Inertial effects in discrete sampling information engines

TL;DR

This study investigates how inertia influences the power output of a discrete sampling information engine using a mechanical oscillator, revealing that inertia can enhance power extraction and aligning experimental results with theoretical predictions.

Contribution

It provides the first experimental analysis of inertial effects in a discrete sampling information engine, demonstrating how inertia can increase power output and comparing results with overdamped models.

Findings

Inertia can be exploited to increase power extraction.

Optimal feedback parameters depend on inertia and sampling frequency.

Work output is bounded by information theory in large sampling interval regimes.

Abstract

We describe an experiment on an underdamped mechanical oscillator used as an information engine. The system is equivalent to an inertial Brownian particle confined in a harmonic potential whose center is controlled by a feedback protocol which measures the particle position at a specific sampling frequency $1/{\tau}$. Several feedback protocols are applied and the power generated by the engine is measured as a function of the oscillator parameters and the sampling frequency. The optimal parameters are then determined. The results are compared to the theoretical predictions and numerical simulations on overdamped systems. We highlight the specific effects of inertia, which can be used to increase the amount of power extracted by the engine. In the regime of large $\tau$, we show that the produced work has a tight bound determined by information theories.