| 16 |
|
|
| 17 |
|
Integration schemes for rotational motion of the rigid molecules in |
| 18 |
|
microcanonical ensemble have been extensively studied in the last |
| 19 |
< |
two decades. Matubayasi and Nakahara developed a time-reversible |
| 20 |
< |
integrator for rigid bodies in quaternion representation. Although |
| 21 |
< |
it is not symplectic, this integrator still demonstrates a better |
| 22 |
< |
long-time energy conservation than traditional methods because of |
| 23 |
< |
the time-reversible nature. Extending Trotter-Suzuki to general |
| 24 |
< |
system with a flat phase space, Miller and his colleagues devised an |
| 25 |
< |
novel symplectic, time-reversible and volume-preserving integrator |
| 26 |
< |
in quaternion representation, which was shown to be superior to the |
| 27 |
< |
time-reversible integrator of Matubayasi and Nakahara. However, all |
| 28 |
< |
of the integrators in quaternion representation suffer from the |
| 19 |
> |
two decades. Matubayasi developed a time-reversible integrator for |
| 20 |
> |
rigid bodies in quaternion representation. Although it is not |
| 21 |
> |
symplectic, this integrator still demonstrates a better long-time |
| 22 |
> |
energy conservation than traditional methods because of the |
| 23 |
> |
time-reversible nature. Extending Trotter-Suzuki to general system |
| 24 |
> |
with a flat phase space, Miller and his colleagues devised an novel |
| 25 |
> |
symplectic, time-reversible and volume-preserving integrator in |
| 26 |
> |
quaternion representation, which was shown to be superior to the |
| 27 |
> |
Matubayasi's time-reversible integrator. However, all of the |
| 28 |
> |
integrators in quaternion representation suffer from the |
| 29 |
|
computational penalty of constructing a rotation matrix from |
| 30 |
|
quaternions to evolve coordinates and velocities at every time step. |
| 31 |
|
An alternative integration scheme utilizing rotation matrix directly |
| 139 |
|
average 7\% increase in computation time using the DLM method in |
| 140 |
|
place of quaternions. This cost is more than justified when |
| 141 |
|
comparing the energy conservation of the two methods as illustrated |
| 142 |
< |
in Fig.~\ref{timestep}. |
| 142 |
> |
in Fig.~\ref{methodFig:timestep}. |
| 143 |
|
|
| 144 |
|
\begin{figure} |
| 145 |
|
\centering |
| 150 |
|
increasing time step. For each time step, the dotted line is total |
| 151 |
|
energy using the DLM integrator, and the solid line comes from the |
| 152 |
|
quaternion integrator. The larger time step plots are shifted up |
| 153 |
< |
from the true energy baseline for clarity.} \label{timestep} |
| 153 |
> |
from the true energy baseline for clarity.} |
| 154 |
> |
\label{methodFig:timestep} |
| 155 |
|
\end{figure} |
| 156 |
|
|
| 157 |
< |
In Fig.~\ref{timestep}, the resulting energy drift at various time |
| 158 |
< |
steps for both the DLM and quaternion integration schemes is |
| 159 |
< |
compared. All of the 1000 molecule water simulations started with |
| 160 |
< |
the same configuration, and the only difference was the method for |
| 161 |
< |
handling rotational motion. At time steps of 0.1 and 0.5 fs, both |
| 162 |
< |
methods for propagating molecule rotation conserve energy fairly |
| 163 |
< |
well, with the quaternion method showing a slight energy drift over |
| 164 |
< |
time in the 0.5 fs time step simulation. At time steps of 1 and 2 |
| 165 |
< |
fs, the energy conservation benefits of the DLM method are clearly |
| 166 |
< |
demonstrated. Thus, while maintaining the same degree of energy |
| 167 |
< |
conservation, one can take considerably longer time steps, leading |
| 168 |
< |
to an overall reduction in computation time. |
| 157 |
> |
In Fig.~\ref{methodFig:timestep}, the resulting energy drift at |
| 158 |
> |
various time steps for both the DLM and quaternion integration |
| 159 |
> |
schemes is compared. All of the 1000 molecule water simulations |
| 160 |
> |
started with the same configuration, and the only difference was the |
| 161 |
> |
method for handling rotational motion. At time steps of 0.1 and 0.5 |
| 162 |
> |
fs, both methods for propagating molecule rotation conserve energy |
| 163 |
> |
fairly well, with the quaternion method showing a slight energy |
| 164 |
> |
drift over time in the 0.5 fs time step simulation. At time steps of |
| 165 |
> |
1 and 2 fs, the energy conservation benefits of the DLM method are |
| 166 |
> |
clearly demonstrated. Thus, while maintaining the same degree of |
| 167 |
> |
energy conservation, one can take considerably longer time steps, |
| 168 |
> |
leading to an overall reduction in computation time. |
| 169 |
|
|
| 170 |
|
\subsection{\label{methodSection:NVT}Nos\'{e}-Hoover Thermostatting} |
| 171 |
|
|
| 172 |
< |
The Nos\'e-Hoover equations of motion are given by\cite{Hoover85} |
| 172 |
> |
The Nos\'e-Hoover equations of motion are given by\cite{Hoover1985} |
| 173 |
|
\begin{eqnarray} |
| 174 |
|
\dot{{\bf r}} & = & {\bf v}, \\ |
| 175 |
|
\dot{{\bf v}} & = & \frac{{\bf f}}{m} - \chi {\bf v} ,\\ |
| 285 |
|
|
| 286 |
|
The Nos\'e-Hoover algorithm is known to conserve a Hamiltonian for |
| 287 |
|
the extended system that is, to within a constant, identical to the |
| 288 |
< |
Helmholtz free energy,\cite{melchionna93} |
| 288 |
> |
Helmholtz free energy,\cite{Melchionna1993} |
| 289 |
|
\begin{equation} |
| 290 |
|
H_{\mathrm{NVT}} = V + K + f k_B T_{\mathrm{target}} \left( |
| 291 |
|
\frac{\tau_{T}^2 \chi^2(t)}{2} + \int_{0}^{t} \chi(t^\prime) |
| 296 |
|
last column of the {\tt .stat} file to allow checks on the quality |
| 297 |
|
of the integration. |
| 298 |
|
|
| 298 |
– |
Bond constraints are applied at the end of both the {\tt moveA} and |
| 299 |
– |
{\tt moveB} portions of the algorithm. Details on the constraint |
