ViewVC Help
View File | Revision Log | Show Annotations | View Changeset | Root Listing
root/group/trunk/tengDissertation/Introduction.tex
(Generate patch)

Comparing trunk/tengDissertation/Introduction.tex (file contents):
Revision 2706 by tim, Thu Apr 13 04:47:47 2006 UTC vs.
Revision 2720 by tim, Wed Apr 19 19:46:53 2006 UTC

# Line 570 | Line 570 | The free rigid body is an example of Poisson system (a
570   \dot x = J(x)\nabla _x H \label{introEquation:poissonHamiltonian}
571   \end{equation}
572   The most obvious change being that matrix $J$ now depends on $x$.
573 The free rigid body is an example of Poisson system (actually a
574 Lie-Poisson system) with Hamiltonian function of angular kinetic
575 energy.
576 \begin{equation}
577 J(\pi ) = \left( {\begin{array}{*{20}c}
578   0 & {\pi _3 } & { - \pi _2 }  \\
579   { - \pi _3 } & 0 & {\pi _1 }  \\
580   {\pi _2 } & { - \pi _1 } & 0  \\
581 \end{array}} \right)
582 \end{equation}
573  
584 \begin{equation}
585 H = \frac{1}{2}\left( {\frac{{\pi _1^2 }}{{I_1 }} + \frac{{\pi _2^2
586 }}{{I_2 }} + \frac{{\pi _3^2 }}{{I_3 }}} \right)
587 \end{equation}
588
574   \subsection{\label{introSection:exactFlow}Exact Flow}
575  
576   Let $x(t)$ be the exact solution of the ODE system,
# Line 837 | Line 822 | q(\Delta t)} \right]. %
822   %
823   q(\Delta t) &= q(0) + \frac{{\Delta t}}{2}\left[ {\dot q(0) + \dot
824   q(\Delta t)} \right]. %
825 < \label{introEquation:positionVerlet1}
825 > \label{introEquation:positionVerlet2}
826   \end{align}
827  
828   \subsubsection{\label{introSection:errorAnalysis}Error Analysis and Higher Order Methods}
# Line 898 | Line 883 | As a special discipline of molecular modeling, Molecul
883  
884   \section{\label{introSection:molecularDynamics}Molecular Dynamics}
885  
886 < As a special discipline of molecular modeling, Molecular dynamics
887 < has proven to be a powerful tool for studying the functions of
888 < biological systems, providing structural, thermodynamic and
889 < dynamical information.
886 > As one of the principal tools of molecular modeling, Molecular
887 > dynamics has proven to be a powerful tool for studying the functions
888 > of biological systems, providing structural, thermodynamic and
889 > dynamical information. The basic idea of molecular dynamics is that
890 > macroscopic properties are related to microscopic behavior and
891 > microscopic behavior can be calculated from the trajectories in
892 > simulations. For instance, instantaneous temperature of an
893 > Hamiltonian system of $N$ particle can be measured by
894 > \[
895 > T(t) = \sum\limits_{i = 1}^N {\frac{{m_i v_i^2 }}{{fk_B }}}
896 > \]
897 > where $m_i$ and $v_i$ are the mass and velocity of $i$th particle
898 > respectively, $f$ is the number of degrees of freedom, and $k_B$ is
899 > the boltzman constant.
900  
901 < \subsection{\label{introSec:mdInit}Initialization}
901 > A typical molecular dynamics run consists of three essential steps:
902 > \begin{enumerate}
903 >  \item Initialization
904 >    \begin{enumerate}
905 >    \item Preliminary preparation
906 >    \item Minimization
907 >    \item Heating
908 >    \item Equilibration
909 >    \end{enumerate}
910 >  \item Production
911 >  \item Analysis
912 > \end{enumerate}
913 > These three individual steps will be covered in the following
914 > sections. Sec.~\ref{introSec:initialSystemSettings} deals with the
915 > initialization of a simulation. Sec.~\ref{introSec:production} will
916 > discusses issues in production run, including the force evaluation
917 > and the numerical integration schemes of the equations of motion .
918 > Sec.~\ref{introSection:Analysis} provides the theoretical tools for
919 > trajectory analysis.
920  
921 < \subsection{\label{introSec:forceEvaluation}Force Evaluation}
921 > \subsection{\label{introSec:initialSystemSettings}Initialization}
922  
923 < \subsection{\label{introSection:mdIntegration} Integration of the Equations of Motion}
923 > \subsubsection{Preliminary preparation}
924 >
925 > When selecting the starting structure of a molecule for molecular
926 > simulation, one may retrieve its Cartesian coordinates from public
927 > databases, such as RCSB Protein Data Bank \textit{etc}. Although
928 > thousands of crystal structures of molecules are discovered every
929 > year, many more remain unknown due to the difficulties of
930 > purification and crystallization. Even for the molecule with known
931 > structure, some important information is missing. For example, the
932 > missing hydrogen atom which acts as donor in hydrogen bonding must
933 > be added. Moreover, in order to include electrostatic interaction,
934 > one may need to specify the partial charges for individual atoms.
935 > Under some circumstances, we may even need to prepare the system in
936 > a special setup. For instance, when studying transport phenomenon in
937 > membrane system, we may prepare the lipids in bilayer structure
938 > instead of placing lipids randomly in solvent, since we are not
939 > interested in self-aggregation and it takes a long time to happen.
940  
941 + \subsubsection{Minimization}
942 +
943 + It is quite possible that some of molecules in the system from
944 + preliminary preparation may be overlapped with each other. This
945 + close proximity leads to high potential energy which consequently
946 + jeopardizes any molecular dynamics simulations. To remove these
947 + steric overlaps, one typically performs energy minimization to find
948 + a more reasonable conformation. Several energy minimization methods
949 + have been developed to exploit the energy surface and to locate the
950 + local minimum. While converging slowly near the minimum, steepest
951 + descent method is extremely robust when systems are far from
952 + harmonic. Thus, it is often used to refine structure from
953 + crystallographic data. Relied on the gradient or hessian, advanced
954 + methods like conjugate gradient and Newton-Raphson converge rapidly
955 + to a local minimum, while become unstable if the energy surface is
956 + far from quadratic. Another factor must be taken into account, when
957 + choosing energy minimization method, is the size of the system.
958 + Steepest descent and conjugate gradient can deal with models of any
959 + size. Because of the limit of computation power to calculate hessian
960 + matrix and insufficient storage capacity to store them, most
961 + Newton-Raphson methods can not be used with very large models.
962 +
963 + \subsubsection{Heating}
964 +
965 + Typically, Heating is performed by assigning random velocities
966 + according to a Gaussian distribution for a temperature. Beginning at
967 + a lower temperature and gradually increasing the temperature by
968 + assigning greater random velocities, we end up with setting the
969 + temperature of the system to a final temperature at which the
970 + simulation will be conducted. In heating phase, we should also keep
971 + the system from drifting or rotating as a whole. Equivalently, the
972 + net linear momentum and angular momentum of the system should be
973 + shifted to zero.
974 +
975 + \subsubsection{Equilibration}
976 +
977 + The purpose of equilibration is to allow the system to evolve
978 + spontaneously for a period of time and reach equilibrium. The
979 + procedure is continued until various statistical properties, such as
980 + temperature, pressure, energy, volume and other structural
981 + properties \textit{etc}, become independent of time. Strictly
982 + speaking, minimization and heating are not necessary, provided the
983 + equilibration process is long enough. However, these steps can serve
984 + as a means to arrive at an equilibrated structure in an effective
985 + way.
986 +
987 + \subsection{\label{introSection:production}Production}
988 +
989 + \subsubsection{\label{introSec:forceCalculation}The Force Calculation}
990 +
991 + \subsubsection{\label{introSection:integrationSchemes} Integration
992 + Schemes}
993 +
994 + \subsection{\label{introSection:Analysis} Analysis}
995 +
996   \section{\label{introSection:rigidBody}Dynamics of Rigid Bodies}
997  
998   Rigid bodies are frequently involved in the modeling of different
# Line 942 | Line 1026 | rotation matrix $A$ and re-formulating Hamiltonian's e
1026   The break through in geometric literature suggests that, in order to
1027   develop a long-term integration scheme, one should preserve the
1028   symplectic structure of the flow. Introducing conjugate momentum to
1029 < rotation matrix $A$ and re-formulating Hamiltonian's equation, a
1029 > rotation matrix $Q$ and re-formulating Hamiltonian's equation, a
1030   symplectic integrator, RSHAKE, was proposed to evolve the
1031   Hamiltonian system in a constraint manifold by iteratively
1032 < satisfying the orthogonality constraint $A_t A = 1$. An alternative
1032 > satisfying the orthogonality constraint $Q_T Q = 1$. An alternative
1033   method using quaternion representation was developed by Omelyan.
