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# Line 315 | Line 315 | partition function like,
315   isolated and conserve energy, Microcanonical ensemble(NVE) has a
316   partition function like,
317   \begin{equation}
318 < \Omega (N,V,E) = e^{\beta TS}
319 < \label{introEqaution:NVEPartition}.
318 > \Omega (N,V,E) = e^{\beta TS} \label{introEquation:NVEPartition}.
319   \end{equation}
320   A canonical ensemble(NVT)is an ensemble of systems, each of which
321   can share its energy with a large heat reservoir. The distribution
# Line 571 | 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 $x$.
574 The free rigid body is an example of Poisson system (actually a
575 Lie-Poisson system) with Hamiltonian function of angular kinetic
576 energy.
577 \begin{equation}
578 J(\pi ) = \left( {\begin{array}{*{20}c}
579   0 & {\pi _3 } & { - \pi _2 }  \\
580   { - \pi _3 } & 0 & {\pi _1 }  \\
581   {\pi _2 } & { - \pi _1 } & 0  \\
582 \end{array}} \right)
583 \end{equation}
573  
585 \begin{equation}
586 H = \frac{1}{2}\left( {\frac{{\pi _1^2 }}{{I_1 }} + \frac{{\pi _2^2
587 }}{{I_2 }} + \frac{{\pi _3^2 }}{{I_3 }}} \right)
588 \end{equation}
589
574   \subsection{\label{introSection:exactFlow}Exact Flow}
575  
576   Let $x(t)$ be the exact solution of the ODE system,
# Line 661 | Line 645 | When designing any numerical methods, one should alway
645   In other words, the flow of this vector field is reversible if and
646   only if $ h \circ \varphi ^{ - 1}  = \varphi  \circ h $.
647  
648 < When designing any numerical methods, one should always try to
648 > A \emph{first integral}, or conserved quantity of a general
649 > differential function is a function $ G:R^{2d}  \to R^d $ which is
650 > constant for all solutions of the ODE $\frac{{dx}}{{dt}} = f(x)$ ,
651 > \[
652 > \frac{{dG(x(t))}}{{dt}} = 0.
653 > \]
654 > Using chain rule, one may obtain,
655 > \[
656 > \sum\limits_i {\frac{{dG}}{{dx_i }}} f_i (x) = f \bullet \nabla G,
657 > \]
658 > which is the condition for conserving \emph{first integral}. For a
659 > canonical Hamiltonian system, the time evolution of an arbitrary
660 > smooth function $G$ is given by,
661 > \begin{equation}
662 > \begin{array}{c}
663 > \frac{{dG(x(t))}}{{dt}} = [\nabla _x G(x(t))]^T \dot x(t) \\
664 >  = [\nabla _x G(x(t))]^T J\nabla _x H(x(t)). \\
665 > \end{array}
666 > \label{introEquation:firstIntegral1}
667 > \end{equation}
668 > Using poisson bracket notion, Equation
669 > \ref{introEquation:firstIntegral1} can be rewritten as
670 > \[
671 > \frac{d}{{dt}}G(x(t)) = \left\{ {G,H} \right\}(x(t)).
672 > \]
673 > Therefore, the sufficient condition for $G$ to be the \emph{first
674 > integral} of a Hamiltonian system is
675 > \[
676 > \left\{ {G,H} \right\} = 0.
677 > \]
678 > As well known, the Hamiltonian (or energy) H of a Hamiltonian system
679 > is a \emph{first integral}, which is due to the fact $\{ H,H\}  =
680 > 0$.
681 >
682 >
683 > When designing any numerical methods, one should always try to
684   preserve the structural properties of the original ODE and its flow.
685  
686   \subsection{\label{introSection:constructionSymplectic}Construction of Symplectic Methods}
# Line 736 | Line 755 | _{1,h/2} ,
755   splitting gives a second-order decomposition,
756   \begin{equation}
757   \varphi _h  = \varphi _{1,h/2}  \circ \varphi _{2,h}  \circ \varphi
758 < _{1,h/2} ,
740 < \label{introEqaution:secondOrderSplitting}
758 > _{1,h/2} , \label{introEquation:secondOrderSplitting}
759   \end{equation}
760   which has a local error proportional to $h^3$. Sprang splitting's
761   popularity in molecular simulation community attribute to its
# Line 872 | Line 890 | dynamical information.
890  
891   \subsection{\label{introSec:mdInit}Initialization}
892  
893 + \subsection{\label{introSec:forceEvaluation}Force Evaluation}
894 +
895   \subsection{\label{introSection:mdIntegration} Integration of the Equations of Motion}
896  
897   \section{\label{introSection:rigidBody}Dynamics of Rigid Bodies}
898  
899 < A rigid body is a body in which the distance between any two given
900 < points of a rigid body remains constant regardless of external
901 < forces exerted on it. A rigid body therefore conserves its shape
902 < during its motion.
