Article(id=1156908032857821510, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156907871645556837, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2402655, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1712851200000, receivedDateStr=2024-04-12, revisedDate=1721232000000, revisedDateStr=2024-07-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1753757969345, onlineDateStr=2025-07-29, pubDate=1737993600000, pubDateStr=2025-01-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753757969345, onlineIssueDateStr=2025-07-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753757969344, creator=13701087609, updateTime=1753757969344, updator=13701087609, issue=Issue{id=1156907871645556837, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='3', pageStart='879', pageEnd='1312', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1753757930909, creator=13701087609, updateTime=1765095544280, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1204461268821320541, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156907871645556837, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1204461268825514846, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156907871645556837, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1165, endPage=1173, ext={EN=ArticleExt(id=1156908033637962057, articleId=1156908032857821510, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Influence of Mountain Height on the Wind Pressure Distribution on the Roof of Airport Terminal Buildings, columnId=1156262729917780302, journalTitle=Science Technology and Engineering, columnName=Papers·Architectural Science, runingTitle=null, highlight=null, articleAbstract=

To study the wind pressure distribution characteristics of long-span roofs of airport terminals at different mountain heights in mountainous areas, a rigid model wind tunnel pressure measurement test of airport terminals roofs at mountain heights of 0 m, 30 m, 60 m, and 90 m was conducted to compare and analyze the effects of the heights on the surface mean and pulsating wind pressure, non-Gaussian characteristics of pulsating wind pressure, peak factor, and extreme wind pressure of the roof surface. The results show that the increase in mountain height significantly increases the mean and fluctuating wind pressure coefficient at the windward leading edge of the roof, and also intensifies the degree of flow separation at the leading edge of the roof. This causes the skewness, kurtosis, and probability density function of the fluctuating wind pressure at the windward leading edge of the roof to deviate significantly from the standard Gaussian distribution, exhibiting strong non-Gaussian characteristics. At the same time, the Hermite moment model was used to calculate the peak factor, and it was found that the peak factor of most measuring points on the roof surface was mainly distributed in the range of 3.5~4, which was much higher than the recommended value of 2.5 in GB 50009—2012. The extreme wind pressure value at the front edge of the roof also increased with the increase of the mountain height, and there was a similar variation pattern at the edge of the roof under all wind directions. Among them, the most unfavorable extreme negative pressure on the roof surface at a mountain height of 90m decreased by 44.9% compared to the 0m mountain height. Research can provide useful suggestions and references for the design and construction of terminals in similar airports.

, correspAuthors=Cheng PEI, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Xiao-min ZHANG, Cheng PEI, Xiao-kang CHENG, Xiong-wei YANG, Cun-ming MA), CN=ArticleExt(id=1156908109575836610, articleId=1156908032857821510, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=山体高度对机场航站楼屋盖风压分布的影响, columnId=1156262730517565784, journalTitle=科学技术与工程, columnName=论文·建筑科学, runingTitle=null, highlight=null, articleAbstract=

为研究山区不同山体高度上机场航站楼大跨屋盖的风压分布特性,对机场航站楼屋盖在0、30、60、90 m山体高度上的刚性模型,进行的风洞测压试验,对比分析了不同山体高度对屋盖表面平均风压、脉动风压、脉动风压的非高斯特性、峰值因子以及极值风压的影响。结果表明:山体高度的增加显著增加了屋盖迎风前缘的平均风压与脉动风压系数,也加剧了屋盖前缘流动分离的程度,使得屋盖迎风前缘脉动风压的偏度、峰度以及概率密度函数明显偏离标准高斯分布,表现出强烈的非高斯特性。同时通过Hermite矩模型对峰值因子进行计算发现,屋盖表面大部分测点的峰值因子主要分布在3.5~4范围内,远高于GB 50009—2012的建议值2.5,屋盖前缘的极值风压值也会随山体高度的增加而增大,并且全风向角下屋盖边缘有着类似的变化规律,其中90 m山体高度上屋盖表面最不利极值负压比0 m山体高度上降低了44.9%。研究可为同类型机场航站楼的设计与建造提供有益的建议和参考。