1034   However, both of these methods are iterative and inefficient. In
1035   this section, we will present a symplectic Lie-Poisson integrator
1036 < for rigid body developed by Dullweber and his coworkers\cite{}.
1036 > for rigid body developed by Dullweber and his
1037 > coworkers\cite{Dullweber1997} in depth.
1038  
954 \subsection{\label{introSection:lieAlgebra}Lie Algebra}
955
1039   \subsection{\label{introSection:constrainedHamiltonianRB}Constrained Hamiltonian for Rigid Body}
1040 <
1040 > The motion of the rigid body is Hamiltonian with the Hamiltonian
1041 > function
1042   \begin{equation}
1043   H = \frac{1}{2}(p^T m^{ - 1} p) + \frac{1}{2}tr(PJ^{ - 1} P) +
1044   V(q,Q) + \frac{1}{2}tr[(QQ^T  - 1)\Lambda ].
# Line 975 | Line 1059 | Q^t PJ^{ - 1}  + J^{ - 1} P^t Q = 0 . \\
1059   Differentiating \ref{introEquation:orthogonalConstraint} and using
1060   Equation \ref{introEquation:RBMotionMomentum}, one may obtain,
1061   \begin{equation}
1062 < Q^t PJ^{ - 1}  + J^{ - 1} P^t Q = 0 . \\
1062 > Q^T PJ^{ - 1}  + J^{ - 1} P^T Q = 0 . \\
1063   \label{introEquation:RBFirstOrderConstraint}
1064   \end{equation}
1065  
# Line 987 | Line 1071 | the equations of motion,
1071   \frac{{dq}}{{dt}} = \frac{p}{m} \label{introEquation:RBMotionPosition}\\
1072   \frac{{dp}}{{dt}} =  - \nabla _q V(q,Q) \label{introEquation:RBMotionMomentum}\\
1073   \frac{{dQ}}{{dt}} = PJ^{ - 1}  \label{introEquation:RBMotionRotation}\\
1074 < \frac{{dP}}{{dt}} =  - \nabla _q V(q,Q) - 2Q\Lambda . \label{introEquation:RBMotionP}\\
1074 > \frac{{dP}}{{dt}} =  - \nabla _Q V(q,Q) - 2Q\Lambda . \label{introEquation:RBMotionP}\\
1075   \end{array}
1076   \]
1077  
1078 <
1078 > In general, there are two ways to satisfy the holonomic constraints.
1079 > We can use constraint force provided by lagrange multiplier on the
1080 > normal manifold to keep the motion on constraint space. Or we can
1081 > simply evolve the system in constraint manifold. The two method are
1082 > proved to be equivalent. The holonomic constraint and equations of
1083 > motions define a constraint manifold for rigid body
1084   \[
1085 < M = \left\{ {(Q,P):Q^T Q = 1,Q^t PJ^{ - 1}  + J^{ - 1} P^t Q = 0}
1086 < \right\} .
1085 > M = \left\{ {(Q,P):Q^T Q = 1,Q^T PJ^{ - 1}  + J^{ - 1} P^T Q = 0}
1086 > \right\}.
1087   \]
1088  
1089 < \subsection{\label{introSection:DLMMotionEquation}The Euler Equations of Rigid Body Motion}
1090 <
1091 < \subsection{\label{introSection:symplecticDiscretizationRB}Symplectic Discretization of Euler Equations}
1092 <
1004 <
1005 < \section{\label{introSection:langevinDynamics}Langevin Dynamics}
1006 <
1007 < \subsection{\label{introSection:LDIntroduction}Introduction and application of Langevin Dynamics}
1008 <
1009 < \subsection{\label{introSection:generalizedLangevinDynamics}Generalized Langevin Dynamics}
1010 <
1011 < \begin{equation}
1012 < H = \frac{{p^2 }}{{2m}} + U(x) + H_B  + \Delta U(x,x_1 , \ldots x_N)
1013 < \label{introEquation:bathGLE}
1014 < \end{equation}
1015 < where $H_B$ is harmonic bath Hamiltonian,
1089 > Unfortunately, this constraint manifold is not the cotangent bundle
1090 > $T_{\star}SO(3)$. However, it turns out that under symplectic
1091 > transformation, the cotangent space and the phase space are
1092 > diffeomorphic. Introducing
1093   \[
1094 < H_B  =\sum\limits_{\alpha  = 1}^N {\left\{ {\frac{{p_\alpha ^2
1018 < }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha  w_\alpha ^2 } \right\}}
1094 > \tilde Q = Q,\tilde P = \frac{1}{2}\left( {P - QP^T Q} \right),
1095   \]
1096 < and $\Delta U$ is bilinear system-bath coupling,
1096 > the mechanical system subject to a holonomic constraint manifold $M$
1097 > can be re-formulated as a Hamiltonian system on the cotangent space
1098   \[
1099 < \Delta U =  - \sum\limits_{\alpha  = 1}^N {g_\alpha  x_\alpha  x}
1099 > T^* SO(3) = \left\{ {(\tilde Q,\tilde P):\tilde Q^T \tilde Q =
1100 > 1,\tilde Q^T \tilde PJ^{ - 1}  + J^{ - 1} P^T \tilde Q = 0} \right\}
1101   \]
1102 < Completing the square,
1102 >
1103 > For a body fixed vector $X_i$ with respect to the center of mass of
1104 > the rigid body, its corresponding lab fixed vector $X_0^{lab}$  is
1105 > given as
1106 > \begin{equation}
1107 > X_i^{lab} = Q X_i + q.
1108 > \end{equation}
1109 > Therefore, potential energy $V(q,Q)$ is defined by
1110   \[
1111 < H_B  + \Delta U = \sum\limits_{\alpha  = 1}^N {\left\{
1027 < {\frac{{p_\alpha ^2 }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha
1028 < w_\alpha ^2 \left( {x_\alpha   - \frac{{g_\alpha  }}{{m_\alpha
1029 < w_\alpha ^2 }}x} \right)^2 } \right\}}  - \sum\limits_{\alpha  =
1030 < 1}^N {\frac{{g_\alpha ^2 }}{{2m_\alpha  w_\alpha ^2 }}} x^2
1111 > V(q,Q) = V(Q X_0 + q).
1112   \]
1113 < and putting it back into Eq.~\ref{introEquation:bathGLE},
1113 > Hence, the force and torque are given by
1114   \[
1115 < H = \frac{{p^2 }}{{2m}} + W(x) + \sum\limits_{\alpha  = 1}^N
1035 < {\left\{ {\frac{{p_\alpha ^2 }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha
1036 < w_\alpha ^2 \left( {x_\alpha   - \frac{{g_\alpha  }}{{m_\alpha
1037 < w_\alpha ^2 }}x} \right)^2 } \right\}}
1115 > \nabla _q V(q,Q) = F(q,Q) = \sum\limits_i {F_i (q,Q)},
1116   \]
1117 < where
1117 > and
1118   \[
1119 < W(x) = U(x) - \sum\limits_{\alpha  = 1}^N {\frac{{g_\alpha ^2
1042 < }}{{2m_\alpha  w_\alpha ^2 }}} x^2
1119 > \nabla _Q V(q,Q) = F(q,Q)X_i^t
1120   \]
1121 < Since the first two terms of the new Hamiltonian depend only on the
1045 < system coordinates, we can get the equations of motion for
1046 < Generalized Langevin Dynamics by Hamilton's equations
1047 < \ref{introEquation:motionHamiltonianCoordinate,
1048 < introEquation:motionHamiltonianMomentum},
1049 < \begin{align}
1050 < \dot p &=  - \frac{{\partial H}}{{\partial x}}
1051 <       &= m\ddot x
1052 <       &= - \frac{{\partial W(x)}}{{\partial x}} - \sum\limits_{\alpha  = 1}^N {g_\alpha  \left( {x_\alpha   - \frac{{g_\alpha  }}{{m_\alpha  w_\alpha ^2 }}x} \right)}
1053 < \label{introEquation:Lp5}
1054 < \end{align}
1055 < , and
1056 < \begin{align}
1057 < \dot p_\alpha   &=  - \frac{{\partial H}}{{\partial x_\alpha  }}
1058 <                &= m\ddot x_\alpha
1059 <                &= \- m_\alpha  w_\alpha ^2 \left( {x_\alpha   - \frac{{g_\alpha}}{{m_\alpha  w_\alpha ^2 }}x} \right)
1060 < \end{align}
1121 > respectively.