899 > Rigid bodies are frequently involved in the modeling of different
900 > areas, from engineering, physics, to chemistry. For example,
901 > missiles and vehicle are usually modeled by rigid bodies.  The
902 > movement of the objects in 3D gaming engine or other physics
903 > simulator is governed by the rigid body dynamics. In molecular
904 > simulation, rigid body is used to simplify the model in
905 > protein-protein docking study{\cite{Gray03}}.
906  
907 < Applications of dynamics of rigid bodies.
908 <
909 < \subsection{\label{introSection:lieAlgebra}Lie Algebra}
910 <
911 < \subsection{\label{introSection:DLMMotionEquation}The Euler Equations of Rigid Body Motion}
912 <
913 < \subsection{\label{introSection:otherRBMotionEquation}Other Formulations for Rigid Body Motion}
914 <
915 < \section{\label{introSection:correlationFunctions}Correlation Functions}
916 <
917 < \section{\label{introSection:langevinDynamics}Langevin Dynamics}
907 > It is very important to develop stable and efficient methods to
908 > integrate the equations of motion of orientational degrees of
909 > freedom. Euler angles are the nature choice to describe the
910 > rotational degrees of freedom. However, due to its singularity, the
911 > numerical integration of corresponding equations of motion is very
912 > inefficient and inaccurate. Although an alternative integrator using
913 > different sets of Euler angles can overcome this difficulty\cite{},
914 > the computational penalty and the lost of angular momentum
915 > conservation still remain. A singularity free representation
916 > utilizing quaternions was developed by Evans in 1977. Unfortunately,
917 > this approach suffer from the nonseparable Hamiltonian resulted from
918 > quaternion representation, which prevents the symplectic algorithm
919 > to be utilized. Another different approach is to apply holonomic
920 > constraints to the atoms belonging to the rigid body. Each atom
921 > moves independently under the normal forces deriving from potential
922 > energy and constraint forces which are used to guarantee the
923 > rigidness. However, due to their iterative nature, SHAKE and Rattle
924 > algorithm converge very slowly when the number of constraint
925 > increases.
926  
927 < \subsection{\label{introSection:LDIntroduction}Introduction and application of Langevin Dynamics}
927 > The break through in geometric literature suggests that, in order to
928 > develop a long-term integration scheme, one should preserve the
929 > symplectic structure of the flow. Introducing conjugate momentum to
930 > rotation matrix $A$ and re-formulating Hamiltonian's equation, a
931 > symplectic integrator, RSHAKE, was proposed to evolve the
932 > Hamiltonian system in a constraint manifold by iteratively
933 > satisfying the orthogonality constraint $A_t A = 1$. An alternative
934 > method using quaternion representation was developed by Omelyan.
935 > However, both of these methods are iterative and inefficient. In
936 > this section, we will present a symplectic Lie-Poisson integrator
937 > for rigid body developed by Dullweber and his
938 > coworkers\cite{Dullweber1997} in depth.
939 >
940 > \subsection{\label{introSection:constrainedHamiltonianRB}Constrained Hamiltonian for Rigid Body}
941 > The motion of the rigid body is Hamiltonian with the Hamiltonian
942 > function
943 > \begin{equation}
944 > H = \frac{1}{2}(p^T m^{ - 1} p) + \frac{1}{2}tr(PJ^{ - 1} P) +
945 > V(q,Q) + \frac{1}{2}tr[(QQ^T  - 1)\Lambda ].
946 > \label{introEquation:RBHamiltonian}
947 > \end{equation}
948 > Here, $q$ and $Q$  are the position and rotation matrix for the
949 > rigid-body, $p$ and $P$  are conjugate momenta to $q$  and $Q$ , and
950 > $J$, a diagonal matrix, is defined by
951 > \[
952 > I_{ii}^{ - 1}  = \frac{1}{2}\sum\limits_{i \ne j} {J_{jj}^{ - 1} }
953 > \]
954 > where $I_{ii}$ is the diagonal element of the inertia tensor. This
955 > constrained Hamiltonian equation subjects to a holonomic constraint,
956 > \begin{equation}
957 > Q^T Q = 1$, \label{introEquation:orthogonalConstraint}
958 > \end{equation}
959 > which is used to ensure rotation matrix's orthogonality.
960 > Differentiating \ref{introEquation:orthogonalConstraint} and using
961 > Equation \ref{introEquation:RBMotionMomentum}, one may obtain,
962 > \begin{equation}
963 > Q^T PJ^{ - 1}  + J^{ - 1} P^T Q = 0 . \\
964 > \label{introEquation:RBFirstOrderConstraint}
965 > \end{equation}
966 >
967 > Using Equation (\ref{introEquation:motionHamiltonianCoordinate},
968 > \ref{introEquation:motionHamiltonianMomentum}), one can write down
969 > the equations of motion,
970 > \[
971 > \begin{array}{c}
972 > \frac{{dq}}{{dt}} = \frac{p}{m} \label{introEquation:RBMotionPosition}\\
973 > \frac{{dp}}{{dt}} =  - \nabla _q V(q,Q) \label{introEquation:RBMotionMomentum}\\
974 > \frac{{dQ}}{{dt}} = PJ^{ - 1}  \label{introEquation:RBMotionRotation}\\
975 > \frac{{dP}}{{dt}} =  - \nabla _Q V(q,Q) - 2Q\Lambda . \label{introEquation:RBMotionP}\\
976 > \end{array}
977 > \]
978 >
979 > In general, there are two ways to satisfy the holonomic constraints.
