Article(id=1324426184649482522, tenantId=1146029695717560320, journalId=1240685776644648972, issueId=1324426184037114137, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1007-7294.2026.08.004, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1768838400000, receivedDateStr=2026-01-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793697409069, onlineDateStr=2026-11-03, pubDate=1786723200000, pubDateStr=2026-08-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793697409069, onlineIssueDateStr=2026-11-03, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793697409069, creator=13701087609, updateTime=1793697409069, updator=13701087609, issue=Issue{id=1324426184037114137, tenantId=1146029695717560320, journalId=1240685776644648972, year='2026', volume='30', issue='8', pageStart='1221', pageEnd='1396', issueExtLink='null', onlineDate='null', pubDate='1786723200000', pubDateStr='2026-08-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793697408922, creator='13701087609', updateTime=1793697408922, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1255, endPage=1267, ext={EN=ArticleExt(id=1324426184863392027, articleId=1324426184649482522, tenantId=1146029695717560320, journalId=1240685776644648972, language=EN, title=CFD-based numerical study on hydrodynamic performance of underwater vehicles in turning maneuvers, columnId=1241023037940748650, journalTitle=Journal of Ship Mechanics, columnName=Hydrodynamics, runingTitle=null, highlight=null, articleAbstract=

Turning maneuver is a typical operational condition for underwater vehicles. This paper establishes a self-propulsion numerical simulation method for underwater vehicles based on Computational Fluid Dynamics (CFD) and conducts numerical investigations on the hydrodynamic characteristics during turning maneuvers. The proposed method employs the Unsteady Reynolds-Averaged Navier-Stokes (URANS) equations to solve the viscous flow field, while a Proportional-Derivative (PD) control algorithm and overset grid technique are applied to regulate the steering states of the vehicle, thereby enabling simulations of depth-keeping straight-line motion and complex maneuvers. Using this method, the turning maneuver process of an underwater vehicle at ±20° rudder angles was simulated, and an in-depth analysis was performed on the motion responses and flow field characteristics during starboard turning. The results demonstrate that the proposed self-propulsion numerical method effectively captures the motion responses during turning maneuvers, with simulation results agreeing well with experimental data (relative errors of key response parameters within 7%). The turning motion significantly influences the pressure field, velocity field, and vortex structures, particularly near the parallel middle body and wake region. As the heading angle gradually increases, the flow field stabilizes and reaches a quasi-steady state at approximately 180°.

, authors=Jun-bo GE1, 2, Yao-hua KONG2, Pan-pan HAN1, 3, Yun-xiang YOU1, 3, authorsList=Jun-bo GE, Yao-hua KONG, Pan-pan HAN, Yun-xiang YOU, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright ©2026 Journal of Ship Mechanics. All rights reserved., 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=1324426206631829923, articleId=1324426184649482522, tenantId=1146029695717560320, journalId=1240685776644648972, language=CN, title=基于CFD的水下航行器回转机动水动力特性数值模拟研究, columnId=1241023038087549292, journalTitle=船舶力学, columnName=流体力学, runingTitle=null, highlight=null, articleAbstract=

回转机动是水下航行器的典型操纵工况,本文建立了基于CFD的水下航行器自航数值模拟方法,并针对航行器的回转机动水动力特性进行了数值模拟研究。该方法采用非定常雷诺平均方程(URANS)求解粘性流场,通过比例–微分(PD)控制算法及重叠网格技术控制航行器的操舵状态,从而实现水下航行器的定深直航及复杂机动模拟。基于该方法,对水下航行器在±20°舵角下的回转机动过程进行了模拟,并对右回转运动中的运动响应与流场特性进行了深入分析。研究表明,该自航数值模拟方法可有效捕捉水下航行器回转过程中的运动响应,模拟结果与实验数据吻合良好,关键响应参数的相对误差不超过7%;回转运动显著影响水下航行器的压力场、速度场及涡结构,尤其在平行中体内侧及尾流区域表现明显;随着艏向角逐步增大,流场结构趋于稳定,至艏向角约180°时基本达到稳定。

, authors=葛俊波1, 2, 孔耀华2, 韩盼盼1, 3, 尤云祥1, 3, authorsList=葛俊波, 孔耀华, 韩盼盼, 尤云祥, authorCompany=null, correspAuthors=null, authorNote=

葛俊波(1988−),男,博士研究生,E-mail:

韩盼盼(1990−),男,博士,助理研究员,通讯作者,E-mail:

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Autopilot control parameters (model scale/full scale)

, figureFileSmall=null, figureFileBig=null, tableContent=
比例系数P微分系数D
Py (º/m)18.3/1.0Dy /(°·s/m)−
Pz (º/m)−55.0/−3.0Dz /(°·s/m)−12.85/3.0
Pψ (º/º)3.0/3.0 Dψ /s2.8/12.0
Pθ (º/º)3.0/3.0 Dθ/s0.7/3.0
), ArticleFig(id=1324426605300429406, tenantId=1146029695717560320, journalId=1240685776644648972, articleId=1324426184649482522, language=CN, label=表1, caption=

自动舵控制参数(模型尺度/全尺度)