1122  
1123 < \subsection{\label{introSection:laplaceTransform}The Laplace Transform}
1124 <
1123 > As a common choice to describe the rotation dynamics of the rigid
1124 > body, angular momentum on body frame $\Pi  = Q^t P$ is introduced to
1125 > rewrite the equations of motion,
1126 > \begin{equation}
1127 > \begin{array}{l}
1128 > \mathop \Pi \limits^ \bullet   = J^{ - 1} \Pi ^T \Pi  + Q^T \sum\limits_i {F_i (q,Q)X_i^T }  - \Lambda  \\
1129 > \mathop Q\limits^{{\rm{   }} \bullet }  = Q\Pi {\rm{ }}J^{ - 1}  \\
1130 > \end{array}
1131 > \label{introEqaution:RBMotionPI}
1132 > \end{equation}
1133 > , as well as holonomic constraints,
1134   \[
1135 < L(x) = \int_0^\infty  {x(t)e^{ - pt} dt}
1135 > \begin{array}{l}
1136 > \Pi J^{ - 1}  + J^{ - 1} \Pi ^t  = 0 \\
1137 > Q^T Q = 1 \\
1138 > \end{array}
1139   \]
1140  
1141 + For a vector $v(v_1 ,v_2 ,v_3 ) \in R^3$ and a matrix $\hat v \in
1142 + so(3)^ \star$, the hat-map isomorphism,
1143 + \begin{equation}
1144 + v(v_1 ,v_2 ,v_3 ) \Leftrightarrow \hat v = \left(
1145 + {\begin{array}{*{20}c}
1146 +   0 & { - v_3 } & {v_2 }  \\
1147 +   {v_3 } & 0 & { - v_1 }  \\
1148 +   { - v_2 } & {v_1 } & 0  \\
1149 + \end{array}} \right),
1150 + \label{introEquation:hatmapIsomorphism}
1151 + \end{equation}
1152 + will let us associate the matrix products with traditional vector
1153 + operations
1154   \[
1155 < L(x + y) = L(x) + L(y)
1155 > \hat vu = v \times u
1156   \]
1157  
1158 < \[
1159 < L(ax) = aL(x)
1160 < \]
1158 > Using \ref{introEqaution:RBMotionPI}, one can construct a skew
1159 > matrix,
1160 > \begin{equation}
1161 > (\mathop \Pi \limits^ \bullet   - \mathop \Pi \limits^ \bullet  ^T
1162 > ){\rm{ }} = {\rm{ }}(\Pi  - \Pi ^T ){\rm{ }}(J^{ - 1} \Pi  + \Pi J^{
1163 > - 1} ) + \sum\limits_i {[Q^T F_i (r,Q)X_i^T  - X_i F_i (r,Q)^T Q]} -
1164 > (\Lambda  - \Lambda ^T ) . \label{introEquation:skewMatrixPI}
1165 > \end{equation}
1166 > Since $\Lambda$ is symmetric, the last term of Equation
1167 > \ref{introEquation:skewMatrixPI} is zero, which implies the Lagrange
1168 > multiplier $\Lambda$ is absent from the equations of motion. This
1169 > unique property eliminate the requirement of iterations which can
1170 > not be avoided in other methods\cite{}.
1171  
1172 + Applying hat-map isomorphism, we obtain the equation of motion for
1173 + angular momentum on body frame
1174 + \begin{equation}
1175 + \dot \pi  = \pi  \times I^{ - 1} \pi  + \sum\limits_i {\left( {Q^T
1176 + F_i (r,Q)} \right) \times X_i }.
1177 + \label{introEquation:bodyAngularMotion}
1178 + \end{equation}
1179 + In the same manner, the equation of motion for rotation matrix is
1180 + given by
1181   \[
1182 < L(\dot x) = pL(x) - px(0)
1182 > \dot Q = Qskew(I^{ - 1} \pi )
1183   \]
1184  
1185 + \subsection{\label{introSection:SymplecticFreeRB}Symplectic
1186 + Lie-Poisson Integrator for Free Rigid Body}
1187 +
1188 + If there is not external forces exerted on the rigid body, the only
1189 + contribution to the rotational is from the kinetic potential (the
1190 + first term of \ref{ introEquation:bodyAngularMotion}). The free
1191 + rigid body is an example of Lie-Poisson system with Hamiltonian
1192 + function
1193 + \begin{equation}
1194 + T^r (\pi ) = T_1 ^r (\pi _1 ) + T_2^r (\pi _2 ) + T_3^r (\pi _3 )
1195 + \label{introEquation:rotationalKineticRB}
1196 + \end{equation}
1197 + where $T_i^r (\pi _i ) = \frac{{\pi _i ^2 }}{{2I_i }}$ and
1198 + Lie-Poisson structure matrix,
1199 + \begin{equation}
1200 + J(\pi ) = \left( {\begin{array}{*{20}c}
1201 +   0 & {\pi _3 } & { - \pi _2 }  \\
1202 +   { - \pi _3 } & 0 & {\pi _1 }  \\
1203 +   {\pi _2 } & { - \pi _1 } & 0  \\
1204 + \end{array}} \right)
1205 + \end{equation}
1206 + Thus, the dynamics of free rigid body is governed by
1207 + \begin{equation}
1208 + \frac{d}{{dt}}\pi  = J(\pi )\nabla _\pi  T^r (\pi )
1209 + \end{equation}
1210 +
1211 + One may notice that each $T_i^r$ in Equation
1212 + \ref{introEquation:rotationalKineticRB} can be solved exactly. For
1213 + instance, the equations of motion due to $T_1^r$ are given by
1214 + \begin{equation}
1215 + \frac{d}{{dt}}\pi  = R_1 \pi ,\frac{d}{{dt}}Q = QR_1
1216 + \label{introEqaution:RBMotionSingleTerm}
1217 + \end{equation}
1218 + where
1219 + \[ R_1  = \left( {\begin{array}{*{20}c}
1220 +   0 & 0 & 0  \\
1221 +   0 & 0 & {\pi _1 }  \\
1222 +   0 & { - \pi _1 } & 0  \\
1223 + \end{array}} \right).
1224 + \]
1225 + The solutions of Equation \ref{introEqaution:RBMotionSingleTerm} is
1226   \[
1227 < L(\ddot x) = p^2 L(x) - px(0) - \dot x(0)
1227 > \pi (\Delta t) = e^{\Delta tR_1 } \pi (0),Q(\Delta t) =
1228 > Q(0)e^{\Delta tR_1 }
1229   \]
1230 <
1230 > with
1231   \[
1232 < L\left( {\int_0^t {g(t - \tau )h(\tau )d\tau } } \right) = G(p)H(p)
1232 > e^{\Delta tR_1 }  = \left( {\begin{array}{*{20}c}
1233 >   0 & 0 & 0  \\
1234 >   0 & {\cos \theta _1 } & {\sin \theta _1 }  \\
1235 >   0 & { - \sin \theta _1 } & {\cos \theta _1 }  \\
1236 > \end{array}} \right),\theta _1  = \frac{{\pi _1 }}{{I_1 }}\Delta t.
1237   \]
1238 + To reduce the cost of computing expensive functions in $e^{\Delta
1239 + tR_1 }$, we can use Cayley transformation,
1240 + \[
1241 + e^{\Delta tR_1 }  \approx (1 - \Delta tR_1 )^{ - 1} (1 + \Delta tR_1
1242 + )
1243 + \]
1244 + The flow maps for $T_2^r$ and $T_3^r$ can be found in the same
1245 + manner.
