Article(id=1239217292546986549, tenantId=1146029695717560320, journalId=1238823019242635269, issueId=1239217289715839002, articleNumber=null, orderNo=null, doi=10.12465/j.issn.0253-4339.2025.05.133, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1717344000000, receivedDateStr=2024-06-03, revisedDate=1720886400000, revisedDateStr=2024-07-14, acceptedDate=1725206400000, acceptedDateStr=2024-09-02, onlineDate=1773382026086, onlineDateStr=2026-03-13, pubDate=1760544000000, pubDateStr=2025-10-16, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773382026086, onlineIssueDateStr=2026-03-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773382026086, creator=13701087609, updateTime=1773382026086, updator=13701087609, issue=Issue{id=1239217289715839002, tenantId=1146029695717560320, journalId=1238823019242635269, year='2025', volume='46', issue='5', pageStart='1', pageEnd='174', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1773382025412, creator=13701087609, updateTime=1773382179485, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1239217936007753991, tenantId=1146029695717560320, journalId=1238823019242635269, issueId=1239217289715839002, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1239217936007753992, tenantId=1146029695717560320, journalId=1238823019242635269, issueId=1239217289715839002, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=133, endPage=141, ext={EN=ArticleExt(id=1239217292760896058, articleId=1239217292546986549, tenantId=1146029695717560320, journalId=1238823019242635269, language=EN, title=Development and Validation of the Water Pit Numerical Model for Seasonal Thermal Energy Storage, columnId=null, journalTitle=Journal of Refrigeration, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Seasonal thermal energy storage (STES) can effectively mitigate the supply and demand imbalance of solar energy between winter and summer. Large-scale water pit thermal storage systems require efficient and accurate computational simulations to avoid investment waste. This study proposes a simplified numerical analysis method and establishes a cylindrical underground pit with a total volume of 11 304 m3 to describe the operation of a STES system. The model establishes a one-dimensional heat transfer model for the water body and a two-dimensional heat transfer model for the soil, separately solving for the water and the soil temperature field. The two models are connected through the temperature boundary at the pool wall to simulate the entire system. To comprehensively verify the accuracy of the numerical simulation model, validation was conducted under standby, charging, and discharging modes. The results indicate that the developed model has good accuracy and reliability. Under the standby mode, the temperature error of the five water layers in the sandbox test is less than 10%, with the highest accuracy in the middle and lower-middle water layers, with an average absolute error of 1.75% and 1.24%, respectively. Under the charging mode, the average relative error is 1.57%, and the average temperature error is 0.44 ℃. Under the discharging mode, the average relative error is 0.46%, and the average temperature error is 0.24 ℃.

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Tian Zhiyong, male, Ph.D., associate professor, School of Environmental Science and Engineering, Huazhong University of Science and Technology, 86-13522166267, E-mail: . Research fields: thermal energy storage and solar energy.
