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Article(id=1322161778335798217, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322161751467078485, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1731945600000, receivedDateStr=2024-11-19, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793157532533, onlineDateStr=2026-10-28, pubDate=1758729600000, pubDateStr=2025-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793157532533, onlineIssueDateStr=2026-10-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793157532533, creator=13701087609, updateTime=1793157532533, updator=13701087609, issue=Issue{id=1322161751467078485, tenantId=1146029695717560320, journalId=1225396423026438145, year='2025', volume='45', issue='9', pageStart='1', pageEnd='314', issueExtLink='null', onlineDate='null', pubDate='1758729600000', pubDateStr='2025-09-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793157526127, creator='13701087609', updateTime=1793158877659, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322167420266074322, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322161751467078485, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322167420266074323, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322161751467078485, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=7, ext={EN=ArticleExt(id=1322161778562290634, articleId=1322161778335798217, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Research on Performance Optimization of Deep-Sea Microbial Sampling Device Based on Porous Step Model, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Taking the filter membrane of a microbial sampler as the research background, a porous step model was used to simulate the structure of the microbial sampling membrane. The internal flow field of the sampler was numerically investigated using the finite element method. Simulations were conducted to analyze the filtration pressure drop, flow velocity variation patterns, and the distribution of velocity and pressure contours within the sampler. The influence of fluid inlet velocity parameters on the performance of the deep-sea microbial sampling device was studied, thereby determining the optimal fluid inlet velocity parameters for the deep-sea microbial sampler. The results show that under the conditions of inlet velocity of 0.1, 0.2, 0.3, 0.4, 0.5, the pressure drops on both sides of the filter membrane of the sampler are 36, 82, 139, 207, 288 Pa, and the differences of flow velocity on both sides of the filter membrane of the sampler are 0.05, 0.16, 0.26, 0.34, 0.38, respectively. The pressure drop across the filter membrane of the sampler and the velocity difference between both sides of the membrane increase with the increase in the inlet velocity, while the pressure behind the filter membrane decreases as the velocity increases. When the fluid inlet velocity is 0.3, the filtration performance of the sampler reaches its optimum. Numerical simulation results can provide a theoretical basis for researching the filtration performance of deep-sea microbial sampling devices.

, authors=Yang AI, Long ZANG, Bingzheng CHEN, Jiangming LI, Min JIANG, authorsList=Yang AI, Long ZANG, Bingzheng CHEN, Jiangming LI, Min JIANG, authorCompany=null, correspAuthors=null, 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, fund=null), CN=ArticleExt(id=1322161783092138996, articleId=1322161778335798217, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于多孔阶跃模型的深海微生物取样装置性能优化研究, columnId=1226462294582936453, journalTitle=矿业研究与开发, columnName=深部、深海矿产资源勘探与开发, runingTitle=null, highlight=null, articleAbstract=

以微生物取样器过滤膜为研究背景，采用多孔阶跃模型模拟微生物取样膜的结构，通过有限元方法对取样器内部流场进行数值模拟研究，从过滤压降、流速变化规律和取样器内部速度与压力云图分布情况进行模拟分析，研究流体入口速度参数对深海微生物取样装置性能的影响，从而确定深海微生物取样器的最优流体入口速度参数。研究结果表明：在入口速度分别为0.1, 0.2, 0.3, 0.4, 0.5 m/s条件下，取样器过滤膜两侧的压降分别为36, 82, 139, 207, 288 Pa，取样器过滤膜两侧的流速差分别为0.05, 0.16, 0.26, 0.34, 0.38 m/s；取样器过滤膜两侧的压降和膜两侧速度差均随入口速度的增大而增大，且过滤膜后的压力随速度的增大呈减小趋势；当流体入口速度为0.3 m/s时，取样器的过滤性能最优。数值模拟结果可为深海微生物取样装置的过滤性能研究提供理论依据。

, authors=艾杨, 臧龙, 陈秉正, 李江明, 江敏, authorsList=艾杨, 臧龙, 陈秉正, 李江明, 江敏, authorCompany=null, correspAuthors=null, authorNote=

艾杨（1993—），男，湖南长沙人，工程师，主要从事海洋装备技术研究工作。E-mail:

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艾杨（1993—），男，湖南长沙人，工程师，主要从事海洋装备技术研究工作。E-mail:

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艾杨（1993—），男，湖南长沙人，工程师，主要从事海洋装备技术研究工作。E-mail:

