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Article(id=1156912572009107808, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156908295593223005, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2401685, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1710086400000, receivedDateStr=2024-03-11, revisedDate=1728576000000, revisedDateStr=2024-10-11, acceptedDate=null, acceptedDateStr=null, onlineDate=1753759051562, onlineDateStr=2025-07-29, pubDate=1736265600000, pubDateStr=2025-01-08, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753759051562, onlineIssueDateStr=2025-07-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753759051562, creator=13701087609, updateTime=1753759051562, updator=13701087609, issue=Issue{id=1156908295593223005, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='1', pageStart='1', pageEnd='438', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1753758031985, creator=13701087609, updateTime=1765425680602, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1205845960933049001, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156908295593223005, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1205845960933049002, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156908295593223005, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=404, endPage=409, ext={EN=ArticleExt(id=1156912572638253409, articleId=1156912572009107808, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Prediction of the Motion Statistical Characteristics of Amphibious Aircraft in Waves, columnId=1156262731079607234, journalTitle=Science Technology and Engineering, columnName=Papers·Aeronautics and Astronautics, runingTitle=null, highlight=null, articleAbstract=

Aiming at the problem of short effective prediction time for the movement history of amphibious aircraft in waves, the statistical values of amphibious aircraft movement over a period of time were proposed to predict, and a prediction model for the statistical characteristics of amphibious aircraft movement was constructed based on long short-term memory neural networks(LSTM). Taking the NACA TN 2929 amphibious aircraft as an example, based on its numerical simulation data, the statistical values of the three degrees of freedom motion of heave, roll, and pitch of amphibious aircraft under sea conditions of level 3, 4, and 5 were predicted, and their prediction effects were analyzed in detail. The results show that the LSTM neural network-based model for predicting the statistical characteristics of amphibious aircraft motion has good prediction accuracy. In practical engineering applications, this model can accurately predict the statistical values of amphibious aircraft motion in the future, providing auxiliary decision-making information for offshore operations.

, correspAuthors=Guo-da CHENG, 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=Gao-xiang SUN, Chan-ying QI, Peng-tao HU, Guo-da CHENG, Jue GONG), CN=ArticleExt(id=1156912593874014693, articleId=1156912572009107808, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=水陆两栖飞机波浪中运动统计特性预报, columnId=1156262731373208516, journalTitle=科学技术与工程, columnName=论文·航空、航天, runingTitle=null, highlight=null, articleAbstract=针对当前水陆两栖飞机在波浪中运动时历有效预报时长较短的问题,提出对水陆两栖飞机在一段时间内的运动统计值进行预报,并基于长短期记忆神经(long short-term memory,LSTM)神经网络构建了水陆两栖飞机运动统计特性预报模型。以NACA TN 2929水陆两栖飞机为例,基于其数值仿真数据分别对三、四、五级海况下水陆两栖飞机的垂荡、横摇和纵摇三自由度运动统计值进行预报并对其预报效果进行详细分析。结果表明:基于LSTM神经网络的水陆两栖飞机运动统计特性预报模型具有较好的预报精度,在实际工程应用中可以通过该模型对水陆两栖飞机未来一段时间内的运动统计值实现精确预报,为海上作业提供辅助决策信息。, correspAuthors=成果达, authorNote=null, correspAuthorsNote=
* 成果达(1988—),男,汉族,青海人,硕士,高级工程师。研究方向:系统对准、组合导航及完好性。E-mail:。
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=i829CA8Vr+S7eFmvvMR1Qw==, magXml=kxVU+Qamln/q/4zPwWfsVQ==, pdfUrl=null, pdf=iZyPprr9QRuUkn/UZ9O+YA==, pdfFileSize=5983205, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=4NALLfgiITA9bAhsAokbLg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=ikfQi5GtVIAqNjXM322bKw==, mapNumber=null, authorCompany=null, fund=null, authors=

孙高翔(2001—),男,汉族,山东临沂人,硕士研究生。研究方向:水面浮体运动极短期预报。E-mail:。

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孙高翔(2001—),男,汉族,山东临沂人,硕士研究生。研究方向:水面浮体运动极短期预报。E-mail:。

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孙高翔(2001—),男,汉族,山东临沂人,硕士研究生。研究方向:水面浮体运动极短期预报。E-mail:。

