Abstract

In this work, we consider the choice of a system suitable for the formulation of principles in nonequilibrium thermodynamics. It is argued that an isolated system is a much better candidate than a system in contact with a bath. In other words, relaxation processes rather than stationary processes are more appropriate for the formulation of principles in nonequilibrium thermodynamics. Arguing that slow varying relaxation can be described with quasi-stationary process, it is shown for two special cases, linear nonequilibrium thermodynamics and linearized Boltzmann equation, that solutions of these problems are in accordance with the maximum entropy production principle.

Highlights

  • Physicists develop an axiomatic theory whenever possible

  • In order to avoid such interference between a subject and a system being examined, we propose the use of relaxation processes for a formulation of physical principles in nonequilibrium thermodynamics

  • We argue that relaxation processes are more suitable than stationary processes for the formulation of principles in nonequilibrium thermodynamics

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Summary

Introduction

Physicists develop an axiomatic theory whenever possible. In the case of thermodynamics, the equilibrium properties of macroscopic systems can be well understood with three (or four) laws. Claiming that our knowledge of the initial state of a system is not a matter of physics but information theory, he introduced the concept of information entropy and proposed that the probabilities of states, under certain constraints, make information entropy the maximum possible. This procedure is known as the MaxEnt formalism [10,19,20]. Values of fluxes are considered as key elements in his approach in the sense that their mean values are constraints Assuming local equilibrium he found, under certain conditions, that MaxEnt leads to maximum entropy production. We ask which system is the best one to use when one aims for the definition of principles in nonequilibrium thermodynamics

Interference of Subject and Object in Stationary Processes
Conclusions

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