, figureFileSmall=null, figureFileBig=null, tableContent=
比例系数P微分系数D
Py (º/m)18.3/1.0Dy /(°·s/m)−
Pz (º/m)−55.0/−3.0Dz /(°·s/m)−12.85/3.0
Pψ (º/º)3.0/3.0 Dψ /s2.8/12.0
Pθ (º/º)3.0/3.0 Dθ/s0.7/3.0
), ArticleFig(id=1324426605417869919, tenantId=1146029695717560320, journalId=1240685776644648972, articleId=1324426184649482522, language=EN, label=Tab.2, caption=

Model parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
参数名及符号 /单位实尺度模型尺度
艇长L /m70.23.826
艇宽B /m9.60.523
排水量Δ /t44400.701
重心纵向位置(距首部)XCG /m32.311.761
重心垂向位置(距螺旋桨中轴线)ZCG /m0.0440.002
横摇惯性半径rx /m3.4330.187
纵摇惯性半径ry /m17.60.959
艏摇惯性半径rz /m17.5220.955
), ArticleFig(id=1324426605505950304, tenantId=1146029695717560320, journalId=1240685776644648972, articleId=1324426184649482522, language=CN, label=表2, caption=

模型参数

, figureFileSmall=null, figureFileBig=null, tableContent=
参数名及符号 /单位实尺度模型尺度
艇长L /m70.23.826
艇宽B /m9.60.523
排水量Δ /t44400.701
重心纵向位置(距首部)XCG /m32.311.761
重心垂向位置(距螺旋桨中轴线)ZCG /m0.0440.002
横摇惯性半径rx /m3.4330.187
纵摇惯性半径ry /m17.60.959
艏摇惯性半径rz /m17.5220.955
), ArticleFig(id=1324426605745025633, tenantId=1146029695717560320, journalId=1240685776644648972, articleId=1324426184649482522, language=EN, label=Tab.3, caption=

Grid convergence analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
网格方案网格总数 ($ \times {10}^{6} $)阻力/N与Grid 1的差异(%)旋转速率/(rpm)与Grid 1的差异(%)
Grid 116.022.61/270/
Grid 26.022.640.1269−0.4
Grid 32.023.694.82623.0
试验值///266/
), ArticleFig(id=1324426605812134498, tenantId=1146029695717560320, journalId=1240685776644648972, articleId=1324426184649482522, language=CN, label=表3, caption=

网格收敛性分析

, figureFileSmall=null, figureFileBig=null, tableContent=
网格方案网格总数 ($ \times {10}^{6} $)阻力/N与Grid 1的差异(%)旋转速率/(rpm)与Grid 1的差异(%)
Grid 116.022.61/270/
Grid 26.022.640.1269−0.4
Grid 32.023.694.82623.0
试验值///266/
), ArticleFig(id=1324426605891826275, tenantId=1146029695717560320, journalId=1240685776644648972, articleId=1324426184649482522, language=EN, label=Tab.4, caption=

Parameters for vehicle during straight-line motion

, figureFileSmall=null, figureFileBig=null, tableContent=
参 数 (单位)仿真值试验值误差 (%)
航速/(m·s−1)1.191.2−0.83%
旋转速率/(rpm)2692661.13%
扭矩/(N·m)1.171.133.54%
纵倾角 (º)−0.32−−
垂荡/艇长 (%)0.3−−
横倾角 (º)−0.45−−
), ArticleFig(id=1324426605963129444, tenantId=1146029695717560320, journalId=1240685776644648972, articleId=1324426184649482522, language=CN, label=表4, caption=

直线航行时参数

, figureFileSmall=null, figureFileBig=null, tableContent=
参 数 (单位)仿真值试验值误差 (%)
航速/(m·s−1)1.191.2−0.83%
旋转速率/(rpm)2692661.13%
扭矩/(N·m)1.171.133.54%
纵倾角 (º)−0.32−−
垂荡/艇长 (%)0.3−−
横倾角 (º)−0.45−−
), ArticleFig(id=1324426606030238309, tenantId=1146029695717560320, journalId=1240685776644648972, articleId=1324426184649482522, language=EN, label=Tab.5, caption=

Turning maneuver parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
参数−20°(左回转)20°(右回转)
仿真
值
试验
值
误差
(%)
仿真
值
试验
值
误差
(%)
纵距 (L)2.612.600.42.592.610.7
横距 (L)1.651.584.41.621.63−0.6
战术直径 (L)3.383.283.03.243.33−2.7
90° 回转时间/s11.8812.27−3.1811.8212.62−6.3
180° 回转时间 (s)23.5324.27−3.0523.3124.78−5.9
), ArticleFig(id=1324426606114124390, tenantId=1146029695717560320, journalId=1240685776644648972, articleId=1324426184649482522, language=CN, label=表5, caption=