1246  
1247 < Some relatively important transformation,
1247 > In order to construct a second-order symplectic method, we split the
1248 > angular kinetic Hamiltonian function can into five terms
1249   \[
1250 < L(\cos at) = \frac{p}{{p^2  + a^2 }}
1250 > T^r (\pi ) = \frac{1}{2}T_1 ^r (\pi _1 ) + \frac{1}{2}T_2^r (\pi _2
1251 > ) + T_3^r (\pi _3 ) + \frac{1}{2}T_2^r (\pi _2 ) + \frac{1}{2}T_1 ^r
1252 > (\pi _1 )
1253 > \].
1254 > Concatenating flows corresponding to these five terms, we can obtain
1255 > an symplectic integrator,
1256 > \[
1257 > \varphi _{\Delta t,T^r }  = \varphi _{\Delta t/2,\pi _1 }  \circ
1258 > \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t,\pi _3 }
1259 > \circ \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t/2,\pi
1260 > _1 }.
1261   \]
1262  
1263 + The non-canonical Lie-Poisson bracket ${F, G}$ of two function
1264 + $F(\pi )$ and $G(\pi )$ is defined by
1265   \[
1266 < L(\sin at) = \frac{a}{{p^2  + a^2 }}
1266 > \{ F,G\} (\pi ) = [\nabla _\pi  F(\pi )]^T J(\pi )\nabla _\pi  G(\pi
1267 > )
1268   \]
1269 + If the Poisson bracket of a function $F$ with an arbitrary smooth
1270 + function $G$ is zero, $F$ is a \emph{Casimir}, which is the
1271 + conserved quantity in Poisson system. We can easily verify that the
1272 + norm of the angular momentum, $\parallel \pi
1273 + \parallel$, is a \emph{Casimir}. Let$ F(\pi ) = S(\frac{{\parallel
1274 + \pi \parallel ^2 }}{2})$ for an arbitrary function $ S:R \to R$ ,
1275 + then by the chain rule
1276 + \[
1277 + \nabla _\pi  F(\pi ) = S'(\frac{{\parallel \pi \parallel ^2
1278 + }}{2})\pi
1279 + \]
1280 + Thus $ [\nabla _\pi  F(\pi )]^T J(\pi ) =  - S'(\frac{{\parallel \pi
1281 + \parallel ^2 }}{2})\pi  \times \pi  = 0 $. This explicit
1282 + Lie-Poisson integrator is found to be extremely efficient and stable
1283 + which can be explained by the fact the small angle approximation is
1284 + used and the norm of the angular momentum is conserved.
1285  
1286 + \subsection{\label{introSection:RBHamiltonianSplitting} Hamiltonian
1287 + Splitting for Rigid Body}
1288 +
1289 + The Hamiltonian of rigid body can be separated in terms of kinetic
1290 + energy and potential energy,
1291   \[
1292 < L(1) = \frac{1}{p}
1292 > H = T(p,\pi ) + V(q,Q)
1293   \]
1294 + The equations of motion corresponding to potential energy and
1295 + kinetic energy are listed in the below table,
1296 + \begin{center}
1297 + \begin{tabular}{|l|l|}
1298 +  \hline
1299 +  % after \\: \hline or \cline{col1-col2} \cline{col3-col4} ...
1300 +  Potential & Kinetic \\
1301 +  $\frac{{dq}}{{dt}} = \frac{p}{m}$ & $\frac{d}{{dt}}q = p$ \\
1302 +  $\frac{d}{{dt}}p =  - \frac{{\partial V}}{{\partial q}}$ & $ \frac{d}{{dt}}p = 0$ \\
1303 +  $\frac{d}{{dt}}Q = 0$ & $ \frac{d}{{dt}}Q = Qskew(I^{ - 1} j)$ \\
1304 +  $ \frac{d}{{dt}}\pi  = \sum\limits_i {\left( {Q^T F_i (r,Q)} \right) \times X_i }$ & $\frac{d}{{dt}}\pi  = \pi  \times I^{ - 1} \pi$\\
1305 +  \hline
1306 + \end{tabular}
1307 + \end{center}
1308 + A second-order symplectic method is now obtained by the composition
1309 + of the flow maps,
1310 + \[
1311 + \varphi _{\Delta t}  = \varphi _{\Delta t/2,V}  \circ \varphi
1312 + _{\Delta t,T}  \circ \varphi _{\Delta t/2,V}.
1313 + \]
1314 + Moreover, $\varphi _{\Delta t/2,V}$ can be divided into two
1315 + sub-flows which corresponding to force and torque respectively,
1316 + \[
1317 + \varphi _{\Delta t/2,V}  = \varphi _{\Delta t/2,F}  \circ \varphi
1318 + _{\Delta t/2,\tau }.
1319 + \]
1320 + Since the associated operators of $\varphi _{\Delta t/2,F} $ and
1321 + $\circ \varphi _{\Delta t/2,\tau }$ are commuted, the composition
1322 + order inside $\varphi _{\Delta t/2,V}$ does not matter.
1323  
1324 < First, the bath coordinates,
1324 > Furthermore, kinetic potential can be separated to translational
1325 > kinetic term, $T^t (p)$, and rotational kinetic term, $T^r (\pi )$,
1326 > \begin{equation}
1327 > T(p,\pi ) =T^t (p) + T^r (\pi ).
1328 > \end{equation}
1329 > where $ T^t (p) = \frac{1}{2}p^T m^{ - 1} p $ and $T^r (\pi )$ is
1330 > defined by \ref{introEquation:rotationalKineticRB}. Therefore, the
1331 > corresponding flow maps are given by
1332   \[
1333 < p^2 L(x_\alpha  ) - px_\alpha  (0) - \dot x_\alpha  (0) =  - \omega
1334 < _\alpha ^2 L(x_\alpha  ) + \frac{{g_\alpha  }}{{\omega _\alpha
1105 < }}L(x)
1333 > \varphi _{\Delta t,T}  = \varphi _{\Delta t,T^t }  \circ \varphi
1334 > _{\Delta t,T^r }.
1335   \]
1336 + Finally, we obtain the overall symplectic flow maps for free moving
1337 + rigid body
1338 + \begin{equation}
1339 + \begin{array}{c}
1340 + \varphi _{\Delta t}  = \varphi _{\Delta t/2,F}  \circ \varphi _{\Delta t/2,\tau }  \\
1341 +  \circ \varphi _{\Delta t,T^t }  \circ \varphi _{\Delta t/2,\pi _1 }  \circ \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t,\pi _3 }  \circ \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t/2,\pi _1 }  \\
1342 +  \circ \varphi _{\Delta t/2,\tau }  \circ \varphi _{\Delta t/2,F}  .\\
1343 + \end{array}
1344 + \label{introEquation:overallRBFlowMaps}
1345 + \end{equation}
1346 +
1347 + \section{\label{introSection:langevinDynamics}Langevin Dynamics}
1348 + As an alternative to newtonian dynamics, Langevin dynamics, which
1349 + mimics a simple heat bath with stochastic and dissipative forces,
1350 + has been applied in a variety of studies. This section will review
1351 + the theory of Langevin dynamics simulation. A brief derivation of
1352 + generalized Langevin equation will be given first. Follow that, we
1353 + will discuss the physical meaning of the terms appearing in the
1354 + equation as well as the calculation of friction tensor from
1355 + hydrodynamics theory.
1356 +
1357 + \subsection{\label{introSection:generalizedLangevinDynamics}Derivation of Generalized Langevin Equation}
1358 +
1359 + Harmonic bath model, in which an effective set of harmonic
1360 + oscillators are used to mimic the effect of a linearly responding
1361 + environment, has been widely used in quantum chemistry and
1362 + statistical mechanics. One of the successful applications of
1363 + Harmonic bath model is the derivation of Deriving Generalized
1364 + Langevin Dynamics. Lets consider a system, in which the degree of
1365 + freedom $x$ is assumed to couple to the bath linearly, giving a
1366 + Hamiltonian of the form
1367 + \begin{equation}
1368 + H = \frac{{p^2 }}{{2m}} + U(x) + H_B  + \Delta U(x,x_1 , \ldots x_N)
1369 + \label{introEquation:bathGLE}.
