68 |
|
The stochastic nature of Langevin dynamics also enhances the sampling |
69 |
|
of the system and increases the probability of crossing energy |
70 |
|
barriers.\cite{Cui2003,Banerjee2004} Combining Langevin dynamics with |
71 |
< |
Kramers's theory, Klimov and Thirumalai identified free-energy |
71 |
> |
Kramers' theory, Klimov and Thirumalai identified free-energy |
72 |
|
barriers by studying the viscosity dependence of the protein folding |
73 |
|
rates.\cite{Klimov1997} In order to account for solvent induced |
74 |
|
interactions missing from the implicit solvent model, Kaya |
85 |
|
finite structures.\cite{Berkov2005a}] |
86 |
|
|
87 |
|
In typical LD simulations, the friction and random forces on |
88 |
< |
individual atoms are taken from the Stokes-Einstein hydrodynamic |
89 |
< |
approximation, |
88 |
> |
individual atoms are taken from Stokes' law, |
89 |
|
\begin{eqnarray} |
90 |
|
m \dot{v}(t) & = & -\nabla U(x) - \xi m v(t) + R(t) \\ |
91 |
|
\langle R(t) \rangle & = & 0 \\ |
94 |
|
where $\xi \approx 6 \pi \eta a$. Here $\eta$ is the viscosity of the |
95 |
|
implicit solvent, and $a$ is the hydrodynamic radius of the atom. |
96 |
|
|
97 |
< |
The use of rigid substructures,\cite{???} |
98 |
< |
coarse-graining,\cite{Ayton,Sun,Zannoni} and ellipsoidal |
99 |
< |
representations of protein side chains~\cite{Schulten} has made the |
100 |
< |
use of the Stokes-Einstein approximation problematic. A rigid |
101 |
< |
substructure moves as a single unit with orientational as well as |
102 |
< |
translational degrees of freedom. This requires a more general |
97 |
> |
The use of rigid substructures,\cite{Chun:2000fj} |
98 |
> |
coarse-graining,\cite{Ayton01,Golubkov06,Orlandi:2006fk,SunGezelter08} |
99 |
> |
and ellipsoidal representations of protein side chains~\cite{Fogolari:1996lr} |
100 |
> |
has made the use of the Stokes-Einstein approximation problematic. A |
101 |
> |
rigid substructure moves as a single unit with orientational as well |
102 |
> |
as translational degrees of freedom. This requires a more general |
103 |
|
treatment of the hydrodynamics than the spherical approximation |
104 |
|
provides. The atoms involved in a rigid or coarse-grained structure |
105 |
|
should properly have solvent-mediated interactions with each |
106 |
|
other. The theory of interactions {\it between} bodies moving through |
107 |
|
a fluid has been developed over the past century and has been applied |
108 |
|
to simulations of Brownian |
109 |
< |
motion.\cite{MarshallNewton,GarciaDeLaTorre} There a need to have a |
111 |
< |
more thorough treatment of hydrodynamics included in the library of |
112 |
< |
methods available for performing Langevin simulations. |
109 |
> |
motion.\cite{FIXMAN:1986lr,Ramachandran1996} |
110 |
|
|
111 |
+ |
In order to account for the diffusion anisotropy of arbitrarily-shaped |
112 |
+ |
particles, Fernandes and Garc\'{i}a de la Torre improved the original |
113 |
+ |
Brownian dynamics simulation algorithm~\cite{Ermak1978,Allison1991} by |
114 |
+ |
incorporating a generalized $6\times6$ diffusion tensor and |
115 |
+ |
introducing a rotational evolution scheme consisting of three |
116 |
+ |
consecutive rotations.\cite{Fernandes2002} Unfortunately, biases are |
117 |
+ |
introduced into the system due to the arbitrary order of applying the |
118 |
+ |
noncommuting rotation operators.\cite{Beard2003} Based on the |
119 |
+ |
observation the momentum relaxation time is much less than the time |
120 |
+ |
step, one may ignore the inertia in Brownian dynamics. However, the |
121 |
+ |
assumption of zero average acceleration is not always true for |
122 |
+ |
cooperative motion which is common in proteins. An inertial Brownian |
123 |
+ |
dynamics (IBD) was proposed to address this issue by adding an |
124 |
+ |
inertial correction term.\cite{Beard2000} As a complement to IBD which |
