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

TL;DR

This study uses a bio-inspired digital trout model with deep reinforcement learning to analyze neuromuscular control strategies that optimize propulsive efficiency during steady swimming.

Contribution

It introduces a novel digital trout model combining biomechanics, fluid dynamics, and neural control to systematically study muscle activation patterns for efficient swimming.

Findings

Axial myomere coupling is crucial for stable wave propagation.

Moderate muscle contraction duration reduces energy consumption.

Activation phase lag influences body wave shape and thrust efficiency.

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.