, correspAuthors=裴城, authorNote=null, correspAuthorsNote=
* 裴城(1992—),男,汉族,四川成都人,博士,讲师。研究方向:结构抗风设计。E-mail:
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张小敏(1999—),女,汉族,河南周口人,硕士研究生。研究方向:大跨屋盖抗风设计。E-mail:

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张小敏(1999—),女,汉族,河南周口人,硕士研究生。研究方向:大跨屋盖抗风设计。E-mail:

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张小敏(1999—),女,汉族,河南周口人,硕士研究生。研究方向:大跨屋盖抗风设计。E-mail:

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U为风速,H为高度,IU为湍流强度,Uref为模型参考点风速,Href为模型参考点高度,a为风速剖面指数,Su为风速功率谱密度,k1为一个特定频率, σ u 2为风速信号的方差

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Test conditions

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山体高度H/m 风向角α/(°)
H1=0 0、15、30、…、180
H2=30 0、15、30、…、180
H3=60 0、15、30、…、180
H4=90 0、15、30、…、180
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试验工况

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山体高度H/m 风向角α/(°)
H1=0 0、15、30、…、180
H2=30 0、15、30、…、180
H3=60 0、15、30、…、180
H4=90 0、15、30、…、180
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山体高度对机场航站楼屋盖风压分布的影响
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张小敏 1 , 裴城 1, 2, * , 程小慷 1 , 杨雄伟 3 , 马存明 4
科学技术与工程 | 论文·建筑科学 2025,25(3): 1165-1173
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科学技术与工程 | 论文·建筑科学 2025, 25(3): 1165-1173
山体高度对机场航站楼屋盖风压分布的影响
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张小敏1 , 裴城1, 2, * , 程小慷1, 杨雄伟3, 马存明4
作者信息
  • 1.中国民用航空飞行学院机场学院, 广汉 618307
  • 2.民航机场智慧运营与运维四川省工程研究中心, 广汉 618300
  • 3.河北地质大学城市地质与工程学院, 石家庄 050031
  • 4.西南交通大学, 四川省风工程试验研究中心, 成都 610031
  • 张小敏(1999—),女,汉族,河南周口人,硕士研究生。研究方向:大跨屋盖抗风设计。E-mail:

通讯作者:

* 裴城(1992—),男,汉族,四川成都人,博士,讲师。研究方向:结构抗风设计。E-mail:
Influence of Mountain Height on the Wind Pressure Distribution on the Roof of Airport Terminal Buildings
Xiao-min ZHANG1 , Cheng PEI1, 2, * , Xiao-kang CHENG1, Xiong-wei YANG3, Cun-ming MA4
Affiliations
  • 1. Airport College, Civil Aviation Flight University of China, Guanghan 618307, China
  • 2. Sichuan Provincial Engineering Research Center of Smart Operation and Maintenance of Civil Aviation Airports, Guanghan 618300, China
  • 3. School of Urban Geology and Engineering, Hebei GEO University, Shijiazhuang 050031, China
  • 4. Research Center for Wind Engineering, Southwest Jiaotong University, Chengdu 610031, China
出版时间: 2025-01-28 doi: 10.12404/j.issn.1671-1815.2402655
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为研究山区不同山体高度上机场航站楼大跨屋盖的风压分布特性,对机场航站楼屋盖在0、30、60、90 m山体高度上的刚性模型,进行的风洞测压试验,对比分析了不同山体高度对屋盖表面平均风压、脉动风压、脉动风压的非高斯特性、峰值因子以及极值风压的影响。结果表明:山体高度的增加显著增加了屋盖迎风前缘的平均风压与脉动风压系数,也加剧了屋盖前缘流动分离的程度,使得屋盖迎风前缘脉动风压的偏度、峰度以及概率密度函数明显偏离标准高斯分布,表现出强烈的非高斯特性。同时通过Hermite矩模型对峰值因子进行计算发现,屋盖表面大部分测点的峰值因子主要分布在3.5~4范围内,远高于GB 50009—2012的建议值2.5,屋盖前缘的极值风压值也会随山体高度的增加而增大,并且全风向角下屋盖边缘有着类似的变化规律,其中90 m山体高度上屋盖表面最不利极值负压比0 m山体高度上降低了44.9%。研究可为同类型机场航站楼的设计与建造提供有益的建议和参考。