回转参数

, figureFileSmall=null, figureFileBig=null, tableContent=
参数−20°(左回转)20°(右回转)
仿真
值
试验
值
误差
(%)
仿真
值
试验
值
误差
(%)
纵距 (L)2.612.600.42.592.610.7
横距 (L)1.651.584.41.621.63−0.6
战术直径 (L)3.383.283.03.243.33−2.7
90° 回转时间/s11.8812.27−3.1811.8212.62−6.3
180° 回转时间 (s)23.5324.27−3.0523.3124.78−5.9
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基于CFD的水下航行器回转机动水动力特性数值模拟研究
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葛俊波 1, 2 , 孔耀华 2 , 韩盼盼 1, 3 , 尤云祥 1, 3
船舶力学 | 流体力学 2026,30(8): 1255-1267
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船舶力学 |流体力学 2026 , 30 (8) : 1255 -1267
基于CFD的水下航行器回转机动水动力特性数值模拟研究
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葛俊波1, 2 , 孔耀华2, 韩盼盼1, 3 , 尤云祥1, 3
作者信息
  • 1上海交通大学 海洋工程全国重点试验室,上海 200240
  • 2中国船舶及海洋工程设计研究院,上海 200011
  • 3上海交通大学 海南研究院,海南 三亚 572000
作者简介:

葛俊波(1988−),男,博士研究生,E-mail:

韩盼盼(1990−),男,博士,助理研究员,通讯作者,E-mail:

CFD-based numerical study on hydrodynamic performance of underwater vehicles in turning maneuvers
Jun-bo GE1, 2 , Yao-hua KONG2, Pan-pan HAN1, 3 , Yun-xiang YOU1, 3
Affiliations
  • 1State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • 2Marine Design & Research Institute of China, Shanghai 200011, China
  • 3Hainan Research Institute, Shanghai Jiao Tong University, Sanya 572000, China
出版时间: 2026-08-15 doi: 10.3969/j.issn.1007-7294.2026.08.004
文章导航
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回转机动是水下航行器的典型操纵工况,本文建立了基于CFD的水下航行器自航数值模拟方法,并针对航行器的回转机动水动力特性进行了数值模拟研究。该方法采用非定常雷诺平均方程(URANS)求解粘性流场,通过比例–微分(PD)控制算法及重叠网格技术控制航行器的操舵状态,从而实现水下航行器的定深直航及复杂机动模拟。基于该方法,对水下航行器在±20°舵角下的回转机动过程进行了模拟,并对右回转运动中的运动响应与流场特性进行了深入分析。研究表明,该自航数值模拟方法可有效捕捉水下航行器回转过程中的运动响应,模拟结果与实验数据吻合良好,关键响应参数的相对误差不超过7%;回转运动显著影响水下航行器的压力场、速度场及涡结构,尤其在平行中体内侧及尾流区域表现明显;随着艏向角逐步增大,流场结构趋于稳定,至艏向角约180°时基本达到稳定。

水下航行器  /  自航模拟  /  回转机动  /  水动力特性

Turning maneuver is a typical operational condition for underwater vehicles. This paper establishes a self-propulsion numerical simulation method for underwater vehicles based on Computational Fluid Dynamics (CFD) and conducts numerical investigations on the hydrodynamic characteristics during turning maneuvers. The proposed method employs the Unsteady Reynolds-Averaged Navier-Stokes (URANS) equations to solve the viscous flow field, while a Proportional-Derivative (PD) control algorithm and overset grid technique are applied to regulate the steering states of the vehicle, thereby enabling simulations of depth-keeping straight-line motion and complex maneuvers. Using this method, the turning maneuver process of an underwater vehicle at ±20° rudder angles was simulated, and an in-depth analysis was performed on the motion responses and flow field characteristics during starboard turning. The results demonstrate that the proposed self-propulsion numerical method effectively captures the motion responses during turning maneuvers, with simulation results agreeing well with experimental data (relative errors of key response parameters within 7%). The turning motion significantly influences the pressure field, velocity field, and vortex structures, particularly near the parallel middle body and wake region. As the heading angle gradually increases, the flow field stabilizes and reaches a quasi-steady state at approximately 180°.

