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# Line 5 | Line 5 | main classes of molecular simulation: Molecular Dynami
5  
6   The techniques used in the course of this research fall under the two
7   main classes of molecular simulation: Molecular Dynamics and Monte
8 < Carlo. Molecular Dynamic simulations integrate the equations of motion
9 < for a given system of particles, allowing the researcher to gain
10 < insight into the time dependent evolution of a system. Diffusion
8 > Carlo. Molecular Dynamics simulations integrate the equations of
9 > motion for a given system of particles, allowing the researcher to
10 > gain insight into the time dependent evolution of a system. Diffusion
11   phenomena are readily studied with this simulation technique, making
12   Molecular Dynamics the main simulation technique used in this
13   research. Other aspects of the research fall under the Monte Carlo
14   class of simulations. In Monte Carlo, the configuration space
15 < available to the collection of particles is sampled stochastically,
16 < or randomly. Each configuration is chosen with a given probability
17 < based on the Maxwell Boltzmann distribution. These types of simulations
18 < are best used to probe properties of a system that are only dependent
19 < only on the state of the system. Structural information about a system
20 < is most readily obtained through these types of methods.
15 > available to the collection of particles is sampled stochastically, or
16 > randomly. Each configuration is chosen with a given probability based
17 > on the Maxwell Boltzmann distribution. These types of simulations are
18 > best used to probe properties of a system that are dependent only on
19 > the state of the system. Structural information about a system is most
20 > readily obtained through these types of methods.
21  
22   Although the two techniques employed seem dissimilar, they are both
23   linked by the overarching principles of Statistical
24 < Thermodynamics. Statistical Thermodynamics governs the behavior of
24 > Mechanics. Statistical Meachanics governs the behavior of
25   both classes of simulations and dictates what each method can and
26   cannot do. When investigating a system, one most first analyze what
27   thermodynamic properties of the system are being probed, then chose
# Line 31 | Line 31 | Statistical Mechanics concepts present in this dissert
31  
32   The following section serves as a brief introduction to some of the
33   Statistical Mechanics concepts present in this dissertation.  What
34 < follows is a brief derivation of Boltzmann weighted statistics, and an
34 > follows is a brief derivation of Boltzmann weighted statistics and an
35   explanation of how one can use the information to calculate an
36   observable for a system.  This section then concludes with a brief
37   discussion of the ergodic hypothesis and its relevance to this
# Line 39 | Line 39 | research.
39  
40   \subsection{\label{introSec:boltzman}Boltzmann weighted statistics}
41  
42 < Consider a system, $\gamma$, with some total energy,, $E_{\gamma}$.
43 < Let $\Omega(E_{\gamma})$ represent the number of degenerate ways
44 < $\boldsymbol{\Gamma}$, the collection of positions and conjugate
45 < momenta of system $\gamma$, can be configured to give
46 < $E_{\gamma}$. Further, if $\gamma$ is in contact with a bath system
47 < where energy is exchanged between the two systems, $\Omega(E)$, where
48 < $E$ is the total energy of both systems, can be represented as
42 > Consider a system, $\gamma$, with total energy $E_{\gamma}$.  Let
43 > $\Omega(E_{\gamma})$ represent the number of degenerate ways
44 > $\boldsymbol{\Gamma}\{r_1,r_2,\ldots r_n,p_1,p_2,\ldots p_n\}$, the
45 > collection of positions and conjugate momenta of system $\gamma$, can
46 > be configured to give $E_{\gamma}$. Further, if $\gamma$ is a subset
47 > of a larger system, $\boldsymbol{\Lambda}\{E_1,E_2,\ldots
48 > E_{\gamma},\ldots E_n\}$, the total degeneracy of the system can be
49 > expressed as,
50   \begin{equation}
51 + \Omega(\boldsymbol{\Lambda}) = \Omega(E_1) \times \Omega(E_2) \times \ldots
52 +        \Omega(E_{\gamma}) \times \ldots \Omega(E_n)
53 + \label{introEq:SM0.1}
54 + \end{equation}
55 + This multiplicative combination of degeneracies is illustrated in
56 + Fig.~\ref{introFig:degenProd}.
57 +
58 + \begin{figure}
59 + \centering
60 + \includegraphics[width=\linewidth]{omegaFig.eps}
61 + \caption[An explanation of the combination of degeneracies]{Systems A and B both have three energy levels and two indistinguishable particles. When the total energy is 2, there are two ways for each system to disperse the energy. However, for system C, the superset of A and B, the total degeneracy is the product of the degeneracy of each system. In this case $\Omega(\text{C})$ is 4.}
62 + \label{introFig:degenProd}
63 + \end{figure}
64 +
65 + Next, consider the specific case of $\gamma$ in contact with a
66 + bath. Exchange of energy is allowed between the bath and the system,
67 + subject to the constraint that the total energy
68 + ($E_{\text{bath}}+E_{\gamma}$) remain constant. $\Omega(E)$, where $E$
69 + is the total energy of both systems, can be represented as
70 + \begin{equation}
71   \Omega(E) = \Omega(E_{\gamma}) \times \Omega(E - E_{\gamma})
72   \label{introEq:SM1}
73   \end{equation}
# Line 644 | Line 665 | defined as follows:
665   \begin{figure}
666   \centering
667   \includegraphics[width=\linewidth]{eulerRotFig.eps}
668 < \caption[Euler rotation of Cartesian coordinates]{The rotation scheme for Euler angles. First is a rotation of $\phi$ about the $z$ axis (blue rotation). Next is a rotation of $\theta$ about the new $x\prime$ axis (green rotation). Lastly is a final rotation of $\psi$ about the new $z\prime$ axis (red rotation).}
668 > \caption[Euler rotation of Cartesian coordinates]{The rotation scheme for Euler angles. First is a rotation of $\phi$ about the $z$ axis (blue rotation). Next is a rotation of $\theta$ about the new $x$ axis (green rotation). Lastly is a final rotation of $\psi$ about the new $z$ axis (red rotation).}
669   \label{introFig:eulerAngles}
670   \end{figure}
671  

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