Optimal work extraction and mutual information in a generalized Szilárd engine
| Autor: | Juyong Song, Susanne Still, Isaac Pérez Castillo, Matteo Marsili, Rafael Díaz Hernández Rojas |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Physics
Szilard engine Mutual information 01 natural sciences 010305 fluids & plasmas Symmetric configuration Combinatorics 0103 physical sciences Partition (number theory) entropy maximisation 010306 general physics Condensed Matter - Statistical Mechanics information theory Szilard engine entropy maximisation information theory |
| Popis: | A 1929 Gedankenexperiment proposed by Szil\'ard, often referred to as "Szil\'ard's engine", has served as a foundation for computing fundamental thermodynamic bounds to information processing. While Szil\'ard's original box could be partitioned into two halves and contains one gas molecule, we calculate here the maximal average work that can be extracted in a system with $N$ particles and $q$ partitions, given an observer which counts the molecules in each partition, and given a work extraction mechanism that is limited to pressure equalization. We find that the average extracted work is proportional to the mutual information between the one-particle position and the vector containing the counts of how many particles are in each partition. We optimize this quantity over the initial locations of the dividing walls, and find that there exists a critical number of particles $N^{\star}(q)$ below which the extracted work is maximized by a symmetric configuration of the $q$ partitions, and above which the optimal partitioning is asymmetric. Overall, the average extracted work is maximized for a number of particles $\hat{N}(q) Comment: Updated with published version |
| Databáze: | OpenAIRE |
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