980 > We can use constraint force provided by lagrange multiplier on the
981 > normal manifold to keep the motion on constraint space. Or we can
982 > simply evolve the system in constraint manifold. The two method are
983 > proved to be equivalent. The holonomic constraint and equations of
984 > motions define a constraint manifold for rigid body
985 > \[
986 > M = \left\{ {(Q,P):Q^T Q = 1,Q^T PJ^{ - 1}  + J^{ - 1} P^T Q = 0}
987 > \right\}.
988 > \]
989 >
990 > Unfortunately, this constraint manifold is not the cotangent bundle
991 > $T_{\star}SO(3)$. However, it turns out that under symplectic
992 > transformation, the cotangent space and the phase space are
993 > diffeomorphic. Introducing
994 > \[
995 > \tilde Q = Q,\tilde P = \frac{1}{2}\left( {P - QP^T Q} \right),
996 > \]
997 > the mechanical system subject to a holonomic constraint manifold $M$
998 > can be re-formulated as a Hamiltonian system on the cotangent space
999 > \[
1000 > T^* SO(3) = \left\{ {(\tilde Q,\tilde P):\tilde Q^T \tilde Q =
1001 > 1,\tilde Q^T \tilde PJ^{ - 1}  + J^{ - 1} P^T \tilde Q = 0} \right\}
1002 > \]
1003  
1004 + For a body fixed vector $X_i$ with respect to the center of mass of
1005 + the rigid body, its corresponding lab fixed vector $X_0^{lab}$  is
1006 + given as
1007 + \begin{equation}
1008 + X_i^{lab} = Q X_i + q.
1009 + \end{equation}
1010 + Therefore, potential energy $V(q,Q)$ is defined by
1011 + \[
1012 + V(q,Q) = V(Q X_0 + q).
1013 + \]
1014 + Hence, the force and torque are given by
1015 + \[
1016 + \nabla _q V(q,Q) = F(q,Q) = \sum\limits_i {F_i (q,Q)},
1017 + \]
1018 + and
1019 + \[
1020 + \nabla _Q V(q,Q) = F(q,Q)X_i^t
1021 + \]
1022 + respectively.
1023 +
1024 + As a common choice to describe the rotation dynamics of the rigid
1025 + body, angular momentum on body frame $\Pi  = Q^t P$ is introduced to
1026 + rewrite the equations of motion,
1027 + \begin{equation}
1028 + \begin{array}{l}
1029 + \mathop \Pi \limits^ \bullet   = J^{ - 1} \Pi ^T \Pi  + Q^T \sum\limits_i {F_i (q,Q)X_i^T }  - \Lambda  \\
1030 + \mathop Q\limits^{{\rm{   }} \bullet }  = Q\Pi {\rm{ }}J^{ - 1}  \\
1031 + \end{array}
1032 + \label{introEqaution:RBMotionPI}
1033 + \end{equation}
1034 + , as well as holonomic constraints,
1035 + \[
1036 + \begin{array}{l}
1037 + \Pi J^{ - 1}  + J^{ - 1} \Pi ^t  = 0 \\
1038 + Q^T Q = 1 \\
1039 + \end{array}
1040 + \]
1041 +
1042 + For a vector $v(v_1 ,v_2 ,v_3 ) \in R^3$ and a matrix $\hat v \in
1043 + so(3)^ \star$, the hat-map isomorphism,
1044 + \begin{equation}
1045 + v(v_1 ,v_2 ,v_3 ) \Leftrightarrow \hat v = \left(
1046 + {\begin{array}{*{20}c}
1047 +   0 & { - v_3 } & {v_2 }  \\
1048 +   {v_3 } & 0 & { - v_1 }  \\
1049 +   { - v_2 } & {v_1 } & 0  \\
1050 + \end{array}} \right),
1051 + \label{introEquation:hatmapIsomorphism}
1052 + \end{equation}
1053 + will let us associate the matrix products with traditional vector
1054 + operations
1055 + \[
1056 + \hat vu = v \times u
1057 + \]
1058 +
1059 + Using \ref{introEqaution:RBMotionPI}, one can construct a skew
1060 + matrix,
1061 + \begin{equation}
1062 + (\mathop \Pi \limits^ \bullet   - \mathop \Pi \limits^ \bullet  ^T
1063 + ){\rm{ }} = {\rm{ }}(\Pi  - \Pi ^T ){\rm{ }}(J^{ - 1} \Pi  + \Pi J^{
1064 + - 1} ) + \sum\limits_i {[Q^T F_i (r,Q)X_i^T  - X_i F_i (r,Q)^T Q]} -
1065 + (\Lambda  - \Lambda ^T ) . \label{introEquation:skewMatrixPI}
1066 + \end{equation}
1067 + Since $\Lambda$ is symmetric, the last term of Equation
1068 + \ref{introEquation:skewMatrixPI} is zero, which implies the Lagrange
1069 + multiplier $\Lambda$ is absent from the equations of motion. This
1070 + unique property eliminate the requirement of iterations which can
1071 + not be avoided in other methods\cite{}.