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跨季节储热可有效缓解太阳能在冬夏季的供需矛盾。大规模水池蓄热系统需要高效准确的计算模拟以避免投资浪费。研究提出了一个简化的数值分析方法,建立了总体积为11 304 m3的圆柱形地下水池模型用于描述季节性蓄热系统的运行。该模型分别建立水体一维传热模型和土壤二维传热模型,求解水体和土壤温度场,通过水池壁温度边界连接两者以实现对整个系统的模拟。为全方面验证数值仿真模型的正确性,分别在待机工况、充能工况和放能工况进行了验证,结果表明:所开发模型具有较好的准确性和可靠性。在待机工况下,砂箱实验台5个水体分层温度误差均低于10%,中层和中下层水体温度准确性最高,平均绝对误差分别为1.75%和1.24%。在充能工况下,平均相对误差仅为1.57%,平均误差温度为0.44 ℃。在放能工况下,平均相对误差仅为0.46%,平均误差温度为0.24 ℃。

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田志勇,男,博士,副教授,华中科技大学环境科学与工程学院,13522166267,E-mail:。研究方向:储热与太阳能。
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材料尺寸/mm密度/(kg/m3导热系数/[W/(m·K)]比热容/[kJ/(kg·K)]
木板200.052
细干沙子1 5200.591 410
聚苯板50280.0421 500
9800.694 195
空气501.120.023005 1
304不锈钢2.515.2
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材料尺寸/mm密度/(kg/m3导热系数/[W/(m·K)]比热容/[kJ/(kg·K)]
木板200.052
细干沙子1 5200.591 410
聚苯板50280.0421 500
9800.694 195
空气501.120.023005 1
304不锈钢2.515.2
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跨季节储热水体数值模型开发及验证
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周超辉 1 , 刘育策 1 , 胡跃 1 , 章继成 2, 3 , 田志勇 4 , 阮宇 2, 3 , 罗惠恒 1 , 罗勇强 4 , 许国治 4
制冷学报 | 2025,46(5): 133-141
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制冷学报 | 2025, 46(5): 133-141
跨季节储热水体数值模型开发及验证
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周超辉1, 刘育策1, 胡跃1, 章继成2, 3, 田志勇4 , 阮宇2, 3, 罗惠恒1, 罗勇强4, 许国治4
作者信息
  • 1中国长江三峡集团有限公司 武汉 430010
  • 2中国长江电力股份有限公司 武汉 430010
  • 3三峡电能有限公司 武汉 430022
  • 4华中科技大学环境科学与工程学院 武汉 430074

通讯作者:

田志勇,男,博士,副教授,华中科技大学环境科学与工程学院,13522166267,E-mail:。研究方向:储热与太阳能。
Development and Validation of the Water Pit Numerical Model for Seasonal Thermal Energy Storage
Chaohui Zhou1, Yuce Liu1, Yue Hu1, Jicheng Zhang2, 3, Zhiyong Tian4 , Yu Ruan2, 3, Huiheng Luo1, Yongqiang Luo4, Guozhi Xu4
Affiliations
  • 1.China Three Gorges Corporation, Wuhan, 430010, China
  • 2.China Yangtze Power Co., Ltd., Wuhan, 430010, China
  • 3.Three Gorges Electric Energy Co., Ltd., Wuhan, 430022, China
  • 4.School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
出版时间: 2025-10-16 doi: 10.12465/j.issn.0253-4339.2025.05.133
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跨季节储热可有效缓解太阳能在冬夏季的供需矛盾。大规模水池蓄热系统需要高效准确的计算模拟以避免投资浪费。研究提出了一个简化的数值分析方法,建立了总体积为11 304 m3的圆柱形地下水池模型用于描述季节性蓄热系统的运行。该模型分别建立水体一维传热模型和土壤二维传热模型,求解水体和土壤温度场,通过水池壁温度边界连接两者以实现对整个系统的模拟。为全方面验证数值仿真模型的正确性,分别在待机工况、充能工况和放能工况进行了验证,结果表明:所开发模型具有较好的准确性和可靠性。在待机工况下,砂箱实验台5个水体分层温度误差均低于10%,中层和中下层水体温度准确性最高,平均绝对误差分别为1.75%和1.24%。在充能工况下,平均相对误差仅为1.57%,平均误差温度为0.44 ℃。在放能工况下,平均相对误差仅为0.46%,平均误差温度为0.24 ℃。

太阳能  /  跨季节储热  /  大规模水池蓄热系统  /  数值模型

Seasonal thermal energy storage (STES) can effectively mitigate the supply and demand imbalance of solar energy between winter and summer. Large-scale water pit thermal storage systems require efficient and accurate computational simulations to avoid investment waste. This study proposes a simplified numerical analysis method and establishes a cylindrical underground pit with a total volume of 11 304 m3 to describe the operation of a STES system. The model establishes a one-dimensional heat transfer model for the water body and a two-dimensional heat transfer model for the soil, separately solving for the water and the soil temperature field. The two models are connected through the temperature boundary at the pool wall to simulate the entire system. To comprehensively verify the accuracy of the numerical simulation model, validation was conducted under standby, charging, and discharging modes. The results indicate that the developed model has good accuracy and reliability. Under the standby mode, the temperature error of the five water layers in the sandbox test is less than 10%, with the highest accuracy in the middle and lower-middle water layers, with an average absolute error of 1.75% and 1.24%, respectively. Under the charging mode, the average relative error is 1.57%, and the average temperature error is 0.44 ℃. Under the discharging mode, the average relative error is 0.46%, and the average temperature error is 0.24 ℃.