山体高度  /  机场航站楼  /  大跨屋盖  /  极值风压  /  非高斯特性  /  风洞试验

To study the wind pressure distribution characteristics of long-span roofs of airport terminals at different mountain heights in mountainous areas, a rigid model wind tunnel pressure measurement test of airport terminals roofs at mountain heights of 0 m, 30 m, 60 m, and 90 m was conducted to compare and analyze the effects of the heights on the surface mean and pulsating wind pressure, non-Gaussian characteristics of pulsating wind pressure, peak factor, and extreme wind pressure of the roof surface. The results show that the increase in mountain height significantly increases the mean and fluctuating wind pressure coefficient at the windward leading edge of the roof, and also intensifies the degree of flow separation at the leading edge of the roof. This causes the skewness, kurtosis, and probability density function of the fluctuating wind pressure at the windward leading edge of the roof to deviate significantly from the standard Gaussian distribution, exhibiting strong non-Gaussian characteristics. At the same time, the Hermite moment model was used to calculate the peak factor, and it was found that the peak factor of most measuring points on the roof surface was mainly distributed in the range of 3.5~4, which was much higher than the recommended value of 2.5 in GB 50009—2012. The extreme wind pressure value at the front edge of the roof also increased with the increase of the mountain height, and there was a similar variation pattern at the edge of the roof under all wind directions. Among them, the most unfavorable extreme negative pressure on the roof surface at a mountain height of 90m decreased by 44.9% compared to the 0m mountain height. Research can provide useful suggestions and references for the design and construction of terminals in similar airports.