1072 +
1073 + Applying hat-map isomorphism, we obtain the equation of motion for
1074 + angular momentum on body frame
1075 + \begin{equation}
1076 + \dot \pi  = \pi  \times I^{ - 1} \pi  + \sum\limits_i {\left( {Q^T
1077 + F_i (r,Q)} \right) \times X_i }.
1078 + \label{introEquation:bodyAngularMotion}
1079 + \end{equation}
1080 + In the same manner, the equation of motion for rotation matrix is
1081 + given by
1082 + \[
1083 + \dot Q = Qskew(I^{ - 1} \pi )
1084 + \]
1085 +
1086 + \subsection{\label{introSection:SymplecticFreeRB}Symplectic
1087 + Lie-Poisson Integrator for Free Rigid Body}
1088 +
1089 + If there is not external forces exerted on the rigid body, the only
1090 + contribution to the rotational is from the kinetic potential (the
1091 + first term of \ref{ introEquation:bodyAngularMotion}). The free
1092 + rigid body is an example of Lie-Poisson system with Hamiltonian
1093 + function
1094 + \begin{equation}
1095 + T^r (\pi ) = T_1 ^r (\pi _1 ) + T_2^r (\pi _2 ) + T_3^r (\pi _3 )
1096 + \label{introEquation:rotationalKineticRB}
1097 + \end{equation}
1098 + where $T_i^r (\pi _i ) = \frac{{\pi _i ^2 }}{{2I_i }}$ and
1099 + Lie-Poisson structure matrix,
1100 + \begin{equation}
1101 + J(\pi ) = \left( {\begin{array}{*{20}c}
1102 +   0 & {\pi _3 } & { - \pi _2 }  \\
1103 +   { - \pi _3 } & 0 & {\pi _1 }  \\
1104 +   {\pi _2 } & { - \pi _1 } & 0  \\
1105 + \end{array}} \right)
1106 + \end{equation}
1107 + Thus, the dynamics of free rigid body is governed by
1108 + \begin{equation}
1109 + \frac{d}{{dt}}\pi  = J(\pi )\nabla _\pi  T^r (\pi )
1110 + \end{equation}
1111 +
1112 + One may notice that each $T_i^r$ in Equation
1113 + \ref{introEquation:rotationalKineticRB} can be solved exactly. For
1114 + instance, the equations of motion due to $T_1^r$ are given by
1115 + \begin{equation}
1116 + \frac{d}{{dt}}\pi  = R_1 \pi ,\frac{d}{{dt}}Q = QR_1
1117 + \label{introEqaution:RBMotionSingleTerm}
1118 + \end{equation}
1119 + where
1120 + \[ R_1  = \left( {\begin{array}{*{20}c}
1121 +   0 & 0 & 0  \\
1122 +   0 & 0 & {\pi _1 }  \\
1123 +   0 & { - \pi _1 } & 0  \\
1124 + \end{array}} \right).
1125 + \]
1126 + The solutions of Equation \ref{introEqaution:RBMotionSingleTerm} is
1127 + \[
1128 + \pi (\Delta t) = e^{\Delta tR_1 } \pi (0),Q(\Delta t) =
1129 + Q(0)e^{\Delta tR_1 }
1130 + \]
1131 + with
1132 + \[
1133 + e^{\Delta tR_1 }  = \left( {\begin{array}{*{20}c}
1134 +   0 & 0 & 0  \\
1135 +   0 & {\cos \theta _1 } & {\sin \theta _1 }  \\
1136 +   0 & { - \sin \theta _1 } & {\cos \theta _1 }  \\
1137 + \end{array}} \right),\theta _1  = \frac{{\pi _1 }}{{I_1 }}\Delta t.
1138 + \]
1139 + To reduce the cost of computing expensive functions in e^{\Delta
1140 + tR_1 }, we can use Cayley transformation,
1141 + \[
1142 + e^{\Delta tR_1 }  \approx (1 - \Delta tR_1 )^{ - 1} (1 + \Delta tR_1
1143 + )
1144 + \]
1145 +
1146 + The flow maps for $T_2^r$ and $T_2^r$ can be found in the same
1147 + manner.
1148 +
1149 + In order to construct a second-order symplectic method, we split the
1150 + angular kinetic Hamiltonian function can into five terms
1151 + \[
1152 + T^r (\pi ) = \frac{1}{2}T_1 ^r (\pi _1 ) + \frac{1}{2}T_2^r (\pi _2
1153 + ) + T_3^r (\pi _3 ) + \frac{1}{2}T_2^r (\pi _2 ) + \frac{1}{2}T_1 ^r
1154 + (\pi _1 )
1155 + \].