solar energy  /  seasonal thermal energy storage  /  large-scale water pit thermal energy storage system  /  numerical model
周超辉, 刘育策, 胡跃, 章继成, 田志勇, 阮宇, 罗惠恒, 罗勇强, 许国治. 跨季节储热水体数值模型开发及验证. 制冷学报, 2025 , 46 (5) : 133 -141 . DOI: 10.12465/j.issn.0253-4339.2025.05.133
Chaohui Zhou, Yuce Liu, Yue Hu, Jicheng Zhang, Zhiyong Tian, Yu Ruan, Huiheng Luo, Yongqiang Luo, Guozhi Xu. Development and Validation of the Water Pit Numerical Model for Seasonal Thermal Energy Storage[J]. Journal of Refrigeration, 2025 , 46 (5) : 133 -141 . DOI: 10.12465/j.issn.0253-4339.2025.05.133
建筑能耗约占总能源消费总量的40%,其中大部分用于夏季制冷和冬季供暖[1]。使用季节性蓄热(seasonal thermal energy storage,STES)的可再生能源区域供暖可以更好地为建筑提供低碳空间供暖,因为它们提供了可调度性和灵活性[2-3]。根据存储介质的不同,STES可分为显热存储、潜热存储和化学能存储[4-5]。其中,显热存储是最常用的STES类型。关于太阳能区域供暖(solar district heating,SDH)系统的STES,商业应用中存在4种主要类型,包括储罐蓄热系统(tank thermal energy storage,TTES)、水池蓄热系统(pit thermal energy storage,PTES)、钻孔蓄热系统(borehole thermal energy storage,BTES)和含水层蓄热系统(aquifer thermal energy storage,ATES)[6-7]
在这些显热类型的STES中,PTES是目前最可靠、使用最广泛的季节性储热系统[8],可将热水储存在带有隔热盖的大型挖掘水池中,其侧面、底部通常由聚合物衬里覆盖。然而,如此大规模的系统需仔细规划以避免高昂的投资成本。因此,建立高效准确的计算模型越来越重要。
当前的STES仿真工具可分为3类:1)用于部件级建模的计算流体动力学(computational fluid dynamics,CFD);2)用于系统级建模的简化2D/3D数值方法;3)使用TRNSYS等软件对STES系统进行的长时间多系统耦合研究。该类数值模型中通常也考虑了诸多因素,包括储存介质的热性质、设计和几何结构、周围土壤条件以及传热和传质机制[9]
周喜超等[10]基于TRNSYS模拟对张家口某太阳能储热、供热综合能源实验系统的性能和运行策略进行了研究。贾鹏琦等[11]同样基于TRNSYS建模对城市中水余热跨季节储热系统的长期运行性能进行了分析,并对该系统的经济性进行了研究。王春林等[12]结合TRNSYS模拟软件以及Hooke-Jeeves算法[13]对太阳能跨季节储热系统进行数值模拟以及优化分析。此外,J. Fan等[14]利用CFD软件对Marstal 75 000 m3水池储热系统的热特性进行了实验和数值研究,监测分析了储热水池中不同蓄水层的温度和水池周围的地面不同深度的温度。A. Dahash等[15]建立了大型储罐和水池的数值有限元模型,考虑2个能效指标和2个分层质量指标来评估系统性能。Pan Xinyu等[16]基于TRNSYS中的XST(seasonal ground heat storage,跨季节地埋管储热)模块,提出了一种改进的二维模型以研究PTES中温度、热流和热分层情况。S. Raab等[17]基于TRNSYS建立了跨季节储热系统模型,并根据德国汉诺威的项目数据进行了模型验证,偏差在5%以内。类似的,Xie Zichan等[18]基于TRNSYS开发的仿真模型依据丹麦储热水体项目进行了模型验证。