underwater vehicle  /  self-propulsion simulation  /  turning maneuvering  /  hydrodynamic characteristics
葛俊波, 孔耀华, 韩盼盼, 尤云祥. 基于CFD的水下航行器回转机动水动力特性数值模拟研究. 船舶力学, 2026 , 30 (8) : 1255 -1267 . DOI: 10.3969/j.issn.1007-7294.2026.08.004
Jun-bo GE, Yao-hua KONG, Pan-pan HAN, Yun-xiang YOU. CFD-based numerical study on hydrodynamic performance of underwater vehicles in turning maneuvers[J]. Journal of Ship Mechanics, 2026 , 30 (8) : 1255 -1267 . DOI: 10.3969/j.issn.1007-7294.2026.08.004
水下航行器的操纵性能是衡量其水下机动能力的关键指标,直接关系到水下航行器在复杂海洋环境中的运动控制能力。具体而言,操纵性能表征了水下航行器在六自由度空间内精确调控航速、姿态角(横滚/纵倾/艏摇)、航向及深度的能力。开展水下航行器的操纵性研究,对优化其外形设计、提升海洋环境探测能力,都具有至关重要的意义[1−2]。
水下航行器操纵性的研究早期主要以试验为主,比如拘束模测试和自由航行测试。拘束模试验用到的主要试验装置包括平面运动机构、旋转臂等[3−5],通过这些机械装置强制模型运动(如纯横荡、纯艏摇),但该类试验方法难以考虑水下航行器的垂向运动和横滚运动,因此无法模拟其真实的六自由度耦合运动。相比于拘束模测试,自由航行的模型测试更接近于真实运动状态,可用于研究水下航行器复杂机动状态(如回转、Z型操纵、紧急上浮等),在水下航行器操纵性研究中具有明显的优势。Jun等[6]设计了一种小型自主水下航行器,用于开发和验证各类能增强AUV功能的算法与仪器设备,并验证了一系列AUV自由航行测试结果。Overpelt等[7]基于“Joubert”船体设计了一种现代通用SSK级水下航行器BB2,该水下航行器是专门为操纵性测试和验证而设计的一艘虚构模型,并进行了BB2水下航行器的自由航行机动模型试验。Carrica等[8]进行了通用水下航行器Joubert BB2在自航和自由航行机动条件下的试验,包括近水面和深度的直航、回转以及深度的垂直和水平“Z型”机动,以及通过紧急倒车实现停止的上浮机动,并且在试验中使用了自动驾驶仪(Autopilot)控制俯仰和深度的垂直指令,以及控制偏航和横移的水平指令。
近年来,基于CFD(Computational Fluid Dynamics)的自由航行数值模拟研究也取得了显著进展。Carrica等[8]进行了Joubert BB2的CFD模拟,结果表明:CFD能够实现潜艇的自航运动数值模拟,其预测结果与试验数据吻合良好。Kim等[9]对六自由度下BB2水下航行器进行自由操纵模拟,并采用虚拟盘体积力方法模拟实际螺旋桨,结果表明:CFD方法可以很好地预测六自由度水下航行器的操纵运动,其结果与试验吻合度较高。郭恩锴等[10]对近水面潜艇操纵运动进行模拟,分析了自由面对潜艇自航点及运动特性的影响。韩琨羽[11]采用CFD方法对BB2水下航行器模型的六自由度自航操纵进行数值预测,结果表明:转向运动对水下航行器的深度和纵倾姿态有较大影响,水下航行器在转向后会出现“尾重”现象。
上述文献对于水下航行器操纵性能的研究,多集中于运动响应特性的分析,而对机动过程的流场结构演化机理关注相对不足。尽管已有研究通过模型试验与数值模拟手段积累了丰富的运动性能数据,但在真实自由航行条件下,尤其是复杂机动过程中,流场结构与航行体运动之间的内在关联仍未得到充分揭示。有鉴于此,本文将PD控制算法与重叠网格技术结合,建立了基于CFD的自航数值模拟方法,并重点针对回转机动工况,系统分析了水下航行器的六自由度运动响应与流场动态特性,揭示航行器在机动过程中流场结构的演变规律,为水下航行器操纵性的精细化预测提供支撑。
本研究采用商业CFD软件STAR-CCM+对水下航行器自航操纵过程开展非定常数值模拟研究。采用非定常雷诺平均 (Unsteady Reynolds-Averaged Navier-Stokes, URANS)方法求解流场,控制方程为
$\begin{gathered}[b]\nabla \cdot U=0\\\rho \frac{\partial \overline{{u}_{i}}}{\partial t}+\rho \frac{\partial \overline{{u}_{i}{u}_{j}}}{\partial {x}_{j}}=-\frac{\partial p}{\partial {x}_{i}}+\rho \frac{\partial }{\partial {x}_{i}}\left(v\frac{\partial \overline{{u}_{i}}}{\partial {x}_{j}}-\overline{{{{u}^{\prime}_{i}}}{{{u}^{\prime}_{j}}}}\right)\end{gathered}$
式中:$ \overline{{u}_{i}} $代表时均速度分量; $ {{{u}^{\prime}_{i}}} $代表脉动速度分量; $ \rho \overline{{{{u}^{\prime}_{i}}}{{{u}^{\prime}_{j}}}} $代表雷诺应力。