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articleId=1322161778335798217, language=EN, label=Table 1, caption=

Flow field parameters corresponding to different grid sizes

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网格尺寸/mm网格数量最大压力/Pa最大速度/(m/s)
0.6620 502204.460.432
0.8263 329204.130.412
1.0133 756204.020.409
2.018 918200.570.395
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不同网格尺寸对应的流场参数

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网格尺寸/mm网格数量最大压力/Pa最大速度/(m/s)
0.6620 502204.460.432
0.8263 329204.130.412
1.0133 756204.020.409
2.018 918200.570.395
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基于多孔阶跃模型的深海微生物取样装置性能优化研究
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艾杨 , 臧龙 , 陈秉正 , 李江明 , 江敏
矿业研究与开发 | 深部、深海矿产资源勘探与开发 2025,45(9): 1-7
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矿业研究与开发 |深部、深海矿产资源勘探与开发 2025 , 45 (9) : 1 -7
基于多孔阶跃模型的深海微生物取样装置性能优化研究
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艾杨 , 臧龙, 陈秉正, 李江明, 江敏
作者信息
  • 长沙矿山研究院有限责任公司，湖南　长沙　410012
作者简介:

艾杨（1993—），男，湖南长沙人，工程师，主要从事海洋装备技术研究工作。E-mail:

Research on Performance Optimization of Deep-Sea Microbial Sampling Device Based on Porous Step Model
Yang AI , Long ZANG, Bingzheng CHEN, Jiangming LI, Min JIANG
Affiliations
  • Changsha Institute of Mining Research Co., Ltd., Changsha, Hunan 410012, China
出版时间: 2025-09-25
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摘要
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以微生物取样器过滤膜为研究背景，采用多孔阶跃模型模拟微生物取样膜的结构，通过有限元方法对取样器内部流场进行数值模拟研究，从过滤压降、流速变化规律和取样器内部速度与压力云图分布情况进行模拟分析，研究流体入口速度参数对深海微生物取样装置性能的影响，从而确定深海微生物取样器的最优流体入口速度参数。研究结果表明：在入口速度分别为0.1, 0.2, 0.3, 0.4, 0.5 m/s条件下，取样器过滤膜两侧的压降分别为36, 82, 139, 207, 288 Pa，取样器过滤膜两侧的流速差分别为0.05, 0.16, 0.26, 0.34, 0.38 m/s；取样器过滤膜两侧的压降和膜两侧速度差均随入口速度的增大而增大，且过滤膜后的压力随速度的增大呈减小趋势；当流体入口速度为0.3 m/s时，取样器的过滤性能最优。数值模拟结果可为深海微生物取样装置的过滤性能研究提供理论依据。

深海采矿  /  深海微生物取样器  /  多孔阶跃模型  /  数值模拟

Taking the filter membrane of a microbial sampler as the research background, a porous step model was used to simulate the structure of the microbial sampling membrane. The internal flow field of the sampler was numerically investigated using the finite element method. Simulations were conducted to analyze the filtration pressure drop, flow velocity variation patterns, and the distribution of velocity and pressure contours within the sampler. The influence of fluid inlet velocity parameters on the performance of the deep-sea microbial sampling device was studied, thereby determining the optimal fluid inlet velocity parameters for the deep-sea microbial sampler. The results show that under the conditions of inlet velocity of 0.1, 0.2, 0.3, 0.4, 0.5, the pressure drops on both sides of the filter membrane of the sampler are 36, 82, 139, 207, 288 Pa, and the differences of flow velocity on both sides of the filter membrane of the sampler are 0.05, 0.16, 0.26, 0.34, 0.38, respectively. The pressure drop across the filter membrane of the sampler and the velocity difference between both sides of the membrane increase with the increase in the inlet velocity, while the pressure behind the filter membrane decreases as the velocity increases. When the fluid inlet velocity is 0.3, the filtration performance of the sampler reaches its optimum. Numerical simulation results can provide a theoretical basis for researching the filtration performance of deep-sea microbial sampling devices.

