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.

翻译：理解鱼类高效游动机制可为生物力学、流体动力学及工程学提供重要见解。传统研究常忽略神经肌肉控制与全身运动之间的关联。为探究鲹科鱼类游动中的能量传递机制，我们构建了一种仿生数字鳟鱼模型。该模型融合了多体动力学、希尔型肌肉建模及高保真流固耦合算法，精确复现了真实鳟鱼的形态与物理特性。通过深度强化学习，鳟鱼神经系统实现了肌肉激活的层级化时空控制。我们系统研究了激活策略对游速与能量消耗的影响。结果表明：轴向肌节的耦合激活（激活范围跨越0.5个以上体长）对稳定体波传递至关重要；适中的肌肉收缩持续时间（占摆尾周期的[0.1,0.3]）可使身体与流体形成被动阻尼系统，显著降低能耗；此外，肌节间的激活相位差决定了体波形态，若相位差过大将引发拮抗性收缩，阻碍推进力生成。这些发现深化了对仿生运动机制的理解，并为设计高能效水下系统提供了理论依据。