1370 + \end{equation}
1371 + Here $p$ is a momentum conjugate to $q$, $m$ is the mass associated
1372 + with this degree of freedom, $H_B$ is harmonic bath Hamiltonian,
1373   \[
1374 < L(x_\alpha  ) = \frac{{\frac{{g_\alpha  }}{{\omega _\alpha  }}L(x) +
1375 < px_\alpha  (0) + \dot x_\alpha  (0)}}{{p^2  + \omega _\alpha ^2 }}
1374 > H_B  = \sum\limits_{\alpha  = 1}^N {\left\{ {\frac{{p_\alpha ^2
1375 > }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha  \omega _\alpha ^2 }
1376 > \right\}}
1377   \]
1378 < Then, the system coordinates,
1379 < \begin{align}
1380 < mL(\ddot x) &=  - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} -
1381 < \sum\limits_{\alpha  = 1}^N {\left\{ {\frac{{\frac{{g_\alpha
1382 < }}{{\omega _\alpha  }}L(x) + px_\alpha  (0) + \dot x_\alpha
1383 < (0)}}{{p^2  + \omega _\alpha ^2 }} - \frac{{g_\alpha ^2 }}{{m_\alpha
1384 < }}\omega _\alpha ^2 L(x)} \right\}}
1385 < %
1386 < &= - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} -
1387 < \sum\limits_{\alpha  = 1}^N {\left\{ { - \frac{{g_\alpha ^2 }}{{m_\alpha  \omega _\alpha ^2 }}\frac{p}{{p^2  + \omega _\alpha ^2 }}pL(x)
1388 < - \frac{p}{{p^2  + \omega _\alpha ^2 }}g_\alpha  x_\alpha  (0)
1389 < - \frac{1}{{p^2  + \omega _\alpha ^2 }}g_\alpha  \dot x_\alpha  (0)} \right\}}
1390 < \end{align}
1391 < Then, the inverse transform,
1378 > where the index $\alpha$ runs over all the bath degrees of freedom,
1379 > $\omega _\alpha$ are the harmonic bath frequencies, $m_\alpha$ are
1380 > the harmonic bath masses, and $\Delta U$ is bilinear system-bath
1381 > coupling,
1382 > \[
1383 > \Delta U =  - \sum\limits_{\alpha  = 1}^N {g_\alpha  x_\alpha  x}
1384 > \]
1385 > where $g_\alpha$ are the coupling constants between the bath and the
1386 > coordinate $x$. Introducing
1387 > \[
1388 > W(x) = U(x) - \sum\limits_{\alpha  = 1}^N {\frac{{g_\alpha ^2
1389 > }}{{2m_\alpha  w_\alpha ^2 }}} x^2
1390 > \] and combining the last two terms in Equation
1391 > \ref{introEquation:bathGLE}, we may rewrite the Harmonic bath
1392 > Hamiltonian as
1393 > \[
1394 > H = \frac{{p^2 }}{{2m}} + W(x) + \sum\limits_{\alpha  = 1}^N
1395 > {\left\{ {\frac{{p_\alpha ^2 }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha
1396 > w_\alpha ^2 \left( {x_\alpha   - \frac{{g_\alpha  }}{{m_\alpha
1397 > w_\alpha ^2 }}x} \right)^2 } \right\}}
1398 > \]
1399 > Since the first two terms of the new Hamiltonian depend only on the
1400 > system coordinates, we can get the equations of motion for
1401 > Generalized Langevin Dynamics by Hamilton's equations
1402 > \ref{introEquation:motionHamiltonianCoordinate,
1403 > introEquation:motionHamiltonianMomentum},
1404 > \begin{equation}
1405 > m\ddot x =  - \frac{{\partial W(x)}}{{\partial x}} -
1406 > \sum\limits_{\alpha  = 1}^N {g_\alpha  \left( {x_\alpha   -
1407 > \frac{{g_\alpha  }}{{m_\alpha  w_\alpha ^2 }}x} \right)},
1408 > \label{introEquation:coorMotionGLE}
1409 > \end{equation}
1410 > and
1411 > \begin{equation}
1412 > m\ddot x_\alpha   =  - m_\alpha  w_\alpha ^2 \left( {x_\alpha   -
1413 > \frac{{g_\alpha  }}{{m_\alpha  w_\alpha ^2 }}x} \right).
1414 > \label{introEquation:bathMotionGLE}
1415 > \end{equation}
1416  
1417 + In order to derive an equation for $x$, the dynamics of the bath
1418 + variables $x_\alpha$ must be solved exactly first. As an integral
1419 + transform which is particularly useful in solving linear ordinary
1420 + differential equations, Laplace transform is the appropriate tool to
1421 + solve this problem. The basic idea is to transform the difficult
1422 + differential equations into simple algebra problems which can be
1423 + solved easily. Then applying inverse Laplace transform, also known
1424 + as the Bromwich integral, we can retrieve the solutions of the
1425 + original problems.
1426 +
1427 + Let $f(t)$ be a function defined on $ [0,\infty ) $. The Laplace
1428 + transform of f(t) is a new function defined as
1429 + \[
1430 + L(f(t)) \equiv F(p) = \int_0^\infty  {f(t)e^{ - pt} dt}
1431 + \]
1432 + where  $p$ is real and  $L$ is called the Laplace Transform
1433 + Operator. Below are some important properties of Laplace transform
1434 + \begin{equation}
1435 + \begin{array}{c}
1436 + L(x + y) = L(x) + L(y) \\
1437 + L(ax) = aL(x) \\
1438 + L(\dot x) = pL(x) - px(0) \\
1439 + L(\ddot x) = p^2 L(x) - px(0) - \dot x(0) \\
1440 + L\left( {\int_0^t {g(t - \tau )h(\tau )d\tau } } \right) = G(p)H(p) \\
1441 + \end{array}
1442 + \end{equation}
1443 +
1444 + Applying Laplace transform to the bath coordinates, we obtain
1445 + \[
1446 + \begin{array}{c}
1447 + p^2 L(x_\alpha  ) - px_\alpha  (0) - \dot x_\alpha  (0) =  - \omega _\alpha ^2 L(x_\alpha  ) + \frac{{g_\alpha  }}{{\omega _\alpha  }}L(x) \\
1448 + L(x_\alpha  ) = \frac{{\frac{{g_\alpha  }}{{\omega _\alpha  }}L(x) + px_\alpha  (0) + \dot x_\alpha  (0)}}{{p^2  + \omega _\alpha ^2 }} \\
1449 + \end{array}
1450 + \]
1451 + By the same way, the system coordinates become
1452 + \[
1453 + \begin{array}{c}
1454 + mL(\ddot x) =  - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} \\
1455 +  - \sum\limits_{\alpha  = 1}^N {\left\{ { - \frac{{g_\alpha ^2 }}{{m_\alpha  \omega _\alpha ^2 }}\frac{p}{{p^2  + \omega _\alpha ^2 }}pL(x) - \frac{p}{{p^2  + \omega _\alpha ^2 }}g_\alpha  x_\alpha  (0) - \frac{1}{{p^2  + \omega _\alpha ^2 }}g_\alpha  \dot x_\alpha  (0)} \right\}}  \\
1456 + \end{array}
1457 + \]
1458 +
1459 + With the help of some relatively important inverse Laplace
1460 + transformations:
1461 + \[
1462 + \begin{array}{c}
1463 + L(\cos at) = \frac{p}{{p^2  + a^2 }} \\
1464 + L(\sin at) = \frac{a}{{p^2  + a^2 }} \\
1465 + L(1) = \frac{1}{p} \\
1466 + \end{array}
1467 + \]
1468 + , we obtain
1469   \begin{align}
1470   m\ddot x &=  - \frac{{\partial W(x)}}{{\partial x}} -
1471   \sum\limits_{\alpha  = 1}^N {\left\{ {\left( { - \frac{{g_\alpha ^2
# Line 1142 | Line 1485 | t)\dot x(t - \tau )d} \tau }  + \sum\limits_{\alpha  =
1485   (\omega _\alpha  t)} \right\}}
1486   \end{align}
1487  
1488 + Introducing a \emph{dynamic friction kernel}
1489   \begin{equation}
1490 + \xi (t) = \sum\limits_{\alpha  = 1}^N {\left( { - \frac{{g_\alpha ^2
1491 + }}{{m_\alpha  \omega _\alpha ^2 }}} \right)\cos (\omega _\alpha  t)}
1492 + \label{introEquation:dynamicFrictionKernelDefinition}
1493 + \end{equation}
1494 + and \emph{a random force}
1495 + \begin{equation}
1496 + R(t) = \sum\limits_{\alpha  = 1}^N {\left( {g_\alpha  x_\alpha  (0)
1497 + - \frac{{g_\alpha ^2 }}{{m_\alpha  \omega _\alpha ^2 }}x(0)}
1498 + \right)\cos (\omega _\alpha  t)}  + \frac{{\dot x_\alpha
1499 + (0)}}{{\omega _\alpha  }}\sin (\omega _\alpha  t),
1500 + \label{introEquation:randomForceDefinition}
1501 + \end{equation}
1502 + the equation of motion can be rewritten as
1503 + \begin{equation}
1504   m\ddot x =  - \frac{{\partial W}}{{\partial x}} - \int_0^t {\xi
1505   (t)\dot x(t - \tau )d\tau }  + R(t)
1506   \label{introEuqation:GeneralizedLangevinDynamics}
1507   \end{equation}
1508 < %where $ {\xi (t)}$ is friction kernel, $R(t)$ is random force and
1509 < %$W$ is the potential of mean force. $W(x) =  - kT\ln p(x)$
1508 > which is known as the \emph{generalized Langevin equation}.
