间的恒定粒子浓度，进而获取高质量的瞬时速度场时间序列数据。为了不影响实验舱室内的流场，粒子在主风道内被释放。采用功率为350 mJ/pluse的双脉冲激光器(YAG double pulse laser)以照亮视场，使用带有尼康镜头的高速摄像机(Nikon Nikkor 50 mm Lens)进行拍摄。测量的样本频率为3 Hz，共采集了1 000个样本数据。详细信息可在本团队之前的研究中发现[12]。
1.2 研究方法
1.2.1 缩比实验
进行通风缩比舱室实验需满足几何相似、流动相似和传热相似，本文几何相似比为1∶5，根据相关文献本实验的流动相似和传热相似的准则数分别为雷诺数Re和格拉晓夫数Gr，计算如下[12-13]:
式中：V为送风速度，m/s；L为特征尺寸，m；ν为空气的运动黏度，m2/s；g为重力加速度，m/s2；β为流体的体积膨胀系数；ΔT为送风温度和热源温度的温差，℃。
以文献[14]中的地铁车厢为原型，计算得到缩比模型和原型的Re和Gr，如表2所示。由表2可知，缩比模型和原型之间Re和Gr的相对误差分别为11.94%和10.53%。相对误差均低于15%，可以认为二者气流组织相似，实验结果可以推广至全尺寸模型。
涡识别方法主要划分为3代，第1代主要以涡量识别来判定涡旋结构，但后续研究表明涡量和涡的概念完全不同，S. K. Robinson[15]报道了涡量和涡结构之间的关联性非常低。因此本文主要介绍第2代和第3代涡识别方法。
1.2.2 第2代涡识别方法
1）Q准则。
Q准则将速度场分解为旋转率部分和应变率部分，表达式如下[5]:
式中：Q为识别参数，s-2；u和v分别为水平和竖直方向的速度，m/s。当Q>0时表明局部空间内的旋转率大于应变率，即局部区域有涡旋的存在[16]。
2）Δ准则。
M. S. Chong等[17]根据临界点理论，提出Δ准则涡识别方法，表达式为：
式中：Δ为识别参数，s-2。Δ<0的区域判定该区域存在涡结构，相比于Q准则，Δ准则识别出的涡的区域比Q准则更大些[18]。
3）λci准则。
λci准则是用速度梯度张量特征方程的共轭复根的虚部来量化了涡旋的强度。在二维流场中，共轭复根的虚部可以用下式表示[5]:
式中：λci为识别参数，s-1。
1.2.3 第3代涡识别方法
1）Ω准则。
自2013年，UTA团队开始研究新一代的涡识别方法，以克服第2代涡识别方法存在的问题。2016年，Liu Chaoqun等[19]提出了Ω涡识别方法，Ω准则将涡量进一步分解为旋转部分和非旋转部分，引入无量纲Ω参数来表示旋转部分涡量占总涡量的比例，计算式为[19]:
为了避免零除现象，在分母上加上一个很小的正数ε，可以表示为[20]:
式中：Qmax为Q准则中Q的最大值，s-2。
2）Liutex准则。
Liutex准则分解出流场中的刚性旋转部分，更能表示涡旋强度，Liutex是带有方向的向量，其大小是刚体转动的旋转角速度，方向代表涡旋旋转轴的方向，二维流动下的表达式为[21]:
式中：R为识别参数，s-1，代表流体流动中的刚性旋转部分，即涡旋的强度；α为流场平面的主应变率，s-1；β为涡量，s-1。
3）ΩR准则。
ΩR为无量纲识别参数，是Liutex准则和Ω准则结合的产物，既具有Liutex准则反映刚体旋转部分的优点，又具有Ω准则不依赖阈值选择的特性，计算如下[5]:
ΩR方法和Ω方法涡的判别涡的边界相同，即当ΩR>0.52时判定该区域存在涡结构。
1.2.4 涡心识别算法
涡心识别算法由L. Graftieaux等[22]首次提出，通过流场的几何拓扑来表征涡旋的中心和边界的位置。其离散格式的定义式如下：
式中：S为任意平面，点P和点M位于S中；k为S平面的单位法向量；rPM为由P点至M点的矢量半径，m；uM为点M处的速度矢量，m/s；θM为rPM和uM的夹角，(°)；Γ为0~1的无量纲数，其值越接近1，说明在面域S内，θM的面积平均值越接近90°，即越接近圆形的涡旋；N为矩形区域内M点的数量，N起到面积平均的作用。
2 结果与分析
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2.1 涡识别方法对比
由于CS3截面第77个样本(即第25.67 s)的速度场较为对称，存在明显的涡旋结构，因此选择该样本来评判不同的涡识别方法。图3所示为该样本的流线图和使用Γ方程计算得出的涡心分布图。可以发现涡心识别算法和流线图得出的涡旋位置是高度相似的。因此，可以以涡心识别算法得出的涡心分布为基准来评价上文提出的6种涡识别算法的准确性。
图4和图5分别为第2代涡识别方法和第3代涡识别方法的结果图。可以看出这些方法均可识别出碰撞区左侧明显的涡旋结构，但不同于涡心识别算法的几何拓扑识别，这些方法将射流主流附近的涡列也识别了出来。由于这些涡列未呈现出严格的圆形使得捕捉圆形结构的涡心识别算法没有很好地识别出来。J. H. Thysen等[7]也报道了射流两侧的涡列，并提出这些涡列是由Kelvin-Helmholtz不稳定性产生的。