Deep-sea mining  /  Deep-sea microbial sampler  /  Porous step model  /  Numerical simulation
艾杨, 臧龙, 陈秉正, 李江明, 江敏. 基于多孔阶跃模型的深海微生物取样装置性能优化研究. 矿业研究与开发, 2025 , 45 (9) : 1 -7 .
Yang AI, Long ZANG, Bingzheng CHEN, Jiangming LI, Min JIANG. Research on Performance Optimization of Deep-Sea Microbial Sampling Device Based on Porous Step Model[J]. Mining Research and Development, 2025 , 45 (9) : 1 -7 .
正文
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0　引言
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深海缺乏阳光，静水压力大、寡营养、低温/高温的环境下，孕育了海底火山、深海海山、深渊等特殊生命系统，蕴藏着丰富的微生物资源[1]。海底微生物是指活动于深海中的生命结构简单的单细胞或多细胞生物。作为生活在海底极端条件下的特殊生物，它们是海洋生态系统能量流动的重要环节，为海洋中的生物提供大量的能量和营养来源，并在海洋污染的降解以及海底沉积矿物的形成过程中扮演着重要的角色[2]。对生活在海洋特殊环境中的微生物开展研究，不仅可以了解物种在极端环境下的生存方式，也有助于对深海资源的了解与开发。
研究深海微生物的前提条件是获得大量具有原位特征的深海微生物样本。国内现有的深海微生物取样器主要是由浙江大学研究团队[3]牵头研制的深海微生物采样及富集装置，可在深海中以泵吸、机械手抓取捕捞、多级循环过滤等流程捕获深海微生物。中南大学研究团队则重点研究微生物取样器的保压功能及提升最大浓缩比[4]。深海微生物研究也是国外的研究热点，日本、德国、奥地利等国家设计并测试了原位微生物培养箱(ISMI)，可在原位压力条件下培养和研究微生物，以更真实地反映深海微生物的生存状态[5]。随着海洋开发技术的进步，对深海微生物取样器的取样效率提出了更高要求。
传统取样器通常依赖经验设计参数，导致取样成功率和效率难以保证。随着有限元分析技术的发展，可通过预置多组参数进行数值模拟，优化设计方案，从而增加从深海中获取微生物的数量，提高取样的质量。在有限元仿真软件中，可采用流体分析模块对取样装置进行仿真[6]。本文以多孔阶跃模型生成的单滤膜流场为研究对象，通过对流体速度参数的设置，并结合过滤膜内外两侧压差是否满足过滤条件进行仿真分析，最终确定特定多孔阶跃模型参数下滤膜的最大过滤效率。影响多孔阶跃模型生成过滤膜的关键因素主要包括滤液中颗粒(微生物)的物理特性以及过滤过程的各项参数[7]。本研究重点在于明确过滤效率、压降与速度之间的相互关系，特别是流体速度与滤膜两侧压差之间的关系[8]。通过系统分析这些参数，可确定最优的过滤装置固定参数，为高效捕获深海微生物提供理论依据和技术参考。
1　模型的建立
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首先，利用三维建模软件构建取样器的简易三维模型，并将文件保存为step格式。随后，将该模型导入流体仿真软件的设计模块，完成流体域的填充，并将流体域划分为4个关键观测区域。最后，将模型导入有限元软件的流体仿真模块，采用SIMPLE算法求解取样器的压力–速度耦合方程，从而确定最优的压力与速度参数[9-10]。
1.1　几何模型
由于深海微生物取样器结构复杂、体积较大，若对整个模型进行分析计算，将导致计算量过大且难以获得精准的计算结果[11]。为此，在确保计算精度的前提下，对模型进行合理简化，重点分析关键功能模块（包括入水系统、过滤层系统、出水系统）[12]。经查阅资料，深海微生物取样器为圆柱形，其进出水口呈直线排列。模型参数初始设定为：孔隙率0.8，多孔介质区间厚度为0.4 mm。本研究主要针对流体流速及膜两侧压差等关键参数进行模拟分析，故采用简化模型设计，具体尺寸为：筒体直径60 mm、高150 mm，进出口直径均为20 mm。基于此建立相应的物理模型，图1为取样器简化物理模型及其剖视图。
1.2　数学模型
基于深海微生物体积微小（细胞级）的特性，本研究采用FLUENT-DPM模型[13]开展多相耦合数值模拟，具体计算过程为：先求解连续相流场，随后建立离散相与连续相的射流耦合计算模型，实现对微生物颗粒运动轨迹的精确计算。
模型的标准输送方程为：