1156 + Concatenating flows corresponding to these five terms, we can obtain
1157 + an symplectic integrator,
1158 + \[
1159 + \varphi _{\Delta t,T^r }  = \varphi _{\Delta t/2,\pi _1 }  \circ
1160 + \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t,\pi _3 }
1161 + \circ \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t/2,\pi
1162 + _1 }.
1163 + \]
1164 +
1165 + The non-canonical Lie-Poisson bracket ${F, G}$ of two function
1166 + $F(\pi )$ and $G(\pi )$ is defined by
1167 + \[
1168 + \{ F,G\} (\pi ) = [\nabla _\pi  F(\pi )]^T J(\pi )\nabla _\pi  G(\pi
1169 + )
1170 + \]
1171 + If the Poisson bracket of a function $F$ with an arbitrary smooth
1172 + function $G$ is zero, $F$ is a \emph{Casimir}, which is the
1173 + conserved quantity in Poisson system. We can easily verify that the
1174 + norm of the angular momentum, $\parallel \pi
1175 + \parallel$, is a \emph{Casimir}. Let$ F(\pi ) = S(\frac{{\parallel
1176 + \pi \parallel ^2 }}{2})$ for an arbitrary function $ S:R \to R$ ,
1177 + then by the chain rule
1178 + \[
1179 + \nabla _\pi  F(\pi ) = S'(\frac{{\parallel \pi \parallel ^2
1180 + }}{2})\pi
1181 + \]
1182 + Thus $ [\nabla _\pi  F(\pi )]^T J(\pi ) =  - S'(\frac{{\parallel \pi
1183 + \parallel ^2 }}{2})\pi  \times \pi  = 0 $. This explicit
1184 + Lie-Poisson integrator is found to be extremely efficient and stable
1185 + which can be explained by the fact the small angle approximation is
1186 + used and the norm of the angular momentum is conserved.
1187 +
1188 + \subsection{\label{introSection:RBHamiltonianSplitting} Hamiltonian
1189 + Splitting for Rigid Body}
1190 +
1191 + The Hamiltonian of rigid body can be separated in terms of kinetic
1192 + energy and potential energy,
1193 + \[
1194 + H = T(p,\pi ) + V(q,Q)
1195 + \]
1196 + The equations of motion corresponding to potential energy and
1197 + kinetic energy are listed in the below table,
1198 + \begin{center}
1199 + \begin{tabular}{|l|l|}
1200 +  \hline
1201 +  % after \\: \hline or \cline{col1-col2} \cline{col3-col4} ...
1202 +  Potential & Kinetic \\
1203 +  $\frac{{dq}}{{dt}} = \frac{p}{m}$ & $\frac{d}{{dt}}q = p$ \\
1204 +  $\frac{d}{{dt}}p =  - \frac{{\partial V}}{{\partial q}}$ & $ \frac{d}{{dt}}p = 0$ \\
1205 +  $\frac{d}{{dt}}Q = 0$ & $ \frac{d}{{dt}}Q = Qskew(I^{ - 1} j)$ \\
1206 +  $ \frac{d}{{dt}}\pi  = \sum\limits_i {\left( {Q^T F_i (r,Q)} \right) \times X_i }$ & $\frac{d}{{dt}}\pi  = \pi  \times I^{ - 1} \pi$\\
1207 +  \hline
1208 + \end{tabular}
1209 + \end{center}
1210 + A second-order symplectic method is now obtained by the composition
1211 + of the flow maps,
1212 + \[
1213 + \varphi _{\Delta t}  = \varphi _{\Delta t/2,V}  \circ \varphi
1214 + _{\Delta t,T}  \circ \varphi _{\Delta t/2,V}.
1215 + \]
1216 + Moreover, \varphi _{\Delta t/2,V} can be divided into two sub-flows
1217 + which corresponding to force and torque respectively,
1218 + \[
1219 + \varphi _{\Delta t/2,V}  = \varphi _{\Delta t/2,F}  \circ \varphi
1220 + _{\Delta t/2,\tau }.
1221 + \]
1222 + Since the associated operators of $\varphi _{\Delta t/2,F} $ and
1223 + $\circ \varphi _{\Delta t/2,\tau }$ are commuted, the composition
1224 + order inside \varphi _{\Delta t/2,V} does not matter.
1225 +
1226 + Furthermore, kinetic potential can be separated to translational
1227 + kinetic term, $T^t (p)$, and rotational kinetic term, $T^r (\pi )$,
1228 + \begin{equation}
1229 + T(p,\pi ) =T^t (p) + T^r (\pi ).
1230 + \end{equation}
1231 + where $ T^t (p) = \frac{1}{2}p^T m^{ - 1} p $ and $T^r (\pi )$ is
1232 + defined by \ref{introEquation:rotationalKineticRB}. Therefore, the
1233 + corresponding flow maps are given by
1234 + \[
1235 + \varphi _{\Delta t,T}  = \varphi _{\Delta t,T^t }  \circ \varphi
1236 + _{\Delta t,T^r }.