| 300 |
– |
algorithms are given in section \ref{oopseSec:rattle}. |
| 301 |
– |
|
| 299 |
|
\subsection{\label{methodSection:NPTi}Constant-pressure integration with |
| 300 |
|
isotropic box deformations (NPTi)} |
| 301 |
|
|
| 302 |
|
To carry out isobaric-isothermal ensemble calculations {\sc oopse} |
| 303 |
|
implements the Melchionna modifications to the |
| 304 |
< |
Nos\'e-Hoover-Andersen equations of motion,\cite{melchionna93} |
| 304 |
> |
Nos\'e-Hoover-Andersen equations of motion,\cite{Melchionna1993} |
| 305 |
|
|
| 306 |
|
\begin{eqnarray} |
| 307 |
|
\dot{{\bf r}} & = & {\bf v} + \eta \left( {\bf r} - {\bf R}_0 \right), \\ |
| 474 |
|
\end{equation} |
| 475 |
|
|
| 476 |
|
Bond constraints are applied at the end of both the {\tt moveA} and |
| 477 |
< |
{\tt moveB} portions of the algorithm. Details on the constraint |
| 481 |
< |
algorithms are given in section \ref{oopseSec:rattle}. |
| 477 |
> |
{\tt moveB} portions of the algorithm. |
| 478 |
|
|
| 479 |
|
\subsection{\label{methodSection:NPTf}Constant-pressure integration with a |
| 480 |
|
flexible box (NPTf)} |
| 607 |
|
|
| 608 |
|
\subsection{\label{methodSection:otherSpecialEnsembles}Other Special Ensembles} |
| 609 |
|
|
| 610 |
< |
\subsubsection{\label{methodSection:NPAT}NPAT Ensemble} |
| 610 |
> |
\subsubsection{\label{methodSection:NPAT}\textbf{NPAT Ensemble}} |
| 611 |
|
|
| 612 |
|
A comprehensive understanding of structure¨Cfunction relations of |
| 613 |
|
biological membrane system ultimately relies on structure and |
| 630 |
|
Note that the iterative schemes for NPAT are identical to those |
| 631 |
|
described for the NPTi integrator. |
| 632 |
|
|
| 633 |
< |
\subsubsection{\label{methodSection:NPrT}NP$\gamma$T Ensemble} |
| 633 |
> |
\subsubsection{\label{methodSection:NPrT}\textbf{NP$\gamma$T Ensemble}} |
| 634 |
|
|
| 635 |
|
Theoretically, the surface tension $\gamma$ of a stress free |
| 636 |
|
membrane system should be zero since its surface free energy $G$ is |
| 655 |
|
where $ \gamma _{{\rm{target}}}$ is the external surface tension and |
| 656 |
|
the instantaneous surface tensor $\gamma _\alpha$ is given by |
| 657 |
|
\begin{equation} |
| 658 |
< |
\gamma _\alpha = - h_z |
| 659 |
< |
(\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftrightarrow$}} |
| 664 |
< |
\over P} _{\alpha \alpha } - P_{{\rm{target}}} ) |
| 658 |
> |
\gamma _\alpha = - h_z( \overleftrightarrow{P} _{\alpha \alpha } |
| 659 |
> |
- P_{{\rm{target}}} ) |
| 660 |
|
\label{methodEquation:instantaneousSurfaceTensor} |
| 661 |
|
\end{equation} |
| 662 |
|
|
| 668 |
|
integrator is a special case of $NP\gamma T$ if the surface tension |
| 669 |
|
$\gamma$ is set to zero. |
| 670 |
|
|
| 671 |
< |
%\section{\label{methodSection:constraintMethod}Constraint Method} |
| 671 |
> |
\section{\label{methodSection:zcons}Z-Constraint Method} |
| 672 |
|
|
| 673 |
< |
%\subsection{\label{methodSection:bondConstraint}Bond Constraint for Rigid Body} |
| 673 |
> |
Based on the fluctuation-dissipation theorem, a force |
| 674 |
> |
auto-correlation method was developed by Roux and Karplus to |
| 675 |
> |
investigate the dynamics of ions inside ion channels\cite{Roux1991}. |
| 676 |
> |
The time-dependent friction coefficient can be calculated from the |
| 677 |
> |
deviation of the instantaneous force from its mean force. |
| 678 |
> |
\begin{equation} |
| 679 |
> |
\xi(z,t)=\langle\delta F(z,t)\delta F(z,0)\rangle/k_{B}T, |
| 680 |
> |
\end{equation} |
| 681 |
> |
where% |
| 682 |
> |
\begin{equation} |
| 683 |
> |
\delta F(z,t)=F(z,t)-\langle F(z,t)\rangle. |
| 684 |
> |
\end{equation} |
| 685 |
|
|
| 686 |
< |
%\subsection{\label{methodSection:zcons}Z-constraint Method} |
| 686 |
> |
If the time-dependent friction decays rapidly, the static friction |
| 687 |
> |
coefficient can be approximated by |
| 688 |
> |
\begin{equation} |
| 689 |
> |
\xi_{\text{static}}(z)=\int_{0}^{\infty}\langle\delta F(z,t)\delta |
| 690 |
> |
F(z,0)\rangle dt. |
| 691 |
> |
\end{equation} |
| 692 |
> |
Allowing diffusion constant to then be calculated through the |
| 693 |
> |
Einstein relation:\cite{Marrink1994} |
| 694 |
> |
\begin{equation} |
| 695 |
> |
D(z)=\frac{k_{B}T}{\xi_{\text{static}}(z)}=\frac{(k_{B}T)^{2}}{\int_{0}^{\infty |
| 696 |
> |
}\langle\delta F(z,t)\delta F(z,0)\rangle dt}.% |
| 697 |
> |
\end{equation} |
| 698 |
|
|
| 699 |
+ |
The Z-Constraint method, which fixes the z coordinates of the |
| 700 |
+ |
molecules with respect to the center of the mass of the system, has |
| 701 |
+ |
been a method suggested to obtain the forces required for the force |
| 702 |
+ |
auto-correlation calculation.\cite{Marrink1994} However, simply |
| 703 |
+ |
resetting the coordinate will move the center of the mass of the |
| 704 |
+ |
whole system. To avoid this problem, we reset the forces of |
| 705 |
+ |
z-constrained molecules as well as subtract the total constraint |
| 706 |
+ |
forces from the rest of the system after the force calculation at |
| 707 |
+ |
each time step instead of resetting the coordinate. |
| 708 |
+ |
|
| 709 |
+ |
After the force calculation, define $G_\alpha$ as |
| 710 |
+ |
\begin{equation} |
| 711 |
+ |
G_{\alpha} = \sum_i F_{\alpha i}, \label{oopseEq:zc1} |
| 712 |
+ |
\end{equation} |
| 713 |
+ |
where $F_{\alpha i}$ is the force in the z direction of atom $i$ in |
| 714 |
+ |
z-constrained molecule $\alpha$. The forces of the z constrained |
| 715 |
+ |
molecule are then set to: |
| 716 |
+ |
\begin{equation} |
| 717 |
+ |
F_{\alpha i} = F_{\alpha i} - |
| 718 |
+ |
\frac{m_{\alpha i} G_{\alpha}}{\sum_i m_{\alpha i}}. |
| 719 |
+ |
\end{equation} |
| 720 |
+ |
Here, $m_{\alpha i}$ is the mass of atom $i$ in the z-constrained |
| 721 |
+ |
molecule. Having rescaled the forces, the velocities must also be |