考虑到STES和整个太阳能区域供热系统的动态特征,进行长期耦合模拟并不容易。且由于只有少数大型PTES正在运行,实验数据有限,很难验证和修改现有模型[19]。相关研究多对STES模型中土壤区域或水域的计算进行简化以实现快速分析,但这种简化一般偏离实际。此外,当使用计算流体动力学软件(如CFD/COMSOL)进行模拟时,大时间尺度的模拟耗时较长且需要专业的建模。且由于组件的复杂性,TRNSYS软件在运行和分析STES时也需要仔细控制和大量时间[15]
因此,本研究提出了一个简化的数值模型-跨季节储热水体系统级耦合数值模型,可用于描述STES的运行状况,考虑了太阳能集热器组件、水域和周围土壤区域以及加热组件的运行。该模型的待机工况根据缩尺砂箱实验台的实测数据进行了验证,充能工况和放能工况基于中国黄帝城项目的实验数据进行了验证。该研究对模型应用和系统分析具有指导意义,也同时为圆柱形PTES的规模设计和操作优化提供了参考。
由于PTES中水与周围土壤之间的热传递属于不同领域,因此应分别求解其温度场的解。2个部件的控制方程和边界条件不同,一旦获得这2个部件的分析结果,就可以通过水池壁温度边界连接水和土壤的传热来执行PTES系统的整体模拟,计算原理具体如图1所示。在模型建立和计算中,PTES中的水体模型采用一维传热模型,土壤传热则采用二维传热模型。
目前水体一维传热存在的问题是:水体各层之间的纯一维传热具有不一致的散热率,容易导致下部水体比上部水体更热,这不符合实际情况。考虑到水池内部水域的传热,本研究中所用的处理反温跃层的方法是在一个步骤内计算每一层水的温度序列。对于底层温度高于上层水的地方,将2层水融合成一个等温层,并在一个时间步骤中从水池底层进行检查和计算。这在实践中是合理的,理论上,当下层的水温高于上层时,它们会在重力作用下混合,直至温度相同。需要注意的是,这种混合需要时间,因此对计算的时间步长和计算的水层体积有一定的要求。
对于土壤传热温度场的求解,此时STES水池的热流是土壤升温的热源,并作为土壤温度场求解的边界条件,通过时间步长模拟,可更新土壤温度。对于跨季节储热水体池内水体的传热,更新后的土壤温度可作为水传热的重要热边界,通过时间步长模拟,则可以进一步更新水体温度。应注意的是,在每个时间步长内,流体的稳态传热模型均会得到求解,池壁温度会通过瞬态土壤传热模型进行更新,因为时间步长通常为1 h,这足以使流体在该时间间隔内达到稳态。
基于以下假设,为快速计算,开发了一个半径为15 m、深度为16 m、总体积为11 304 m3的圆柱形地下水池的简化数值模型:
1)水池表面无降低传热率的水垢或杂质。
2)土壤是均质的,未考虑地下水渗透和其他因素对传热的影响。
3)未考虑温度对水的物理参数的影响。
4)每个高度水池内的水具有相同的温度,且在径向方向上无温度梯度[8]
5)盖板具有相同的温度,且在径向上无温度梯度。
6)盖子和水面之间有一层空气。由于密封,水蒸发的潜热很小,空气几乎不流动,类似于固体传热。
7)水池壁有隔热层和混凝土层。由于两者厚度与池直径相比非常小,因此它们的物理性质仅用于计算热阻。
8)由于圆柱体的对称性,本文采用了一半的模型进行研究。
基于模型假设,PTES中具有一个顶部空气层的水域可以简化为一维模型以及土壤和混凝土墙组成的二维模型。水池上方有一层0.3 m厚的空气层。空气层上方为0.3 m厚的隔热盖板。水池的周围和底部由一层0.3 m厚的混凝土层建造。水域平均分为nw个节点。水层中的节点从上至下从1至nw编号,如图2所示。与PTES的半径R和深度H相比,空气层、盖板和混凝土层的厚度非常小。因此,在耦合时,它们的物理存在被忽略,仅用作传热计算中的热阻,计算如式(1)所示。
