In fish, caudally propagating waves of neural activity produce muscle bending moments. These moments, coupled with forces due to the body's elastic properties and forces due to fluid-body interactions, determine the deformation kinematics for swimming. Fully resolved simulations of neurally-activated swimming can be used to decode activation patterns underlying observed behaviors in a swimming animal. These computations are expensive; the time stepping requirement is onerous due to the canonically used explicit coupling between the elastic body and the fluid. To overcome this barrier, we use our prior result that deformation kinematics closely follow the preferred kinematics due to muscle activation when a swimmer has a sufficiently stiff body. Thus, we can impose the preferred deformation kinematics directly on the body immersed in the fluid. In this way, the need to solve the elastic equations is eliminated. Here, we couple physiochemical and physiomechanical equations to a constraint-based self-propulsion formulation. With this method, we demonstrate how different behaviors, such as turning, emerge from varying the neural signal. Experimentalists have consistently reported a difference between the electromyogram (EMG) and curvature wave speeds. The EMG wave speed has been found to correlate with the cross-sectional moment wave. The correlation, however, remains unexplained. Using feedback dependent controller models in the context of reduced order fluid flow simulations where the elasticity of the body is resolved, we demonstrate two scenarios -- one at higher passive elastic stiffness and another at lower passive elastic stiffness of the body. The former case becomes equivalent to the penalty type mathematical model for swimming used in prior literature and it does not reproduce neuromechanical wave speed discrepancy. The latter case at lower elastic stiffness does reproduce the wave speed discrepancy and appears to be biologically most relevant. The swimmer retains a large effective stiffness, even for small passive elastic stiffness. Thus, feedback driven neural controllers may be integrated into the constraint-based formulation to simulate fully-resolved neuromechanical problem of swimming.

Last modified

• 01/29/2019

Creator

• Namrata Kartik Patel

DOI

• https://doi.org/10.21985/N2VV0W

Subject

• Mathematics

Keyword

• biomechanics
• fluid structure interaction
• locomotion
• neuromechanics
• neuromechanical phase lag
• immersed boundary method

Date created

• 2017-01-01

Resource type

• Dissertation

Rights statement

• In Copyright