| 722 |
+ |
rescaled to subtract out any center of mass velocity in the z |
| 723 |
+ |
direction. |
| 724 |
+ |
\begin{equation} |
| 725 |
+ |
v_{\alpha i} = v_{\alpha i} - |
| 726 |
+ |
\frac{\sum_i m_{\alpha i} v_{\alpha i}}{\sum_i m_{\alpha i}}, |
| 727 |
+ |
\end{equation} |
| 728 |
+ |
where $v_{\alpha i}$ is the velocity of atom $i$ in the z direction. |
| 729 |
+ |
Lastly, all of the accumulated z constrained forces must be |
| 730 |
+ |
subtracted from the system to keep the system center of mass from |
| 731 |
+ |
drifting. |
| 732 |
+ |
\begin{equation} |
| 733 |
+ |
F_{\beta i} = F_{\beta i} - \frac{m_{\beta i} \sum_{\alpha} |
| 734 |
+ |
G_{\alpha}} |
| 735 |
+ |
{\sum_{\beta}\sum_i m_{\beta i}}, |
| 736 |
+ |
\end{equation} |
| 737 |
+ |
where $\beta$ are all of the unconstrained molecules in the system. |
| 738 |
+ |
Similarly, the velocities of the unconstrained molecules must also |
| 739 |
+ |
be scaled. |
| 740 |
+ |
\begin{equation} |
| 741 |
+ |
v_{\beta i} = v_{\beta i} + \sum_{\alpha} |
| 742 |
+ |
\frac{\sum_i m_{\alpha i} v_{\alpha i}}{\sum_i m_{\alpha i}}. |
| 743 |
+ |
\end{equation} |
| 744 |
+ |
|
| 745 |
+ |
At the very beginning of the simulation, the molecules may not be at |
| 746 |
+ |
their constrained positions. To move a z-constrained molecule to its |
| 747 |
+ |
specified position, a simple harmonic potential is used |
| 748 |
+ |
\begin{equation} |
| 749 |
+ |
U(t)=\frac{1}{2}k_{\text{Harmonic}}(z(t)-z_{\text{cons}})^{2},% |
| 750 |
+ |
\end{equation} |
| 751 |
+ |
where $k_{\text{Harmonic}}$ is the harmonic force constant, $z(t)$ |
| 752 |
+ |
is the current $z$ coordinate of the center of mass of the |
| 753 |
+ |
constrained molecule, and $z_{\text{cons}}$ is the constrained |
| 754 |
+ |
position. The harmonic force operating on the z-constrained molecule |
| 755 |
+ |
at time $t$ can be calculated by |
| 756 |
+ |
\begin{equation} |
| 757 |
+ |
F_{z_{\text{Harmonic}}}(t)=-\frac{\partial U(t)}{\partial z(t)}= |
| 758 |
+ |
-k_{\text{Harmonic}}(z(t)-z_{\text{cons}}). |
| 759 |
+ |
\end{equation} |
| 760 |
+ |
|
| 761 |
+ |
|
| 762 |
|
\section{\label{methodSection:langevin}Integrators for Langevin Dynamics of Rigid Bodies} |
| 763 |
|
|
| 764 |
< |
\subsection{\label{methodSection:temperature}Temperature Control} |
| 764 |
> |
%\subsection{\label{methodSection:temperature}Temperature Control} |
| 765 |
|
|
| 766 |
< |
\subsection{\label{methodSection:pressureControl}Pressure Control} |
| 766 |
> |
%\subsection{\label{methodSection:pressureControl}Pressure Control} |
| 767 |
|
|
| 768 |
< |
\section{\label{methodSection:hydrodynamics}Hydrodynamics} |
| 768 |
> |
%\section{\label{methodSection:hydrodynamics}Hydrodynamics} |
| 769 |
|
|
| 770 |
< |
%\section{\label{methodSection:coarseGrained}Coarse-Grained Modeling} |
| 770 |
> |
%applications of langevin dynamics |
| 771 |
> |
As an excellent alternative to newtonian dynamics, Langevin |
| 772 |
> |
dynamics, which mimics a simple heat bath with stochastic and |
| 773 |
> |
dissipative forces, has been applied in a variety of studies. The |
| 774 |
> |
stochastic treatment of the solvent enables us to carry out |
| 775 |
> |
substantially longer time simulation. Implicit solvent Langevin |
| 776 |
> |
dynamics simulation of met-enkephalin not only outperforms explicit |
| 777 |
> |
solvent simulation on computation efficiency, but also agrees very |
| 778 |
> |
well with explicit solvent simulation on dynamics |
| 779 |
> |
properties\cite{Shen2002}. Recently, applying Langevin dynamics with |
| 780 |
> |
UNRES model, Liow and his coworkers suggest that protein folding |
| 781 |
> |
pathways can be possibly exploited within a reasonable amount of |
| 782 |
> |
time\cite{Liwo2005}. The stochastic nature of the Langevin dynamics |
| 783 |
> |
also enhances the sampling of the system and increases the |
| 784 |
> |
probability of crossing energy barrier\cite{Banerjee2004, Cui2003}. |
| 785 |
> |
Combining Langevin dynamics with Kramers's theory, Klimov and |
| 786 |
> |
Thirumalai identified the free-energy barrier by studying the |
| 787 |
> |
viscosity dependence of the protein folding rates\cite{Klimov1997}. |
| 788 |
> |
In order to account for solvent induced interactions missing from |
| 789 |
> |
implicit solvent model, Kaya incorporated desolvation free energy |
| 790 |
> |
barrier into implicit coarse-grained solvent model in protein |
| 791 |
> |
folding/unfolding study and discovered a higher free energy barrier |
| 792 |
> |
between the native and denatured states. Because of its stability |
| 793 |
> |
against noise, Langevin dynamics is very suitable for studying |
| 794 |
> |
remagnetization processes in various |
| 795 |
> |
systems\cite{Palacios1998,Berkov2002,Denisov2003}. For instance, the |
| 796 |
> |
oscillation power spectrum of nanoparticles from Langevin dynamics |
| 797 |
> |
simulation has the same peak frequencies for different wave |
| 798 |
> |
vectors,which recovers the property of magnetic excitations in small |
| 799 |
> |
finite structures\cite{Berkov2005a}. In an attempt to reduce the |
| 800 |
> |
computational cost of simulation, multiple time stepping (MTS) |
| 801 |
> |
methods have been introduced and have been of great interest to |
| 802 |
> |
macromolecule and protein community\cite{Tuckerman1992}. Relying on |
| 803 |
> |
the observation that forces between distant atoms generally |
| 804 |
> |
demonstrate slower fluctuations than forces between close atoms, MTS |
| 805 |
> |
method are generally implemented by evaluating the slowly |
| 806 |
> |
fluctuating forces less frequently than the fast ones. |
| 807 |
> |
Unfortunately, nonlinear instability resulting from increasing |
| 808 |
> |
timestep in MTS simulation have became a critical obstruction |
