Deriving Statistical Behavior of Particles via Classical Mechanics

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The discussion centers on using computational simulations to derive the statistical behavior of particles through deterministic classical mechanics, emphasizing the practicality of such simulations. It highlights the need for significant computational resources, estimating the number of particles and floating point operations required to achieve meaningful results. Entropy in a deterministic simulation is explored, suggesting it can be defined by the number of microstates corresponding to a macrostate. The conversation also touches on the challenges posed by indistinguishability in classical mechanics, particularly in relation to Gibbs' paradox. Overall, the feasibility of simulating particle behavior hinges on computational capacity and the careful consideration of statistical definitions.
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Hello, using computation simulation, can the statistical behavior of many particles be derived through deterministic classical mechanics?
 
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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.
 
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Nugatory said:
In principle, yes.

How would we define Entropy in a deterministic simulation?
 
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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.
 
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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.
 
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