不同的涡旋识别方法有不同的特点，Q准则识别出的涡旋范围较小，涡核结构没有清晰地识别出来，有些不是涡旋的地方也被识别为涡旋区域，原因是方程把变形剪切误认为涡的一部分。Δ准则是一个数学的判据，即涡区内的Δ必须大于0，可以在图4(a)和图4(b)看到，Δ准则识别出的涡区比Q准则更大，涡结构也较为清晰。相比于Q准则和Δ准则，λci准则可以更为清晰地识别出涡核结构，这是因为λci准则排除了拉伸压缩，但还是会受到剪切的污染。总之，以Q准则、Δ准则、λci准则为主的第2代涡识别方法均会不同程度地受到剪切污染，而且存在阈值选择问题，阈值过小不能显示出清晰的涡结构，阈值过大会造成涡结构的破裂。更为重要的是涡是向量，而第2代涡识别方法表示涡的量均为标量[16]。
Ω准则中的Ω是一个比例参数，可以同时捕捉强涡和弱涡，对比第2代涡识别方法，图5(a)可以清晰地显示出主要的涡结构，而且还可以显示出更多的弱涡结构。但Ω准则无法代表流体运动的刚性部分，而Liutex准则就解决了该问题。由图5(b)可知，Liutex是一个向量，因为是二维测试平面，所以Liutex的方向仅在z轴方向上，正值代表逆时针涡旋，负值代表顺时针涡旋，颜色的深度表示涡旋的强度。ΩR准则结合了Ω方法和Liutex方法的优点，既能准确代表刚体旋转部分，又弱化了阈值对结果的影响，可以看出图5(c)和图5(a)具有相似性。但ΩR准则是一个比例参数，丢失了涡旋方向信息。综上所述，因为Liutex涡识别方法既能识别涡旋的方向，又能准确识别出真正的涡旋结构，因此本文选择Liutex准则数据进行分析。
2.2 涡的瞬态特征分析
因为CS3截面处于腔体中部，不易受到纵向气流的影响，所以该截面的流场结构较为对称，瞬态射流摆动明显、周期性强。而且由于本次实验的采样频率过低，不能捕捉小时间尺度的流动信息，因此无法分析射流自持振荡的特性。本文使用Liutex准则以冷却条件下CS3截面为对象来分析涡的瞬态特征。
图6所示为CS3截面在25.33~29.00 s内的Liutex涡识别图，t=25.33 s时，两射流在中间相遇，流场结构呈现对称的状态，此时左侧射流末端产生大尺度顺时针涡旋。t=25.67 s时，射流碰撞区在向左摆动的同时，涡旋也在向上运动。t=26.00 s时，该涡旋继续向上运动，同时吸收左侧射流下方的小尺度涡旋而变强。t=26.33 s时，之前的涡被气流冲散，而射流碰撞区末端左侧的大尺度涡旋再次出现，且t=26.67 s时向上向右运动。在涡旋和气流相互作用的影响下，t=27.00 s时左侧射流被压缩至顶部区域并贴附顶棚水平流动，在末端集中冲击右侧射流的根部，形成一个7字形的流场。这是一个不稳定的状态，右侧射流的影响开始由盛转衰。
至下一阶段，t=27.33 s时，前期的大尺度顺时针涡旋湮灭而左侧射流起始端下部产生离散的小尺度涡旋，这些涡旋随着主流向左下方运动的同时减缓了气流向下的流动，这加大了冲击右侧射流的区域。而t=27.67 s时，右侧射流开始被左侧射流截为两段，末端的气流由于没有了后续射流的推进作用，开始变得混乱，大尺度顺时针涡旋在混乱气流中再次产生并开始影响流场的结构。t=28.00 s时，右侧射流完全被左侧射流截断，在此过程中右侧射流无向右摆动的现象。
t=28.33 s时，左侧射流压制右侧射流，两射流的碰撞区整体处于右侧，但其末端受到右侧射流的冲击开始向左摆动。在大尺度涡旋的影响下，t=28.67 s时，右侧射流开始向左反弹，射流碰撞区开始向对称状态趋近。t=29.00 s时，流场结构再次呈现对称状态。下一阶段碰撞区会继续向左运动，左侧射流会再次向上运动，以此循环往复，周而复始。在25.33~29.00 s，该摆动现象的周期约为3.67 s。
2.3 涡心频率分布
为了体现涡在时间尺度的不稳定信息，绘制了等温条件和冷却条件下5个截面的涡心相对频率分布图，分别如图7和图8所示。使用式(11)对左右两侧的大尺度环流中心进行追踪，图中颜色代表涡心出现在某一点的次数，某一区域颜色越深代表涡出现在该区域的次数越多、涡的分布也较为集中、流场结构较为稳定。为了统一标准对相对频率进行了归一化处理。
在CS3截面中，涡心集中出现在左右两侧的大尺度环流中心区域，右侧射流的上方也有涡心出现。但冷却条件下左侧环流区内的涡心分布较为发散，而右侧的环流区涡心分布较为集中。表明因温度产生的热羽流使左侧流场更加不稳定，右侧气流趋向稳定。CS3.5截面中涡心主要分布在中部偏上的位置和左侧壁面偏下的区域。冷却条件下涡心分布范围均比等温条件下要小，可能是加热地板产生的热羽流改变了流场的结构，使该截面内涡心的分布更加集中，气流也相对稳定。CS4截面所处位置的几何特征和CS3截面完全相同，但二者的流场结构存在很大差异，CS4截面的涡心集中分布在左侧壁面区域，在右侧环流区，颜色较浅、范围较大表明涡心不集中，也说明右侧的环流中心是不稳定的。