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Xt为从外部输入到模型的数据;Ht为当前LSTM神经元的输出;it、ot、ft分别为输入门、输出门和遗忘门

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Hyper parameter setting for LSTM neural networks

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数据集
大小		 网络
层数		 神经元
个数		 批量
大小		 迭代
次数		 激活
函数
10 000 2 16 128 1 000 ReLU
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LSTM神经网络超参数设置

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数据集
大小		 网络
层数		 神经元
个数		 批量
大小		 迭代
次数		 激活
函数
10 000 2 16 128 1 000 ReLU
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The main scale parameters of NACA TN 2929

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机身长度
L/m		 翼展
B/m		 质量
M/kg		 重心纵向
位置Xg/m		 惯性矩
Iyy/(kg·m2)
1.220 1.680 1.489 0.492L 0.242
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NACA TN 2929水陆两栖飞机模型主尺度参数

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机身长度
L/m		 翼展
B/m		 质量
M/kg		 重心纵向
位置Xg/m		 惯性矩
Iyy/(kg·m2)
1.220 1.680 1.489 0.492L 0.242
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Working condition parameters for numerical simulation

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海况
级别		 有义
波高/m		 平均
周期/s		 航速/节 浪向角/(°)
三级 0.50 3.30 15.00 135.00
四级 1.25 4.50 15.00 135.00
五级 2.50 7.00 15.00 135.00
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数值仿真工况参数

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海况
级别		 有义
波高/m		 平均
周期/s		 航速/节 浪向角/(°)
三级 0.50 3.30 15.00 135.00
四级 1.25 4.50 15.00 135.00
五级 2.50 7.00 15.00 135.00
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Table of Results for Model Prediction Errors

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海况
等级		 水陆两栖飞机运动自由度
垂荡 横摇 纵摇
RMSE/m NRMSE/% RMSE/(°) NRMSE/% RMSE/(°) NRMSE/%
三级 0.070 7 6.39 0.235 0 6.32 0.228 8 6.28
四级 0.182 7 6.96 0.571 2 6.59 0.623 8 6.70
五级 0.405 9 8.44 1.034 0 7.76 1.506 8 7.79
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模型预报误差结果表

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海况
等级		 水陆两栖飞机运动自由度
垂荡 横摇 纵摇
RMSE/m NRMSE/% RMSE/(°) NRMSE/% RMSE/(°) NRMSE/%
三级 0.070 7 6.39 0.235 0 6.32 0.228 8 6.28
四级 0.182 7 6.96 0.571 2 6.59 0.623 8 6.70
五级 0.405 9 8.44 1.034 0 7.76 1.506 8 7.79
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水陆两栖飞机波浪中运动统计特性预报
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孙高翔 1 , 齐婵颖 2 , 胡鹏涛 2 , 成果达 3, * , 宫珏 3
科学技术与工程 | 论文·航空、航天 2025,25(1): 404-409
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科学技术与工程 | 论文·航空、航天 2025, 25(1): 404-409
水陆两栖飞机波浪中运动统计特性预报
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孙高翔1 , 齐婵颖2, 胡鹏涛2, 成果达3, * , 宫珏3
作者信息
  • 1.哈尔滨工程大学青岛创新发展基地, 青岛 266000
  • 2.空装驻西安地区第五军事代表室, 西安 710000
  • 3.中航工业西安飞行自动控制研究所, 西安 710076

    • 孙高翔(2001—),男,汉族,山东临沂人,硕士研究生。研究方向:水面浮体运动极短期预报。E-mail:。

通讯作者:

