Demon algorithm for microcanonical ensemble

In summary, the conversation discusses the simulation of a microcanonical ensemble using a demon algorithm. The algorithm involves conducting a random walk in phase space with the constraint that the demon's energy is non-negative and potentially also has an upper bound. This approximation of the microcanonical ensemble reduces the probability of rejection and allows for more efficient simulations. There is a question about whether there should be an upper bound on the demon's energy, and it is mentioned that in the original paper, there is a positive energy constraint but further limitations may be useful.
  • #1
gre_abandon
13
1
I simulated a microcanonical ensemble of 10 ideal gas particles in one dimension and yielded the expected normal distribution of velocities. However, I still did not get how the algorithm works. The demon has non-negative energy content and the demon together with the system constitutes a closed system with fixed energy. In my view, the demon algorithm amounts to conducting a random walk in phase space where H is less than E_total. Whenever a step of walk carries the particle outside the permitted region this step is rejected. But how is that a sampling of a microcanonical ensemble?
 
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  • #2
It is an approximation of the microcanonical ensemble. There are fluctuations in the total energy of the system, but if the energy of the demon is constrained enough, then the system will stay close to the actual microcanonical energy. The main advantage is to reduce the probability of rejection: if your were to try to simulate the actual microcanonical case, most of the computer time would be used generating configurations that would be rejected.
 
  • #3
DrClaude said:
It is an approximation of the microcanonical ensemble. There are fluctuations in the total energy of the system, but if the energy of the demon is constrained enough, then the system will stay close to the actual microcanonical energy. The main advantage is to reduce the probability of rejection: if your were to try to simulate the actual microcanonical case, most of the computer time would be used generating configurations that would be rejected.
On wikipedia and my textbook the only constraint on the demon is that the demon should hold non-negative energy while in my opinion there also should be an upper bound as well for demon energy. And the energy fluctuation of the system is between E_demon_min and E_demon_max. If the energy fluctuation is small enough compared with the energy scale of the system we can regard the energy of the system fixed. Am I correct that there should be an upper bound for demon energy?

I've also done simulation for only two one dimensional particles. Without an upper bound on demon energy, it seems that we are effectively sampling the phase volume enclosed by the energy surface uniformly and this is by no means what we want.
 
  • #4
gre_abandon said:
Am I correct that there should be an upper bound for demon energy?
It can be useful to limit the energy of the demon. To quote from the original paper:
To keep the demon from running off with all the energy, its energy must be restricted. The simplest constraint is that ##E_D## be a positive number, but further limitations could be useful in certain cases.
The way the algorithm is described in Landau & Binder, A Guide to Monte Carlo Simulations in Statistical Physics, there is also an upper limit to the demon energy.
 

1. What is a demon algorithm for microcanonical ensemble?

A demon algorithm for microcanonical ensemble is a computational method used in statistical mechanics to simulate the behavior of particles in a system that is in a microcanonical (NVE) ensemble. It is named after the "Maxwell's demon" thought experiment, which involves a hypothetical being that can control the movement of particles in a system to violate the second law of thermodynamics.

2. How does a demon algorithm work?

A demon algorithm works by dividing the system into small cells and assigning a "demon" to each cell. The demon keeps track of the number of particles in the cell and their positions and velocities. It then uses this information to make decisions on whether to allow or block the movement of particles between cells in order to maintain the system in a microcanonical ensemble.

3. What are the advantages of using a demon algorithm?

One advantage of using a demon algorithm is that it allows for the simulation of systems with a large number of particles, which would be impractical to do through traditional methods. It also provides a more accurate representation of a microcanonical ensemble compared to other statistical ensembles.

4. Are there any limitations to using a demon algorithm?

One limitation of using a demon algorithm is that it assumes the system is in a state of equilibrium, which may not always be the case. It also requires a significant amount of computational power and may be difficult to implement for systems with complex interactions.

5. How is a demon algorithm used in research?

A demon algorithm is used in research to study the behavior of complex systems, such as gases, liquids, and solids. It can provide insights into the thermodynamic properties of these systems and aid in the development of new materials and technologies. It is also used in the field of quantum computing to simulate the behavior of qubits.

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