A numerical study of fish adaption behaviors in complex environments with a deep reinforcement learning and immersed boundary–lattice Boltzmann method

Zhu, Yi; Tian, Fang-Bao; Young, John; Liao, James C.; Lai, Joseph H.K.

doi:10.1038/s41598-021-81124-8

Cited by 39 publications

(35 citation statements)

References 67 publications

(55 reference statements)

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“…The complex currents and the moving wall require excellent control strategy which has not been studied. The combination of numerical methods with artificial intelligence, which has been introduced in high-Reynolds-number swimmers [70,71], is an attractive strategy to achieve this. Second, the sperm-sperm and sperm-wall interactions are important during this journey.…”

Section: Discussionmentioning

confidence: 99%

Numerical Modeling of Sperm Swimming

Tian

Wang

2021

Fluids

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Due to rising human infertility, sperm motility has been an important subject. Among the hundreds of millions of sperms on the journey up the oviducts, only a few excellent travelers will reach the eggs. This journey is affected by many factors, some of which include sperm quality, sperm density, fluid rheology and chemotaxis. In addition, the sperm swimming through different body tracks and fluids involves complex sperm flagellar, complex fluid environment, and multi-sperm and sperm-wall interactions. Therefore, this topic has generated substantial research interest. In this paper, we present a review of computational studies on sperm swimming from an engineering perspective with focus on both simplified theoretical methods and fluid–structure interaction methods. Several open issues in this field are highlighted.

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Section: Discussionmentioning

confidence: 99%

Numerical Modeling of Sperm Swimming

Tian

Wang

2021

Fluids

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“…The interactions between the intermediate fins were analyzed in detail. The CFD method was used by Macias et al to simulate the swimming process of the fish in undisturbed water flow [42] . Zhu et al combined the immersed boundary-lattice Boltzmann method in numerical simulation with a deep recurrent Q-network to simulate the behavior of fish [43] .…”

Section: Numerical Simulationmentioning

confidence: 99%

“…The hydrodynamic performance of fish of different shapes near the water surface using CFD was studied by Zhan et al [39]. numerical simulation with a deep recurrent Q-network to simulate the behavior of fish [43]. It provided an effective method for researching fish adaptation behaviors in complex environments.…”

Section: Numerical Simulationmentioning

confidence: 99%

Designs, motion mechanism, motion coordination, and communication of bionic robot fishes: a survey

Yu¹,

Li²,

J³

et al. 2022

Intell Robot

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In the last few years, there have been many developments and significant accomplishments in the research of bionic robot fishes. However, in terms of swimming performance, existing bionic robot fishes lag far behind fish, prompting researchers to constantly innovative designs of various bionic robot fishes. In this paper, the latest designs of robot fishes are presented in detail, distinguished by the propulsion mode. New robot fishes are mainly includes soft robot fishes and rigid-soft coupled robot fishes. The latest progress in the study of the swimming mechanism is analyzed on the basis of summarizing the main swimming theories of fish. The current state-of-the-art research in the new field of motion coordination and communication of multiple robot fishes is summarized. The general research trend in robot fishes is to utilize a more efficient and robust methods to best mimic real fish while exhibiting superior swimming performance. The current challenges and potential future research directions are discussed. Various methods are needed to narrow the gap in swimming performance between robot fishes and fish. This paper is a first step to bring together roboticists and marine biologists interested in learning the state-of-the-art research on bionic robot fishes.

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“…Sekar et al (2019) presented a data-driven approach by using the combination of CNN and multilayer perceptron for the prediction of laminar flow around NACA airfoil, which achieves the flow prediction of various airfoils. ML has also been combined with CFD tools to study fish swimming (Zhu et al, 2021(Zhu et al, , 2022. Moreover, ML has been studied in the field of biomedicine, such as prediction of malaria (Lee et al, 2020) and patient quality-of-life after prostate radiation therapy (Yang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Fast prediction of blood flow in stenosed arteries using machine learning and immersed boundary-lattice Boltzmann method

2022

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A fast prediction of blood flow in stenosed arteries with a hybrid framework of machine learning and immersed boundary-lattice Boltzmann method (IB–LBM) is presented. The integrated framework incorporates the immersed boundary method for its excellent capability in handling complex boundaries, the multi-relaxation-time LBM for its efficient modelling for unsteady flows and the deep neural network (DNN) for its high efficiency in artificial learning. Specifically, the stenosed artery is modelled by a channel for two-dimensional (2D) cases or a tube for three-dimensional (3D) cases with a stenosis approximated by a fifth-order polynomial. An IB–LBM is adopted to obtain the training data for the DNN which is constructed to generate an approximate model for the fast flow prediction. In the DNN, the inputs are the characteristic parameters of the stenosis and fluid node coordinates, and the outputs are the mean velocity and pressure at each node. To characterise complex stenosis, a convolutional neural network (CNN) is built to extract the stenosis properties by using the data generated by the aforementioned polynomial. Both 2D and 3D cases (including 3D asymmetrical case) are constructed and examined to demonstrate the effectiveness of the proposed method. Once the DNN model is trained, the prediction efficiency of blood flow in stenosed arteries is much higher compared with the direct computational fluid dynamics simulations. The proposed method has a potential for applications in clinical diagnosis and treatment where the real-time modelling results are desired.

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A numerical study of fish adaption behaviors in complex environments with a deep reinforcement learning and immersed boundary–lattice Boltzmann method

Cited by 39 publications

References 67 publications

Numerical Modeling of Sperm Swimming

Numerical Modeling of Sperm Swimming

Designs, motion mechanism, motion coordination, and communication of bionic robot fishes: a survey

Fast prediction of blood flow in stenosed arteries using machine learning and immersed boundary-lattice Boltzmann method

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