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Behaviors that are produced solely through geometrically complex three-dimensional interactions of soft-tissue muscular elements, and which do not move rigid articulated skeletal elements, are a challenge to mechanically model. This complexity often leads to simulations requiring substantial computational time. We discuss how using a quasi-static approach can greatly reduce the computational time required to model slow-moving soft-tissue structures, and then demonstrate our technique using the biomechanics of feeding behavior by the marine mollusc, Aplysia californica. We used a conventional 2nd order (from Newton’s equations), forward dynamic model, which required 14 s to simulate 1 s of feeding behavior. We then used a quasi-static reformulation of the same model, which only required 0.35 s to perform the same task (a 40-fold improvement in computation speed). Lastly, we re-coded the quasi-static model in Python to further increase computation speed another 3-fold, creating a model that required just 0.12 s to model 1 s of feeding behavior. Both quasi-static models produce results that are nearly indistinguishable from the original 2nd order model, showing that quasi-static formulations can greatly increase the computation speed without sacrificing model accuracy.
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Laminar and Turbulent Behavior Captured by A 3-D Kinetic-Based Discrete Dynamic System
We have derived a 3-D kinetic-based discrete dynamic system (DDS) from the lattice Boltzmann equation (LBE) for incompressible flows through a Galerkin procedure. Expressed by a poor-man lattice Boltzmann equation (PMLBE), it involves five bifurcation parameters including relaxation time from the LBE, splitting factor of large and sub-grid motion scales, and wavevector components from the Fourier space. Numerical experiments have shown that the DDS can capture laminar behaviors of periodic, subharmonic, n-period, and quasi-periodic and turbulent behaviors of noisy periodic with harmonic, noisy subharmonic, noisy quasi-periodic, and broadband power spectra. In this work, we investigated the effects of bifurcation parameters on the capturing of the laminar and turbulent flows in terms of the convergence of time series and the pattern of power spectra. We have found that the 2nd order and 3rd order PMLBEs are both able to capture laminar and turbulent flow behaviors but the 2nd order DDS performs better with lower computation cost and more flow behaviors captured. With the specified ranges of the bifurcation parameters, we have identified two optimal bifurcation parameter sets for laminar and turbulent behaviors. Beyond this work, we are exploring the regime maps for a deeper understanding of the contributions of the bifurcation parameters to the capturing of laminar and turbulent behaviors. Surrogate models (to replace the PMLBE) are being developed using deep learning techniques to overcome the overwhelming computation cost for the regime maps. Meanwhile, the DDS is being employed in the large eddy simulation of turbulent pulsatile flows to provide dynamic sub-grid scale information.
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- Award ID(s):
- 1803845
- PAR ID:
- 10381939
- Editor(s):
- Brehm, Christoph; Pandya, Shishir
- Date Published:
- Journal Name:
- Eleventh International Conference on Computational Fluid Dynamics (ICCFD11)
- Page Range / eLocation ID:
- 1 - 9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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