| 809 |
> |
preventing the long time simulation. Due to the coupling to the heat |
| 810 |
> |
bath, Langevin dynamics has been shown to be able to damp out the |
| 811 |
> |
resonance artifact more efficiently\cite{Sandu1999}. |
| 812 |
|
|
| 813 |
< |
%\section{\label{methodSection:moleculeScale}Molecular-Scale Modeling} |
| 813 |
> |
It is very important to develop stable and efficient methods to |
| 814 |
> |
integrate the equations of motion of orientational degrees of |
| 815 |
> |
freedom. Euler angles are the nature choice to describe the |
| 816 |
> |
rotational degrees of freedom. However, due to its singularity, the |
| 817 |
> |
numerical integration of corresponding equations of motion is very |
| 818 |
> |
inefficient and inaccurate. Although an alternative integrator using |
| 819 |
> |
different sets of Euler angles can overcome this |
| 820 |
> |
difficulty\cite{Ryckaert1977, Andersen1983}, the computational |
| 821 |
> |
penalty and the lost of angular momentum conservation still remain. |
| 822 |
> |
In 1977, a singularity free representation utilizing quaternions was |
| 823 |
> |
developed by Evans\cite{Evans1977}. Unfortunately, this approach |
| 824 |
> |
suffer from the nonseparable Hamiltonian resulted from quaternion |
| 825 |
> |
representation, which prevents the symplectic algorithm to be |
| 826 |
> |
utilized. Another different approach is to apply holonomic |
| 827 |
> |
constraints to the atoms belonging to the rigid |
| 828 |
> |
body\cite{Barojas1973}. Each atom moves independently under the |
| 829 |
> |
normal forces deriving from potential energy and constraint forces |
| 830 |
> |
which are used to guarantee the rigidness. However, due to their |
| 831 |
> |
iterative nature, SHAKE and Rattle algorithm converge very slowly |
| 832 |
> |
when the number of constraint increases. |
| 833 |
> |
|
| 834 |
> |
The break through in geometric literature suggests that, in order to |
| 835 |
> |
develop a long-term integration scheme, one should preserve the |
| 836 |
> |
geometric structure of the flow. Matubayasi developed a |
| 837 |
> |
time-reversible integrator for rigid bodies in quaternion |
| 838 |
> |
representation. Although it is not symplectic, this integrator still |
| 839 |
> |
demonstrates a better long-time energy conservation than traditional |
| 840 |
> |
methods because of the time-reversible nature. Extending |
| 841 |
> |
Trotter-Suzuki to general system with a flat phase space, Miller and |
| 842 |
> |
his colleagues devised an novel symplectic, time-reversible and |
| 843 |
> |
volume-preserving integrator in quaternion representation. However, |
| 844 |
> |
all of the integrators in quaternion representation suffer from the |
| 845 |
> |
computational penalty of constructing a rotation matrix from |
| 846 |
> |
quaternions to evolve coordinates and velocities at every time step. |
| 847 |
> |
An alternative integration scheme utilizing rotation matrix directly |
| 848 |
> |
is RSHAKE\cite{Kol1997}, in which a conjugate momentum to rotation |
| 849 |
> |
matrix is introduced to re-formulate the Hamiltonian's equation and |
| 850 |
> |
the Hamiltonian is evolved in a constraint manifold by iteratively |
| 851 |
> |
satisfying the orthogonality constraint. However, RSHAKE is |
| 852 |
> |
inefficient because of the iterative procedure. An extremely |
| 853 |
> |
efficient integration scheme in rotation matrix representation, |
| 854 |
> |
which also preserves the same structural properties of the |
| 855 |
> |
Hamiltonian flow as Miller's integrator, is proposed by Dullweber, |
| 856 |
> |
Leimkuhler and McLachlan (DLM)\cite{Dullweber1997}. |
| 857 |
> |
|
| 858 |
> |
%review langevin/browninan dynamics for arbitrarily shaped rigid body |
| 859 |
> |
Combining Langevin or Brownian dynamics with rigid body dynamics, |
| 860 |
> |
one can study the slow processes in biomolecular systems. Modeling |
| 861 |
> |
the DNA as a chain of rigid spheres beads, which subject to harmonic |
| 862 |
> |
potentials as well as excluded volume potentials, Mielke and his |
| 863 |
> |
coworkers discover rapid superhelical stress generations from the |
| 864 |
> |
stochastic simulation of twin supercoiling DNA with response to |
| 865 |
> |
induced torques\cite{Mielke2004}. Membrane fusion is another key |
| 866 |
> |
biological process which controls a variety of physiological |
| 867 |
> |
functions, such as release of neurotransmitters \textit{etc}. A |
| 868 |
> |
typical fusion event happens on the time scale of millisecond, which |
| 869 |
> |
is impracticable to study using all atomistic model with newtonian |
| 870 |
> |
mechanics. With the help of coarse-grained rigid body model and |
| 871 |
> |
stochastic dynamics, the fusion pathways were exploited by many |
| 872 |
> |
researchers\cite{Noguchi2001,Noguchi2002,Shillcock2005}. Due to the |
| 873 |
> |
difficulty of numerical integration of anisotropy rotation, most of |
| 874 |
> |
the rigid body models are simply modeled by sphere, cylinder, |
| 875 |
> |
ellipsoid or other regular shapes in stochastic simulations. In an |
| 876 |
> |
effort to account for the diffusion anisotropy of the arbitrary |
| 877 |
> |
particles, Fernandes and de la Torre improved the original Brownian |
| 878 |
> |
dynamics simulation algorithm\cite{Ermak1978,Allison1991} by |
| 879 |
> |
incorporating a generalized $6\times6$ diffusion tensor and |
| 880 |
> |
introducing a simple rotation evolution scheme consisting of three |
| 881 |
> |
consecutive rotations\cite{Fernandes2002}. Unfortunately, unexpected |
| 882 |
> |
error and bias are introduced into the system due to the arbitrary |
| 883 |
> |
order of applying the noncommuting rotation |
| 884 |
> |
operators\cite{Beard2003}. Based on the observation the momentum |