mountain heights  /  airport terminal  /  long-span roof  /  extreme wind pressure  /  non-Gaussian features  /  wind tunnel test
张小敏, 裴城, 程小慷, 杨雄伟, 马存明. 山体高度对机场航站楼屋盖风压分布的影响. 科学技术与工程, 2025 , 25 (3) : 1165 -1173 . DOI: 10.12404/j.issn.1671-1815.2402655
Xiao-min ZHANG, Cheng PEI, Xiao-kang CHENG, Xiong-wei YANG, Cun-ming MA. Influence of Mountain Height on the Wind Pressure Distribution on the Roof of Airport Terminal Buildings[J]. Science Technology and Engineering, 2025 , 25 (3) : 1165 -1173 . DOI: 10.12404/j.issn.1671-1815.2402655
大跨屋盖结构由于质量轻、柔性大、阻尼小等一系列特点,对风荷载的作用十分敏感,给结构设计带来了很大的挑战。风荷载已成为大跨屋盖结构的主要设计荷载,由风导致的屋盖结构的破坏事件时有发生[1-4]。随着“交通强国”的全面实施[5-6],中国西部机场建设蓬勃发展,机场建设体现民航需求,山区机场航站楼多建在山顶或群山环抱的山谷。而山区风场条件复杂,不仅受到大气环流的影响,还受到当地地形和气候的局部影响,因此使得机场航站楼大跨结构的风压分布特性与平原和沿海地区的风场有显著差异。因此,本文中将根据国家发展战略,聚焦西部山区机场航站楼大跨屋盖结构的风压分布特性进行研究。
针对大跨屋盖风压分布特性研究,国内外学者进行了一系列研究。Yang等[7]通过在风洞试验,研究了不同风场对机场航站楼大跨屋盖边界层湍流风荷载的影响,发现湍流积分尺度对非高斯特性有较显著的影响。Song等[8]通过对某机场航站楼大跨结构进行刚性模型测压试验,研究了不同风向角下大跨屋盖结构的风压分布规律,结果发现不同风向角下屋顶上多数平均风压都是负值,屋盖前缘的负压值最为显著。汪之松等[9]基于风洞试验,研究了平均风剖面、风速、风向、湍流强度等因素对屋面风压的影响。结果发现:屋盖迎风前缘负风压最大,风速对屋盖的风压系数均值和极值影响较小,而湍流度对极值风压影响较大。郑德乾等[10]基于风洞试验数据,对不同风向角下机场航站楼屋盖表面平均风压系数和脉动风压系数的变化规律进行了研究,并用计算流体动力学(computational fluid dynamics,CFD)数值模拟揭示了其屋盖风荷载表面的作用机理。李正良等[11]通过风洞试验,研究了周边建筑物对某机场航站楼大跨曲面屋盖表面风压的遮挡效应,发现上游周边建筑对大跨屋盖有一定的遮挡效应,且会减小屋盖表面的风压。李驰宇等[12]对B类地形下的某大跨屋盖进行了风洞测压试验,研究了不同风向角对屋盖结构表面风荷载特性的影响,结果发现:在45°风向角下,脉动风压系数和极值风压系数的最大值大于其他风向角,为最不利风向角。Chen等[13]对大跨度屋盖进行了不同湍流作用下的风洞试验,研究了不同湍流对大跨度屋盖风荷载非高斯特性的影响,结果发现随着湍流的增加,风荷载的非高斯特性更加显著,并且峰值因子呈正相关增加。张建等[14]通过对波纹状大跨屋盖的风压分布特性进行研究,发现波纹形状能加剧了迎风侧屋盖前缘的分离,并对围护结构的负压极值有显著的影响。李波等[15]对不同建筑造型的屋盖风荷载特性进行了研究,发现当风向角与屋盖波纹存在一定的夹角时,波纹间距对屋盖表面风压有一定影响。王程等[16]通过对典型屋盖的二维缩尺模型进行数值模拟。研究了不同坡角对屋面风压分布特性的影响,结果发现:屋脊处的极值吸力随着屋面坡度的增加而增大。王芳等[17]通过对大跨屋盖表面有无建筑装饰条带的刚性模型进行风洞试验,研究了建筑装饰条带对大跨屋盖风荷载特性的影响。发现建筑装饰条带对风荷载的影响与风向角有关,并且建筑装饰条带会增大最不利的极值负压。
