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Information in feedback ratchets.

Natalia Ruiz-Pino1,2, Daniel Villarrubia-Moreno2,3, Antonio Prados1

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This study quantifies entropy reduction in continuous feedback systems using a flashing ratchet model. It demonstrates how information

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Area of Science:

  • Thermodynamics
  • Statistical Mechanics
  • Non-equilibrium Physics

Background:

  • Feedback control systems utilize system state information for actuation, potentially reducing entropy and enhancing performance.
  • Calculating entropy reduction has been established for general and discrete systems.
  • Continuous feedback-controlled systems present unique challenges for entropy reduction computation.

Purpose of the Study:

  • To compute entropy reduction in a spatially continuous feedback-controlled system.
  • To analyze a feedback flashing ratchet as a model system for information's role in transport.
  • To investigate the thermodynamic efficiency and power output of such systems.

Main Methods:

  • Modeling a feedback flashing ratchet with a Brownian particle in a periodic potential.
  • Implementing a controller that measures particle position and switches the potential.
  • Calculating efficiency at maximum power and output power in the long-time dynamical regime.
  • Evaluating entropy reduction from the entropy of non-Markovian control actions.

Main Results:

  • A non-zero mean particle velocity is achieved in a long-time regime, even with symmetric potentials.
  • Entropy reduction is evaluated from the control action's entropy, considering sampling effort.
  • Output power can exceed input power, leading to apparent efficiencies greater than one if entropy reduction is ignored.
  • Including entropy reduction ensures well-behaved efficiency across all parameter ranges.

Conclusions:

  • Entropy reduction by information is crucial for the thermodynamic balance of feedback-controlled devices.
  • Accurate thermodynamic assessments require incorporating information's impact on entropy.
  • The feedback flashing ratchet serves as a key example for understanding information-driven thermodynamics.