结合计算流体力学和实验数据理解鱼群行为

IF 2.2 3区 生物学 Q1 ZOOLOGY Integrative and Comparative Biology Pub Date : 2024-09-27 DOI:10.1093/icb/icae044
Yu Pan, George V Lauder
{"title":"结合计算流体力学和实验数据理解鱼群行为","authors":"Yu Pan, George V Lauder","doi":"10.1093/icb/icae044","DOIUrl":null,"url":null,"abstract":"<p><p>Understanding the flow physics behind fish schooling poses significant challenges due to the difficulties in directly measuring hydrodynamic performance and the three-dimensional, chaotic, and complex flow structures generated by collective moving organisms. Numerous previous simulations and experiments have utilized computational, mechanical, or robotic models to represent live fish. And existing studies of live fish schools have contributed significantly to dissecting the complexities of fish schooling. But the scarcity of combined approaches that include both computational and experimental studies, ideally of the same fish schools, has limited our ability to understand the physical factors that are involved in fish collective behavior. This underscores the necessity of developing new approaches to working directly with live fish schools. An integrated method that combines experiments on live fish schools with computational fluid dynamics (CFD) simulations represents an innovative method of studying the hydrodynamics of fish schooling. CFD techniques can deliver accurate performance measurements and high-fidelity flow characteristics for comprehensive analysis. Concurrently, experimental approaches can capture the precise locomotor kinematics of fish and offer additional flow information through particle image velocimetry (PIV) measurements, potentially enhancing the accuracy and efficiency of CFD studies via advanced data assimilation techniques. The flow patterns observed in PIV experiments with fish schools and the complex hydrodynamic interactions revealed by integrated analyses highlight the complexity of fish schooling, prompting a reevaluation of the classic Weihs model of school dynamics. The synergy between CFD models and experimental data grants us comprehensive insights into the flow dynamics of fish schools, facilitating the evaluation of their functional significance and enabling comparative studies of schooling behavior. In addition, we consider the challenges in developing integrated analytical methods and suggest promising directions for future research.</p>","PeriodicalId":54971,"journal":{"name":"Integrative and Comparative Biology","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Combining Computational Fluid Dynamics and Experimental Data to Understand Fish Schooling Behavior.\",\"authors\":\"Yu Pan, George V Lauder\",\"doi\":\"10.1093/icb/icae044\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Understanding the flow physics behind fish schooling poses significant challenges due to the difficulties in directly measuring hydrodynamic performance and the three-dimensional, chaotic, and complex flow structures generated by collective moving organisms. Numerous previous simulations and experiments have utilized computational, mechanical, or robotic models to represent live fish. And existing studies of live fish schools have contributed significantly to dissecting the complexities of fish schooling. But the scarcity of combined approaches that include both computational and experimental studies, ideally of the same fish schools, has limited our ability to understand the physical factors that are involved in fish collective behavior. This underscores the necessity of developing new approaches to working directly with live fish schools. An integrated method that combines experiments on live fish schools with computational fluid dynamics (CFD) simulations represents an innovative method of studying the hydrodynamics of fish schooling. CFD techniques can deliver accurate performance measurements and high-fidelity flow characteristics for comprehensive analysis. Concurrently, experimental approaches can capture the precise locomotor kinematics of fish and offer additional flow information through particle image velocimetry (PIV) measurements, potentially enhancing the accuracy and efficiency of CFD studies via advanced data assimilation techniques. The flow patterns observed in PIV experiments with fish schools and the complex hydrodynamic interactions revealed by integrated analyses highlight the complexity of fish schooling, prompting a reevaluation of the classic Weihs model of school dynamics. The synergy between CFD models and experimental data grants us comprehensive insights into the flow dynamics of fish schools, facilitating the evaluation of their functional significance and enabling comparative studies of schooling behavior. In addition, we consider the challenges in developing integrated analytical methods and suggest promising directions for future research.</p>\",\"PeriodicalId\":54971,\"journal\":{\"name\":\"Integrative and Comparative Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Integrative and Comparative Biology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1093/icb/icae044\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ZOOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Integrative and Comparative Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1093/icb/icae044","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ZOOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