综上所述,针对机场航站楼大跨屋盖表面风荷载的研究多集中于周边建筑、不同风向角、不同风场、不同坡角、不同湍流强度及建筑装饰条带对大跨屋盖的影响等,关于山区不同山体高度对机场航站楼大跨屋盖风压分布特性影响的研究甚少。为此选取一典型山区机场航站楼大跨屋盖为研究对象,通过在风洞中设置不同山体高度上的机场航站楼进行刚性模型测压试验,对比分析了不同山体高度下屋盖表面平均风压系数、脉动风压系数、脉动风压的非高斯特性、峰值因子以及极值风压的差异。从而得到不同山体高度对航站楼大跨屋盖风压分布特性的影响结果,可为同类型机场航站楼设计及建造提供参考。
本文中风洞试验的研究对象为山区某机场航站楼的大跨度曲面屋盖。此屋盖全长跨度为102 m,最大宽度为53 m,高度为30 m。试验模型按缩尺比1∶200进行设计,模型表面采用2 mm ABS胶板和3d打印的关敏树脂制作而成,能满足风洞试验屋盖测压的要求。为研究山区不同山体高度对机场航站楼大跨屋盖风压分布特性的影响,试验设置了机场航站楼处于0、30、60、90 m山体高度上的刚性模型测压试验,其试验模型照片如图1所示。
本次机场航站楼大跨屋盖刚性模型的风洞试验在西南交通大学XNJD-1风洞实验室中进行。该试验段截面尺寸为长8.0 m、宽3.6 m、高3.0 m,试验段配备有Scanivalve扫描阀和眼镜蛇测速仪器,主要用于压力的测量和风速数据的采集。采样频率为256 Hz,采样时长取60 s,阻塞率小于0.5%。为了在大跨屋盖表面获得准确的风荷载特性,在屋盖表面对称性地布置了205个测压点。并对屋盖曲面边缘、圆孔周围及屋盖侧边缘处存在流动分离的模型测点区域,进行了加密处理,其模型测点布置(A1A2A3,K1K2K3,D1D2D3)和风向角α定义如图2所示。
在工况方面,设置了机场航站楼模型处于0、30、60、90 m山体高度等四种工况,且在每一种山体高度下每隔15°在0°~180°风向角(屋盖两侧区域对称)范围内设置了12种情况,具体试验工况数据如表1所示。
该机场航站楼所处位置在GB 50009—2012《建筑结构荷载规范》[18]中属于D类地貌,为了准确的模拟该地形所受到风场环境,在风洞试验中采用尖劈和粗糙元对风场进行模拟。图3(a)所示为模拟的D类大气边界风场的平均风速和湍流强度剖面。图3(b)为脉动风速功率谱图。从图3风场模拟结果中可知,平均风速剖面、湍流强度剖面的试验值与规范值基本一致,脉动风速功率谱的试验值与von Kárman谱较为吻合,可见试验模拟的风场满足试验要求。
风洞试验获得的试验数据通常是以风压系数的形式给出,对屋盖表面的风压需进行无量纲化处理,得到其风压系数时程[19-20]。计算方法为
Cpi(tj)=[Pi(tj)-P]/0.5ρU02
式(1)中:Cpi(tj)为模型上tj时刻第i个测点的风压系数;Pi为压力扫描阀测得的风压值;P为参考点处平均静压;U0为模型顶部高度处的风速;ρ为空气密度。
根据上述风压系数,计算屋盖表面各测点的平均风压和脉动风压系数,公式为
C p i= j = 1 N C p i ( t j ) M
Cpri= 1 M j = 1 N [ C p i ( t j ) - C p i ] 2
式中: C p i为平均风压系数;Cpri为脉动风压系数;M为测点总采样点数。
以0°风向角下结果为例进行分析,图4给出了航站楼处于四种山体高度下屋盖的平均风压系数等值线云图。可知:机场航站楼在H1~H4山体高度上时,整体上表现为屋盖前缘的平均风压系数变化剧烈,屋盖中间及后缘区域的平均风压系数变化比较缓慢。同时,通过云图可以看出,随着航站楼所处山体高度的增加,屋盖前缘及凸起处的平均风压系数逐渐增大。其中,H4山体高度下屋盖前缘的负压分布范围最大,在-2.62左右,相比于H1的最大平均风压系数增加了107.9%。表明山体高度的增加能加剧屋盖迎风前缘流动分离。