1509 >
1510 > \subsubsection{\label{introSection:randomForceDynamicFrictionKernel}Random Force and Dynamic Friction Kernel}
1511 >
1512 > One may notice that $R(t)$ depends only on initial conditions, which
1513 > implies it is completely deterministic within the context of a
1514 > harmonic bath. However, it is easy to verify that $R(t)$ is totally
1515 > uncorrelated to $x$ and $\dot x$,
1516   \[
1517 < \xi (t) = \sum\limits_{\alpha  = 1}^N {\left( { - \frac{{g_\alpha ^2
1518 < }}{{m_\alpha  \omega _\alpha ^2 }}} \right)\cos (\omega _\alpha  t)}
1517 > \begin{array}{l}
1518 > \left\langle {x(t)R(t)} \right\rangle  = 0, \\
1519 > \left\langle {\dot x(t)R(t)} \right\rangle  = 0. \\
1520 > \end{array}
1521   \]
1522 < For an infinite harmonic bath, we can use the spectral density and
1523 < an integral over frequencies.
1522 > This property is what we expect from a truly random process. As long
1523 > as the model, which is gaussian distribution in general, chosen for
1524 > $R(t)$ is a truly random process, the stochastic nature of the GLE
1525 > still remains.
1526  
1527 + %dynamic friction kernel
1528 + The convolution integral
1529   \[
1530 < R(t) = \sum\limits_{\alpha  = 1}^N {\left( {g_\alpha  x_\alpha  (0)
1161 < - \frac{{g_\alpha ^2 }}{{m_\alpha  \omega _\alpha ^2 }}x(0)}
1162 < \right)\cos (\omega _\alpha  t)}  + \frac{{\dot x_\alpha
1163 < (0)}}{{\omega _\alpha  }}\sin (\omega _\alpha  t)
1530 > \int_0^t {\xi (t)\dot x(t - \tau )d\tau }
1531   \]
1532 < The random forces depend only on initial conditions.
1532 > depends on the entire history of the evolution of $x$, which implies
1533 > that the bath retains memory of previous motions. In other words,
1534 > the bath requires a finite time to respond to change in the motion
1535 > of the system. For a sluggish bath which responds slowly to changes
1536 > in the system coordinate, we may regard $\xi(t)$ as a constant
1537 > $\xi(t) = \Xi_0$. Hence, the convolution integral becomes
1538 > \[
1539 > \int_0^t {\xi (t)\dot x(t - \tau )d\tau }  = \xi _0 (x(t) - x(0))
1540 > \]
1541 > and Equation \ref{introEuqation:GeneralizedLangevinDynamics} becomes
1542 > \[
1543 > m\ddot x =  - \frac{\partial }{{\partial x}}\left( {W(x) +
1544 > \frac{1}{2}\xi _0 (x - x_0 )^2 } \right) + R(t),
1545 > \]
1546 > which can be used to describe dynamic caging effect. The other
1547 > extreme is the bath that responds infinitely quickly to motions in
1548 > the system. Thus, $\xi (t)$ can be taken as a $delta$ function in
1549 > time:
1550 > \[
1551 > \xi (t) = 2\xi _0 \delta (t)
1552 > \]
1553 > Hence, the convolution integral becomes
1554 > \[
1555 > \int_0^t {\xi (t)\dot x(t - \tau )d\tau }  = 2\xi _0 \int_0^t
1556 > {\delta (t)\dot x(t - \tau )d\tau }  = \xi _0 \dot x(t),
1557 > \]
1558 > and Equation \ref{introEuqation:GeneralizedLangevinDynamics} becomes
1559 > \begin{equation}
1560 > m\ddot x =  - \frac{{\partial W(x)}}{{\partial x}} - \xi _0 \dot
1561 > x(t) + R(t) \label{introEquation:LangevinEquation}
1562 > \end{equation}
1563 > which is known as the Langevin equation. The static friction
1564 > coefficient $\xi _0$ can either be calculated from spectral density
1565 > or be determined by Stokes' law for regular shaped particles.A
1566 > briefly review on calculating friction tensor for arbitrary shaped
1567 > particles is given in Sec.~\ref{introSection:frictionTensor}.
1568  
1569   \subsubsection{\label{introSection:secondFluctuationDissipation}The Second Fluctuation Dissipation Theorem}
1570 < So we can define a new set of coordinates,
1570 >
1571 > Defining a new set of coordinates,
1572   \[
1573   q_\alpha  (t) = x_\alpha  (t) - \frac{1}{{m_\alpha  \omega _\alpha
1574   ^2 }}x(0)
1575 < \]
1576 < This makes
1575 > \],
1576 > we can rewrite $R(T)$ as
1577   \[
1578 < R(t) = \sum\limits_{\alpha  = 1}^N {g_\alpha  q_\alpha  (t)}
1578 > R(t) = \sum\limits_{\alpha  = 1}^N {g_\alpha  q_\alpha  (t)}.
1579   \]
1580   And since the $q$ coordinates are harmonic oscillators,
1581   \[
1582 < \begin{array}{l}
1582 > \begin{array}{c}
1583 > \left\langle {q_\alpha ^2 } \right\rangle  = \frac{{kT}}{{m_\alpha  \omega _\alpha ^2 }} \\
1584   \left\langle {q_\alpha  (t)q_\alpha  (0)} \right\rangle  = \left\langle {q_\alpha ^2 (0)} \right\rangle \cos (\omega _\alpha  t) \\
1585   \left\langle {q_\alpha  (t)q_\beta  (0)} \right\rangle  = \delta _{\alpha \beta } \left\langle {q_\alpha  (t)q_\alpha  (0)} \right\rangle  \\
1586 + \left\langle {R(t)R(0)} \right\rangle  = \sum\limits_\alpha  {\sum\limits_\beta  {g_\alpha  g_\beta  \left\langle {q_\alpha  (t)q_\beta  (0)} \right\rangle } }  \\
1587 +  = \sum\limits_\alpha  {g_\alpha ^2 \left\langle {q_\alpha ^2 (0)} \right\rangle \cos (\omega _\alpha  t)}  \\
1588 +  = kT\xi (t) \\
1589   \end{array}
1590   \]
1591 <
1185 < \begin{align}
1186 < \left\langle {R(t)R(0)} \right\rangle  &= \sum\limits_\alpha
1187 < {\sum\limits_\beta  {g_\alpha  g_\beta  \left\langle {q_\alpha
1188 < (t)q_\beta  (0)} \right\rangle } }
1189 < %
1190 < &= \sum\limits_\alpha  {g_\alpha ^2 \left\langle {q_\alpha ^2 (0)}
1191 < \right\rangle \cos (\omega _\alpha  t)}
1192 < %
1193 < &= kT\xi (t)
1194 < \end{align}
1195 <
1591 > Thus, we recover the \emph{second fluctuation dissipation theorem}
1592   \begin{equation}
1593   \xi (t) = \left\langle {R(t)R(0)} \right\rangle
1594 < \label{introEquation:secondFluctuationDissipation}
1594 > \label{introEquation:secondFluctuationDissipation}.
1595   \end{equation}
1596 + In effect, it acts as a constraint on the possible ways in which one
1597 + can model the random force and friction kernel.