125 |
+ |
has a lower bound in time step because of the inertial relaxation |
126 |
+ |
time, long-time-step inertial dynamics (LTID) can be used to |
127 |
+ |
investigate the inertial behavior of linked polymer segments in a low |
128 |
+ |
friction regime.\cite{Beard2000} LTID can also deal with the |
129 |
+ |
rotational dynamics for nonskew bodies without translation-rotation |
130 |
+ |
coupling by separating the translation and rotation motion and taking |
131 |
+ |
advantage of the analytical solution of hydrodynamics |
132 |
+ |
properties. However, typical nonskew bodies like cylinders and |
133 |
+ |
ellipsoids are inadequate to represent most complex macromolecular |
134 |
+ |
assemblies. There is therefore a need for incorporating the |
135 |
+ |
hydrodynamics of complex (and potentially skew) rigid bodies in the |
136 |
+ |
library of methods available for performing Langevin simulations. |
137 |
+ |
|
138 |
|
\subsection{Rigid Body Dynamics} |
139 |
|
Rigid bodies are frequently involved in the modeling of large |
140 |
|
collections of particles that move as a single unit. In molecular |
141 |
|
simulations, rigid bodies have been used to simplify protein-protein |
142 |
< |
docking,\cite{Gray2003} and lipid bilayer simulations.\cite{Sun2008} |
143 |
< |
Many of the water models in common use are also rigid-body |
144 |
< |
models,\cite{TIPs,SPC/E} although they are typically evolved using |
145 |
< |
constraints rather than rigid body equations of motion. |
142 |
> |
docking,\cite{Gray2003} and lipid bilayer |
143 |
> |
simulations.\cite{SunGezelter08} Many of the water models in common |
144 |
> |
use are also rigid-body |
145 |
> |
models,\cite{Jorgensen83,Berendsen81,Berendsen87} although they are |
146 |
> |
typically evolved using constraints rather than rigid body equations |
147 |
> |
of motion. |
148 |
|
|
149 |
< |
Euler angles are a natural choice to describe the rotational |
150 |
< |
degrees of freedom. However, due to $1 \over \sin \theta$ |
151 |
< |
singularities, the numerical integration of corresponding equations of |
152 |
< |
these motion can become inaccurate (and inefficient). Although an |
153 |
< |
alternative integrator using multiple sets of Euler angles can |
154 |
< |
overcome this problem,\cite{Barojas1973} the computational penalty and |
155 |
< |
the loss of angular momentum conservation remain. A singularity-free |
156 |
< |
representation utilizing quaternions was developed by Evans in |
157 |
< |
1977.\cite{Evans1977} Unfortunately, this approach uses a nonseparable |
158 |
< |
Hamiltonian resulting from the quaternion representation, which |
159 |
< |
prevented symplectic algorithms from being utilized until very |
134 |
< |
recently.\cite{Miller2002} Another approach is the application of |
135 |
< |
holonomic constraints to the atoms belonging to the rigid body. Each |
136 |
< |
atom moves independently under the normal forces deriving from |
137 |
< |
potential energy and constraint forces which are used to guarantee the |
138 |
< |
rigidness. However, due to their iterative nature, the SHAKE and |
139 |
< |
Rattle algorithms also converge very slowly when the number of |
140 |
< |
constraints increases.\cite{Ryckaert1977,Andersen1983} |
149 |
> |
Euler angles are a natural choice to describe the rotational degrees |
150 |
> |
of freedom. However, due to $1 \over \sin \theta$ singularities, the |
151 |
> |
numerical integration of corresponding equations of these motion can |
152 |
> |
become inaccurate (and inefficient). Although the use of multiple |
153 |
> |
sets of Euler angles can overcome this problem,\cite{Barojas1973} the |
154 |
> |
computational penalty and the loss of angular momentum conservation |
155 |
> |
remain. A singularity-free representation utilizing quaternions was |
156 |
> |
developed by Evans in 1977.\cite{Evans1977} The Evans quaternion |