1237 + \]
1238 + Finally, we obtain the overall symplectic flow maps for free moving
1239 + rigid body
1240 + \begin{equation}
1241 + \begin{array}{c}
1242 + \varphi _{\Delta t}  = \varphi _{\Delta t/2,F}  \circ \varphi _{\Delta t/2,\tau }  \\
1243 +  \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 }  \\
1244 +  \circ \varphi _{\Delta t/2,\tau }  \circ \varphi _{\Delta t/2,F}  .\\
1245 + \end{array}
1246 + \label{introEquation:overallRBFlowMaps}
1247 + \end{equation}
1248 +
1249 + \section{\label{introSection:langevinDynamics}Langevin Dynamics}
1250 + As an alternative to newtonian dynamics, Langevin dynamics, which
1251 + mimics a simple heat bath with stochastic and dissipative forces,
1252 + has been applied in a variety of studies. This section will review
1253 + the theory of Langevin dynamics simulation. A brief derivation of
1254 + generalized Langevin Dynamics will be given first. Follow that, we
1255 + will discuss the physical meaning of the terms appearing in the
1256 + equation as well as the calculation of friction tensor from
1257 + hydrodynamics theory.
1258 +
1259   \subsection{\label{introSection:generalizedLangevinDynamics}Generalized Langevin Dynamics}
1260  
1261   \begin{equation}
# Line 1087 | Line 1448 | And since the $q$ coordinates are harmonic oscillators
1448   \label{introEquation:secondFluctuationDissipation}
1449   \end{equation}
1450  
1090 \section{\label{introSection:hydroynamics}Hydrodynamics}
1091
1451   \subsection{\label{introSection:frictionTensor} Friction Tensor}
1452 < \subsection{\label{introSection:analyticalApproach}Analytical
1453 < Approach}
1452 > Theoretically, the friction kernel can be determined using velocity
1453 > autocorrelation function. However, this approach become impractical
1454 > when the system become more and more complicate. Instead, various
1455 > approaches based on hydrodynamics have been developed to calculate
1456 > the friction coefficients. The friction effect is isotropic in
1457 > Equation, \zeta can be taken as a scalar. In general, friction
1458 > tensor \Xi is a $6\times 6$ matrix given by
1459 > \[
1460 > \Xi  = \left( {\begin{array}{*{20}c}
1461 >   {\Xi _{}^{tt} } & {\Xi _{}^{rt} }  \\
1462 >   {\Xi _{}^{tr} } & {\Xi _{}^{rr} }  \\
1463 > \end{array}} \right).
1464 > \]
1465 > Here, $ {\Xi^{tt} }$ and $ {\Xi^{rr} }$ are translational friction
1466 > tensor and rotational friction tensor respectively, while ${\Xi^{tr}
1467 > }$ is translation-rotation coupling tensor and $ {\Xi^{rt} }$ is
1468 > rotation-translation coupling tensor.
1469  
1470 < \subsection{\label{introSection:approximationApproach}Approximation
1471 < Approach}
1470 > \[
1471 > \left( \begin{array}{l}
1472 > F_t  \\
1473 > \tau  \\
1474 > \end{array} \right) =  - \left( {\begin{array}{*{20}c}
1475 >   {\Xi ^{tt} } & {\Xi ^{rt} }  \\
1476 >   {\Xi ^{tr} } & {\Xi ^{rr} }  \\
1477 > \end{array}} \right)\left( \begin{array}{l}
1478 > v \\
1479 > w \\
1480 > \end{array} \right)
1481 > \]
1482  
1483 < \subsection{\label{introSection:centersRigidBody}Centers of Rigid
1484 < Body}
1483 > \subsubsection{\label{introSection:analyticalApproach}The Friction Tensor for Regular Shape}
1484 > For a spherical particle, the translational and rotational friction
1485 > constant can be calculated from Stoke's law,
1486 > \[
1487 > \Xi ^{tt}  = \left( {\begin{array}{*{20}c}
1488 >   {6\pi \eta R} & 0 & 0  \\
1489 >   0 & {6\pi \eta R} & 0  \\
1490 >   0 & 0 & {6\pi \eta R}  \\
1491 > \end{array}} \right)
1492 > \]
1493 > and
1494 > \[
1495 > \Xi ^{rr}  = \left( {\begin{array}{*{20}c}
1496 >   {8\pi \eta R^3 } & 0 & 0  \\
1497 >   0 & {8\pi \eta R^3 } & 0  \\
1498 >   0 & 0 & {8\pi \eta R^3 }  \\
1499 > \end{array}} \right)
1500 > \]
1501 > where $\eta$ is the viscosity of the solvent and $R$ is the
1502 > hydrodynamics radius.
1503 >
1504 > Other non-spherical particles have more complex properties.