式中：Gk为由于平均速度梯度引起的湍流动能k的产生项，J；Gb为由于浮力引起的湍动能，J；YM为湍流扩散能，J；C1k，C2k，C3k为经验常数；ε为湍动能耗散数，J；μ为流体动力黏度，mm2/s；ρ为流体的密度，kg/m3；Sk为湍流动能方程式中的自定义源项；Sε为湍流耗散率方程式中的自定义源项；xi、xj分别为空间坐标x方向和y方向；σk为湍动能普朗特数；μt为湍流黏度，流体流速越快，湍流越剧烈，μt值就越大。
1.3　多孔介质阶跃模型
采用流体仿真软件中的多孔介质阶跃模型来模拟滤膜[14]，其多孔介质相关参数表达如下：
式中：c1为阻力系数；c2为惯性损失系数；α为渗透率；D为滤网孔直径，mm；e为孔隙比（筛分面积百分比）。
1.4　边界条件的设定
基于深海微生物取样器实际工作流程的复杂性，作出如下设定：在过滤过程中不考虑膜随时间推移发生的局部或全部堵塞，且流动为定常状态；试验为等温过程，故忽略温度的影响；模型进水口采用速度进口为边界条件，出水口采用压力出口为边界条件；工作压力设为30 MPa，进、出口边界的流动方向均垂直于对应截面；筒体内壁面均按固壁定律处理，采用无滑移边界[15]；过滤膜类型采用多孔阶跃模型生成的不规则纤维膜[10]。取样器的工作模型结构如图2所示。
1.5　网格无关性
三维模型建立后，需将其导入流体仿真软件中进行前处理。前处理的第一步是网格划分，网格划分过大会降低计算精度，网格划分过小会增加计算成本，因此需要选择合适的网格尺寸。网格的划分采用多部件网格划分方法，网格的单元尺寸分别设置为0.6, 0.8, 1.0, 2.0 mm。为保证数值模拟结果的准确性与计算效率之间的平衡，对网格尺寸进行了无关性验证，并以取样器内部流场的最大压力、最大速度与网格数量作为对比分析指标（见表1和图3）。结果表明，当网格尺寸细化至1.0 mm时，流场最大压力和最大速度的变化已趋于稳定，进一步减小网格尺寸至0.6 mm对流场最大压力和最大速度的改善极为有限，却显著增加了计算资源消耗与时间成本。因此，综合考量计算精度与效率，选用1.0 mm网格尺寸可在保证结果可靠性的前提下，实现最优的计算性价比。
为减少数值模拟的计算量并保证计算精度，对过滤器的网格进行优化，采用六面体结构网格划分，网格质量较好，如图4所示。
2　模拟结果与讨论
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2.1　入口速度对取样器内部流体速度的影响
深海取样设备的供水压力应根据水下取样作业要求确定。在综合考虑过滤装置、用水设备的基础上，探究不同入口速度条件下取样器内部流体速度的分布情况[16]。数值模拟中保持取样器的入口压力恒定为30 MPa，即维持入口处过滤流体的压力能不变，出水口的位置坐标设为0 mm。设流体入口速度分别为0.1, 0.2, 0.3, 0.4, 0.5 m/s，该参考直接决定了入口处流体动能。通过多工况数值模拟分析，入口速度与入口压力之间的定量关系由以下数学表达式精确描述：
式中：P0为液体的总压力，即取样器入口的总压，Pa；P1为液体的静压力，即取样器入口的初始设定压力，Pa；P2为液体的动压力，Pa；v为液体的速度，m/s。
取样器进水口直径为20 mm，筒体最大直径为60 mm。由于取样器内部滤膜的实际有效直径小于筒体直径（60 mm），故计算所得的入口速度为近似值。不同入口速度下，取样器内部速度云图见图5。
通过对速度云图进行分析，可得出以下结论。
（1）当取样器初始入口压力恒定时，随着入口速度的逐渐增加，流场速度分布呈现以下特征：入口速度为0.1, 0.2, 0.3, 0.4, 0.5 m/s时，在取样器出水口处产生的最大速度分别为0.12, 0.27, 0.39, 0.52, 0.65 m/s，取样器过滤膜两侧的流速差分别为0.05, 0.16, 0.26, 0.34, 0.38 m/s；出水口最大速度随入口速度增大呈线性增大，进出口速度差值与入口速度成正相关；除进出口区域外，取样器内部其他位置的流速保持相对稳定[17]。
（2）当入口压力恒定时，入口速度的变化对取样器内部流场分布的影响表现为以下特征：流场整体速度分布形态保持不变；取样器顶部区域存在明显的低速区（流速趋近于零），形成流体滞留现象，而该滞留区导致取样器有效工作容积降低，影响设备利用率。
不同初始入口速度条件下各特征位置的速度分布曲线如图6所示。
由图6可知，流体从集水区向出口流动时，速度显著增大，且增速随流动距离增加而增强。进一步观察发现，随着取样器模型入口速度设定值的提高，实际入口速度相应增大，但始终低于预设的入口速度值。当选择入口速度为0.3 m/s，取样器内部的流速变化较为平缓，过滤膜两侧的速度差合理。
2.2　入口速度对取样器内部压力的影响
