Related Experiment Video
Updated: Sep 4, 2025
11:03
An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
Published on: December 4, 2017
8.6K
Stochastic thermodynamics in a non-Markovian dynamical system
Cillian Cockrell1, Ian J Ford2
1School of Physical and Chemical Sciences, Queen Mary University of London, Mile End Road, London E1 4NS, United Kingdom.
Physical Review. E
|July 20, 2022
Summary
Stochastic thermodynamics now includes non-Markovian environments, which have memory. This research explores how these memory effects influence entropy production and system dynamics, revealing unique behaviors compared to standard Markovian models.
Area of Science:
- Physics
- Thermodynamics
- Statistical Mechanics
Background:
- Stochastic thermodynamics applies macroscopic concepts to microscopic systems.
- Current models typically use Markovian (memoryless) environments.
- Extending to non-Markovian environments is crucial for complex systems.
Purpose of the Study:
- To extend stochastic thermodynamics to non-Markovian environments.
- To investigate the impact of environmental memory on system dynamics.
- To analyze entropy production and approach to equilibrium.
Main Methods:
- Developed a theoretical framework for non-Markovian environments.
- Coupled systems to environments with memory.
- Illustrated with simple examples of thermal relaxation and driven systems.
Main Results:
- Non-Markovian dynamics introduce a delay time in entropy production during thermal relaxation.
- Environmental memory can either accelerate or decelerate a system's approach to equilibrium.
- System-environment correlations significantly affect entropy production and dynamics.
Conclusions:
- Non-Markovian environments lead to distinct thermodynamic behaviors.
- Environmental memory introduces novel phenomena in stochastic thermodynamics.
- The framework provides insights into complex system dynamics and energy exchange.
Related Concept Videos
01:14The Second Law of Thermodynamics
5.5K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Scientists refer to the measure of randomness or disorder within a system as entropy. High entropy means high disorder and low energy. To better understand entropy, think of a student’s bedroom. If no energy or work were put into it, the room would quickly become messy. It would exist in a very disordered state, one of high entropy. Energy must be...
5.5K
02:39Entropy
30.9K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
30.9K
02:49Second Law of Thermodynamics
24.3K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic...
24.3K
01:20Entropy and the Second Law of Thermodynamics
3.1K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
The relation between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
3.1K
01:10Entropy Change in Reversible Processes
2.7K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
2.7K
02:21Spontaneity
24.6K
A spontaneous process is one that occurs naturally under certain conditions. A nonspontaneous process, on the other hand, will not take place unless it is “driven” by the continual input of energy from an external source. Processes have a natural tendency to occur in one direction under a given set of conditions. Water will naturally flow downhill (spontaneous process), but uphill flow (nonspontaneous process) requires outside intervention such as the use of a pump. Iron exposed to...
24.6K