为求解式(1),需要建立湍流模型对其中的雷诺应力进行封闭。本文采用船舶与海洋工程领域常用的SST k-ω模型,该模型由Mentor[12]提出,是一种性能出色的RANS湍流模型,能较好地处理湍流剪切应力在逆压梯度和分离边界层中的输运。为了在保证计算精度的同时提升数值鲁棒性,本文采用全$y^+ $壁面处理方法,对潜艇操纵性流场开展数值模拟研究。SST k-ω模型包含湍动能k及比耗散率ω两个输送方程,分别表示为
$ \frac{D\rho k}{Dt}={P}_{k}-\rho {\beta }^{*}\omega k+\frac{\partial }{\partial {x}_{j}}\left[\left(\mu +{\sigma }_{k}{\mu }_{t}\right)\frac{\partial k}{\partial {x}_{j}}\right] $
$ \frac{D\rho \omega }{Dt}={P}_{\omega }-\rho {\beta }^{*}{\omega }^{2}+2\rho \left(1-{F}_{1}\right){\sigma }_{\omega 2}\frac{1}{\omega }\frac{\partial k}{\partial {x}_{j}}\frac{\partial \omega }{\partial {x}_{j}}+\frac{\partial }{\partial {x}_{j}}\left[\left(\mu +{\sigma }_{\omega }{\mu }_{t}\right)\frac{\partial \omega }{\partial {x}_{j}}\right] $
式中参数的具体含义详见Mentor[12]的文献,此处不再赘述。
本文研究的模型为Joubert BB2水下航行器标准模型(以下简称水下航行器),试验中配置的螺旋桨为MARIN-7371R标准桨模。采用真实螺旋桨模型进行数值模拟会显著增加计算成本。相比之下,运用虚拟盘体积力法对真实螺旋桨进行等效模拟,不仅能有效减少网格数量,还可显著降低计算成本,该方法已被验证为高效且精度较高[9,12],其核心思想是在圆柱形虚拟盘面上施加均匀分布且沿径向变化的体积力。其中,体积力分量的径向分布遵循Goldstein最优分布,其具体数学表达由以下公式(4)~(5)给出。
$ {f}_{bx}={A}_{x}{r}^{*}\sqrt{1-{r}^{*}},{f}_{b\theta }={A}_{\theta }\frac{{r}^{*}\sqrt{1-{r}^{*}}}{{r}^{*}\left(1-r_{\text{h}}^{\prime}\right)+r_{\text{h}}^{\prime}},{r}^{*}=\frac{{r}^{\prime}-r_{\text{h}}^{\prime}}{1-r_{\text{h}}^{\prime}},r_{\text{h}}^{\prime}=\frac{{R}_{\text{H}}}{{R}_{\text{p}}},{r}^{\prime}=\frac{r}{{R}_{\text{p}}} $
$ {A}_{x}=\frac{105T}{8{\text{π}t}_{\text{disk}}(3{R}_{\text{H}}+4{R}_{\text{P}})({R}_{\text{P}}-{R}_{\text{H}})},{A}_{\theta }=\frac{105Q}{8\text{π} {t}_{\text{disk}}{R}_{\text{P}}(3{R}_{\text{H}}+4{R}_{\text{P}})({R}_{\text{P}}-{R}_{\text{H}})} $
式中:$ {f}_{bx} $为轴向体积力分量,$ {f}_{b\theta } $为切向体积力分量,$ r $为径向坐标,$ {R}_{\text{H}} $为桨毂半径,$ {R}_{\text{p}} $为螺旋桨叶梢半径,T为螺旋桨推力,Q为扭矩,$ {f}_{\text{disk}} $为虚拟盘体厚度,$ {A}_{x} $与$ {A}_{\theta } $为常数。
因此,本文将采用虚拟盘体积力模拟实际螺旋桨推进,将Kim[9]给出的MARIN-7371R螺旋桨敞水曲线(推力系数KT、力矩系数KQ、敞水效率η)作为虚拟螺旋桨的输入,实现对MARIN-7371R螺旋桨的模拟,其敞水曲线如图1所示。
水下航行器在自由航行时需要保持自身在深度和航向方面的稳定性,因此本文使用PD控制对水下航行器尾部X舵的舵角进行实时控制(未对围壳舵进行任何控制),以实现航行姿态的精准调节。通过监测水下航行器直航时的运动姿态(横荡y、艏摇ψ、升沉z、纵摇θ)瞬时值,控制系统利用PD控制算法实现对水下航行器水平面和垂向面的调控。水平及垂向控制器表达式为
$ \begin{split}&{\delta }_{\text{H}}={P}_{y}{\mathit{\Delta }}_{y}+{D}_{y}\dot{y}+{P}_{\psi }{\mathit{\Delta }}_{\psi }+{D}_{\psi }\dot{\psi }\\&{\delta }_{\text{V}}={P}_{z}{\mathit{\Delta }}_{z}+{D}_{z}\dot{z}+{P}_{\theta }{\mathit{\Delta }}_{\theta }+{D}_{\theta }\dot{\theta }\end{split} $
式(6)里的比例系数P、微分系数D如表1所示。
水下航行器标模尾部配有X型舵,相比于传统的十字舵,其空间布置方便、操纵效率高[13−14],但也存在操作复杂、不直观的缺陷。本文将水平、垂直控制器与X舵的各个舵角(左上舵δPtUp、左下舵δPtDn、右上舵δSbUp、右下舵δSbDn)进行匹配,匹配方案为