157 |
> |
approach uses a nonseparable Hamiltonian, and this has prevented |
158 |
> |
symplectic algorithms from being utilized until very |
159 |
> |
recently.\cite{Miller2002} |
160 |
|
|
161 |
< |
A breakthrough in geometric literature suggests that, in order to |
162 |
< |
develop a long-term integration scheme, one should preserve the |
163 |
< |
symplectic structure of the propagator. By introducing a conjugate |
164 |
< |
momentum to the rotation matrix $Q$ and re-formulating Hamiltonian's |
165 |
< |
equation, a symplectic integrator, RSHAKE,\cite{Kol1997} was proposed |
166 |
< |
to evolve the Hamiltonian system in a constraint manifold by |
167 |
< |
iteratively satisfying the orthogonality constraint $Q^T Q = 1$. An |
149 |
< |
alternative method using the quaternion representation was developed |
150 |
< |
by Omelyan.\cite{Omelyan1998} However, both of these methods are |
151 |
< |
iterative and suffer from some related inefficiencies. A symplectic |
152 |
< |
Lie-Poisson integrator for rigid bodies developed by Dullweber {\it et |
153 |
< |
al.}\cite{Dullweber1997} gets around most of the limitations mentioned |
154 |
< |
above and has become the basis for our Langevin integrator. |
161 |
> |
Another approach is the application of holonomic constraints to the |
162 |
> |
atoms belonging to the rigid body. Each atom moves independently |
163 |
> |
under the normal forces deriving from potential energy and constraints |
164 |
> |
are used to guarantee rigidity. However, due to their iterative |
165 |
> |
nature, the SHAKE and RATTLE algorithms converge very slowly when the |
166 |
> |
number of constraints (and the number of particles that belong to the |
167 |
> |
rigid body) increases.\cite{Ryckaert1977,Andersen1983} |
168 |
|
|
169 |
+ |
In order to develop a stable and efficient integration scheme that |
170 |
+ |
preserves most constants of the motion, symplectic propagators are |
171 |
+ |
necessary. By introducing a conjugate momentum to the rotation matrix |
172 |
+ |
$Q$ and re-formulating Hamilton's equations, a symplectic |
173 |
+ |
orientational integrator, RSHAKE,\cite{Kol1997} was proposed to evolve |
174 |
+ |
rigid bodies on a constraint manifold by iteratively satisfying the |
175 |
+ |
orthogonality constraint $Q^T Q = 1$. An alternative method using the |
176 |
+ |
quaternion representation was developed by Omelyan.\cite{Omelyan1998} |
177 |
+ |
However, both of these methods are iterative and suffer from some |
178 |
+ |
related inefficiencies. A symplectic Lie-Poisson integrator for rigid |
179 |
+ |
bodies developed by Dullweber {\it et al.}\cite{Dullweber1997} removes |
180 |
+ |
most of the limitations mentioned above and is therefore the basis for |
181 |
+ |
our Langevin integrator. |
182 |
|
|
157 |
– |
\subsection{The Hydrodynamic tensor and Brownian dynamics} |
158 |
– |
Combining Brownian dynamics with rigid substructures, one can study |
159 |
– |
slow processes in biomolecular systems. Modeling DNA as a chain of |
160 |
– |
beads which are subject to harmonic potentials as well as excluded |
161 |
– |
volume potentials, Mielke and his coworkers discovered rapid |
162 |
– |
superhelical stress generations from the stochastic simulation of twin |
163 |
– |
supercoiling DNA with response to induced torques.\cite{Mielke2004} |
164 |
– |
Membrane fusion is another key biological process which controls a |
165 |
– |
variety of physiological functions, such as release of |
166 |
– |
neurotransmitters \textit{etc}. A typical fusion event happens on the |
167 |
– |
time scale of a millisecond, which is impractical to study using |
168 |
– |
atomistic models with newtonian mechanics. With the help of |
169 |
– |
coarse-grained rigid body model and stochastic dynamics, the fusion |
170 |
– |
pathways were explored by Noguchi and others.\cite{Noguchi2001,Noguchi2002,Shillcock2005} |