1505 >
1506 > \[
1507 > S = \frac{2}{{\sqrt {a^2  - b^2 } }}\ln \frac{{a + \sqrt {a^2  - b^2
1508 > } }}{b}
1509 > \]
1510 >
1511 >
1512 > \[
1513 > S = \frac{2}{{\sqrt {b^2  - a^2 } }}arctg\frac{{\sqrt {b^2  - a^2 }
1514 > }}{a}
1515 > \]
1516 >
1517 > \[
1518 > \begin{array}{l}
1519 > \Xi _a^{tt}  = 16\pi \eta \frac{{a^2  - b^2 }}{{(2a^2  - b^2 )S - 2a}} \\
1520 > \Xi _b^{tt}  = \Xi _c^{tt}  = 32\pi \eta \frac{{a^2  - b^2 }}{{(2a^2  - 3b^2 )S + 2a}} \\
1521 > \end{array}
1522 > \]
1523 >
1524 > \[
1525 > \begin{array}{l}
1526 > \Xi _a^{rr}  = \frac{{32\pi }}{3}\eta \frac{{(a^2  - b^2 )b^2 }}{{2a - b^2 S}} \\
1527 > \Xi _b^{rr}  = \Xi _c^{rr}  = \frac{{32\pi }}{3}\eta \frac{{(a^4  - b^4 )}}{{(2a^2  - b^2 )S - 2a}} \\
1528 > \end{array}
1529 > \]
1530 >
1531 >
1532 > \subsubsection{\label{introSection:approximationApproach}The Friction Tensor for Arbitrary Shape}
1533 > Unlike spherical and other regular shaped molecules, there is not
1534 > analytical solution for friction tensor of any arbitrary shaped
1535 > rigid molecules. The ellipsoid of revolution model and general
1536 > triaxial ellipsoid model have been used to approximate the
1537 > hydrodynamic properties of rigid bodies. However, since the mapping
1538 > from all possible ellipsoidal space, $r$-space, to all possible
1539 > combination of rotational diffusion coefficients, $D$-space is not
1540 > unique\cite{Wegener79} as well as the intrinsic coupling between
1541 > translational and rotational motion of rigid body\cite{}, general
1542 > ellipsoid is not always suitable for modeling arbitrarily shaped
1543 > rigid molecule. A number of studies have been devoted to determine
1544 > the friction tensor for irregularly shaped rigid bodies using more
1545 > advanced method\cite{} where the molecule of interest was modeled by
1546 > combinations of spheres(beads)\cite{} and the hydrodynamics
1547 > properties of the molecule can be calculated using the hydrodynamic
1548 > interaction tensor. Let us consider a rigid assembly of $N$ beads
1549 > immersed in a continuous medium. Due to hydrodynamics interaction,
1550 > the ``net'' velocity of $i$th bead, $v'_i$ is different than its
1551 > unperturbed velocity $v_i$,
1552 > \[
1553 > v'_i  = v_i  - \sum\limits_{j \ne i} {T_{ij} F_j }
1554 > \]
1555 > where $F_i$ is the frictional force, and $T_{ij}$ is the
1556 > hydrodynamic interaction tensor. The friction force of $i$th bead is
1557 > proportional to its ``net'' velocity
1558 > \begin{equation}
1559 > F_i  = \zeta _i v_i  - \zeta _i \sum\limits_{j \ne i} {T_{ij} F_j }.
1560 > \label{introEquation:tensorExpression}
1561 > \end{equation}
1562 > This equation is the basis for deriving the hydrodynamic tensor. In
1563 > 1930, Oseen and Burgers gave a simple solution to Equation
1564 > \ref{introEquation:tensorExpression}
1565 > \begin{equation}
1566 > T_{ij}  = \frac{1}{{8\pi \eta r_{ij} }}\left( {I + \frac{{R_{ij}
1567 > R_{ij}^T }}{{R_{ij}^2 }}} \right).
1568 > \label{introEquation:oseenTensor}
1569 > \end{equation}
1570 > Here $R_{ij}$ is the distance vector between bead $i$ and bead $j$.
1571 > A second order expression for element of different size was
1572 > introduced by Rotne and Prager\cite{} and improved by Garc\'{i}a de
1573 > la Torre and Bloomfield,
1574 > \begin{equation}
1575 > T_{ij}  = \frac{1}{{8\pi \eta R_{ij} }}\left[ {\left( {I +
1576 > \frac{{R_{ij} R_{ij}^T }}{{R_{ij}^2 }}} \right) + R\frac{{\sigma
1577 > _i^2  + \sigma _j^2 }}{{r_{ij}^2 }}\left( {\frac{I}{3} -
1578 > \frac{{R_{ij} R_{ij}^T }}{{R_{ij}^2 }}} \right)} \right].