$\begin{split}&{\delta }_{{{\text{PtUp}}_{}}}={\delta }_{\text{V}}+{\delta }_{\text{H}}\\& {\delta }_{\text{PtDn}}={\delta }_{\text{V}}-{\delta }_{\text{H}} \\&{\delta }_{\text{SbUp}}=-{\delta }_{\text{V}}+{\delta }_{\text{H}}\\&{\delta }_{\text{SbDn}}=-{\delta }_{\text{V}}-{\delta }_{\text{H}}\end{split} $
根据Joubert BB2水下航行器的试验数据,本文同样对舵角及打舵速率进行限制,最大舵角不超过30°,模型尺度下打舵速率不超过30.5°/s。
本文采用的Joubert BB2水下航行器标模[15](缩尺比1∶18.348),模型的建立使用右手坐标系(o-xyz):x轴正方向指向船艏,y轴正方向指向左舷,z轴正方向指向上。而为了方便与试验数据进行对比,后续的水下航行器运动数据报告采用标准船舶工程坐标系(O-XYZ, X轴指向船艏,Y轴指向右舷,Z轴垂直向下),水下航行器模型与坐标系系统如图2所示。
为减少计算域边界效应对结果的影响,将流场入口设置在距离船艏2倍艇长的位置,定义为速度入口;出口设置在距离船艏7倍艇长的位置,定义为压力出口;上、下平面距离重心位置分别为3倍艇长,并定义为压力出口;前后边界设置在距离重心位置3倍艇长处,定义为压力出口;水下航行器表面定义为无滑移条件。为节省计算资源,本文令计算域在模拟过程中随艇体同步运动。模型基本参数如表2所示。
本文基于重叠网格方法,通过DFBI(Dynamic Fluid Body Interaction)旋转平移技术实现对水下航行器六自由度运动及舵旋转的模拟。在艇体附近建立重叠网格区域,保证背景网格与重叠网格的良好过渡,提升二者之间的数据交换精度,重叠网格区域包括艇体区域和四个尾舵区域,如图3所示。采用非结构化网格对流场进行网格划分,对艇体、尾舵、尾流区域进行网格加密,提升计算精度。此外,还需对虚拟盘体附近进行网格加密,以保证虚拟盘体的模拟精度,网格如图4所示。
为准确捕捉水下航行器运动过程中的边界层流动特性,在壁面网格划分时采用边界层网格进行加密,确保首层网格无量纲壁面距离$y^+ $小于10,边界层增长率设定为 1.1,共划分 7 层边界层网格。水下航行器表面$y^+ $分布如图5所示,水下航行器壁面的整体$y^+ $接近5,在尾舵进行了细化,$y^+ $接近于1,符合本文的预期网格划分结果。基于 ITTC 推荐规程中关于网格细化的尺度比例r=$ \sqrt{2} $,本研究对计算域在三个坐标方向(x, y, z)的网格尺寸进行系统性加密[16−17],并据此设计了三种不同平均表面网格尺寸的模型,网格总量分别为$ 2\times {10}^{6} $ (Grid 3)、$ 6\times {10}^{6} $ (Grid 2)、$ 16\times {10}^{6} $ (Grid 1)。
表3对比了不同网格密度下达到自航点时水下航行器阻力与螺旋桨转速的计算结果,并给出了 Grid 2 和 Grid 3 相对于 Grid 1 的数值偏差。计算结果表明,当网格加密至 Grid 2 时,阻力变化仅为 0.1%,螺旋桨转速偏差仅 0.4%,并且此时螺旋桨转速与试验值高度吻合,说明此时网格已达到收敛,再增加网格量对计算结果影响有限。综合考虑计算效率与数值精度,本研究最终采用 Grid 2 网格方案进行后续数值模拟。
水下航行器的六自由度复杂操纵以其稳定直航运动为基础,因此在实施复杂操纵前须确保其定深直航能力。机动过程中仅采用尾舵进行控制,围壳舵保持固定。水下航行器在稳定直航状态下,需要保持稳定航速为1.2 m/s;垂直面运动(纵倾、垂荡)几乎为0。
表4为水下航行器定深直航状态下运动参数,表中水下航行器稳定直航航速为1.19 m/s,与试验航速(1.2 m/s)偏差仅−0.83%;维持航速所需要的虚拟盘转速269 rpm,与试验值仅差1.13%,并且对于虚拟盘力矩的模拟也与试验值具有较高的吻合度。水下航行器在稳定的直航运动中,纵倾角维持在−0.32º,有轻微的埋首现象;水下航行器垂向位移仅占艇长的0.3%;由于螺旋桨扭矩的影响,水下航行器会存在约−0.45º的横倾角。综合看来,本文已实现对于水下航行器的稳定直航操纵,可作为后续回转等复杂操纵运动的基础。
在水下航行器稳定直航的基础上,使用PD控制对垂直面施加20°/−20°的角度偏转命令,并以最大操舵速度(模型尺度下30.5°/s,实尺度下7.11°/s)打舵,以此实现水下航行器的右回转(Starboard, SB)与左回转(Port, PT)。图6展示了水下航行器的回转运动轨迹与试验值的对比结果,由图可见数值计算结果与试验测量值具有良好的一致性。在左回转工况下,本研究的轨迹预测与实验数据的吻合度高于Chen等[18]和Carrica等[15]的CFD计算结果。表5进一步给出了回转运动关键参数的定量数据,并与试验值对比分析[9,17],结果表明本文计算结果对90°纵距、90°横距和战术直径特征参数的预测具有较高的精度,最大相对误差控制在5%以内。水下航行器在回转90°与180°所使用的时间与试验值也高度吻合,左回转时误差在4%以内,右回转时误差在7%以内,整体看来回转时间相比于试验值偏小。