171 |
– |
|
172 |
– |
Due to the difficulty of numerically integrating anisotropic |
173 |
– |
rotational motion, most of the coarse-grained rigid body models are |
174 |
– |
treated as spheres, cylinders, ellipsoids or other regular shapes in |
175 |
– |
stochastic simulations. In an effort to account for the diffusion |
176 |
– |
anisotropy of arbitrarily-shaped particles, Fernandes and Garc\'{i}a |
177 |
– |
de la Torre improved the original Brownian dynamics simulation |
178 |
– |
algorithm~\cite{Ermak1978,Allison1991} by incorporating a generalized |
179 |
– |
$6\times6$ diffusion tensor and introducing a rotational evolution |
180 |
– |
scheme consisting of three consecutive rotations.\cite{Fernandes2002} |
181 |
– |
Unfortunately, biases are introduced into the system due to the |
182 |
– |
arbitrary order of applying the noncommuting rotation |
183 |
– |
operators.\cite{Beard2003} Based on the observation the momentum |
184 |
– |
relaxation time is much less than the time step, one may ignore the |
185 |
– |
inertia in Brownian dynamics. However, the assumption of zero average |
186 |
– |
acceleration is not always true for cooperative motion which is common |
187 |
– |
in proteins. An inertial Brownian dynamics (IBD) was proposed to |
188 |
– |
address this issue by adding an inertial correction |
189 |
– |
term.\cite{Beard2000} As a complement to IBD which has a lower bound |
190 |
– |
in time step because of the inertial relaxation time, long-time-step |
191 |
– |
inertial dynamics (LTID) can be used to investigate the inertial |
192 |
– |
behavior of the polymer segments in low friction |
193 |
– |
regime.\cite{Beard2000} LTID can also deal with the rotational |
194 |
– |
dynamics for nonskew bodies without translation-rotation coupling by |
195 |
– |
separating the translation and rotation motion and taking advantage of |
196 |
– |
the analytical solution of hydrodynamics properties. However, typical |
197 |
– |
nonskew bodies like cylinders and ellipsoids are inadequate to |
198 |
– |
represent most complex macromolecular assemblies. |
199 |
– |
|
183 |
|
The goal of the present work is to develop a Langevin dynamics |
184 |
|
algorithm for arbitrary-shaped rigid particles by integrating the |
185 |
|
accurate estimation of friction tensor from hydrodynamics theory into |
186 |
|
a symplectic rigid body dynamics propagator. In the sections below, |
187 |
< |
we review some of the theory of hydrodynamic tensors developed for |
188 |
< |
Brownian simulations of rigid multi-particle systems, we then present |
189 |
< |
our integration method for a set of generalized Langevin equations of |
190 |
< |
motion, and we compare the behavior of the new Langevin integrator to |
191 |
< |
dynamical quantities obtained via explicit solvent molecular dynamics. |
187 |
> |
we review some of the theory of hydrodynamic tensors developed |
188 |
> |
primarily for Brownian simulations of multi-particle systems, we then |
189 |
> |
present our integration method for a set of generalized Langevin |
190 |
> |
equations of motion, and we compare the behavior of the new Langevin |
191 |
> |
integrator to dynamical quantities obtained via explicit solvent |
192 |
> |
molecular dynamics. |
193 |
|
|
194 |
|
\subsection{\label{introSection:frictionTensor}The Friction Tensor} |
195 |
|
Theoretically, a complete friction kernel can be determined using the |
196 |
|
velocity autocorrelation function. However, this approach becomes |
197 |
< |
impractical when the solute becomes complex. Instead, various |
197 |
> |
impractical when the solute becomes complex. Instead, various |
198 |
|
approaches based on hydrodynamics have been developed to calculate the |
199 |
|
friction coefficients. In general, the friction tensor $\Xi$ is a |
200 |
|
$6\times 6$ matrix given by |
201 |
|
\begin{equation} |
202 |
< |
\Xi = \left( {\begin{array}{*{20}c} |
