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Research Papers

An Adaptive Multiscaling Approach for Reducing Computation Time in Simulations of Articulated Biopolymers

[+] Author and Article Information
Ashley Guy

Department of Mechanical and
Aerospace Engineering,
University of Texas at Arlington,
Arlington, TX 76019
e-mail: ashley.guy@uta.edu

Alan Bowling

Mem. ASME
The Robotics, Biomechanics, and
Dynamic Systems Laboratory Department of
Mechanical and Aerospace Engineering,
University of Texas at Arlington,
Arlington, TX 76019
e-mail: bowling@uta.edu

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS. Manuscript received July 3, 2018; final manuscript received January 24, 2019; published online March 14, 2019. Assoc. Editor: Zdravko Terze.

J. Comput. Nonlinear Dynam 14(5), 051007 (Mar 14, 2019) (10 pages) Paper No: CND-18-1294; doi: 10.1115/1.4042691 History: Received July 03, 2018; Revised January 24, 2019

Microscale dynamic simulations can require significant computational resources to generate desired time evolutions. Microscale phenomena are often driven by even smaller scale dynamics, requiring multiscale system definitions to combine these effects. At the smallest scale, large active forces lead to large resultant accelerations, requiring small integration time steps to fully capture the motion and dictating the integration time for the entire model. Multiscale modeling techniques aim to reduce this computational cost, often by separating the system into subsystems or coarse graining to simplify calculations. A multiscale method has been previously shown to greatly reduce the time required to simulate systems in the continuum regime while generating equivalent time histories. This method identifies a portion of the active and dissipative forces that cancel and contribute little to the overall motion. The forces are then scaled to eliminate these noncontributing portions. This work extends that method to include an adaptive scaling method for forces that have large changes in magnitude across the time history. Results show that the adaptive formulation generates time histories similar to those of the unscaled truth model. Computation time reduction is consistent with the existing method.

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Figures

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Fig. 1

(top) 3D model of GP1b protein and vWF receptor; image taken from the modeling tools on Ref. [55]; (bottom) model of GP1b coarse grain approximation; dashed lines correlate to the plotted bodies in Figs. 2 and 6

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Fig. 2

Locations of the eight GP1b proteins on the nanoparticle

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Fig. 3

Plots of initial and final positions of the nanoparticle. Bang-bang and hyperbolic shown as Adaptive.

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Fig. 4

Nanoparticle position in N̂1 direction over time

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Fig. 5

Trajectories of centers of mass

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Fig. 6

GP1b proteins approximately 0.3 μs after adaptive scaling enabled

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Fig. 7

Bang-bang controlled system energy. W denotes the work done by friction (subscript d), potential (p), stochastic (s), and conformational (c) forces. T denotes kinetic energy.

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Fig. 8

Hyperbolic controlled system energy

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Fig. 9

Unscaled system energy

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Fig. 10

Force magnitudes from the bang-bang (top) and hyperbolic (bottom) systems. Forces shown are damping (subscript d), conformational (c), potential (p), and stochastic (s). Dashed vertical line denotes time when adaptive scaling is activated.

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Fig. 11

Adaptive scaling factor a2* over time

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Fig. 12

Kinetic energy over time. Dashed vertical line denotes time when bang-bang controller is activated.

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