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How do trout regulate patterns of muscle contraction to optimize propulsive efficiency during steady swimming

Tao Li, Chunze Zhang, Weiwei Yao, Junzhao He, Ji Hou, Qin Zhou, Lu Zhang

TL;DR

The paper addresses how neuromuscular control modulates whole-body undulations to optimize propulsive efficiency in steady swimming. It introduces a high-fidelity digital trout that integrates a Hill-type muscle model, Newton–Euler multibody dynamics, and a bidirectional fluid–structure interaction solver (IB-LBM), driven by Soft Actor-Critic DRL to discover efficient activation patterns. Key findings show that axial myomere coupling spanning more than 0.5 body lengths is essential for stable wave propagation, while shorter contraction durations enable passive damping and energy savings via inertial-elastic-fluid interactions; phase lag between opposing sides shapes wave morphology and thrust, with moderate lag delivering high efficiency and speed. These results illuminate cross-scale energy transfer mechanisms and offer design principles for energy-efficient, bio-inspired underwater robotics.

Abstract

Understanding efficient fish locomotion offers insights for biomechanics, fluid dynamics, and engineering. Traditional studies often miss the link between neuromuscular control and whole-body movement. To explore energy transfer in carangiform swimming, we created a bio-inspired digital trout. This model combined multibody dynamics, Hill-type muscle modeling, and a high-fidelity fluid-structure interaction algorithm, accurately replicating a real trout's form and properties. Using deep reinforcement learning, the trout's neural system achieved hierarchical spatiotemporal control of muscle activation. We systematically examined how activation strategies affect speed and energy use. Results show that axial myomere coupling-with activation spanning over 0.5 body lengths-is crucial for stable body wave propagation. Moderate muscle contraction duration ([0.1,0.3] of a tail-beat cycle) lets the body and fluid act as a passive damping system, cutting energy use. Additionally, the activation phase lag of myomeres shapes the body wave; if too large, it causes antagonistic contractions that hinder thrust. These findings advance bio-inspired locomotion understanding and aid energy-efficient underwater system design.

How do trout regulate patterns of muscle contraction to optimize propulsive efficiency during steady swimming

TL;DR

The paper addresses how neuromuscular control modulates whole-body undulations to optimize propulsive efficiency in steady swimming. It introduces a high-fidelity digital trout that integrates a Hill-type muscle model, Newton–Euler multibody dynamics, and a bidirectional fluid–structure interaction solver (IB-LBM), driven by Soft Actor-Critic DRL to discover efficient activation patterns. Key findings show that axial myomere coupling spanning more than 0.5 body lengths is essential for stable wave propagation, while shorter contraction durations enable passive damping and energy savings via inertial-elastic-fluid interactions; phase lag between opposing sides shapes wave morphology and thrust, with moderate lag delivering high efficiency and speed. These results illuminate cross-scale energy transfer mechanisms and offer design principles for energy-efficient, bio-inspired underwater robotics.

Abstract

Understanding efficient fish locomotion offers insights for biomechanics, fluid dynamics, and engineering. Traditional studies often miss the link between neuromuscular control and whole-body movement. To explore energy transfer in carangiform swimming, we created a bio-inspired digital trout. This model combined multibody dynamics, Hill-type muscle modeling, and a high-fidelity fluid-structure interaction algorithm, accurately replicating a real trout's form and properties. Using deep reinforcement learning, the trout's neural system achieved hierarchical spatiotemporal control of muscle activation. We systematically examined how activation strategies affect speed and energy use. Results show that axial myomere coupling-with activation spanning over 0.5 body lengths-is crucial for stable body wave propagation. Moderate muscle contraction duration ([0.1,0.3] of a tail-beat cycle) lets the body and fluid act as a passive damping system, cutting energy use. Additionally, the activation phase lag of myomeres shapes the body wave; if too large, it causes antagonistic contractions that hinder thrust. These findings advance bio-inspired locomotion understanding and aid energy-efficient underwater system design.

Paper Structure

This paper contains 26 sections, 19 equations, 26 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Fluid-structure interaction
  4. Multibody and muscle dynamics
  5. Deep reinforcement learning
  6. Constructing a multiscale digital trout model
  7. Fish body modeling
  8. Computational parameters and simulation conditions
  9. DRL module configuration
  10. Hyperparameters setting
  11. State space
  12. Action space
  13. Reward function
  14. Hardware configuration
  15. Results and discussion
  16. ...and 11 more sections

Figures (26)

  • Figure 1: Schematic comparison between a live trout and the digital trout model.
  • Figure 2: Anatomical schematic of the digital trout (the anterior 0.25 BL represents the rigid head region).
  • Figure 3: Axial mass distribution of the digital trout.
  • Figure 4: Schematic diagram of the computational domain.
  • Figure 5: Neural activation patterns of bilateral muscles during steady swimming in trout. The start of each black bar indicates the muscle activation phase, and its length represents the contraction duration time (CDT). EMG data are adapted from (williams1989locomotion); the trout image on the left is adapted from (gibbs2024kinematics).
  • ...and 21 more figures