Interacting systems and relaxation times

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In summary: If S2 cannot kick S1 out of equilibrium, then this approach can help solve the problem.In summary, the conversation discusses a method for solving a solid state problem involving two interacting systems, S1 and S2. The approach involves running a Monte Carlo simulation to solve for S1 and deriving S2, and then adjusting the conditions of S1 based on the new state of S2. The validity of this approach is dependent on the rate of change of S2 being slower than S1, and the adiabatic nature of the change can be verified by comparing the energy and temperature of the two systems. If S2 cannot disrupt the equilibrium of S1, this approach can be effective in solving the problem.
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I got a question I'm not sure how to state precisely or is it even valid. Any help is most welcomed.

I stripped the question of all details because I wanted to emphasize my problem, but should someone think they would bring any clarity (it is a solid state problem) I'll present them.

Ok, let say I have two interacting systems. One of them is a system (S1) in a thermodynamical equilibrium and the other is a well defined classical system (S2). I know how to derive S2 from microcanonical state of S1 and how surrounding of a S1 depends on S2. It is very unclear how to combine these two mathematically directly but here is a kick - I THINK that S2 is relaxing much more slowly than S1. So, I was thinking of a iterative approach: to run a Monte Carlo to solve S1, then derive S2, then adjust conditions of S1 based on the new state of S2 and rerun MC etc. So my questions would be: is this approach valid if I assume that S1 is changing adiabatically? Is there a practical way to verify adiabatic change? Is there any circumstance where calculation like this is valid? It feels that if the S2 can't kick S1 out of equilibrium, then I got a powerful edge to clear this problem up - but is this true?
 
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The approach you are proposing is valid, as long as S2 changes more slowly than S1. In order to verify that the change is adiabatic, you can compare the energy of S1 and S2 at different time steps. If the energy of S1 is not significantly changing, then you can assume that the change is adiabatic. You can also compare the temperature of S1 and S2 to make sure that they are in equilibrium.
 

1. What is the meaning of "Interacting systems"?

Interacting systems refer to a complex network of multiple components or elements that interact with each other in a dynamic manner. These interactions can be physical, chemical, or biological in nature and can lead to the emergence of new properties or behaviors in the system as a whole.

2. How do relaxation times play a role in interacting systems?

Relaxation times are the characteristic timescales at which a system returns to its equilibrium state after being perturbed. In interacting systems, relaxation times can indicate the strength and speed of interactions between components, and can also provide information about the stability and dynamics of the system.

3. What are some common examples of interacting systems?

Some common examples of interacting systems include ecosystems, social networks, chemical reactions, and neural networks. These systems are made up of interconnected and interdependent components that interact with each other to form a complex and dynamic whole.

4. How do scientists study interacting systems and relaxation times?

Scientists use a variety of methods and techniques to study interacting systems and relaxation times. These include mathematical modeling, computer simulations, and experimental techniques such as spectroscopy and microscopy. These approaches allow scientists to observe and analyze the behavior of interacting systems and measure their relaxation times.

5. What are the potential applications of understanding interacting systems and relaxation times?

Understanding interacting systems and relaxation times has many potential applications in various fields such as biology, chemistry, physics, and engineering. It can help predict and control the behavior of complex systems, design new materials and technologies, and improve our understanding of natural systems and processes.

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