1598  
1201 \section{\label{introSection:hydroynamics}Hydrodynamics}
1202
1599   \subsection{\label{introSection:frictionTensor} Friction Tensor}
1600 < \subsection{\label{introSection:analyticalApproach}Analytical
1601 < Approach}
1602 <
1603 < \subsection{\label{introSection:approximationApproach}Approximation
1604 < Approach}
1605 <
1606 < \subsection{\label{introSection:centersRigidBody}Centers of Rigid
1607 < Body}
1600 > Theoretically, the friction kernel can be determined using velocity
1601 > autocorrelation function. However, this approach become impractical
1602 > when the system become more and more complicate. Instead, various
1603 > approaches based on hydrodynamics have been developed to calculate
1604 > the friction coefficients. The friction effect is isotropic in
1605 > Equation, \zeta can be taken as a scalar. In general, friction
1606 > tensor \Xi is a $6\times 6$ matrix given by
1607 > \[
1608 > \Xi  = \left( {\begin{array}{*{20}c}
1609 >   {\Xi _{}^{tt} } & {\Xi _{}^{rt} }  \\
1610 >   {\Xi _{}^{tr} } & {\Xi _{}^{rr} }  \\
1611 > \end{array}} \right).
1612 > \]
1613 > Here, $ {\Xi^{tt} }$ and $ {\Xi^{rr} }$ are translational friction
1614 > tensor and rotational resistance (friction) tensor respectively,
1615 > while ${\Xi^{tr} }$ is translation-rotation coupling tensor and $
1616 > {\Xi^{rt} }$ is rotation-translation coupling tensor. When a
1617 > particle moves in a fluid, it may experience friction force or
1618 > torque along the opposite direction of the velocity or angular
1619 > velocity,
1620 > \[
1621 > \left( \begin{array}{l}
1622 > F_R  \\
1623 > \tau _R  \\
1624 > \end{array} \right) =  - \left( {\begin{array}{*{20}c}
1625 >   {\Xi ^{tt} } & {\Xi ^{rt} }  \\
1626 >   {\Xi ^{tr} } & {\Xi ^{rr} }  \\
1627 > \end{array}} \right)\left( \begin{array}{l}
1628 > v \\
1629 > w \\
1630 > \end{array} \right)
1631 > \]
1632 > where $F_r$ is the friction force and $\tau _R$ is the friction
1633 > toque.
1634  
1635 < \section{\label{introSection:correlationFunctions}Correlation Functions}
1635 > \subsubsection{\label{introSection:resistanceTensorRegular}The Resistance Tensor for Regular Shape}
1636 >
1637 > For a spherical particle, the translational and rotational friction
1638 > constant can be calculated from Stoke's law,
1639 > \[
1640 > \Xi ^{tt}  = \left( {\begin{array}{*{20}c}
1641 >   {6\pi \eta R} & 0 & 0  \\
1642 >   0 & {6\pi \eta R} & 0  \\
1643 >   0 & 0 & {6\pi \eta R}  \\
1644 > \end{array}} \right)
1645 > \]
1646 > and
1647 > \[
1648 > \Xi ^{rr}  = \left( {\begin{array}{*{20}c}
1649 >   {8\pi \eta R^3 } & 0 & 0  \\
1650 >   0 & {8\pi \eta R^3 } & 0  \\
1651 >   0 & 0 & {8\pi \eta R^3 }  \\
1652 > \end{array}} \right)
1653 > \]
1654 > where $\eta$ is the viscosity of the solvent and $R$ is the
1655 > hydrodynamics radius.
1656 >
1657 > Other non-spherical shape, such as cylinder and ellipsoid
1658 > \textit{etc}, are widely used as reference for developing new
1659 > hydrodynamics theory, because their properties can be calculated
1660 > exactly. In 1936, Perrin extended Stokes's law to general ellipsoid,
1661 > also called a triaxial ellipsoid, which is given in Cartesian
1662 > coordinates by
1663 > \[
1664 > \frac{{x^2 }}{{a^2 }} + \frac{{y^2 }}{{b^2 }} + \frac{{z^2 }}{{c^2
1665 > }} = 1
1666 > \]
1667 > where the semi-axes are of lengths $a$, $b$, and $c$. Unfortunately,
1668 > due to the complexity of the elliptic integral, only the ellipsoid
1669 > with the restriction of two axes having to be equal, \textit{i.e.}
1670 > prolate($ a \ge b = c$) and oblate ($ a < b = c $), can be solved
1671 > exactly. Introducing an elliptic integral parameter $S$ for prolate,
1672 > \[
1673 > S = \frac{2}{{\sqrt {a^2  - b^2 } }}\ln \frac{{a + \sqrt {a^2  - b^2
1674 > } }}{b},
1675 > \]
1676 > and oblate,
1677 > \[
1678 > S = \frac{2}{{\sqrt {b^2  - a^2 } }}arctg\frac{{\sqrt {b^2  - a^2 }
1679 > }}{a}
1680 > \],
1681 > one can write down the translational and rotational resistance
1682 > tensors
1683 > \[
1684 > \begin{array}{l}
1685 > \Xi _a^{tt}  = 16\pi \eta \frac{{a^2  - b^2 }}{{(2a^2  - b^2 )S - 2a}} \\
1686 > \Xi _b^{tt}  = \Xi _c^{tt}  = 32\pi \eta \frac{{a^2  - b^2 }}{{(2a^2  - 3b^2 )S + 2a}} \\
1687 > \end{array},
1688 > \]
1689 > and
1690 > \[
1691 > \begin{array}{l}
1692 > \Xi _a^{rr}  = \frac{{32\pi }}{3}\eta \frac{{(a^2  - b^2 )b^2 }}{{2a - b^2 S}} \\
1693 > \Xi _b^{rr}  = \Xi _c^{rr}  = \frac{{32\pi }}{3}\eta \frac{{(a^4  - b^4 )}}{{(2a^2  - b^2 )S - 2a}} \\
1694 > \end{array}.
1695 > \]
1696 >
1697 > \subsubsection{\label{introSection:resistanceTensorRegularArbitrary}The Resistance Tensor for Arbitrary Shape}
1698 >
1699 > Unlike spherical and other regular shaped molecules, there is not
1700 > analytical solution for friction tensor of any arbitrary shaped
1701 > rigid molecules. The ellipsoid of revolution model and general
1702 > triaxial ellipsoid model have been used to approximate the
1703 > hydrodynamic properties of rigid bodies. However, since the mapping
1704 > from all possible ellipsoidal space, $r$-space, to all possible
1705 > combination of rotational diffusion coefficients, $D$-space is not
1706 > unique\cite{Wegener79} as well as the intrinsic coupling between
1707 > translational and rotational motion of rigid body\cite{}, general
1708 > ellipsoid is not always suitable for modeling arbitrarily shaped
1709 > rigid molecule. A number of studies have been devoted to determine
1710 > the friction tensor for irregularly shaped rigid bodies using more
1711 > advanced method\cite{} where the molecule of interest was modeled by
1712 > combinations of spheres(beads)\cite{} and the hydrodynamics
1713 > properties of the molecule can be calculated using the hydrodynamic
1714 > interaction tensor. Let us consider a rigid assembly of $N$ beads
1715 > immersed in a continuous medium. Due to hydrodynamics interaction,
1716 > the ``net'' velocity of $i$th bead, $v'_i$ is different than its
1717 > unperturbed velocity $v_i$,
1718 > \[
1719 > v'_i  = v_i  - \sum\limits_{j \ne i} {T_{ij} F_j }
1720 > \]
1721 > where $F_i$ is the frictional force, and $T_{ij}$ is the
1722 > hydrodynamic interaction tensor. The friction force of $i$th bead is
1723 > proportional to its ``net'' velocity
1724 > \begin{equation}
1725 > F_i  = \zeta _i v_i  - \zeta _i \sum\limits_{j \ne i} {T_{ij} F_j }.
1726 > \label{introEquation:tensorExpression}
1727 > \end{equation}
1728 > This equation is the basis for deriving the hydrodynamic tensor. In
1729 > 1930, Oseen and Burgers gave a simple solution to Equation
1730 > \ref{introEquation:tensorExpression}
1731 > \begin{equation}
1732 > T_{ij}  = \frac{1}{{8\pi \eta r_{ij} }}\left( {I + \frac{{R_{ij}
1733 > R_{ij}^T }}{{R_{ij}^2 }}} \right).
1734 > \label{introEquation:oseenTensor}
1735 > \end{equation}
1736 > Here $R_{ij}$ is the distance vector between bead $i$ and bead $j$.