* 成果达(1988—),男,汉族,青海人,硕士,高级工程师。研究方向:系统对准、组合导航及完好性。E-mail:。
Prediction of the Motion Statistical Characteristics of Amphibious Aircraft in Waves
Gao-xiang SUN1 , Chan-ying QI2, Peng-tao HU2, Guo-da CHENG3, * , Jue GONG3
Affiliations
  • 1. Qingdao Innovation and Development Base, Harbin Engineering University, Qingdao 266000, China
  • 2. The Fifth Military Representative Office Stationed in Xi’an with Empty Equipment, Xi’an 710000, China
  • 3. Xi’an Flight Automatic Control Research Institute, AVIC, Xi’an 710076, China
出版时间: 2025-01-08 doi: 10.12404/j.issn.1671-1815.2401685
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针对当前水陆两栖飞机在波浪中运动时历有效预报时长较短的问题,提出对水陆两栖飞机在一段时间内的运动统计值进行预报,并基于长短期记忆神经(long short-term memory,LSTM)神经网络构建了水陆两栖飞机运动统计特性预报模型。以NACA TN 2929水陆两栖飞机为例,基于其数值仿真数据分别对三、四、五级海况下水陆两栖飞机的垂荡、横摇和纵摇三自由度运动统计值进行预报并对其预报效果进行详细分析。结果表明:基于LSTM神经网络的水陆两栖飞机运动统计特性预报模型具有较好的预报精度,在实际工程应用中可以通过该模型对水陆两栖飞机未来一段时间内的运动统计值实现精确预报,为海上作业提供辅助决策信息。
水陆两栖飞机  /  长短期记忆神经网络  /  运动统计特性  /  运动预报

Aiming at the problem of short effective prediction time for the movement history of amphibious aircraft in waves, the statistical values of amphibious aircraft movement over a period of time were proposed to predict, and a prediction model for the statistical characteristics of amphibious aircraft movement was constructed based on long short-term memory neural networks(LSTM). Taking the NACA TN 2929 amphibious aircraft as an example, based on its numerical simulation data, the statistical values of the three degrees of freedom motion of heave, roll, and pitch of amphibious aircraft under sea conditions of level 3, 4, and 5 were predicted, and their prediction effects were analyzed in detail. The results show that the LSTM neural network-based model for predicting the statistical characteristics of amphibious aircraft motion has good prediction accuracy. In practical engineering applications, this model can accurately predict the statistical values of amphibious aircraft motion in the future, providing auxiliary decision-making information for offshore operations.