| 885 |
> |
relaxation time is much less than the time step, one may ignore the |
| 886 |
> |
inertia in Brownian dynamics. However, assumption of the zero |
| 887 |
> |
average acceleration is not always true for cooperative motion which |
| 888 |
> |
is common in protein motion. An inertial Brownian dynamics (IBD) was |
| 889 |
> |
proposed to address this issue by adding an inertial correction |
| 890 |
> |
term\cite{Beard2003}. As a complement to IBD which has a lower bound |
| 891 |
> |
in time step because of the inertial relaxation time, long-time-step |
| 892 |
> |
inertial dynamics (LTID) can be used to investigate the inertial |
| 893 |
> |
behavior of the polymer segments in low friction |
| 894 |
> |
regime\cite{Beard2003}. LTID can also deal with the rotational |
| 895 |
> |
dynamics for nonskew bodies without translation-rotation coupling by |
| 896 |
> |
separating the translation and rotation motion and taking advantage |
| 897 |
> |
of the analytical solution of hydrodynamics properties. However, |
| 898 |
> |
typical nonskew bodies like cylinder and ellipsoid are inadequate to |
| 899 |
> |
represent most complex macromolecule assemblies. These intricate |
| 900 |
> |
molecules have been represented by a set of beads and their |
| 901 |
> |
hydrodynamics properties can be calculated using variant |
| 902 |
> |
hydrodynamic interaction tensors. |
| 903 |
> |
|
| 904 |
> |
The goal of the present work is to develop a Langevin dynamics |
| 905 |
> |
algorithm for arbitrary rigid particles by integrating the accurate |
| 906 |
> |
estimation of friction tensor from hydrodynamics theory into the |
| 907 |
> |
sophisticated rigid body dynamics. |
| 908 |
> |
|
| 909 |
> |
\subsection{\label{introSection:frictionTensor} Friction Tensor} |
| 910 |
> |
Theoretically, the friction kernel can be determined using velocity |
| 911 |
> |
autocorrelation function. However, this approach become impractical |
| 912 |
> |
when the system become more and more complicate. Instead, various |
| 913 |
> |
approaches based on hydrodynamics have been developed to calculate |
| 914 |
> |
the friction coefficients. The friction effect is isotropic in |
| 915 |
> |
Equation, $\zeta$ can be taken as a scalar. In general, friction |
| 916 |
> |
tensor $\Xi$ is a $6\times 6$ matrix given by |
| 917 |
> |
\[ |
| 918 |
> |
\Xi = \left( {\begin{array}{*{20}c} |
| 919 |
> |
{\Xi _{}^{tt} } & {\Xi _{}^{rt} } \\ |
| 920 |
> |
{\Xi _{}^{tr} } & {\Xi _{}^{rr} } \\ |
| 921 |
> |
\end{array}} \right). |
| 922 |
> |
\] |
| 923 |
> |
Here, $ {\Xi^{tt} }$ and $ {\Xi^{rr} }$ are translational friction |
| 924 |
> |
tensor and rotational resistance (friction) tensor respectively, |
| 925 |
> |
while ${\Xi^{tr} }$ is translation-rotation coupling tensor and $ |
| 926 |
> |
{\Xi^{rt} }$ is rotation-translation coupling tensor. When a |
| 927 |
> |
particle moves in a fluid, it may experience friction force or |
| 928 |
> |
torque along the opposite direction of the velocity or angular |
| 929 |
> |
velocity, |
| 930 |
> |
\[ |
| 931 |
> |
\left( \begin{array}{l} |
| 932 |
> |
F_R \\ |
| 933 |
> |
\tau _R \\ |
| 934 |
> |
\end{array} \right) = - \left( {\begin{array}{*{20}c} |
| 935 |
> |
{\Xi ^{tt} } & {\Xi ^{rt} } \\ |
| 936 |
> |
{\Xi ^{tr} } & {\Xi ^{rr} } \\ |
| 937 |
> |
\end{array}} \right)\left( \begin{array}{l} |
| 938 |
> |
v \\ |
| 939 |
> |
w \\ |
| 940 |
> |
\end{array} \right) |
| 941 |
> |
\] |
| 942 |
> |
where $F_r$ is the friction force and $\tau _R$ is the friction |
| 943 |
> |
toque. |
| 944 |
> |
|
| 945 |
> |
\subsubsection{\label{introSection:resistanceTensorRegular}\textbf{The Resistance Tensor for Regular Shape}} |
| 946 |
> |
|
| 947 |
> |
For a spherical particle, the translational and rotational friction |
| 948 |
> |
constant can be calculated from Stoke's law, |
| 949 |
> |
\[ |
| 950 |
> |
\Xi ^{tt} = \left( {\begin{array}{*{20}c} |
| 951 |
> |
{6\pi \eta R} & 0 & 0 \\ |
| 952 |
> |
0 & {6\pi \eta R} & 0 \\ |
| 953 |
> |
0 & 0 & {6\pi \eta R} \\ |
| 954 |
> |
\end{array}} \right) |
| 955 |
> |
\] |
| 956 |
> |
and |
| 957 |
> |
\[ |
| 958 |
> |
\Xi ^{rr} = \left( {\begin{array}{*{20}c} |
| 959 |
> |
{8\pi \eta R^3 } & 0 & 0 \\ |
| 960 |
> |
0 & {8\pi \eta R^3 } & 0 \\ |
| 961 |
> |
0 & 0 & {8\pi \eta R^3 } \\ |
| 962 |
> |
\end{array}} \right) |
| 963 |
> |
\] |
| 964 |
> |
where $\eta$ is the viscosity of the solvent and $R$ is the |
| 965 |
> |
hydrodynamics radius. |
| 966 |
> |
|
| 967 |
> |
Other non-spherical shape, such as cylinder and ellipsoid |
| 968 |
> |
\textit{etc}, are widely used as reference for developing new |
| 969 |
> |
hydrodynamics theory, because their properties can be calculated |
| 970 |
> |
exactly. In 1936, Perrin extended Stokes's law to general ellipsoid, |
| 971 |
> |
also called a triaxial ellipsoid, which is given in Cartesian |
| 972 |
> |
coordinates by\cite{Perrin1934, Perrin1936} |
| 973 |
> |
\[ |
| 974 |
> |
\frac{{x^2 }}{{a^2 }} + \frac{{y^2 }}{{b^2 }} + \frac{{z^2 }}{{c^2 |
| 975 |
> |
}} = 1 |
| 976 |
> |
\] |
| 977 |
> |
where the semi-axes are of lengths $a$, $b$, and $c$. Unfortunately, |
| 978 |
> |
due to the complexity of the elliptic integral, only the ellipsoid |
| 979 |
> |
with the restriction of two axes having to be equal, \textit{i.e.} |
| 980 |
> |
prolate($ a \ge b = c$) and oblate ($ a < b = c $), can be solved |
| 981 |
> |
exactly. Introducing an elliptic integral parameter $S$ for prolate, |
| 982 |
> |
\[ |
| 983 |
> |
S = \frac{2}{{\sqrt {a^2 - b^2 } }}\ln \frac{{a + \sqrt {a^2 - b^2 |
| 984 |
> |
} }}{b}, |
| 985 |
> |
\] |
| 986 |
> |
and oblate, |
| 987 |
> |
\[ |
| 988 |
> |
S = \frac{2}{{\sqrt {b^2 - a^2 } }}arctg\frac{{\sqrt {b^2 - a^2 } |
| 989 |
> |
}}{a} |
| 990 |
> |
\], |
| 991 |
> |