为便于网格划分,在一年运行周期内,使用一致的网格划分土壤区域。根据节省计算资源的需要以及温度梯度的变化规律,可以设置近密和远疏的网格。水池热量损失会影响周围土壤的温度。在数值解的计算中,土壤区域的半径为15 m,水池底下的深度也为15 m,以模拟半无限土壤区域。因此,整个计算域的总半径为rcal=r+15=30 m,总深度为Dcal=H+15=31 m。温度测量点布置在水体的上层、中上层、中层、中下层、下层,以及罐体外部的土壤区域,如图3中蓝色标记点位置所示。测量结果的云图显示,在运营的第一年,边缘区域不受水温影响。因此,认为土壤面积足够大,半无限边界是合适的。但对于多年运行而言,水池热量损失对周围土壤的影响增加,需要取rcal=5r+15;Dcal=5H+15来模拟半无限土壤区域[8]
跨季节储热水池水域传热分析中,第1层水面和外部空气之间有隔热盖板,盖板和水面之间有一层空气。在水面和盖板的受限空间中,空气层流动受到限制。该情况下,水汽化潜热和传质的传热远小于显热,因此忽略计算[20]。在跨季节储热水池水体一维模型传热模型中,流入/流出水池的水的质量保持恒定,因此,水的稳态连续性方程如式(2)所示。
水池水体第1层的传热方程:
其中,m>0表示水池水体第1层有水充入,为充能工况;当m<0时则表示水池水体第1层有水流出,为放能工况;待机工况下,式(3)中流动项为0。
使用McAdam公式计算环境空气和土壤之间的传热系数hev,kt计算式如式(4)所示:
当水池处于充能工况时,水池水体与太阳能集热器直接连接,此时,太阳能集热器入口温度等于水池水体最底层温度,太阳能集热器出口温度等于水池水体第1层温度。
当水池处于放能工况时,水池水体与供热末端(用户、板式换热器或中深层地源热泵)直接连接,即末端入口温度等于水池水体第1层温度,末端出口温度等于水池水体最底层温度,计算如式(5)和式(6)所示。
水池水体域中节点kw(1<kw<nw)方程:
水池水体底部节点的能量方程:
其中,节点kw(1≤kw≤nw)能量损失和底部能量损失计算如式(9)和式(10)所示。
在水池水体温度场求解过程中会出现下层水体比上层温度更高的情况,这与实际情况不符,所以在每个时间步长的STES水池水体温度场求解后,会对所求解出来的温度场进行判断,当下部水体的温度高于上部水体的温度时,应遵循式(11)掺混公式:
在土壤区域的热传导中,土壤区域中节点温度将取决于相邻节点的热传导。采用图2中3个典型点的传热平衡方程对各节点能量平衡进行推导。土壤中节点1的能量平衡计算:
其中,热阻计算如式(13)至式(16)所示。
土壤域中节点2的能量平衡计算:
土壤域中节点3的能量平衡计算:
由于水池土壤域采用的是半无限和对称边界条件。即假设径向和轴向上半无限边界最后一个节点的边界条件为绝热边界条件,计算如式(20)所示。
为验证所提出模型的有效性和准确性,本研究对跨季节储热水体供热待机工况(Standby)采用缩尺砂箱实验台的实测数据进行验证,对动态工况中充能工况(Charging)和放能工况(Discharging)则采用中国科学院黄帝城项目[8]中研究数据进行验证。结合2个数据源的实测数据全方面验证本研究数学仿真模型的正确性。
为了获得Standby工况STES中静态水体热分层以及土壤传热特性,设计了一个水池式储热器测试实验台,原理如图4所示。水池式储热器主要包括木制箱体、干细砂、圆柱形不锈钢储水罐以及聚苯乙烯板保温层;实验台由连接到数据记录器的计算机组成。数据记录器用于表征系统的热性能。
砂箱实验台如图5所示,搭建了1 m×1 m×0.8 m的木制箱体,所制备的箱体中装满了厚度为0.6 m的半干细砂。为观察更明显的热分层以及土壤的热扩散情况,圆柱形不锈钢罐体总深度为0.35 m,土壤埋深为0.3 m,储水深度为0.3 m。在不锈钢罐体柱心处,水体的上层、中上层、中层、中下层、下层均匀设置5个水体温度测点,罐体外部土壤也均匀设有28个土壤温度测点以便可以更加精确地描述土壤中温度扩散的云图。