amphibious aircraft  /  long short-term memory neural network  /  motion statistical characteristics  /  motion prediction
孙高翔, 齐婵颖, 胡鹏涛, 成果达, 宫珏. 水陆两栖飞机波浪中运动统计特性预报. 科学技术与工程, 2025 , 25 (1) : 404 -409 . DOI: 10.12404/j.issn.1671-1815.2401685
Gao-xiang SUN, Chan-ying QI, Peng-tao HU, Guo-da CHENG, Jue GONG. Prediction of the Motion Statistical Characteristics of Amphibious Aircraft in Waves[J]. Science Technology and Engineering, 2025 , 25 (1) : 404 -409 . DOI: 10.12404/j.issn.1671-1815.2401685
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水陆两栖飞机是一种具有独特水陆起降特性的多功能飞机,最早出现于20世纪20年代初期。随着近现代航空工业的发展,水陆两栖飞机已经成为能够执行近海巡逻、应急救援和海上消防等特种任务的有力工具[1]。在新时代,水陆两栖飞机作为国家应急救援及自然灾害防治体系建设必备的航空装备,因其在国家海洋权益维护中的重要战略意义和广泛应用前景,受到各国的广泛关注,俄罗斯、美国和日本等国均已开展对新一代水陆两栖飞机的研制,欧洲也提出了未来水上飞机规划(future seaplane traffic,FUSETRA)加快水陆两栖飞机技术的开发与应用[2]。在中国,水陆两栖飞机的研制同样备受重视,目前我国自主研发的AG600成为全球在研最大的灭火/水上救援水陆两栖飞机,标志着中国航空工业取得了又一重大科技成果,对中国应急救援体系和国防体系的建设和巩固具有重要意义。
由于底部独特的船身或浮筒结构,水陆两栖飞机能够实现在水面上的起降和滑行[3]。在水面滑行过程中,机身会受到波浪等复杂环境因素的作用而不断产生六自由度的摇荡运动,当其摇荡运动幅度超过某一阈值时,将会对其正常的作业流程产生极大的影响,甚至威胁到机体的安全性[4],因此,对水陆两栖飞机在水面中的运动响应进行预报,有利于提前判断其在未来一段时间内的运动趋势,保证海上作业过程中的安全和效率。
当前水陆两栖飞机的研究热点主要集中在基于数值计算方法模拟和研究波浪因素对水陆两栖飞机水动力性能的影响。胡开业等[5]基于数值模型技术开展水陆两栖飞机加速滑行起飞运动特性研究,探讨了波长与波高对飞机升沉、纵摇运动及重心垂向加速度的响应影响规律;张杜文等[6]采用数值计算与模型试验集合的方法对水陆两栖飞机全机模型的水动力性能进行预报与研究。关于水陆两栖飞机在波浪中的运动姿态预报研究,国内外学者的研究还相对较少。
在船舶领域,目前基于船体历史运动时历进行外推的运动姿态预报方法已经相对成熟。时历外推方法的本质是波浪场的记忆效应,由于记忆效应的存在,波浪场的历史数据与未来数据存在关联,由波浪引起的摇荡运动自然也在时序上关联性[7]。水陆两栖飞机同船舶一样,其摇荡运动主要由波浪激励引起,因此水陆两栖飞机的水面运动姿态同样可以通过时序外推预测的方法进行预报。
随着近年来深度学习技术的快速发展,机器学习和深度学习技术逐渐被引入到时间序列的分析和预测中[8-9]。专门用于处理序列化问题的循环神经网络及其变种被越来越多的人关注和使用。循环神经网络能够捕捉时间序列数据中的复杂特征和前后关联性,这使得其在处理和分析具有非线性特征和长时间依赖关系的运动数据时表现更加出色。李昊波等[10]基于长短期记忆神经网络模型,建立了浮式海洋平台运动响应极短期在线预报方法;张茴栋等[11]将经验模态分解与循环神经网络方法相结合对极短期非线性非平稳波浪时序的变化趋势进行预报;何国联等[12]基于长短期记忆神经网络对船舶六自由度运动耦合特征进行建模和预测;Han等[13]设置多组数据采样频率,基于变步长变采样频率长短期记忆神经网络对船舶运动进行预测;He等[14]基于多层长短期记忆模型,分析了不同速度条件下的船舶运动数据,研究了输入步长与输出步长之间的最优关系。但由于波浪激励下的水陆两栖飞机运动响应存在周期性且周期较短,受限于运动时间序列内部的时序关联性,针对时历数据的预报存在有效预报时长不足、预报精度不高的问题,难以满足实际工程的需要。
考虑到在实际的海上作业过程中,往往更关注水陆两栖飞机运动时历数据的幅值大小及其变化趋势,因此可以提取在一段时间内水陆两栖飞机运动幅值的有义值,以此作为该段时间内水陆两栖飞机摇荡运动的主要统计特征,并通过长短期记忆神经网络模型对其开展预报研究,在保证运动时历数据主要特征不丢失的同时,将短周期数据转化为长周期数据,有效提高可预报时长。现选取NACA TN 2929水陆两栖飞机开展数值仿真模拟,以其仿真数据为例开展水陆两栖飞机运动统计特性预报模型验证工作,对该模型在不同工况下各自由度的预报效果进行验证。
1 长短期记忆神经网络原理