203 |
< |
{\Xi _{}^{tt} } & {\Xi _{}^{rt} } \\ |
204 |
< |
{\Xi _{}^{tr} } & {\Xi _{}^{rr} } \\ |
205 |
< |
\end{array}} \right). |
202 |
> |
\Xi = \left( \begin{array}{*{20}c} |
203 |
> |
\Xi^{tt} & \Xi^{rt} \\ |
204 |
> |
\Xi^{tr} & \Xi^{rr} \\ |
205 |
> |
\end{array} \right). |
206 |
|
\end{equation} |
207 |
|
Here, $\Xi^{tt}$ and $\Xi^{rr}$ are $3 \times 3$ translational and |
208 |
|
rotational resistance (friction) tensors respectively, while |
215 |
|
\left( \begin{array}{l} |
216 |
|
\mathbf{F}_f \\ |
217 |
|
\mathbf{\tau}_f \\ |
218 |
< |
\end{array} \right) = - \left( {\begin{array}{*{20}c} |
219 |
< |
\Xi ^{tt} & \Xi ^{rt} \\ |
220 |
< |
\Xi ^{tr} & \Xi ^{rr} \\ |
221 |
< |
\end{array}} \right)\left( \begin{array}{l} |
218 |
> |
\end{array} \right) = - \left( \begin{array}{*{20}c} |
219 |
> |
\Xi^{tt} & \Xi^{rt} \\ |
220 |
> |
\Xi^{tr} & \Xi^{rr} \\ |
221 |
> |
\end{array} \right)\left( \begin{array}{l} |
222 |
|
\mathbf{v} \\ |
223 |
|
\mathbf{\omega} \\ |
224 |
|
\end{array} \right). |
227 |
|
\subsubsection{\label{introSection:resistanceTensorRegular}\textbf{The Resistance Tensor for Regular Shapes}} |
228 |
|
For a spherical particle under ``stick'' boundary conditions, the |
229 |
|
translational and rotational friction tensors can be calculated from |
230 |
< |
Stoke's law, |
230 |
> |
Stokes' law, |
231 |
|
\begin{equation} |
232 |
< |
\Xi^{tt} = \left( {\begin{array}{*{20}c} |
232 |
> |
\Xi^{tt} = \left( \begin{array}{*{20}c} |
233 |
|
{6\pi \eta R} & 0 & 0 \\ |
234 |
|
0 & {6\pi \eta R} & 0 \\ |
235 |
|
0 & 0 & {6\pi \eta R} \\ |
236 |
< |
\end{array}} \right) |
236 |
> |
\end{array} \right) |
237 |
|
\end{equation} |
238 |
|
and |
239 |
|
\begin{equation} |
240 |
< |
\Xi ^{rr} = \left( {\begin{array}{*{20}c} |
240 |
> |
\Xi^{rr} = \left( \begin{array}{*{20}c} |
241 |
|
{8\pi \eta R^3 } & 0 & 0 \\ |
242 |
|
0 & {8\pi \eta R^3 } & 0 \\ |
243 |
|
0 & 0 & {8\pi \eta R^3 } \\ |
244 |
< |
\end{array}} \right) |
244 |
> |
\end{array} \right) |
245 |
|
\end{equation} |
246 |
|
where $\eta$ is the viscosity of the solvent and $R$ is the |
247 |
|
hydrodynamic radius. |
249 |
|
Other non-spherical shapes, such as cylinders and ellipsoids, are |
250 |
|
widely used as references for developing new hydrodynamics theories, |
251 |
|
because their properties can be calculated exactly. In 1936, Perrin |
252 |
< |
extended Stokes's law to general ellipsoids, also called a triaxial |
253 |
< |
ellipsoid, which is given in Cartesian coordinates |
270 |
< |
by\cite{Perrin1934,Perrin1936} |
252 |
> |
extended Stokes' law to general ellipsoids which are given in |
253 |
> |
Cartesian coordinates by~\cite{Perrin1934,Perrin1936} |
254 |
|
\begin{equation} |
255 |
< |
\frac{x^2 }{a^2} + \frac{y^2}{b^2} + \frac{z^2 }{c^2} = 1 |
255 |
> |
\frac{x^2 }{a^2} + \frac{y^2}{b^2} + \frac{z^2 }{c^2} = 1. |
256 |
|
\end{equation} |
257 |
< |
where the semi-axes are of lengths $a$, $b$, and $c$. Due to the |
258 |
< |
complexity of the elliptic integral, only uniaxial ellipsoids, |
259 |
< |
{\it i.e.} prolate ($ a \ge b = c$) and oblate ($ a < b = c $), can |
260 |
< |
be solved exactly. Introducing an elliptic integral parameter $S$ for |
278 |
< |
prolate ellipsoids : |
257 |
> |
Here, the semi-axes are of lengths $a$, $b$, and $c$. Due to the |
258 |
> |
complexity of the elliptic integral, only uniaxial ellipsoids, either |
259 |
> |
prolate ($a \ge b = c$) or oblate ($a < b = c$), can be solved |
260 |
> |
exactly. Introducing an elliptic integral parameter $S$ for prolate, |
261 |
|
\begin{equation} |
262 |
|
S = \frac{2}{\sqrt{a^2 - b^2}} \ln \frac{a + \sqrt{a^2 - b^2}}{b}, |
263 |
|
\end{equation} |
264 |
< |
and oblate ellipsoids: |
264 |
> |
and oblate, |
265 |
|
\begin{equation} |
266 |
|
S = \frac{2}{\sqrt {b^2 - a^2 }} \arctan \frac{\sqrt {b^2 - a^2}}{a}, |
267 |
|
\end{equation} |
268 |
< |