实验开始时圆柱形不锈钢罐体里装满0.6 m热水,并埋在木制箱体内的干燥细砂中。不锈钢罐的顶面高于半干细砂的顶面0.05 m。保温层为聚苯乙烯板,覆盖不锈钢罐顶面和裸露的半干细砂表面。各材料物性参数以及尺寸如表1所示。
测量系统由37个Pt100型热电偶(-70~500 ℃)、无纸数据记录器和计算机组成。这些Pt100型热电偶在实验前分别用0 ℃冰水混合物及正在沸腾100 ℃的热水进行了校准和误差标记。水体加热由外部90 L的加热水箱完成,当水体温度加热至指定温度时,采用水泵转移至实验台的不锈钢罐体中。
在缩尺砂箱实验台实验开始时,需对未储热水工况下的土壤温度场进行标定并运用标定点的温度进行线性插值得到土壤的初始温度场。
由于该缩尺砂箱实验台主要是对STES储热的待机工况进行验证,所以实验采用在外部水箱加热水体并快速注入该缩尺砂箱实验台储热水箱中的方式进行。每个工况下,采用30 s的扫描时间步长,设置扫描3 000次(25 h)后结束扫描并保存数据。每个工况结束后,需要去除该缩尺砂箱实验台储热水箱中的热水,并冷却至少1 d,使砂箱中土壤的余温分布趋于平稳而不对第2次实验造成显著影响。在每次测量开始时,均对砂箱实验台的土壤温度进行测量以及温度场的标定。
在数值模型中,其初始条件与边界条件设置与该圆柱形砂箱实验台一致,包括初始不均匀土壤温度场、空气温度、接触土壤面的地面温度、物性参数参见表1。数值模型运算时,其时间步长采用与实验扫描间隔相同的30 s,计算步数也同实验有效扫描次数3 000次(25 h)一致,得到水体温度变化趋势如图6所示。
图6可知,砂箱实验台水体温度变化数据与数值解得到的数据拟合情况良好,5个水体分层温度测点误差均低于10%,表现出较高准确性。误差主要集中于顶部以及底部区域水体。对比数值解结果与实验台结果可知,水池最上部水层、中上层、中层、中下层、最下层水体温度平均绝对误差分别为2%、2.1%、1.75%、1.24%、3.24%、3.67%。总体来看实验验证效果较好,数值解与实验台数据吻合良好。
STES水池系统充能、放能工况使用黄帝城项目的运行数据进行验证。在进行系统时间步长鲁棒性验证中,在典型充能日(2018年6月1日—2018年6月8日)和典型放能日(2018年10月11日—2018年10月16日)中均存在待机工况,这里不再对典型待机工况进行验证。将河北省1年内测得的每小时气温、辐射强度和风速数据传输至曲线上,然后输入到模拟中。在黄帝城项目中,水池中水体的温度测量点分别位于距水池底部4.25、2.65和0.25 m的高度。坑内初始水温为15 ℃,初始土壤温度为10 ℃。
1)充能工况。
采用60 s的时间步长,将项目典型充能日中充入的水体流量、温度按时间步长截取输入至模型中,通过模型168 h的运行和中国科学院黄帝城项目实验测量数据进行对比,结果如图7所示,充能模型的平均相对误差仅为1.57%,平均误差温度为0.44 ℃,模拟结果与实测结果吻合良好。
2)放能工况。
在STES典型放能工况的项目数据验证中,同样采用60 s的时间步长,将项目典型放能工况中的流出流量与温度按时间步长截取输入到模型中,通过模型120 h的仿真模拟运行数据和中国科学院黄帝城项目实验测量数据进行对比,结果如图8所示,模拟结果与实测结果差异小于1%,在可接受范围。模拟模型中放能工况温度数据与中国科学院黄帝城项目实测温度数据的平均相对误差仅为0.46%,平均误差温度为0.24 ℃。
在系统的初始设置中,初始运行水温为15 ℃,土壤温度为10 ℃,气象数据边界条件输入如图9所示,计算时间步长为3 600 s。系统充能工况的初始运行时间设置为北方供暖结束后的4月1日,放能工况的初始操作时间设置为北方开始集中供暖的11月15日。在系统的全年运行中,充能(集热)时间为225 d,放能(供热)时间为135 d。在系统充能工况下,当太阳能集热器不能加热进水温度或太阳辐射为0时,系统处于待机状态,反之亦然。