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循环神经网络(recurrent neural network,RNN)是一类以序列数据为输入,在序列的时间演化方向进行递归的神经网络,其内部的循环结构和记忆单元能够保证序列数据点之间的时间依赖性不被忽略。但传统RNN在处理长输入序列问题中表现不佳,这是由于RNN内部的循环机制使得模型会保持对先前所有信息的记忆。一方面,新加入的信息会逐渐降低先前信息的权重占比,可能会导致保留的特征信息并不准确;另一方面,大量的信息留存也会导致网络出现数值不稳定的状况。
在传统循环神经网络的基础上,人们开始对其结构进行优化和改良,得到了RNN的特例:长短期记忆神经网络(long short-term memory,LSTM)。LSTM模型在保留了RNN记忆能力的基础上,通过门控机制选择性地处理输入信息,使模型更容易捕捉到内含长期依赖关系的关键时间点。
在执行序列分析任务时,LSTM模型使用其特有的输入门、遗忘门和输出门来决定信息的遗忘与保留,单个LSTM神经元的结构如图1所示。LSTM模型根据输入数据计算当前神经元隐藏状态和输出,具体计算公式如下。
fn=σ(Wxfxn+Wsfsn-1+Wcfcn-1+bf)
in=σ(Wxixn+Wsisn-1+Wsicn-1+bi)
cn=fncn-1+intanh(Wxcxn+Wscsn-1+bc)
on=σ(Wxoxn+Whosn-1+Wcocn+bo)
sn=ontanh(cn)
式中:xn为当前神经元的输入数据,Wij为权重系数矩阵;bi为偏置向量;in、cn、fn、on分别为第n个隐藏层的输入门、遗忘门、神经元记忆流和输出门;σ为sigmoid激活函数。
经过正反向传播过程,模型可以计算得到模型输出与真实值之间的损失函数,根据损失函数对参数的梯度,使用优化算法更新模型的参数,最终模型将逐步收敛到最优解。
2 运动统计特性预报模型构建
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2.1 运动统计特性序列获取
在模型构建前,首先对水陆两栖飞机的运动统计特性序列进行获取,作为模型的数据集参与模型的训练与测试。选取运动幅值的有义值作为主要统计特征进行相关序列构建。
基于P-M谱(Pierson-Moskowitz spectrum, P-M)生成不同海况下的波浪仿真数据,并结合水陆两栖飞机自身水动力特征对其六自由度运动响应进行水动力仿真,求解得到水陆两栖飞机各自由度下的仿真运动时历数据。
在获取仿真运动时历数据的基础上,对一段时历数据的峰谷值进行提取,计算每个相邻的峰值和谷值间的高度差,即水陆两栖飞机的运动幅值;将计算得到的运动幅值按数值从大到小排列,对前1/3的数据取平均值即可得到该段运动幅值的有义值。对各个自由度中每隔90 s的水陆两栖飞机运动仿真数据提取一个运动幅值有义值,构成该自由度下的运动统计特性序列。以横摇运动为例,基于上述方法得到的水陆两栖飞机横摇运动统计特性序列片段如图2所示。
2.2 模型构建与训练
基于处理好的运动统计特性序列,构建基于LSTM神经网络的水陆两栖飞机运动统计特性预报模型。
首先,对数据进行预处理。根据水陆两栖飞机运动统计特性预报的要求,确定预报模型的历史输入步长与预报步长,采用滑动窗口的方法对已建立好的运动统计特征序列进行片段分割,构建模型的输入输出矩阵。
其次,对构建好的输入输出矩阵进行切分,按照7∶2∶1的比例划分训练集、验证集和测试集,依次用于模型超参数训练、性能评估和预报效果验证。
最后,对模型结构进行搭建。基于Python开发环境构建LSTM模型,确定神经网络层数、神经元个数与激活函数等参数,并接入全连接层对模型输出结果进行展平和输出,选择合适的损失函数用于模型训练过程中的参数更新。
LSTM模型构建完毕后,利用训练集数据进行训练。定义模型训练的批次大小、训练轮数、学习率等超参数如表1所示,按照设置好的批次依次将训练集数据输入神经网络中,通过执行正向传播和反向传播以更新模型的权重和偏置。如图3所示,若达到预设的训练轮数时神经网络模型的损失函数计算结果已不再下降,可认为该模型已经收敛,输出该模型。否则需重新设置更多的训练轮数,直至模型训练结果收敛。
2.3 模型评价指标
均方根误差(root mean squared error, RMSE)是一种衡量预测值与真实值之间的平均偏差程度的指标,往往用于衡量预报模型输出结果的绝对误差。同时,为了更好地比较不同数据集下的模型预报效果,采用正则化均方根误差(normalized root mean square error,NRMSE)作为相对误差的衡量标准,充分衡量模型在不同数据分布特征下的预报性能,实现不同工况下整体时间序列预测精度评估[15]。RMSE和NRMSE公式如式(6)与式(7)所示。
RMSE = 1 m ∑ i = 1 m ( y i - y ∧ i ) 2
NRMSE = 1 H s 1 m ∑ i = 1 m ( y i - y ∧ i ) 2
式中:yi和 y ∧ i分别为每一时刻下实际的运动统计特征时历与模型输出的预测时历;m为整段序列包含的时间点个数;Hs为整段序列的有义值。
3 预报结果分析
