Deriving Statistical Behavior of Particles via Classical Mechanics

Click For Summary
SUMMARY

The discussion focuses on deriving the statistical behavior of particles through deterministic classical mechanics using computational simulations. Participants emphasize the importance of understanding Boyle's law for estimating particle velocity and mean free path, which are crucial for determining simulation granularity. The conversation also touches on defining entropy within deterministic simulations, particularly in relation to microstates and macrostates. Additionally, the implications of indistinguishability and Gibbs' paradox in classical mechanics are discussed, highlighting the complexities of simulating large particle systems.

PREREQUISITES
  • Understanding of Boyle's law and its application in particle simulations
  • Familiarity with classical mechanics and Hamiltonian equations
  • Knowledge of statistical mechanics concepts, including microstates and macrostates
  • Experience with computational simulations and floating point operations
NEXT STEPS
  • Research computational methods for simulating particle dynamics in classical mechanics
  • Learn about entropy calculations in deterministic simulations
  • Explore the implications of Gibbs' paradox in classical systems
  • Investigate the use of dynamical systems to model electron behavior in atoms
USEFUL FOR

Researchers, physicists, and computational scientists interested in particle dynamics, statistical mechanics, and the application of classical mechanics in simulations.

thaiqi
Messages
160
Reaction score
8
Hello, using computation simulation, can the statistical behavior of many particles be derived through deterministic classical mechanics?
 
Physics news on Phys.org
In principle, yes. In practice... how long are you willing to wait on the computation?

Consider Boyle's law, which might be the most tractable case. Google will give you reasonable estimates for the velocity and mean free path of a particle; these will give you order-of-magnitude values for the time and space granularity you’ll need. Figure something ##10^{22}## particles in your simulation. How many floating point operations do you need to simulate one second? Divide that by what your hardware is capable of to know how long the simulation will take.
 
  • Like
Likes   Reactions: atyy, Vanadium 50 and FactChecker
Nugatory said:
In principle, yes.

How would we define Entropy in a deterministic simulation?
 
  • Like
Likes   Reactions: FactChecker
Stephen Tashi said:
How would we define Entropy in a deterministic simulation?
I’m thinking the same way as always: number of microstates corresponding to a given macrostate. The simulation of course takes the system to a particular microstate, but we can still consider how many other microstates would produce the same macrostate.
 
  • Like
Likes   Reactions: Stephen Tashi
Thanks.
Nugatory said:
In principle, yes. In practice... how long are you willing to wait on the computation?

Consider Boyle's law, which might be the most tractable case. Google will give you reasonable estimates for the velocity and mean free path of a particle; these will give you order-of-magnitude values for the time and space granularity you’ll need. Figure something ##10^{22}## particles in your simulation. How many floating point operations do you need to simulate one second? Divide that by what your hardware is capable of to know how long the simulation will take.
Can dynamical system be used to describe the behavior of the electron in the atom?
 
Nugatory said:
I’m thinking the same way as always: number of microstates corresponding to a given macrostate. The simulation of course takes the system to a particular microstate, but we can still consider how many other microstates would produce the same macrostate.
Don't want to go off-topic, but you have to assume indistinguishability is not relevant to the problem you are simulating otherwise you could run into problems (like Gibbs' paradox). That's really an extreme case and is more of a question whether classical mechanics could be applied.

PS: You could still solve Gibbs paradox in the framework of classical mechanics, but you'll need to take care of the indistinguishability separately and it will not follow directly from the equations of motion (hamilton equations) since distinguishability implies a greater number of microstates. The interesting thing (to me) is that Gibbs' paradox arise in a pretty "classical" context (a box full of gas) which classical mechanics is perfectly fit to describe.
 
Stephen Tashi said:
How would we define Entropy in a deterministic simulation?
You mean in a classical molecular-dynamics simulation? That's very difficult. One way is to consider some subsystem (e.g., considering only particles in a certain partial volume) and calculating the corresponding averages on the one-particle distribution.
 

Similar threads

  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 4 ·
Replies
4
Views
1K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 6 ·
Replies
6
Views
5K
  • · Replies 1 ·
Replies
1
Views
1K
  • · Replies 1 ·
Replies
1
Views
1K
  • · Replies 14 ·
Replies
14
Views
7K
  • · Replies 5 ·
Replies
5
Views
1K
  • · Replies 3 ·
Replies
3
Views
1K
  • · Replies 9 ·
Replies
9
Views
2K