one can write down the translational and rotational resistance |
| 992 |
> |
tensors |
| 993 |
> |
\[ |
| 994 |
> |
\begin{array}{l} |
| 995 |
> |
\Xi _a^{tt} = 16\pi \eta \frac{{a^2 - b^2 }}{{(2a^2 - b^2 )S - 2a}} \\ |
| 996 |
> |
\Xi _b^{tt} = \Xi _c^{tt} = 32\pi \eta \frac{{a^2 - b^2 }}{{(2a^2 - 3b^2 )S + 2a}} \\ |
| 997 |
> |
\end{array}, |
| 998 |
> |
\] |
| 999 |
> |
and |
| 1000 |
> |
\[ |
| 1001 |
> |
\begin{array}{l} |
| 1002 |
> |
\Xi _a^{rr} = \frac{{32\pi }}{3}\eta \frac{{(a^2 - b^2 )b^2 }}{{2a - b^2 S}} \\ |
| 1003 |
> |
\Xi _b^{rr} = \Xi _c^{rr} = \frac{{32\pi }}{3}\eta \frac{{(a^4 - b^4 )}}{{(2a^2 - b^2 )S - 2a}} \\ |
| 1004 |
> |
\end{array}. |
| 1005 |
> |
\] |
| 1006 |
> |
|
| 1007 |
> |
\subsubsection{\label{introSection:resistanceTensorRegularArbitrary}\textbf{The Resistance Tensor for Arbitrary Shape}} |
| 1008 |
> |
|
| 1009 |
> |
Unlike spherical and other regular shaped molecules, there is not |
| 1010 |
> |
analytical solution for friction tensor of any arbitrary shaped |
| 1011 |
> |
rigid molecules. The ellipsoid of revolution model and general |
| 1012 |
> |
triaxial ellipsoid model have been used to approximate the |
| 1013 |
> |
hydrodynamic properties of rigid bodies. However, since the mapping |
| 1014 |
> |
from all possible ellipsoidal space, $r$-space, to all possible |
| 1015 |
> |
combination of rotational diffusion coefficients, $D$-space is not |
| 1016 |
> |
unique\cite{Wegener1979} as well as the intrinsic coupling between |
| 1017 |
> |
translational and rotational motion of rigid body, general ellipsoid |
| 1018 |
> |
is not always suitable for modeling arbitrarily shaped rigid |
| 1019 |
> |
molecule. A number of studies have been devoted to determine the |
| 1020 |
> |
friction tensor for irregularly shaped rigid bodies using more |
| 1021 |
> |
advanced method where the molecule of interest was modeled by |
| 1022 |
> |
combinations of spheres(beads)\cite{Carrasco1999} and the |
| 1023 |
> |
hydrodynamics properties of the molecule can be calculated using the |
| 1024 |
> |
hydrodynamic interaction tensor. Let us consider a rigid assembly of |
| 1025 |
> |
$N$ beads immersed in a continuous medium. Due to hydrodynamics |
| 1026 |
> |
interaction, the ``net'' velocity of $i$th bead, $v'_i$ is different |
| 1027 |
> |
than its unperturbed velocity $v_i$, |
| 1028 |
> |
\[ |
| 1029 |
> |
v'_i = v_i - \sum\limits_{j \ne i} {T_{ij} F_j } |
| 1030 |
> |
\] |
| 1031 |
> |
where $F_i$ is the frictional force, and $T_{ij}$ is the |
| 1032 |
> |
hydrodynamic interaction tensor. The friction force of $i$th bead is |
| 1033 |
> |
proportional to its ``net'' velocity |
| 1034 |
> |
\begin{equation} |
| 1035 |
> |
F_i = \zeta _i v_i - \zeta _i \sum\limits_{j \ne i} {T_{ij} F_j }. |
| 1036 |
> |
\label{introEquation:tensorExpression} |
| 1037 |
> |
\end{equation} |
| 1038 |
> |
This equation is the basis for deriving the hydrodynamic tensor. In |
| 1039 |
> |
1930, Oseen and Burgers gave a simple solution to Equation |
| 1040 |
> |
\ref{introEquation:tensorExpression} |
| 1041 |
> |
\begin{equation} |
| 1042 |
> |
T_{ij} = \frac{1}{{8\pi \eta r_{ij} }}\left( {I + \frac{{R_{ij} |
| 1043 |
> |
R_{ij}^T }}{{R_{ij}^2 }}} \right). \label{introEquation:oseenTensor} |
| 1044 |
> |
\end{equation} |
| 1045 |
> |
Here $R_{ij}$ is the distance vector between bead $i$ and bead $j$. |
| 1046 |
> |
A second order expression for element of different size was |
| 1047 |
> |
introduced by Rotne and Prager\cite{Rotne1969} and improved by |
| 1048 |
> |
Garc\'{i}a de la Torre and Bloomfield\cite{Torre1977}, |
| 1049 |
> |
\begin{equation} |
| 1050 |
> |
T_{ij} = \frac{1}{{8\pi \eta R_{ij} }}\left[ {\left( {I + |
| 1051 |
> |
\frac{{R_{ij} R_{ij}^T }}{{R_{ij}^2 }}} \right) + R\frac{{\sigma |
| 1052 |
> |
_i^2 + \sigma _j^2 }}{{r_{ij}^2 }}\left( {\frac{I}{3} - |
| 1053 |
> |
\frac{{R_{ij} R_{ij}^T }}{{R_{ij}^2 }}} \right)} \right]. |
| 1054 |
> |
\label{introEquation:RPTensorNonOverlapped} |
| 1055 |
> |
\end{equation} |
| 1056 |
> |
Both of the Equation \ref{introEquation:oseenTensor} and Equation |
| 1057 |
> |
\ref{introEquation:RPTensorNonOverlapped} have an assumption $R_{ij} |
| 1058 |
> |
\ge \sigma _i + \sigma _j$. An alternative expression for |
| 1059 |
> |
overlapping beads with the same radius, $\sigma$, is given by |
| 1060 |
> |
\begin{equation} |
| 1061 |
> |
T_{ij} = \frac{1}{{6\pi \eta R_{ij} }}\left[ {\left( {1 - |
| 1062 |
> |
\frac{2}{{32}}\frac{{R_{ij} }}{\sigma }} \right)I + |
| 1063 |
> |
\frac{2}{{32}}\frac{{R_{ij} R_{ij}^T }}{{R_{ij} \sigma }}} \right] |
| 1064 |
> |
\label{introEquation:RPTensorOverlapped} |
| 1065 |
> |
\end{equation} |
| 1066 |
> |
|
| 1067 |
> |
To calculate the resistance tensor at an arbitrary origin $O$, we |
| 1068 |
> |
construct a $3N \times 3N$ matrix consisting of $N \times N$ |
| 1069 |
> |
$B_{ij}$ blocks |
| 1070 |
> |
\begin{equation} |
| 1071 |
> |
B = \left( {\begin{array}{*{20}c} |
| 1072 |
> |
{B_{11} } & \ldots & {B_{1N} } \\ |
| 1073 |
> |
\vdots & \ddots & \vdots \\ |
| 1074 |
> |
{B_{N1} } & \cdots & {B_{NN} } \\ |
| 1075 |
> |
\end{array}} \right), |
| 1076 |
> |
\end{equation} |
| 1077 |
> |
where $B_{ij}$ is given by |
| 1078 |
> |
\[ |
| 1079 |
> |
B_{ij} = \delta _{ij} \frac{I}{{6\pi \eta R}} + (1 - \delta _{ij} |
| 1080 |
> |
)T_{ij} |
| 1081 |
> |
\] |
| 1082 |
> |
where $\delta _{ij}$ is Kronecker delta function. Inverting matrix |
| 1083 |
> |
$B$, we obtain |
| 1084 |
> |
|
| 1085 |
> |
\[ |
| 1086 |
> |
C = B^{ - 1} = \left( {\begin{array}{*{20}c} |
| 1087 |
> |
{C_{11} } & \ldots & {C_{1N} } \\ |
| 1088 |
> |
\vdots & \ddots & \vdots \\ |
| 1089 |
> |
{C_{N1} } & \cdots & {C_{NN} } \\ |
| 1090 |
> |
\end{array}} \right) |
| 1091 |
> |
\] |
| 1092 |
> |
, which can be partitioned into $N \times N$ $3 \times 3$ block |
| 1093 |