1737 > A second order expression for element of different size was
1738 > introduced by Rotne and Prager\cite{} and improved by Garc\'{i}a de
1739 > la Torre and Bloomfield,
1740 > \begin{equation}
1741 > T_{ij}  = \frac{1}{{8\pi \eta R_{ij} }}\left[ {\left( {I +
1742 > \frac{{R_{ij} R_{ij}^T }}{{R_{ij}^2 }}} \right) + R\frac{{\sigma
1743 > _i^2  + \sigma _j^2 }}{{r_{ij}^2 }}\left( {\frac{I}{3} -
1744 > \frac{{R_{ij} R_{ij}^T }}{{R_{ij}^2 }}} \right)} \right].
1745 > \label{introEquation:RPTensorNonOverlapped}
1746 > \end{equation}
1747 > Both of the Equation \ref{introEquation:oseenTensor} and Equation
1748 > \ref{introEquation:RPTensorNonOverlapped} have an assumption $R_{ij}
1749 > \ge \sigma _i  + \sigma _j$. An alternative expression for
1750 > overlapping beads with the same radius, $\sigma$, is given by
1751 > \begin{equation}
1752 > T_{ij}  = \frac{1}{{6\pi \eta R_{ij} }}\left[ {\left( {1 -
1753 > \frac{2}{{32}}\frac{{R_{ij} }}{\sigma }} \right)I +
1754 > \frac{2}{{32}}\frac{{R_{ij} R_{ij}^T }}{{R_{ij} \sigma }}} \right]
1755 > \label{introEquation:RPTensorOverlapped}
1756 > \end{equation}
1757 >
1758 > To calculate the resistance tensor at an arbitrary origin $O$, we
1759 > construct a $3N \times 3N$ matrix consisting of $N \times N$
1760 > $B_{ij}$ blocks
1761 > \begin{equation}
1762 > B = \left( {\begin{array}{*{20}c}
1763 >   {B_{11} } &  \ldots  & {B_{1N} }  \\
1764 >    \vdots  &  \ddots  &  \vdots   \\
1765 >   {B_{N1} } &  \cdots  & {B_{NN} }  \\
1766 > \end{array}} \right),
1767 > \end{equation}
1768 > where $B_{ij}$ is given by
1769 > \[
1770 > B_{ij}  = \delta _{ij} \frac{I}{{6\pi \eta R}} + (1 - \delta _{ij}
1771 > )T_{ij}
1772 > \]
1773 > where $\delta _{ij}$ is Kronecker delta function. Inverting matrix
1774 > $B$, we obtain
1775 >
1776 > \[
1777 > C = B^{ - 1}  = \left( {\begin{array}{*{20}c}
1778 >   {C_{11} } &  \ldots  & {C_{1N} }  \\
1779 >    \vdots  &  \ddots  &  \vdots   \\
1780 >   {C_{N1} } &  \cdots  & {C_{NN} }  \\
1781 > \end{array}} \right)
1782 > \]
1783 > , which can be partitioned into $N \times N$ $3 \times 3$ block
1784 > $C_{ij}$. With the help of $C_{ij}$ and skew matrix $U_i$
1785 > \[
1786 > U_i  = \left( {\begin{array}{*{20}c}
1787 >   0 & { - z_i } & {y_i }  \\
1788 >   {z_i } & 0 & { - x_i }  \\
1789 >   { - y_i } & {x_i } & 0  \\
1790 > \end{array}} \right)
1791 > \]
1792 > where $x_i$, $y_i$, $z_i$ are the components of the vector joining
1793 > bead $i$ and origin $O$. Hence, the elements of resistance tensor at
1794 > arbitrary origin $O$ can be written as
1795 > \begin{equation}
1796 > \begin{array}{l}
1797 > \Xi _{}^{tt}  = \sum\limits_i {\sum\limits_j {C_{ij} } } , \\
1798 > \Xi _{}^{tr}  = \Xi _{}^{rt}  = \sum\limits_i {\sum\limits_j {U_i C_{ij} } } , \\
1799 > \Xi _{}^{rr}  =  - \sum\limits_i {\sum\limits_j {U_i C_{ij} } } U_j  \\
1800 > \end{array}
1801 > \label{introEquation:ResistanceTensorArbitraryOrigin}
1802 > \end{equation}
1803 >
1804 > The resistance tensor depends on the origin to which they refer. The
1805 > proper location for applying friction force is the center of
1806 > resistance (reaction), at which the trace of rotational resistance
1807 > tensor, $ \Xi ^{rr}$ reaches minimum. Mathematically, the center of
1808 > resistance is defined as an unique point of the rigid body at which
1809 > the translation-rotation coupling tensor are symmetric,
1810 > \begin{equation}
1811 > \Xi^{tr}  = \left( {\Xi^{tr} } \right)^T
1812 > \label{introEquation:definitionCR}
1813 > \end{equation}
1814 > Form Equation \ref{introEquation:ResistanceTensorArbitraryOrigin},
1815 > we can easily find out that the translational resistance tensor is
1816 > origin independent, while the rotational resistance tensor and
1817 > translation-rotation coupling resistance tensor depend on the
1818 > origin. Given resistance tensor at an arbitrary origin $O$, and a
1819 > vector ,$r_{OP}(x_{OP}, y_{OP}, z_{OP})$, from $O$ to $P$, we can
1820 > obtain the resistance tensor at $P$ by
1821 > \begin{equation}
1822 > \begin{array}{l}
1823 > \Xi _P^{tt}  = \Xi _O^{tt}  \\
1824 > \Xi _P^{tr}  = \Xi _P^{rt}  = \Xi _O^{tr}  - U_{OP} \Xi _O^{tt}  \\
1825 > \Xi _P^{rr}  = \Xi _O^{rr}  - U_{OP} \Xi _O^{tt} U_{OP}  + \Xi _O^{tr} U_{OP}  - U_{OP} \Xi _O^{tr} ^{^T }  \\
1826 > \end{array}
1827 > \label{introEquation:resistanceTensorTransformation}
1828 > \end{equation}
1829 > where
1830 > \[
1831 > U_{OP}  = \left( {\begin{array}{*{20}c}
1832 >   0 & { - z_{OP} } & {y_{OP} }  \\
1833 >   {z_i } & 0 & { - x_{OP} }  \\
1834 >   { - y_{OP} } & {x_{OP} } & 0  \\
1835 > \end{array}} \right)
1836 > \]
1837 > Using Equations \ref{introEquation:definitionCR} and
1838 > \ref{introEquation:resistanceTensorTransformation}, one can locate
1839 > the position of center of resistance,
1840 > \[
1841 > \left( \begin{array}{l}
1842 > x_{OR}  \\
1843 > y_{OR}  \\
1844 > z_{OR}  \\
1845 > \end{array} \right) = \left( {\begin{array}{*{20}c}
1846 >   {(\Xi _O^{rr} )_{yy}  + (\Xi _O^{rr} )_{zz} } & { - (\Xi _O^{rr} )_{xy} } & { - (\Xi _O^{rr} )_{xz} }  \\
1847 >   { - (\Xi _O^{rr} )_{xy} } & {(\Xi _O^{rr} )_{zz}  + (\Xi _O^{rr} )_{xx} } & { - (\Xi _O^{rr} )_{yz} }  \\
1848 >   { - (\Xi _O^{rr} )_{xz} } & { - (\Xi _O^{rr} )_{yz} } & {(\Xi _O^{rr} )_{xx}  + (\Xi _O^{rr} )_{yy} }  \\
1849 > \end{array}} \right)^{ - 1} \left( \begin{array}{l}
1850 > (\Xi _O^{tr} )_{yz}  - (\Xi _O^{tr} )_{zy}  \\
1851 > (\Xi _O^{tr} )_{zx}  - (\Xi _O^{tr} )_{xz}  \\
1852 > (\Xi _O^{tr} )_{xy}  - (\Xi _O^{tr} )_{yx}  \\
1853 > \end{array} \right).
1854 > \]
1855 > where $x_OR$, $y_OR$, $z_OR$ are the components of the vector
1856 > joining center of resistance $R$ and origin $O$.
1857 >
1858 > %\section{\label{introSection:correlationFunctions}Correlation Functions}

Diff Legend

Removed lines
+ Added lines
< Changed lines
> Changed lines