Maximal fluctuation exploitation in Gaussian information engines
| Autor: | Joseph N. E. Lucero, Jannik Ehrich, David A. Sivak, John Bechhoefer |
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| Rok vydání: | 2021 |
| Předmět: |
Work (thermodynamics)
Statistical Mechanics (cond-mat.stat-mech) Computer science Gaussian Feedback control media_common.quotation_subject FOS: Physical sciences Control engineering Second law of thermodynamics 01 natural sciences 7. Clean energy Energy storage 010305 fluids & plasmas symbols.namesake Simple (abstract algebra) 0103 physical sciences symbols Limit (mathematics) 010306 general physics Condensed Matter - Statistical Mechanics Computer Science::Databases Energy (signal processing) media_common |
| Zdroj: | Physical Review E. 104 |
| ISSN: | 2470-0053 2470-0045 |
| DOI: | 10.1103/physreve.104.044122 |
| Popis: | Understanding the connections between information and thermodynamics has been among the most visible applications of stochastic thermodynamics. While recent theoretical advances have established that the second law of thermodynamics sets limits on information-to-energy conversion, it is currently unclear to what extent real systems can achieve the predicted theoretical limits. Using a simple model of an information engine that has recently been experimentally implemented, we explore the limits of information-to-energy conversion when an information engine's benefit is limited to output energy that can be stored. We find that restricting the engine's output in this way can limit its ability to convert information to energy. Nevertheless, a feedback control that inputs work can allow the engine to store energy at the highest achievable rate. These results sharpen our theoretical understanding of the limits of real systems that convert information to energy. 17 pages, 17 figures |
| Databáze: | OpenAIRE |
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