图5给出了0°风向角时航站楼处于四种山体高度下屋盖表面的脉动风压系数等值线云图。可知, 航站楼在四种山体高度上时,其屋盖表面的脉动风压系数整体上表现为屋盖前缘及曲面凸起处的脉动风压系数较大,而屋盖迎风侧后方绝大部分区域的脉动风压系数相对较小。同时,通过云图可以看出,屋盖表面的脉动风压系数与平均风压系数具有类似的分布规律,即随着航站楼所处山体高度的增加,屋盖前缘的脉动风压值逐渐增大。其中,屋盖在山体高度H2~H4上的脉动风压系数相比于山体高度H1最大脉动风压系数分别增加了4.7%、9.4%、20.3%。
由上述结果可知,山体高度的增加能加剧航站楼屋盖前缘及凸起处流动分离的剧烈程度,可能使其表面风压呈现出较显著的非高斯特性。因此,有必要对不同山体高度下屋盖表面脉动风压的非高斯性进行研究。
偏度和峰度是非高斯概率分布区别于标准Gaussian分布的重要指标。偏度和峰度的计算公式为
Ski= 1 M j = 1 M C p i ( t j ) - C p i C p r i 3
Kui= 1 M j = 1 M C p i ( t j ) - C p i C p r i 4
式中:SkiKui分别为第i个测点的脉动风压的偏度和峰度。
图6给出了0°风向角时机场航站楼在不同山体高度上屋盖表面所有测点脉动风压的偏度-峰度散点图。由结果可以看出,机场航站楼在H1~H4四种山体高度时,其屋盖模型表面各测点脉动风压的偏度与峰度存在一定的偏差,但从整体上看屋盖表面的偏度与峰度间呈现非线性关系。在四种山体高度上屋盖表面大多数测点风压的偏度值与峰度值都分布在-1.0<S<0及3.5<K<6.0范围内,其偏度与峰度值明显偏离标准Gaussian分布。且不同山体高度上屋盖表面各测点脉动风压的偏度值大多数为负值,长尾在负向(吸力一侧)。
图7给出了0°风向角时航站楼处于四种山体高度上屋盖模型表面所有测点脉动风压的偏度与峰度等值线云图。由图可知,机场航站楼在四种山体高度上时,其屋盖表面的偏度和峰度整体上表现为屋盖前缘及两侧的偏度和峰度较大,而屋盖后方的绝大部分区域的负偏度和峰度值相对较小。同时,通过云图可以看出,机场航站楼屋盖在山体高度H2~H4上的偏度相比于山体高度H1最大偏度分别增加了14.6%、8.9%、11.4%、峰度分别增加了21.8%、1.4%、12.2%。表明随着航站楼所处山体高度的增加,屋盖前缘及两侧的偏度和峰度值呈现出明显的先增大后减小再增大变化趋势。
由上述结果可知,山体高度H1~H4对屋盖前缘及两侧偏度和峰度的影响最为剧烈,使得屋盖前缘及两侧脉动风压的非高斯性更显著。
为进一步研究机场航站楼在不同山体高度上屋盖表面脉动风压的非高斯特性,图8给出了0°风向角时机场航站楼在四种山体高度上屋盖表面典型测点(测点位置及编号见图2)的脉动风压概率分布函数,并将其与标准Gaussian曲线分布进行对比。图中纵坐标为脉动风压概率密度(probability density function,PDF),横坐标为数据归一化处理后的风压系数(Cp- C p)/Cpr。由图8(a)~图8(c)可知,在四种山体高度上屋盖前缘测点A1和圆孔周围测点A2的脉动风压概率密度函数与标准Gaussian分布曲线出现严重偏离,不满足高斯分布。而屋盖后缘测点A3的脉动风压概率密度分布与标准Gaussian分布基本吻合,满足高斯分布。通过将不同山体高度上相应测点值与标准Gaussian分布曲线进行对比可知,随着航站楼所处山体高度的增加,屋盖表面的脉动风压概率密度函数呈现先增大后减小的变化趋势。其中测点A1在山体高度H3下由于受流动分离影响最为显著,其最大偏差达到17.9%,测点A2由于处于屋盖圆孔周围导致其处于分离泡内,其最大偏差达到15.4%。由图8(d)~图8(f)可知,屋盖前缘测点K1的风压概率密度分布也显著偏离标准Gaussian分布,且其偏离程度随着航站楼所处山体高度的增加,呈现先增大后减小的变化趋势。而对于测点K2K3,由于距离屋盖前缘和边缘较远,其风压概率密度函数基本满足高斯分布。由图8(g)~图8(i)可知,位于屋盖边缘的测点D1D2D3在四种山体高度上的脉动风压概率密度函数均明显偏离标准Gaussian分布曲线,不满足高斯分布, 且山体高度H4上的非高斯特性较其他山体高度下更显著。