为研究入口速度对流体压力分布的影响，本文所建数值模型采用以下参数设置：恒定入口压力30 MPa(维持压力能恒定)，出水口的位置坐标设为0 mm，过滤膜精确布置在入水口与出水口的中间位置。通过调节流体入口速度（0.1, 0.2, 0.3, 0.4, 0.5 m/s）改变流体动能，系统分析了5种工况下取样器内部的压力场分布特征，所得压力云图如图7所示。
由图7可得到以下结论。
（1）当初始入口压力保持不变时，随着入口速度的逐渐增大，过滤膜模型承受的最大压力逐渐增大，且在过滤膜的上方中间区域形成了局部高压区。这种压力分布不均现象可能会导致过滤时过滤膜受力不均匀，或更易造成过滤物质的堵塞。
（2）流速增大不会改变过滤器内部的压力分布规律（入口高压、出口低压的基本特征保持不变），但会影响压力的绝对值大小，且随着流速的不断增大，过滤膜两侧的压力均有不同程度的增大。
不同初始速度对静压力的影响如图8所示。由图8可知，当初始压力恒定时，改变初始入口速度对整个取样器模型内部压力分布的影响可以忽略不计。入口速度从0.1 m/s增加到0.5 m/s，取样器模型压力的下降规律保持一致。但是可以明显观察到，在入口速度分别为0.1, 0.2, 0.3, 0.4, 0.5 m/s时，过滤膜处的压降分别为36, 82, 139, 207, 288 Pa，随着入口速度的增大，流体的静压力在过滤膜处的压降也剧烈增大。同时，流体在过滤器模型集水区部分的压力明显增大。综合评估显示，0.3 m/s入口速度条件下取样器内的压力分布较为均匀，且膜两侧的压差合理。
3　结论
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（1）基于多孔介质模型的计算参数，对取样器在不同的入口速度(0.1, 0.2, 0.3, 0.4, 0.5 m/s)下的速度和压力开展数值模拟研究。通过分析纵向中心对称面的流场特征，获得了该区域压力分布云图和速度分布云图。从速度云图分布可以明显发现，在取样器顶部和侧部出现明显的流动滞止区，该区域流体不流动，降低了取样器的利用率。
（2）微生物取样器的流体入口速度是影响内部流场特性的关键参数。取样器入口速度分别为0.1, 0.2, 0.3, 0.4, 0.5 m/s时，取样器过滤膜两侧的压降分别为36, 82, 139, 207, 288 Pa，取样器过滤膜两侧的流速差分别为0.05, 0.16, 0.26, 0.34, 0.38 m/s。取样器过滤膜两侧的压降和膜两侧速度差均随入口速度的增大而增大，且过滤膜后的压力均随速度的增大呈减小趋势。综合考虑，流体入口速度为0.3 m/s时取样器的过滤性能最优。
（3）深海微生物取样装置在深海矿产资源开发领域具有巨大的应用潜力，其优化设计和应用将为深海资源的可持续利用和生态保护提供有力保障。通过优化微生物取样器的设计，特别是针对深海高压、低温、黑暗等特殊环境进行适应性改进，可以实现对深海微生物群落的高效、准确取样。此外，深海微生物取样装置还可以作为深海矿产资源开发过程中的环境监测工具。通过定期取样和分析，可以实时监测深海环境的变化，评估矿产资源开发活动对深海生态系统的影响，为制定科学合理的开发策略提供数据支持。未来，期待看到更多关于深海微生物取样技术的研究进展，以及这些技术在深海矿产资源开发中的实际应用成果。
基金
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  • 国家重点研发计划项目(2021YFC2801702)
文献
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2025年第45卷第9期
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  • 接收时间：2024-11-19
  • 首发时间：2026-10-28
  • 出版时间：2025-09-25
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  • 收稿日期：2024-11-19
基金
国家重点研发计划项目(2021YFC2801702)
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    长沙矿山研究院有限责任公司，湖南　长沙　410012
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Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
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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
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