one can write down the translational and rotational resistance |
269 |
< |
tensors for oblate, |
268 |
> |
ellipsoids, one can write down the translational and rotational |
269 |
> |
resistance tensors: |
270 |
|
\begin{eqnarray*} |
271 |
|
\Xi_a^{tt} & = & 16\pi \eta \frac{a^2 - b^2}{(2a^2 - b^2 )S - 2a}. \\ |
272 |
|
\Xi_b^{tt} = \Xi_c^{tt} & = & 32\pi \eta \frac{a^2 - b^2 }{(2a^2 - 3b^2 )S + 2a}, |
273 |
|
\end{eqnarray*} |
274 |
< |
and prolate, |
274 |
> |
for oblate, and |
275 |
|
\begin{eqnarray*} |
276 |
|
\Xi_a^{rr} & = & \frac{32\pi}{3} \eta \frac{(a^2 - b^2 )b^2}{2a - b^2 S}, \\ |
277 |
|
\Xi_b^{rr} = \Xi_c^{rr} & = & \frac{32\pi}{3} \eta \frac{(a^4 - b^4)}{(2a^2 - b^2 )S - 2a} |
278 |
|
\end{eqnarray*} |
279 |
< |
ellipsoids. For both spherical and ellipsoidal particles, the |
280 |
< |
translation-rotation and rotation-translation coupling tensors are |
279 |
> |
for prolate ellipsoids. For both spherical and ellipsoidal particles, |
280 |
> |
the translation-rotation and rotation-translation coupling tensors are |
281 |
|
zero. |
282 |
|
|
283 |
|
\subsubsection{\label{introSection:resistanceTensorRegularArbitrary}\textbf{The Resistance Tensor for Arbitrary Shapes}} |
302 |
– |
|
284 |
|
Unlike spherical and other simply shaped molecules, there is no |
285 |
|
analytical solution for the friction tensor for arbitrarily shaped |
286 |
|
rigid molecules. The ellipsoid of revolution model and general |
296 |
|
using more advanced methods where the molecule of interest was modeled |
297 |
|
by a combinations of spheres\cite{Carrasco1999} and the hydrodynamics |
298 |
|
properties of the molecule can be calculated using the hydrodynamic |
299 |
< |
interaction tensor. Let us consider a rigid assembly of $N$ beads |
300 |
< |
immersed in a continuous medium. Due to hydrodynamic interaction, the |
301 |
< |
``net'' velocity of $i$th bead, $v'_i$ is different than its |
302 |
< |
unperturbed velocity $v_i$, |
303 |
< |
\[ |
299 |
> |
interaction tensor. |
300 |
> |
|
301 |
> |
Consider a rigid assembly of $N$ beads immersed in a continuous |
302 |
> |
medium. Due to hydrodynamic interaction, the ``net'' velocity of $i$th |
303 |
> |
bead, $v'_i$ is different than its unperturbed velocity $v_i$, |
304 |
> |
\begin{equation} |
305 |
|
v'_i = v_i - \sum\limits_{j \ne i} {T_{ij} F_j } |
306 |
< |
\] |
307 |
< |
where $F_i$ is the frictional force, and $T_{ij}$ is the |
308 |
< |
hydrodynamic interaction tensor. The friction force of $i$th bead is |
309 |
< |
proportional to its ``net'' velocity |
306 |
> |
\end{equation} |
307 |
> |
where $F_i$ is the frictional force, and $T_{ij}$ is the hydrodynamic |
308 |
> |
interaction tensor. The frictional force on the $i^\mathrm{th}$ bead |
309 |
> |
is proportional to its ``net'' velocity |
310 |
|
\begin{equation} |
311 |
|
F_i = \zeta _i v_i - \zeta _i \sum\limits_{j \ne i} {T_{ij} F_j }. |
312 |
|
\label{introEquation:tensorExpression} |
343 |
|
construct a $3N \times 3N$ matrix consisting of $N \times N$ |
344 |
|
$B_{ij}$ blocks |
345 |
|
\begin{equation} |
346 |
< |
B = \left( {\begin{array}{*{20}c} |
347 |
< |
{B_{11} } & \ldots & {B_{1N} } \\ |
346 |
> |
B = \left( \begin{array}{*{20}c} |
347 |
> |
B_{11} & \ldots & B_{1N} \\ |
348 |
|
\vdots & \ddots & \vdots \\ |
349 |
< |
{B_{N1} } & \cdots & {B_{NN} } \\ |
350 |
< |
\end{array}} \right), |
349 |
> |
B_{N1} & \cdots & B_{NN} \\ |
350 |
> |
\end{array} \right), |
351 |
|
\end{equation} |
352 |
|
where $B_{ij}$ is given by |
353 |
< |
\[ |
353 |
> |
\begin{equation} |
354 |
|
B_{ij} = \delta _{ij} \frac{I}{{6\pi \eta R}} + (1 - \delta _{ij} |
355 |
|
)T_{ij} |
356 |
< |
\] |
356 |
> |
\end{equation} |
357 |
|
where $\delta _{ij}$ is the Kronecker delta function. Inverting the |
358 |
|
$B$ matrix, we obtain |
359 |
|
\[ |
438 |
|
x_{OR} \\ |
439 |
|
y_{OR} \\ |
440 |
|
z_{OR} \\ |
441 |
< |
\end{array} \right) & = &\left( {\begin{array}{*{20}c} |