由上述分析可知,山体高度的增加能加剧屋盖前缘及两侧脉动风压概率密度函数偏离标准Gaussian分布曲线的程度,使得屋盖前缘及两侧边缘的非高斯性更显著。因此,下面将基于不同山体高度上屋盖表面脉动风压的非高斯特性,对屋盖非高斯区域测点的峰值因子展开计算,以便为此类山区机场航站楼屋盖脉动风压非高斯峰值因子的取值提供一定的借鉴和参考。
峰值因子是计算屋盖表面极值风压的关键参数,在传统的大跨屋盖抗风设计中,通常假定屋盖表面脉动风压符合高斯分布,然后采用规范中统一的峰值因子对屋盖表面的极值风压进行计算[21-25]。然而,这可能会对屋盖表面呈现非高斯特性区域的抗风安全性带来隐患。因此,本文中将在风压时程三阶统计量偏度和四阶统计量峰度的基础上,将非高斯过程转换成高斯过程的Hermite级数[26-27],进而来求解屋盖表面的峰值因子,其对应的峰值因子g的表达式为
$\begin{aligned} g= & \kappa\left\{\left(\beta+\frac{\gamma}{\beta}\right)+h_{3}\left[\beta^{2}+(2 \gamma-1)+\frac{1.98}{\beta^{2}}\right]+\right. \\ & h_{4}\left[\beta^{3}+3 \beta(\gamma-1)+\frac{3}{\beta}\left(\frac{\pi^{2}}{6}-\gamma+\gamma^{2}\right)+\right. \\ & \left.\left.\frac{5.44}{\beta^{3}}\right]\right\} \end{aligned}$
式(6)中:κh3h4为Hermite级数法的参数,分别为
κ= 1 1 + 2 h 3 2 + 6 h 4 2
h3= S k 4 + 2 1 + 1.5 K u
h4= 1 + 1.5 K u - 1 18
式中:SkKu风压时程的三阶统计量偏度和四阶统计量峰度;γ为欧拉常数,γ=0.577 2;β= 2 l n ( v 0 T );T为时距;v0为零穿越率(即v0= m 2 / m 0);mi= 0  niSiy(n)dn;mii阶谱矩;n为频率;Siy(n)为单边功率谱密度。
为进一步研究不同山体高度上屋盖表面脉动风压非高斯峰值因子的变化规律,这里选取上述非高斯区域前缘和后缘典型测点为对象,采用Hermite级数法对不同山体高度上屋盖表面典型
测点在各风向角下的风压非高斯峰值因子进行计算,其非高斯区域典型测点峰值因子随风向角α的变化情况,如图9所示。
图9可知,屋盖前缘测点A1K1D1在多个风向角下,其脉动风压峰值因子在四种山体高度上均超过了4,且在0°及75°~90°风向角下,不同山体高度对三个测点的脉动风压峰值因子影响最为显著,其最大脉动风压峰值因子超过了5。同时,从三个测点的放大图可以发现,在75°~90°风向角下,测点A1K1D1的脉动风压峰值因子会随山体高度的增加而有所增大。而屋盖后缘测点A3K3D3在多个风向角下,其脉动风压峰值因子都超过了4,特别是在75°~90°与165°~180°风向角下,出现了峰值因子上凸的两个最大值区域,其最大脉动风压峰值因子都超过了5,表明不同山体高度在75°~90°与165°~180°风向角下对屋盖后缘部分测点的脉动风压峰值因子有较显著的影响。
由上述结果知,非高斯区的脉动风压峰值因子与山体高度、测点位置及风向角有一定的关系,且四种山体高度上屋盖表面多数测点的峰值因子都超过4,远大于建筑荷载规范中的建议值2。
极值风压是围护结构抗风设计的重要参考依据,它的准确与否直接关系到围护结构的安全性。为此,本节将研究不同山体高度对屋盖表面极值风压的影响。极值风压Cpi-ex的表达式为
Cpi-ex= C p i-giCpri