图7~图10进一步对比分析了水下航行器回转运动过程中的横摇角(roll)、艏摇角速度(yaw rate)、垂荡(heave)及纵倾角(pitch)的时历响应曲线。图7显示,水下航行器在左、右回转初期(约30°转向角位置)会出现一个横摇角极值,随后趋于稳定。左/右回转稳态横摇角分别为−1.9°和1.1°,呈现典型的内倾特性。与试验数据对比显示,数值模拟能准确捕捉横摇运动趋势,但预测的横摇角极值略低于试验值(最大偏差小于1°)。该微小偏差可能源于数值求解误差或试验的不确定性,尽管存在此偏差,但整体运动趋势高度一致,表明整体模拟精度可靠。
图8表明,本文CFD模拟的艏摇角速度时历曲线与试验值高度一致,但数值模拟的极值响应略高于试验测量值,Carrica模拟的左回转时艏摇角极值在时间上存在滞后现象。
图9显示,水下航行器在左/右回转过程中均表现出明显的垂向下沉现象,这是因为在螺旋桨定转速回转时,水下航行器航速会显著降低约35%(如图11所示,图中V代表沿艇体方向的航速),导致舵效不足。另外,水下航行器大幅度回转会引起尾部流体分离,进一步削弱舵效。这些影响导致对垂直面升沉调控不足,最终引起水下航行器下沉。CFD预测的下沉深度略大于试验值(约0.05L),最终稳定在约0.06L左右,时历曲线变化趋势与试验结果保持一致,而Carrica的模拟结果由于模拟初始时水下航行器位置上浮约0.01L,导致在最终稳定状态比本文结果更接近试验值。
图10进一步揭示了回转机动中的纵倾角变化规律。与升沉特性表现一致,由于水下航行器回转过程中舵效削弱,因此其会出现较大的纵倾角,本文模拟的纵倾角稳定在3°左右,与试验值相差约0.4°。结合图9的垂荡变化,表明水下航行器在回转机动过程中能够维持稳定的俯仰姿态和定深性能。综合上述分析,本研究基于PD控制策略成功实现了六自由度水下航行器深水回转运动的数值模拟。计算结果与试验数据及其他学者(如Carrica等)的CFD结果具有较高的吻合度。
本节针对水下航行器回转机动(以右回转为例)中的流场特征展开研究,重点分析右回转工况下伴流场的演化,并揭示流场分布与航行器运动之间的内在关联。图12为水下航行器右回转过程中不同艏向角下流场压力系数$ {C}_{\text{p}} $分布图,$ {C}_{\text{p}}=\left(p-{p}_{\text{ref}}\right)/0.5\rho {U}^{2} $,式中$ {p}_{\text{ref}} $为参考压力,$ U $为水下航行器航速。在回转运动初始($ \psi =0{^{\circ}} $)的图12(a)中,在水下航行器首部及尾部区域可观察到呈对称漏斗状分布的大范围高压区,而在艇体中纵剖面尾端,由于螺旋桨影响,流体加速形成明显的细长低压区。平行中体段呈现出“葫芦状”低压区分布特征。在图12(b)~(h)中,流场压力分布呈现显著的非对称特性:平行中体右侧低压区范围扩大,而左侧则出现高压聚集现象;尾部螺旋桨诱导的低压区发生明显偏转,形成具有一定弧度的弧形低压区;右侧低压区范围在艏向角超过$ \psi =180{^{\circ}} $后趋于稳定,同时尾部弧形低压区也逐渐达到稳定状态。
图13为水下航行器在右回转过程中不同艏向角下壁面$ {C}_{\text{p}} $分布云图。在回转运动初始时刻($ \psi =0{^{\circ}} $),水下航行器顶端、围壳前端及尾舵前端由于结构突起对流体流动的阻碍作用形成明显驻点,并在邻近区域产生高压区;同时,围壳尾缘与平行中体连接处存在局部高压区。在平行中体与尾部的过渡区域,由于几何曲率变化诱发的流动分离形成一定范围的低压区。而下表面艇首区域呈现顺压梯度(favorable pressure gradient, FPG)分布,流动状态相对稳定;尾部表现为逆压梯度(adverse pressure gradient, APG)区,易诱发流动分离;平行中体则保持零压梯度(zero pressure gradient, ZPG)特性。当水下航行器开始做右回转运动时,壁面$ {C}_{\text{p}} $分布呈现显著非对称演化:水下航行器顶端部分的高压区向右侧扩散;平行中体段呈现右低压−左高压的特征,该现象与图14速度云图呈现的流动特征具有良好相关性。尾部区域上、下表面与围壳上表面出现大面积低压区,表明回转运动导致的流动分离效应显著增强,引起该位置压力急剧降低。另外,当艏向角在$ \psi =180{^{\circ}} $及以后,水下航行器整体压力分布趋于稳定,仅尾舵区域表现出明显差异,这种差异主要源于舵角持续调整。
图15为不同艏摇角(ψ=0°, 90°, 180°, 270°, 360°)下水下航行器纵剖面压力系数云图。可以看出,随着回转过程的持续进行,航行器上、下表面附近的低压区逐渐变得明显,其中下表面低压现象尤为显著。这导致了上下表面之间的压差持续增大,成为水下航行器在回转过程中显著下沉的重要原因。
在自由航行的数值模拟中,水下航行器在全局坐标系($ o\text{-}xyz $)下位置不断变化,无法对水下航行器流场进行定性分析,因此以艇体重心为原点建立局部坐标系($ {o}^{\prime}\text{-}{x}^{\prime}{y}^{\prime}{z}^{\prime} $),该坐标系符合右手准则,随水下航行器运动,该坐标系仅用以表述速度场分布特征。