> |
$C_{ij}$. With the help of $C_{ij}$ and skew matrix $U_i$ |
| 1094 |
> |
\[ |
| 1095 |
> |
U_i = \left( {\begin{array}{*{20}c} |
| 1096 |
> |
0 & { - z_i } & {y_i } \\ |
| 1097 |
> |
{z_i } & 0 & { - x_i } \\ |
| 1098 |
> |
{ - y_i } & {x_i } & 0 \\ |
| 1099 |
> |
\end{array}} \right) |
| 1100 |
> |
\] |
| 1101 |
> |
where $x_i$, $y_i$, $z_i$ are the components of the vector joining |
| 1102 |
> |
bead $i$ and origin $O$. Hence, the elements of resistance tensor at |
| 1103 |
> |
arbitrary origin $O$ can be written as |
| 1104 |
> |
\begin{equation} |
| 1105 |
> |
\begin{array}{l} |
| 1106 |
> |
\Xi _{}^{tt} = \sum\limits_i {\sum\limits_j {C_{ij} } } , \\ |
| 1107 |
> |
\Xi _{}^{tr} = \Xi _{}^{rt} = \sum\limits_i {\sum\limits_j {U_i C_{ij} } } , \\ |
| 1108 |
> |
\Xi _{}^{rr} = - \sum\limits_i {\sum\limits_j {U_i C_{ij} } } U_j \\ |
| 1109 |
> |
\end{array} |
| 1110 |
> |
\label{introEquation:ResistanceTensorArbitraryOrigin} |
| 1111 |
> |
\end{equation} |
| 1112 |
> |
|
| 1113 |
> |
The resistance tensor depends on the origin to which they refer. The |
| 1114 |
> |
proper location for applying friction force is the center of |
| 1115 |
> |
resistance (reaction), at which the trace of rotational resistance |
| 1116 |
> |
tensor, $ \Xi ^{rr}$ reaches minimum. Mathematically, the center of |
| 1117 |
> |
resistance is defined as an unique point of the rigid body at which |
| 1118 |
> |
the translation-rotation coupling tensor are symmetric, |
| 1119 |
> |
\begin{equation} |
| 1120 |
> |
\Xi^{tr} = \left( {\Xi^{tr} } \right)^T |
| 1121 |
> |
\label{introEquation:definitionCR} |
| 1122 |
> |
\end{equation} |
| 1123 |
> |
Form Equation \ref{introEquation:ResistanceTensorArbitraryOrigin}, |
| 1124 |
> |
we can easily find out that the translational resistance tensor is |
| 1125 |
> |
origin independent, while the rotational resistance tensor and |
| 1126 |
> |
translation-rotation coupling resistance tensor depend on the |
| 1127 |
> |
origin. Given resistance tensor at an arbitrary origin $O$, and a |
| 1128 |
> |
vector ,$r_{OP}(x_{OP}, y_{OP}, z_{OP})$, from $O$ to $P$, we can |
| 1129 |
> |
obtain the resistance tensor at $P$ by |
| 1130 |
> |
\begin{equation} |
| 1131 |
> |
\begin{array}{l} |
| 1132 |
> |
\Xi _P^{tt} = \Xi _O^{tt} \\ |
| 1133 |
> |
\Xi _P^{tr} = \Xi _P^{rt} = \Xi _O^{tr} - U_{OP} \Xi _O^{tt} \\ |
| 1134 |
> |
\Xi _P^{rr} = \Xi _O^{rr} - U_{OP} \Xi _O^{tt} U_{OP} + \Xi _O^{tr} U_{OP} - U_{OP} \Xi _O^{{tr} ^{^T }} \\ |
| 1135 |
> |
\end{array} |
| 1136 |
> |
\label{introEquation:resistanceTensorTransformation} |
| 1137 |
> |
\end{equation} |
| 1138 |
> |
where |
| 1139 |
> |
\[ |
| 1140 |
> |
U_{OP} = \left( {\begin{array}{*{20}c} |
| 1141 |
> |
0 & { - z_{OP} } & {y_{OP} } \\ |
| 1142 |
> |
{z_i } & 0 & { - x_{OP} } \\ |
| 1143 |
> |
{ - y_{OP} } & {x_{OP} } & 0 \\ |
| 1144 |
> |
\end{array}} \right) |
| 1145 |
> |
\] |
| 1146 |
> |
Using Equations \ref{introEquation:definitionCR} and |
| 1147 |
> |
\ref{introEquation:resistanceTensorTransformation}, one can locate |
| 1148 |
> |
the position of center of resistance, |
| 1149 |
> |
\begin{eqnarray*} |
| 1150 |
> |
\left( \begin{array}{l} |
| 1151 |
> |
x_{OR} \\ |
| 1152 |
> |
y_{OR} \\ |
| 1153 |
> |
z_{OR} \\ |
| 1154 |
> |
\end{array} \right) & = &\left( {\begin{array}{*{20}c} |
| 1155 |
> |
{(\Xi _O^{rr} )_{yy} + (\Xi _O^{rr} )_{zz} } & { - (\Xi _O^{rr} )_{xy} } & { - (\Xi _O^{rr} )_{xz} } \\ |
| 1156 |
> |
{ - (\Xi _O^{rr} )_{xy} } & {(\Xi _O^{rr} )_{zz} + (\Xi _O^{rr} )_{xx} } & { - (\Xi _O^{rr} )_{yz} } \\ |
| 1157 |
> |
{ - (\Xi _O^{rr} )_{xz} } & { - (\Xi _O^{rr} )_{yz} } & {(\Xi _O^{rr} )_{xx} + (\Xi _O^{rr} )_{yy} } \\ |
| 1158 |
> |
\end{array}} \right)^{ - 1} \\ |
| 1159 |
> |
& & \left( \begin{array}{l} |
| 1160 |
> |
(\Xi _O^{tr} )_{yz} - (\Xi _O^{tr} )_{zy} \\ |
| 1161 |
> |
(\Xi _O^{tr} )_{zx} - (\Xi _O^{tr} )_{xz} \\ |
| 1162 |
> |
(\Xi _O^{tr} )_{xy} - (\Xi _O^{tr} )_{yx} \\ |
| 1163 |
> |
\end{array} \right) \\ |
| 1164 |
> |
\end{eqnarray*} |
| 1165 |
> |
|
| 1166 |
> |
where $x_OR$, $y_OR$, $z_OR$ are the components of the vector |
| 1167 |
> |
joining center of resistance $R$ and origin $O$. |
| 1168 |
> |
|
| 1169 |
> |
\subsection{Langevin dynamics for rigid particles of arbitrary shape} |
| 1170 |
> |
|
| 1171 |
> |
Consider a Langevin equation of motions in generalized coordinates |
| 1172 |
> |
\begin{equation} |
| 1173 |
> |
M_i \dot V_i (t) = F_{s,i} (t) + F_{f,i(t)} + F_{r,i} (t) |
| 1174 |
> |
\label{LDGeneralizedForm} |
| 1175 |
> |
\end{equation} |
| 1176 |
> |
where $M_i$ is a $6\times6$ generalized diagonal mass (include mass |
| 1177 |
> |
and moment of inertial) matrix and $V_i$ is a generalized velocity, |
| 1178 |
> |
$V_i = V_i(v_i,\omega _i)$. The right side of Eq. |
| 1179 |
> |
(\ref{LDGeneralizedForm}) consists of three generalized forces in |
| 1180 |
> |
lab-fixed frame, systematic force $F_{s,i}$, dissipative force |
| 1181 |
> |
$F_{f,i}$ and stochastic force $F_{r,i}$. While the evolution of the |
| 1182 |
> |
system in Newtownian mechanics typically refers to lab-fixed frame, |
| 1183 |
> |
it is also convenient to handle the rotation of rigid body in |
| 1184 |
> |
body-fixed frame. Thus the friction and random forces are calculated |
| 1185 |
> |
in body-fixed frame and converted back to lab-fixed frame by: |
| 1186 |
> |
\[ |
| 1187 |
> |
\begin{array}{l} |
| 1188 |
> |
F_{f,i}^l (t) = A^T F_{f,i}^b (t), \\ |
| 1189 |
> |
F_{r,i}^l (t) = A^T F_{r,i}^b (t) \\ |
| 1190 |
> |
\end{array}. |
| 1191 |
> |
\] |
| 1192 |
> |
Here, the body-fixed friction force $F_{r,i}^b$ is proportional to |
| 1193 |
> |
the body-fixed velocity at center of resistance $v_{R,i}^b$ and |
| 1194 |
> |
angular velocity $\omega _i$, |
| 1195 |
> |
\begin{equation} |
| 1196 |
> |