441 |
> |
\end{array} \right) & = &\left( \begin{array}{*{20}c} |
442 |
|
{(\Xi _O^{rr} )_{yy} + (\Xi _O^{rr} )_{zz} } & { - (\Xi _O^{rr} )_{xy} } & { - (\Xi _O^{rr} )_{xz} } \\ |
443 |
|
{ - (\Xi _O^{rr} )_{xy} } & {(\Xi _O^{rr} )_{zz} + (\Xi _O^{rr} )_{xx} } & { - (\Xi _O^{rr} )_{yz} } \\ |
444 |
|
{ - (\Xi _O^{rr} )_{xz} } & { - (\Xi _O^{rr} )_{yz} } & {(\Xi _O^{rr} )_{xx} + (\Xi _O^{rr} )_{yy} } \\ |
445 |
< |
\end{array}} \right)^{ - 1} \\ |
445 |
> |
\end{array} \right)^{ - 1} \\ |
446 |
|
& & \left( \begin{array}{l} |
447 |
|
(\Xi _O^{tr} )_{yz} - (\Xi _O^{tr} )_{zy} \\ |
448 |
|
(\Xi _O^{tr} )_{zx} - (\Xi _O^{tr} )_{xz} \\ |
456 |
|
\section{Langevin Dynamics for Rigid Particles of Arbitrary Shape\label{LDRB}} |
457 |
|
Consider the Langevin equations of motion in generalized coordinates |
458 |
|
\begin{equation} |
459 |
< |
M_i \dot V_i (t) = F_{s,i} (t) + F_{f,i(t)} + F_{r,i} (t) |
459 |
> |
\mathbf{M}_i \dot \mathbf{V}_i(t) = \mathbf{F}_{s,i}(t) + \mathbf{F}_{f,i}(t) + \mathbf{R}_{i}(t) |
460 |
|
\label{LDGeneralizedForm} |
461 |
|
\end{equation} |
462 |
< |
where $M_i$ is a $6\times6$ generalized diagonal mass (include mass |
463 |
< |
and moment of inertial) matrix and $V_i$ is a generalized velocity, |
464 |
< |
$V_i = V_i(v_i,\omega _i)$. The right side of |
465 |
< |
Eq.~\ref{LDGeneralizedForm} consists of three generalized forces in |
466 |
< |
lab-fixed frame, systematic force $F_{s,i}$, dissipative force |
467 |
< |
$F_{f,i}$ and stochastic force $F_{r,i}$. While the evolution of the |
468 |
< |
system in Newtownian mechanics typically refers to lab-fixed frame, |
469 |
< |
it is also convenient to handle the rotation of rigid body in |
470 |
< |
body-fixed frame. Thus the friction and random forces are calculated |
471 |
< |
in body-fixed frame and converted back to lab-fixed frame by: |
472 |
< |
\[ |
462 |
> |
where $\mathbf{M}_i$ is a $6\times6$ diagonal mass matrix (which |
463 |
> |
includes the rigid body mass and moments of inertia) and $\mathbf{V}_i$ is a |
464 |
> |
generalized velocity, $\mathbf{V}_i = |
465 |
> |
\left\{\mathbf{v}_i,\mathbf{\omega}_i \right\}$. The right side of |
466 |
> |
Eq.~\ref{LDGeneralizedForm} consists of three generalized forces: a |
467 |
> |
system force $\mathbf{F}_{s,i}$, a frictional or dissipative force |
468 |
> |
$\mathbf{F}_{f,i}$ and stochastic force $\mathbf{R}_{i}$. While the |
469 |
> |
evolution of the system in Newtownian mechanics is typically done in the |
470 |
> |
lab-fixed frame, it is convenient to handle the rotation of rigid |
471 |
> |
bodies in the body-fixed frame. Thus the friction and random forces are |
472 |
> |
calculated in body-fixed frame and converted back to lab-fixed frame |
473 |
> |
using the rigid body's rotation matrix ($Q_i$): |
474 |
> |
\begin{equation} |
475 |
|
\begin{array}{l} |
476 |
< |
F_{f,i}^l (t) = Q^T F_{f,i}^b (t), \\ |
477 |
< |
F_{r,i}^l (t) = Q^T F_{r,i}^b (t). \\ |
476 |
> |
\mathbf{F}_{f,i}(t) = Q_{i}^{T} \mathbf{F}_{f,i}^b (t), \\ |
477 |
> |
\mathbf{R}_{i}(t) = Q_{i}^{T} \mathbf{R}_{i}^b (t). \\ |
478 |
|
\end{array} |
479 |
< |
\] |
480 |
< |
Here, the body-fixed friction force $F_{r,i}^b$ is proportional to |
481 |
< |
the body-fixed velocity at center of resistance $v_{R,i}^b$ and |
482 |
< |
angular velocity $\omega _i$ |
479 |
> |
\end{equation} |
480 |
> |
Here, the body-fixed friction force $\mathbf{F}_{f,i}^b$ is proportional to |
481 |
> |
the body-fixed velocity at the center of resistance $\mathbf{v}_{R,i}^b$ and |
482 |
> |
angular velocity $\mathbf{\omega}_i$ |
483 |
|
\begin{equation} |
484 |
< |
F_{r,i}^b (t) = \left( \begin{array}{l} |
485 |
< |
f_{r,i}^b (t) \\ |
486 |
< |
\tau _{r,i}^b (t) \\ |
487 |
< |
\end{array} \right) = - \left( {\begin{array}{*{20}c} |
488 |
< |
{\Xi _{R,t} } & {\Xi _{R,c}^T } \\ |
489 |
< |
{\Xi _{R,c} } & {\Xi _{R,r} } \\ |
490 |
< |