在跨季节储热水体供热系统中,储热子系统承担了整个系统最大的热量负荷,也是整个系统中热损失最大的部分。系统在第1年第1天至第2年第1天的完整运行周期中,太阳能集热器给水池充入的总能量为683 MW·h。图10所示为系统逐时热损失情况。在所有热损失中,顶部总热损失为13.91 MW·h,这是因为顶部有一层绝热良好的空气层,使顶部热损失较小。底部热损失大于顶部热损失,为31.74 MW·h,因为在整个运行阶段底部温度均较低。若水池的保温措施得当,当经过长时间运行后,水池周围的土壤达到稳态,热损失率可以降至10%以下。
本研究建立了一个总体积为11 304 m3的圆柱形跨季节储热水体系统级耦合数值模型。采用一维模型计算水池水区域,采用二维模型计算土壤区域,再通过水池壁温度边界连接水和土壤传热以对PTES系统进行整体模拟。为验证模型的准确性,通过缩尺砂箱实验台对待机工况进行了验证,并采用黄帝城项目典型的日充放能实验数据对动态工况进行验证,得到如下结论:
1)砂箱实验台水体温度变化数据与数值解模拟数据拟合情况良好,在24 h的模拟中,5个水体分层温度误差均低于10%。其中,中层和中下层水体温度准确性最高,平均绝对误差分别为1.75%和1.24%。误差相对较大的顶部以及底部区域水体平均绝对误差也仅有2.1%和3.24%。
2)在典型充能日(2018-06-01—2018-08-08)和典型放能日(2018-10-11—2018-10-16)的数据验证中,充能工况下的平均相对误差仅为1.57%,平均误差温度为0.44 ℃;放能工况的平均相对误差仅为0.46%,平均误差温度为0.24 ℃。模型模拟的温度数据与中国科学院黄帝城项目实测温度数据基本吻合。
3)系统在第1年完整运行周期中,太阳能集热器给水池充入的总能量为683 MW·h。在所有热损失中,顶部热损失较小,为13.91 MW·h。底部热损失大于顶部热损失,为31.74 MW·h。在长周期运行后,水池周围土壤温度稳定,热损失率可在10%以下。
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doi: 10.12465/j.issn.0253-4339.2025.05.133
  • 接收时间:2024-06-03
  • 首发时间:2026-03-13
  • 出版时间:2025-10-16
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  • 收稿日期:2024-06-03
  • 修回日期:2024-07-14
  • 录用日期:2024-09-02
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    1中国长江三峡集团有限公司 武汉 430010
    2中国长江电力股份有限公司 武汉 430010
    3三峡电能有限公司 武汉 430022
    4华中科技大学环境科学与工程学院 武汉 430074

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田志勇,男,博士,副教授,华中科技大学环境科学与工程学院,13522166267,E-mail:。研究方向:储热与太阳能。
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2种不同金属材料的力学参数

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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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