Modeling fish swimming trajectories in a sudden expansion flow based on eulerian lagrangian agent method (ELAM): A case study of red crucian carp

IF 3.2 3区 环境科学与生态学 Q2 ECOLOGY
Dongjin Gao , Xin Zhu , Minghai Huang , Siying Wang , Hui Guo
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Abstract

Based on the experimental data of Red Crucian Carp swimming in a sudden expansion flow, this study established a fish swimming trajectory prediction model using the Eulerian–Lagrangian–agent method (ELAM). Considering the observed phenomenon that fish may exhibit different swimming behaviors under identical hydrodynamic conditions in both real-world and experiments, we innovativily summarize that fish may randomly adopt two distinct behavioral strategies: aggressive and conservative navigation in various environments. Through a systematic analysis of the experimental results of fish swimming trajectories and the numerical simulation results of the sudden expansion flow, we quantified the preferred value range and sensitivity of fish to the key hydraulic parameters (including flow velocity, turbulent kinetic energy, and strain rate) under each strategies. Based on these findings, we constructed a trajectories prediction model for fish swimming in complex flow conditions within the ELAM framework, which incorporates real-time hydrodynamic feedback mechanisms. This model is then used to simulate and analyze the swimming trajectories of Red Crucian Carp of three different body sizes in sudden expansion flows with inlet velocities ranging from 0.2 to 1.0 m/s. The results demonstrate that the prediction model successfully replicates the four typical trajectory patterns observed in the experiments, namely Main Current Migration, Recirculation Bypass Migration, Corner Retention, and Failed Migration. Moreover, the predicted trajectories' dependence on fish size and flow velocity aligns well with the experimental results. The model developed in this study can effectively capture the interaction between the fish and the surrounding flow, enable the prediction of the success rate and the efficiency of fish navigating through complex flow fields, which offers a potential alternative to traditional fish passage experiments, thereby providing a valuable tool for the design and optimization of fish protection measures.
基于欧拉-拉格朗日代理法(ELAM)的突然膨胀流中鱼类游动轨迹建模——以红鲫鱼为例
基于红鲫在突然膨胀流中游动的实验数据,采用欧拉-拉格朗日- agent方法(ELAM)建立了红鲫游动轨迹预测模型。考虑到鱼在现实世界和实验中在相同的水动力条件下可能表现出不同的游泳行为,我们创新地总结了鱼在不同环境中可能随机采取两种不同的行为策略:积极和保守导航。通过系统分析鱼类游动轨迹的实验结果和突然膨胀流的数值模拟结果,量化了各策略下鱼类对关键水力参数(流速、湍流动能、应变率)的偏好取值范围和敏感性。基于这些发现,我们在ELAM框架内构建了一个包含实时水动力反馈机制的复杂流动条件下鱼类游动轨迹预测模型。利用该模型模拟分析了三种不同体型的红鲫在入口速度为0.2 ~ 1.0 m/s的突然膨胀流中的游动轨迹。结果表明,该预测模型成功地复制了实验中观察到的四种典型轨迹模式,即主流偏移、再循环旁路偏移、角区保留和失败偏移。此外,预测的轨迹对鱼的大小和流速的依赖与实验结果很好地吻合。本研究建立的模型能够有效捕捉鱼类与周围水流的相互作用,预测鱼类在复杂流场中航行的成功率和效率,为传统的鱼类通道实验提供了潜在的替代方案,从而为鱼类保护措施的设计和优化提供了有价值的工具。
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来源期刊
Ecological Modelling
Ecological Modelling 环境科学-生态学
CiteScore
5.60
自引率
6.50%
发文量
259
审稿时长
69 days
期刊介绍: The journal is concerned with the use of mathematical models and systems analysis for the description of ecological processes and for the sustainable management of resources. Human activity and well-being are dependent on and integrated with the functioning of ecosystems and the services they provide. We aim to understand these basic ecosystem functions using mathematical and conceptual modelling, systems analysis, thermodynamics, computer simulations, and ecological theory. This leads to a preference for process-based models embedded in theory with explicit causative agents as opposed to strictly statistical or correlative descriptions. These modelling methods can be applied to a wide spectrum of issues ranging from basic ecology to human ecology to socio-ecological systems. The journal welcomes research articles, short communications, review articles, letters to the editor, book reviews, and other communications. The journal also supports the activities of the [International Society of Ecological Modelling (ISEM)](http://www.isemna.org/).
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