图中还显示出两射流向左偏斜的现象，可能是由于在右侧的涡心处存在纵向气流，一方面阻止了左侧射流向右侧摆动，另一面托着右侧的射流使其处于靠上的位置。因为左侧长条形涡心分布范围变短，右侧也出现了颜色较深的区域，所以冷却条件下流场也趋向稳定、涡分布也较为集中。在CS4.5截面涡心集中出现在左右两侧的壁面区域，还有少部分出现在舱室的右上角区域。冷却条件下左右两侧的涡心分布范围均有不同程度的增大，但涡心的分布还是呈现出相对集中的态势，这可由深色区域较为集中来体现。这表明热羽流使涡的分布向不稳定的态势发展，但幅度较小。CS5截面涡心主要集中出现在左右两侧的壁面区和射流合并区的上方。热羽流对涡分布范围和位置均产生了影响，冷却条件下左右壁面的涡心分布位置更靠上。在右侧壁面区域的涡心分布范围明显扩大，使该区域的流场更具不稳定性。两射流上部中间区域涡心分布的区域呈现下沉的态势，且范围也有一定程度的扩大。
表3所示为涡心出现最大次数及其位置的结果，量化了涡分布的集中性和稳定性。其中CS4截面左侧涡心在同一点出现的次数高达21次，说明涡的分布是相对稳定的。而且由表3可知，冷却条件下同一位置涡出现的最大次数多数均比等温条件下的多，可见冷却条件下涡的分布更加集中，流场结构也更为稳定。
3 结论
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本文搭建了对置射流封闭人居空间的缩比模型，使用PIV测速系统对等温条件和冷却条件下的气流结构进行实验研究。对比了多种涡识别方法，分析了纵向不同截面中涡心及其频率的分布特征，以涡旋的视角分析了射流摆动的周期，得到如下结论：
1）第2代和第3代涡识别方法得出的涡分布图和几何拓扑得出的涡心分布图高度相似，相比于Q准则、Δ准则、λci准则、Ω准则和ΩR准则，Liutex准则既能准确表示涡旋的强度，又能表示涡的旋转方向。
2）采用Liutex涡识别方法对CS3截面某一周期内的涡旋运动进行了分析，发现逆时针和顺时针方向涡旋强度不同，最大强度分别为50 s-1和110 s-1，得出涡旋的运动、合并、湮灭会改变流场结构，射流摆动的周期约为3.67 s。
3）虽然对置射流的流场是非定常的，但涡的分布位置是相对稳定的，其中CS4截面左侧涡心在同一点出现的次数高达21次。涡聚集的区域气流混合较差且对污染物具有锁定作用，使在该区域的人热舒适较差，且容易被病毒感染。
4）温度产生热羽流使涡的分布位置更集中、流场更稳定，表明污染物被锁定在更小的区域，即冷却条件有助于遏制封闭人居环境中污染物的传播。
基金
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  • 北京市科技计划项目(Z231100006123014)
  • 北京市委组织部青年骨干人才项目(24269220005)
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2025年第46卷第1期
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doi: 10.12465/j.issn.0253-4339.2025.01.140
  • 接收时间：2023-10-27
  • 首发时间：2026-03-13
  • 出版时间：2025-02-16
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  • 收稿日期：2023-10-27
  • 修回日期：2023-12-17
  • 录用日期：2024-01-16
基金
Beijing Science and Technology Project(Z231100006123014)
北京市科技计划项目(Z231100006123014)
Beijing Municipal Committee Organization Department Youth Backbone Talent Program(24269220005)
北京市委组织部青年骨干人才项目(24269220005)
作者信息
    1北京建筑大学环境与能源工程学院　北京　102616
    2北京市高强混凝土有限责任公司　北京　100071

通讯作者:

王聪聪，男，讲师，北京建筑大学环境与能源工程学院，18222202285，E-mail：。研究方向：建筑室内环境与绿色能源。
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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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
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