由于难以直接测量流体力学性能以及集体运动的生物体产生的三维、混乱和复杂的流动结构,了解鱼群游动背后的流动物理学构成了重大挑战。以前的许多模拟和实验都使用计算、机械或机器人模型来表示活鱼。现有的活体鱼群研究也为剖析鱼群活动的复杂性做出了重要贡献。但是,同时进行计算研究和实验研究(最好是对同一鱼群进行研究)的综合方法非常缺乏,这限制了我们了解鱼群集体行为所涉及的物理因素的能力。这凸显了开发直接研究活鱼群的新方法的必要性。将活体鱼群实验与计算流体动力学(CFD)模拟相结合的综合方法是研究鱼群水动力的创新方法。CFD 技术可提供精确的性能测量和高保真流动特性,以便进行综合分析。与此同时,实验方法可以捕捉鱼类精确的运动学特征,并通过粒子图像测速仪(PIV)测量提供额外的流动信息,从而通过先进的数据同化技术提高 CFD 研究的准确性和效率。在鱼群 PIV 实验中观察到的流动模式以及综合分析所揭示的复杂流体动力学相互作用凸显了鱼群活动的复杂性,促使人们重新评估经典的鱼群动力学 Weihs 模型。CFD 模型和实验数据之间的协同作用让我们对鱼群的流动动力学有了全面的了解,有助于评估鱼群的功能意义,并能对鱼群行为进行比较研究。此外,我们还探讨了开发综合分析方法所面临的挑战,并为未来的研究提出了有前景的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Combining Computational Fluid Dynamics and Experimental Data to Understand Fish Schooling Behavior.

Understanding the flow physics behind fish schooling poses significant challenges due to the difficulties in directly measuring hydrodynamic performance and the three-dimensional, chaotic, and complex flow structures generated by collective moving organisms. Numerous previous simulations and experiments have utilized computational, mechanical, or robotic models to represent live fish. And existing studies of live fish schools have contributed significantly to dissecting the complexities of fish schooling. But the scarcity of combined approaches that include both computational and experimental studies, ideally of the same fish schools, has limited our ability to understand the physical factors that are involved in fish collective behavior. This underscores the necessity of developing new approaches to working directly with live fish schools. An integrated method that combines experiments on live fish schools with computational fluid dynamics (CFD) simulations represents an innovative method of studying the hydrodynamics of fish schooling. CFD techniques can deliver accurate performance measurements and high-fidelity flow characteristics for comprehensive analysis. Concurrently, experimental approaches can capture the precise locomotor kinematics of fish and offer additional flow information through particle image velocimetry (PIV) measurements, potentially enhancing the accuracy and efficiency of CFD studies via advanced data assimilation techniques. The flow patterns observed in PIV experiments with fish schools and the complex hydrodynamic interactions revealed by integrated analyses highlight the complexity of fish schooling, prompting a reevaluation of the classic Weihs model of school dynamics. The synergy between CFD models and experimental data grants us comprehensive insights into the flow dynamics of fish schools, facilitating the evaluation of their functional significance and enabling comparative studies of schooling behavior. In addition, we consider the challenges in developing integrated analytical methods and suggest promising directions for future research.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
4.70
自引率
7.70%
发文量
150
审稿时长
6-12 weeks
期刊介绍: Integrative and Comparative Biology ( ICB ), formerly American Zoologist , is one of the most highly respected and cited journals in the field of biology. The journal''s primary focus is to integrate the varying disciplines in this broad field, while maintaining the highest scientific quality. ICB''s peer-reviewed symposia provide first class syntheses of the top research in a field. ICB also publishes book reviews, reports, and special bulletins.
期刊最新文献
Big fish can't jump? Allometry of terrestrial jumping in cyprinodontiform fishes. Combining Morphological Characteristics and DNA Barcoding Techniques Confirm Sea Urchins of the Genus Echinometra (Echinodermata: Echinoidea) in Marine Habitat Located at Extreme Regions of the Caribbean Sea. Marine Debris Harbor Unique, yet Functionally Similar Cryptofauna Communities. The Young and the Resilient: Investigating Coral Thermal Resilience in Early Life Stages. Hurricane Irma Linked to Coral Skeletal Density Shifts on the Florida Keys Reef Tract.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1