\end{array}} \right)\left( \begin{array}{l} |
491 |
< |
v_{R,i}^b (t) \\ |
492 |
< |
\omega _i (t) \\ |
484 |
> |
\mathbf{F}_{f,i}^b (t) = \left( \begin{array}{l} |
485 |
> |
\mathbf{f}_{f,i}^b (t) \\ |
486 |
> |
\mathbf{\tau}_{f,i}^b (t) \\ |
487 |
> |
\end{array} \right) = - \left( \begin{array}{*{20}c} |
488 |
> |
\Xi_{R,t} & \Xi_{R,c}^T \\ |
489 |
> |
\Xi_{R,c} & \Xi_{R,r} \\ |
490 |
> |
\end{array} \right)\left( \begin{array}{l} |
491 |
> |
\mathbf{v}_{R,i}^b (t) \\ |
492 |
> |
\mathbf{\omega}_i (t) \\ |
493 |
|
\end{array} \right), |
494 |
|
\end{equation} |
495 |
< |
while the random force $F_{r,i}^l$ is a Gaussian stochastic variable |
495 |
> |
while the random force $\mathbf{R}_{i}^l$ is a Gaussian stochastic variable |
496 |
|
with zero mean and variance |
497 |
|
\begin{equation} |
498 |
< |
\left\langle {F_{r,i}^l (t)(F_{r,i}^l (t'))^T } \right\rangle = |
499 |
< |
\left\langle {F_{r,i}^b (t)(F_{r,i}^b (t'))^T } \right\rangle = |
500 |
< |
2k_B T\Xi _R \delta (t - t'). \label{randomForce} |
498 |
> |
\left\langle {\mathbf{R}_{i}^l (t) (\mathbf{R}_{i}^l (t'))^T } \right\rangle = |
499 |
> |
\left\langle {\mathbf{R}_{i}^b (t) (\mathbf{R}_{i}^b (t'))^T } \right\rangle = |
500 |
> |
2 k_B T \Xi_R \delta(t - t'). \label{randomForce} |
501 |
|
\end{equation} |
502 |
< |
The equation of motion for $v_i$ can be written as |
502 |
> |
Once the $6\times6$ resistance tensor at the center of resistance |
503 |
> |
($\Xi_R$) is known, obtaining a stochastic vector that has the |
504 |
> |
properties in Eq. (\ref{eq:randomForce}) can be done efficiently by |
505 |
> |
carrying out a one-time Cholesky decomposition to obtain the square |
506 |
> |
root matrix of $\Xi_R$.\cite{SchlickBook} Each time a random force |
507 |
> |
vector is needed, a gaussian random vector is generated and then the |
508 |
> |
square root matrix is multiplied onto this vector. |
509 |
> |
|
510 |
> |
The equation of motion for $\mathbf{v}_i$ can be written as |
511 |
|
\begin{equation} |
512 |
< |
m\dot v_i (t) = f_{t,i} (t) = f_{s,i} (t) + f_{f,i}^l (t) + |
513 |
< |
f_{r,i}^l (t) |
512 |
> |
m\dot \mathbf{v}_i (t) = \mathbf{f}_{s,i} (t) + \mathbf{f}_{f,i}^l (t) + |
513 |
> |
\mathbf{R}_{i}^l (t) |
514 |
|
\end{equation} |
515 |
|
Since the frictional force is applied at the center of resistance |
516 |
|
which generally does not coincide with the center of mass, an extra |
517 |
|
torque is exerted at the center of mass. Thus, the net body-fixed |
518 |
< |
frictional torque at the center of mass, $\tau _{n,i}^b (t)$, is |
518 |
> |
frictional torque at the center of mass, $\tau_{f,i}^b (t)$, is |
519 |
|
given by |
520 |
|
\begin{equation} |
521 |
< |
\tau _{r,i}^b = \tau _{r,i}^b +r_{MR} \times f_{r,i}^b |
521 |
> |
\tau_{f,i}^b \leftarrow \tau_{f,i}^b + \mathbf{r}_{MR} \times \mathbf{f}_{r,i}^b |
522 |
|
\end{equation} |
523 |
|
where $r_{MR}$ is the vector from the center of mass to the center |
524 |
|
of the resistance. Instead of integrating the angular velocity in |
525 |
|
lab-fixed frame, we consider the equation of angular momentum in |
526 |
|
body-fixed frame |
527 |
|
\begin{equation} |
528 |
< |
\dot j_i (t) = \tau _{t,i} (t) = \tau _{s,i} (t) + \tau _{f,i}^b (t) |
537 |
< |
+ \tau _{r,i}^b(t) |
528 |
> |
\dot j_i (t) = \tau_{s,i} (t) + \tau_{f,i}^b (t) + \mathbf{R}_{i}^b(t) |
529 |
|
\end{equation} |
530 |
< |
Embedding the friction terms into force and torque, one can |
531 |
< |
integrate the langevin equations of motion for rigid body of |
532 |
< |
arbitrary shape in a velocity-Verlet style 2-part algorithm, where |
542 |
< |
$h= \delta t$: |
530 |
> |
Embedding the friction terms into force and torque, one can integrate |
531 |
> |
the Langevin equations of motion for rigid body of arbitrary shape in |
532 |
> |
a velocity-Verlet style 2-part algorithm, where $h= \delta t$: |
533 |
|
|
534 |
|
{\tt moveA:} |
535 |
|
\begin{align*} |