F_{r,i}^b (t) = \left( \begin{array}{l} |
| 1197 |
> |
f_{r,i}^b (t) \\ |
| 1198 |
> |
\tau _{r,i}^b (t) \\ |
| 1199 |
> |
\end{array} \right) = - \left( {\begin{array}{*{20}c} |
| 1200 |
> |
{\Xi _{R,t} } & {\Xi _{R,c}^T } \\ |
| 1201 |
> |
{\Xi _{R,c} } & {\Xi _{R,r} } \\ |
| 1202 |
> |
\end{array}} \right)\left( \begin{array}{l} |
| 1203 |
> |
v_{R,i}^b (t) \\ |
| 1204 |
> |
\omega _i (t) \\ |
| 1205 |
> |
\end{array} \right), |
| 1206 |
> |
\end{equation} |
| 1207 |
> |
while the random force $F_{r,i}^l$ is a Gaussian stochastic variable |
| 1208 |
> |
with zero mean and variance |
| 1209 |
> |
\begin{equation} |
| 1210 |
> |
\left\langle {F_{r,i}^l (t)(F_{r,i}^l (t'))^T } \right\rangle = |
| 1211 |
> |
\left\langle {F_{r,i}^b (t)(F_{r,i}^b (t'))^T } \right\rangle = |
| 1212 |
> |
2k_B T\Xi _R \delta (t - t'). |
| 1213 |
> |
\end{equation} |
| 1214 |
> |
The equation of motion for $v_i$ can be written as |
| 1215 |
> |
\begin{equation} |
| 1216 |
> |
m\dot v_i (t) = f_{t,i} (t) = f_{s,i} (t) + f_{f,i}^l (t) + |
| 1217 |
> |
f_{r,i}^l (t) |
| 1218 |
> |
\end{equation} |
| 1219 |
> |
Since the frictional force is applied at the center of resistance |
| 1220 |
> |
which generally does not coincide with the center of mass, an extra |
| 1221 |
> |
torque is exerted at the center of mass. Thus, the net body-fixed |
| 1222 |
> |
frictional torque at the center of mass, $\tau _{n,i}^b (t)$, is |
| 1223 |
> |
given by |
| 1224 |
> |
\begin{equation} |
| 1225 |
> |
\tau _{r,i}^b = \tau _{r,i}^b +r_{MR} \times f_{r,i}^b |
| 1226 |
> |
\end{equation} |
| 1227 |
> |
where $r_{MR}$ is the vector from the center of mass to the center |
| 1228 |
> |
of the resistance. Instead of integrating angular velocity in |
| 1229 |
> |
lab-fixed frame, we consider the equation of motion of angular |
| 1230 |
> |
momentum in body-fixed frame |
| 1231 |
> |
\begin{equation} |
| 1232 |
> |
\dot \pi _i (t) = \tau _{t,i} (t) = \tau _{s,i} (t) + \tau _{f,i}^b |
| 1233 |
> |
(t) + \tau _{r,i}^b(t) |
| 1234 |
> |
\end{equation} |
| 1235 |
> |
|
| 1236 |
> |
Embedding the friction terms into force and torque, one can |
| 1237 |
> |
integrate the langevin equations of motion for rigid body of |
| 1238 |
> |
arbitrary shape in a velocity-Verlet style 2-part algorithm, where |
| 1239 |
> |
$h= \delta t$: |
| 1240 |
> |
|
| 1241 |
> |
{\tt part one:} |
| 1242 |
> |
\begin{align*} |
| 1243 |
> |
v_i (t + h/2) &\leftarrow v_i (t) + \frac{{hf_{t,i}^l (t)}}{{2m_i }} \\ |
| 1244 |
> |
\pi _i (t + h/2) &\leftarrow \pi _i (t) + \frac{{h\tau _{t,i}^b (t)}}{2} \\ |
| 1245 |
> |
r_i (t + h) &\leftarrow r_i (t) + hv_i (t + h/2) \\ |
| 1246 |
> |
A_i (t + h) &\leftarrow rotate\left( {h\pi _i (t + h/2)I^{ - 1} } \right) \\ |
| 1247 |
> |
\end{align*} |
| 1248 |
> |
In this context, the $\mathrm{rotate}$ function is the reversible |
| 1249 |
> |
product of five consecutive body-fixed rotations, |
| 1250 |
> |
\begin{equation} |
| 1251 |
> |
\mathrm{rotate}({\bf a}) = \mathsf{G}_x(a_x / 2) \cdot |
| 1252 |
> |
\mathsf{G}_y(a_y / 2) \cdot \mathsf{G}_z(a_z) \cdot \mathsf{G}_y(a_y |
| 1253 |
> |
/ 2) \cdot \mathsf{G}_x(a_x /2), |
| 1254 |
> |
\end{equation} |
| 1255 |
> |
where each rotational propagator, $\mathsf{G}_\alpha(\theta)$, |
| 1256 |
> |
rotates both the rotation matrix ($\mathsf{A}$) and the body-fixed |
| 1257 |
> |
angular momentum ($\pi$) by an angle $\theta$ around body-fixed axis |
| 1258 |
> |
$\alpha$, |
| 1259 |
> |
\begin{equation} |
| 1260 |
> |
\mathsf{G}_\alpha( \theta ) = \left\{ |
| 1261 |
> |
\begin{array}{lcl} |
| 1262 |
> |
\mathsf{A}(t) & \leftarrow & \mathsf{A}(0) \cdot \mathsf{R}_\alpha(\theta)^T, \\ |
| 1263 |
> |
{\bf j}(t) & \leftarrow & \mathsf{R}_\alpha(\theta) \cdot {\bf |
| 1264 |
> |
j}(0). |
| 1265 |
> |
\end{array} |
| 1266 |
> |
\right. |
| 1267 |
> |
\end{equation} |
| 1268 |
> |
$\mathsf{R}_\alpha$ is a quadratic approximation to the single-axis |
| 1269 |
> |
rotation matrix. For example, in the small-angle limit, the |
| 1270 |
> |
rotation matrix around the body-fixed x-axis can be approximated as |
| 1271 |
> |
\begin{equation} |
| 1272 |
> |
\mathsf{R}_x(\theta) \approx \left( |
| 1273 |
> |
\begin{array}{ccc} |
| 1274 |
> |
1 & 0 & 0 \\ |
| 1275 |
> |
0 & \frac{1-\theta^2 / 4}{1 + \theta^2 / 4} & -\frac{\theta}{1+ |
| 1276 |
> |
\theta^2 / 4} \\ |
| 1277 |
> |
0 & \frac{\theta}{1+ \theta^2 / 4} & \frac{1-\theta^2 / 4}{1 + |
| 1278 |
> |
\theta^2 / 4} |
| 1279 |
> |
\end{array} |
| 1280 |
> |
\right). |
| 1281 |
> |
\end{equation} |
| 1282 |
> |
All other rotations follow in a straightforward manner. |
| 1283 |
> |
|
| 1284 |
> |
After the first part of the propagation, the friction and random |
| 1285 |
> |
forces are generated at the center of resistance in body-fixed frame |
| 1286 |
> |
and converted back into lab-fixed frame |
| 1287 |
> |
\[ |
| 1288 |
> |
f_{t,i}^l (t + h) = - \left( {\frac{{\partial V}}{{\partial r_i }}} |
| 1289 |
> |
\right)_{r_i (t + h)} + A_i^T (t + h)[F_{f,i}^b (t + h) + F_{r,i}^b |
| 1290 |
> |
(t + h)], |
| 1291 |
> |
\] |
| 1292 |
> |
while the system torque in lab-fixed frame is transformed into |
| 1293 |
> |
body-fixed frame, |
| 1294 |
> |
\[ |
| 1295 |
> |
\tau _{t,i}^b (t + h) = A\tau _{s,i}^l (t) + \tau _{n,i}^b (t) + |
| 1296 |
> |
\tau _{r,i}^b (t). |
| 1297 |
> |
\] |
| 1298 |
> |
Once the forces and torques have been obtained at the new time step, |
| 1299 |
> |
the velocities can be advanced to the same time value. |
| 1300 |
> |
|
| 1301 |
> |
{\tt part two:} |
| 1302 |
> |
\begin{align*} |
| 1303 |
> |
v_i (t) &\leftarrow v_i (t + h/2) + \frac{{hf_{t,i}^l (t + h)}}{{2m_i }} \\ |
| 1304 |
> |
\pi _i (t) &\leftarrow \pi _i (t + h/2) + \frac{{h\tau _{t,i}^b (t + h)}}{2} \\ |
| 1305 |
> |
\end{align*} |
| 1306 |
> |
|
| 1307 |
> |
\subsection{Results and discussion} |