图14展示了水下航行器右回转运动过程中,不同艏向角($ \psi =0{^{\circ}},90{^{\circ}},180{^{\circ}},270{^{\circ}},360{^{\circ}} $)下三个方向速度分量(Ux, Uy, Uz)的云图。在初始时刻,尽管水下航行器存在轻微横倾,其对整体速度场的影响较小,速度分布在三个方向上基本保持对称。随着回转运动的开始,Ux分量出现明显变化,特别是在平行中体和尾部区域,平行中体右侧形成大范围向$ -{x}^{\prime} $方向运动的流动区,其流动方向与壁面附近的流动相反,易诱发旋涡结构,而左侧则未表现出类似特征,可能导致右侧相对左侧涡量更强。在尾流区域,初始时由螺旋桨产生的轴向条形射流较为明显,回转开始后,尾流呈现一定的弧形偏转。Uy分量方面,初始时首部流体向两侧分散,而尾部因壳体收缩呈向内汇聚态势。随回转进程推进,平行中体及尾部区域的Uy分布发生剧烈变化,水下航行器带动周围流体向$ +{y}^{\prime} $方向运动,尤其水下航行器内侧表现最为显著。Uz分量的变化趋势受水下航行器整体下潜趋势影响,$ -{z}^{\prime} $方向流动占据主导。综上,在回转过程中,水下航行器内侧的流场变化更为剧烈,尾部区域易形成大尺度扰动结构,流场呈现明显弧形特征。当艏向角超过一定值($ \psi =180{^{\circ}} $)后,三个方向的速度分布趋于稳定。
图16与图17分别给出了水下航行器在运动过程中所受侧向力和回转力矩的时历变化曲线,相关数据均已进行无量纲化处理。在直航阶段,侧向力与回转力矩均在零值附近小幅波动。当水下航行器开始施加右舵并进入回转运动后,侧向力与回转力矩迅速增大并出现正向峰值,同时左右两侧流场呈现明显的非对称分布:右侧流速增大、压力降低,而左侧流速减小、压力升高,从而进一步驱动航行器持续向右回转。随着回转过程逐步趋于稳定,侧向力与回转力矩随之减小并最终稳定在一定水平,其变化规律与前述流场分析结果一致。
图18为水下航行器在右回转过程中不同艏向角下涡结构分布图(等值面Q=0.5 s−2),并以$ {C}_{\text{p}} $进行染色。在图18(a)(初始时刻$ \psi =0{^{\circ}} $)中,可观察到围壳与平行中体交接处存在显著的马蹄涡结构,该涡结构沿平行中体延伸至末端,并与艇体保持平行。此外,围壳舵、围壳上表面尾缘及尾舵下游区域形成了丰富的梢涡结构,而在水下航行器尾部,由于几何收缩效应及螺旋桨的影响,形成了大尺度的尾涡结构,此时艇体与附体的涡结构互相影响较小。当水下航行器开始回转运动时,涡结构分布呈现明显变化:平行中体左侧涡量显著减弱,主要由于该区域高压聚集,抑制了涡结构的形成,而右侧涡量明显变大,如图18(e)所示;围壳舵上表面尾缘的梢涡进一步发展,其直径增大、尺度延长,并呈现一定的弯曲形态(往右舷偏转),同时伴随明显的逆压梯度特征,围壳舵附近的梢涡亦表现出相似特性。值得注意的是,围壳舵左右两侧的梢涡发展呈现非对称性,左侧梢涡更为显著,但整体均较初始时刻增强。同时,围壳根部的马蹄涡在回转过程中不再与艇体保持平行(如图18(b)所示)。尾涡结构相较于初始时刻扩散范围更广,同样伴随显著的逆压梯度。此外,艇体尾部与尾舵所产生的涡结构,产生了明显的干涉,而当艏向角在$ \psi =180{^{\circ}} $及以后,涡结构逐渐趋于稳定,与前述压力场、速度场分布规律一致。
本文基于PD控制策略,结合重叠网格技术,建立了基于CFD的水下航行器六自由度自航模拟数值方法,实现了水下航行器在±20°舵角下的回转机动数值模拟,并分析了右回转过程中水下航行器的运动响应及其伴随流场特性,从而揭示了水下航行器运动行为与流场变化之间的内在关联。主要结论如下:
(1)该方法可实现水下航行器定深直航及回转机动仿真;回转运动响应与试验值吻合度较高,定量参数误差不超过7%;基于该方法开展航行器六自由度自航模拟是可行的。
(2)在航行器回转过程中,上、下表面低压区逐渐增强,尤其是下表面低压更为显著,导致压差持续增大,从而诱发明显的下沉趋势。同时,航行器右侧形成低压高速区、左侧形成高压低速区,平行中体及尾部区域的流场非对称特征尤为突出。
(3)随着回转过程的推进,航行器尾流中的速度场及涡结构逐渐向内侧以一定角度发生弯曲和偏转,其扩散范围相较初始时刻明显扩大。然而,随着艏向角的不断增大,流场结构的演化幅度逐渐减弱,整体流场分布趋于稳定。
需要指出,URANS模型在模拟此类流动时对精细涡结构的解析能力有限,本文选用该模型主要基于其对宏观运动的模拟效率和较低的计算成本。后续研究将重点考虑采用IDDES (Improved Delay Detached-Eddy Simulation)或LES (Large Eddy Simulation)等方法,旨在对伴流场开展更高精度的数值解析。

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2026年第30卷第8期
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doi: 10.3969/j.issn.1007-7294.2026.08.004
  • 接收时间:2026-01-20
  • 首发时间:2026-11-03
  • 出版时间:2026-08-15
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  • 收稿日期:2026-01-20
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    1上海交通大学 海洋工程全国重点试验室,上海 200240
    2中国船舶及海洋工程设计研究院,上海 200011
    3上海交通大学 海南研究院,海南 三亚 572000
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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
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