图10给出了航站楼屋盖在四种山体高度上全风向角下的极小值风压系数包络云图。由图可知:四种山体高度上屋盖表面最不利极值负压均分布在各风向角的屋盖前缘、后缘及侧边缘处,而中间区域的极值负压值较小。同时,由图10也可以看出,随着航站楼所处山体高度的增加,全风向角下屋盖前缘、后缘及侧边缘的最不利极值负压值均呈现逐渐降低的趋势,其中航站楼在90 m山体高度上屋盖边缘(前缘、后缘及侧边缘)的最不利极值负压值为-6.58,比0 m山体高度上屋盖的最不利极值负压降低了44.9%。此外,除屋盖边缘以外的区域,山体高度的增加也使得极小值风压系数普遍降低。可见,山体高度的增加会使航站楼屋盖最不利极值负压值有所减小,从而对围护结构的抗风设计产生不利影响。
选取了一典型山区机场航站楼为工程背景,通过对航站楼模型进行风洞测压试验,对比分析了不同山体高度对屋盖表面风压系数、脉动风压的非高斯特性、峰值因子以及极值风压的影响,得出以下主要结论。
(1) 机场航站楼屋盖的表面风压在四种山体高度上的较大值均出现在受流动分离影响显著的屋盖迎风前缘及曲面凸起处,且随着航站楼所处山体高度的增加,屋盖前缘的平均和脉动风压系数显著增大。
(2) 随着航站楼所处山体高度的增加,屋盖前缘及两侧的偏度、峰度和概率密度函数呈现先增大后减小再增大变化趋势,且屋盖前缘及两侧脉动风压的非高斯性更显著。
(3) 在75°~90°风向角下,屋盖前缘典型测点的峰值因子会随山体高度的增加而有所增大。且多数测点的峰值因子分布在3.5~4范围内,远大于建筑荷载规范中的建议值2.5。若仍采用规范中假定为高斯分布的峰值因子值进行屋盖的抗风设计,可能会使其结构存在一定的安全隐患。
(4) 四种山体高度上屋盖表面最不利极值负压均分布在各风向角的屋盖边缘(前缘、后缘及侧边缘)处。同时,随着航站楼所处山体高度的增加,全风向角下屋盖前缘、后缘及侧边缘的最不利极值负压值均呈现逐渐降低的趋势,其中航站楼在90 m山体高度上屋盖边缘处的最不利极值比0 m山体高度上的最不利极值负压降低了44.9%。因此,在山区围护结构抗风设计时应当考虑山体高度效应。
  • 中央高校基本科研业务费(J2022-016)
  • 中央高校基本科研业务费(24CAFUC10210)
  • 中央高校基本科研业务费(PHD2023-024)
  • 国家重点研发计划(2022YFC3005301)
  • 国家自然科学基金(52078438)
  • 河北省自然科学基金(E2023403007)
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doi: 10.12404/j.issn.1671-1815.2402655
  • 接收时间:2024-04-12
  • 首发时间:2025-07-29
  • 出版时间:2025-01-28
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  • 收稿日期:2024-04-12
  • 修回日期:2024-07-18
基金
中央高校基本科研业务费(J2022-016)
中央高校基本科研业务费(24CAFUC10210)
中央高校基本科研业务费(PHD2023-024)
国家重点研发计划(2022YFC3005301)
国家自然科学基金(52078438)
河北省自然科学基金(E2023403007)
作者信息
    1.中国民用航空飞行学院机场学院, 广汉 618307
    2.民航机场智慧运营与运维四川省工程研究中心, 广汉 618300
    3.河北地质大学城市地质与工程学院, 石家庄 050031
    4.西南交通大学, 四川省风工程试验研究中心, 成都 610031

通讯作者:

* 裴城(1992—),男,汉族,四川成都人,博士,讲师。研究方向:结构抗风设计。E-mail:
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2种不同金属材料的力学参数

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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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