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3.1 仿真数据介绍
选取NACA TN 2929水陆两栖飞机模型开展数值模拟仿真计算,并基于其垂荡、横摇和纵摇三自由度运动仿真时历数据开展运动统计特征预报,以验证本文所构建的水陆两栖飞机运动统计特性预报模型的预报效果。表2为NACA TN 2929水陆两栖飞机模型的主尺度信息。
选取三级、四级和五级海况下典型的艏斜浪工况作为水陆两栖飞机运动统计特性预报模型的验证工况,具体工况参数如表3所示。
3.2 模型验证与分析
基于LSTM神经网络构建水陆两栖飞机运动统计特性预报模型,设置输入步长为20,输出步长为1,每一步表示水陆两栖飞机90 s内的运动有义值。以艏斜浪工况下水陆两栖飞机的垂荡运动有义值为例,验证模型的预报效果,其垂荡运动有义值预报结果如图4所示。从整体曲线拟合情况来看,预报有义值曲线整体的预报幅度均处于实际有义值范围以内,且相位基本一致,预报结果在趋势上整体吻合实际运动有义值分布情况,误差仅集中于高频振荡处,模型输出的预报曲线能够为实际工程需要提供有效的信息,因此使用LSTM神经网络模型对水陆两栖飞机运动统计特征进行预报是可行的。
同样地,基于该模型对艏斜浪工况下水陆两栖飞机的横摇与纵摇运动有义值进行预报,其预报结果如图5、图6所示。将模型预测结果与实际运动统计特征序列进行比较,计算各个工况下垂荡、横摇、纵摇运动统计特征预报值的RMSE与NRMSE如表4所示。
从模型预报结果图和模型误差统计表中可以看出,模型对于不同海况下的同一自由度运动,其预报能力不同。以垂荡运动为例,三、四、五级海况下模型的NRMSE分别为6.39%、6.96%与8.44%,随海况等级的提高,模型预报结果的NRMSE也随之增大,这是由于在高海况下,水陆两栖飞机受到的波浪非线性扰动会更强,相应的运动统计特征序列也会表现出更强的非线性特征,深度学习神经网络模型虽然可以通过神经元之间的训练实现对数据非线性特征的提取与拟合,但随着非线性特征的增强,模型的训练难度也会随之增大,一定程度上限制了模型的拟合效果,使其无法保持与低海况相同的预报精度。
针对同一海况下的不同自由度运动,模型的预报能力也有所不同,结合图5所示的横摇预报结果可以看出,预报的横摇统计特征变化基本与原序列保持一致,明显优于图4与图6所示的垂荡与纵摇预报结果,这是由于横摇运动数据变化范围较大,数据波动明显,其数据特征更容易被模型识别和捕捉,因此模型在训练中能够更好地对横摇数据特征进行拟合。从误差统计结果来看,横摇运动统计特征的预报误差相较于纵摇和垂荡运动来说,其均方根误差较大而相对误差较小,这是由于横摇运动绝对值较大导致了该自由度预报结果对应的均方根误差偏大,但同时横摇运动的变化范围区间也较大,因此相对误差反而较小。
4 结论
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基于数值仿真模拟对NACA TN 2929水陆两栖飞机在波浪中的运动时历数据进行计算,并基于LSTM模型建立了水陆两栖飞机运动统计特性预报模型,对不同海况下的水陆两栖飞机垂荡、横摇、纵摇三自由度运动有义值进行预报,分析模型在不同海况与自由度下的预报精度,研究结果表明。
(1)所构建的水陆两栖飞机运动统计特性预报模型,可基于过去1 800 s的运动数据对未来90 s内的运动统计特征值进行预报,在不同海况下,各自由度预报结果的相对误差均小于10%,预报精度较高。
(2)随着海况等级的增加,水陆两栖飞机所受到的波浪非线性扰动加剧,模型预报精度会随着海况等级的增加逐渐下降。
(3)在同一海况下,模型预报精度往往会受到运动幅值的影响,在运动幅值范围较大时,模型更易捕捉数据点之间的波动变化模式和起伏特征,其预报精度也会随之提高。
文献
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doi: 10.12404/j.issn.1671-1815.2401685
  • 接收时间：2024-03-11
  • 首发时间：2025-07-29
  • 出版时间：2025-01-08
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  • 收稿日期：2024-03-11
  • 修回日期：2024-10-11
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    1.哈尔滨工程大学青岛创新发展基地, 青岛 266000
    2.空装驻西安地区第五军事代表室, 西安 710000
    3.中航工业西安飞行自动控制研究所, 西安 710076

通讯作者:

* 成果达(1988—),男,汉族,青海人,硕士,高级工程师。研究方向:系统对准、组合导航及完好性。E-mail:。
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
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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