1579 > \label{introEquation:RPTensorNonOverlapped}
1580 > \end{equation}
1581 > Both of the Equation \ref{introEquation:oseenTensor} and Equation
1582 > \ref{introEquation:RPTensorNonOverlapped} have an assumption $R_{ij}
1583 > \ge \sigma _i  + \sigma _j$. An alternative expression for
1584 > overlapping beads with the same radius, $\sigma$, is given by
1585 > \begin{equation}
1586 > T_{ij}  = \frac{1}{{6\pi \eta R_{ij} }}\left[ {\left( {1 -
1587 > \frac{2}{{32}}\frac{{R_{ij} }}{\sigma }} \right)I +
1588 > \frac{2}{{32}}\frac{{R_{ij} R_{ij}^T }}{{R_{ij} \sigma }}} \right]
1589 > \label{introEquation:RPTensorOverlapped}
1590 > \end{equation}
1591 >
1592 > %Bead Modeling
1593 >
1594 > \[
1595 > B = \left( {\begin{array}{*{20}c}
1596 >   {T_{11} } &  \ldots  & {T_{1N} }  \\
1597 >    \vdots  &  \ddots  &  \vdots   \\
1598 >   {T_{N1} } &  \cdots  & {T_{NN} }  \\
1599 > \end{array}} \right)
1600 > \]
1601 >
1602 > \[
1603 > C = B^{ - 1}  = \left( {\begin{array}{*{20}c}
1604 >   {C_{11} } &  \ldots  & {C_{1N} }  \\
1605 >    \vdots  &  \ddots  &  \vdots   \\
1606 >   {C_{N1} } &  \cdots  & {C_{NN} }  \\
1607 > \end{array}} \right)
1608 > \]
1609 >
1610 > \begin{equation}
1611 > \begin{array}{l}
1612 > \Xi _{}^{tt}  = \sum\limits_i {\sum\limits_j {C_{ij} } } , \\
1613 > \Xi _{}^{tr}  = \Xi _{}^{rt}  = \sum\limits_i {\sum\limits_j {U_i C_{ij} } } , \\
1614 > \Xi _{}^{rr}  =  - \sum\limits_i {\sum\limits_j {U_i C_{ij} } } U_j  \\
1615 > \end{array}
1616 > \end{equation}
1617 > where
1618 > \[
1619 > U_i  = \left( {\begin{array}{*{20}c}
1620 >   0 & { - z_i } & {y_i }  \\
1621 >   {z_i } & 0 & { - x_i }  \\
1622 >   { - y_i } & {x_i } & 0  \\
1623 > \end{array}} \right)
1624 > \]
1625 >
1626 > \[
1627 > r_{OR}  = \left( \begin{array}{l}
1628 > x_{OR}  \\
1629 > y_{OR}  \\
1630 > z_{OR}  \\
1631 > \end{array} \right) = \left( {\begin{array}{*{20}c}
1632 >   {\Xi _{yy}^{rr}  + \Xi _{zz}^{rr} } & { - \Xi _{xy}^{rr} } & { - \Xi _{xz}^{rr} }  \\
1633 >   { - \Xi _{yx}^{rr} } & {\Xi _{zz}^{rr}  + \Xi _{xx}^{rr} } & { - \Xi _{yz}^{rr} }  \\
1634 >   { - \Xi _{zx}^{rr} } & { - \Xi _{yz}^{rr} } & {\Xi _{xx}^{rr}  + \Xi _{yy}^{rr} }  \\
1635 > \end{array}} \right)^{ - 1} \left( \begin{array}{l}
1636 > \Xi _{yz}^{tr}  - \Xi _{zy}^{tr}  \\
1637 > \Xi _{zx}^{tr}  - \Xi _{xz}^{tr}  \\
1638 > \Xi _{xy}^{tr}  - \Xi _{yx}^{tr}  \\
1639 > \end{array} \right)
1640 > \]
1641 >
1642 > \[
1643 > U_{OR}  = \left( {\begin{array}{*{20}c}
1644 >   0 & { - z_{OR} } & {y_{OR} }  \\
1645 >   {z_i } & 0 & { - x_{OR} }  \\
1646 >   { - y_{OR} } & {x_{OR} } & 0  \\
1647 > \end{array}} \right)
1648 > \]
1649 >
1650 > \[
1651 > \begin{array}{l}
1652 > \Xi _R^{tt}  = \Xi _{}^{tt}  \\
1653 > \Xi _R^{tr}  = \Xi _R^{rt}  = \Xi _{}^{tr}  - U_{OR} \Xi _{}^{tt}  \\
1654 > \Xi _R^{rr}  = \Xi _{}^{rr}  - U_{OR} \Xi _{}^{tt} U_{OR}  + \Xi _{}^{tr} U_{OR}  - U_{OR} \Xi _{}^{tr} ^{^T }  \\
1655 > \end{array}
1656 > \]
1657 >
1658 > \[
1659 > D_R  = \left( {\begin{array}{*{20}c}
1660 >   {D_R^{tt} } & {D_R^{rt} }  \\
1661 >   {D_R^{tr} } & {D_R^{rr} }  \\
1662 > \end{array}} \right) = k_b T\left( {\begin{array}{*{20}c}
1663 >   {\Xi _R^{tt} } & {\Xi _R^{rt} }  \\
1664 >   {\Xi _R^{tr} } & {\Xi _R^{rr} }  \\
1665 > \end{array}} \right)^{ - 1}
1666 > \]
1667 >
1668 >
1669 > %Approximation Methods
1670 >
1671 > %\section{\label{introSection:correlationFunctions}Correlation Functions}

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