Newton’s Equation of Motion
First consider an inertial frame of reference, which is a reference environment for a mechanical system such that when a material point or particle is subject to no net force, the velocity of the particle is unchanged. In such a reference frame, particles follow Newton’s equation of motion,
where
which for a closed path is expressed as,
where the symbol
in which
By noting that the position vector is also a function of time, we can rewrite
and we call this work due to the motion of the particle the work from the kinetic energy,
Integrating over the external force gives the work done on the system by the force,
which gives the classic expression that the work performed on the system is equal to the change in mechanical energy of the system,
Thus, under no external force (
Of course, in the statistical theory of liquids we need to extend Newton’s equations of motion to systems of many particles. The result is a system of equations describing the positions and velocities of each particle,
where
where the total energy
Numerical Time-Evolution of the Equations of Motion
Numerically, we will solve Newton’s equation of motion iteratively over a series of short timesteps,
- Given
and , calculate the force on particle , . - Integrate equations of motion over a timestep,
, to obtain and . - Calculate new forces,
- Repeat.
The Euler Algorithm
Let’s think about how we can move between step 1 and 2 in our general strategy. We want the quantity
and recognizing the definition of velocity, acceleration and force in the previous equation gives,
where the
where once again we assume that
Let’s now consider how we could implement this algorithm numerically. We know
The Velocity-Verlet Algorithm
We can amend the problem with the Euler algorithm by including an additional term to the velocity so that,
In general, we don’t know the time-derivative of the force, but we can express the force at time
so that,
to the first order. Now, we can just substitute this into our velocity equation to obtain,
which is known as the Velocity-Verlet algorithm. One interesting thing to notice here is that we now have a “future” term in our velocity equation,
Technically, no. We can solve for the force
The implementation looks like this:
- Using current the current velocity, calculate the next positions.
Given
, compute force .Finally, compute new velocities.
- Repeat.
The Velocity-Verlet integrator is easy to implement, has excellent numerical stability and long time energy conservation, but does have poor short time energy conservation. Alternative integrators that are equivalent to Velocity-Verlet are the Verlet and leapfrog algorithms.
The Langrangian, Action and Hamilton’s Principle
For convenience, we introduce a quantity known as the Lagrangian, which for generalized coordinates
whose time integral is defined as the action,
A useful relation between the actions of a particle following two different paths is known as Hamilton’s principle, which states that the difference between the actions of a particle taking two different paths with the same start and end points is zero in the first order terms,
where
Let’s expand the relation given by Hamilton’s principle in terms of the notation of the Lagrangian,
Applying a Taylor expansion in the first order to the integrand gives,
where h.o. refers to “higher-order” terms. Then, integrating by parts on the second term and recognizing that the surface term must vanish according to Hamilton’s principle we obtain,
which can be evaluated to give the Lagrange equation of motion,
Conservation Laws Derived from the Lagrangian
As a mechanical system evolves according to the classical equations of motion, its generalized coordinates and velocities change. However, the values of specific variables may remain constant - these are referred to as constants of motion or conserved quantities. First consider a mechanical system A composed of n-particles. The Lagrangian of this system is given by a function of the form,
Now, let’s create an “identical copy” of mechanical system A and translate it to a different point in space. We will give it the name mechanical system B. The only difference between these two states is their position in space, and we assume there exist no interactions between them. The Lagrangian for system B is thus,
If the two systems are sufficiently far away that they do not interact with each other, the Lagrangian for each system should be the same. This is known as the homogeneity of space or translational invariance. Translational invariance will occur if there are no external fields, because if
We can rexpress this relation in terms of the momentum
where we have used the definition of momentum,
Thus, the total sum of the linear momentum of all N particles, defined as the total linear momentum,
Let’s now turn our attention to the time dependence of the Lagrangian. The Lagrangian for our hypothetical system of f degrees of freedom has the Lagrangian given by,
The chain rule tells us that,
Applying the Lagrange equation of motion, we obtain,
In which we find that,
By the definition for energy given before, this means that,
Now we may ask what would happen if we solve the Lagrangian today (
Hamilton’s Equations of Motion
The Lagrange equation of motion is an expression of the relationships between the generalized position coordinates,
First, let’s consider the Lagrangian for a system with an arbitrary number of generalized coordinates,
is a function of
that can recover the Lagrangian for the classical time evolution of the system. One way to construct this function is to consider that the momentum corresponding to a specific generalized velocity,
leading to the following relation,
where
This transformation is known as a Legendre transformation and it will appear in thermodynamics with respect to transformation of thermodynamic variables.
We can now derive equations of motion from the Hamiltonian by considering the total derivative,
but
which after substituting into the expression for
Since
so that,
which are known as Hamilton’s equations of motion.
References
- Kusaka, I. Statistical Mechanics for Engineers. (Springer International Publishing, 2015). doi:10.1007/978-3-319-13809-1.