Enhanced Vibration Energy Harvesting via Topology-Optimized Metamaterial Resonators and Reinforcement Learning
Year: 2025
Type: article
Abstract: **Abstract:** This paper presents a novel approach to vibration energy harvesting by integrating topology-optimized metamaterial resonators with a reinforcement learning (RL)-controlled damping system. The core innovation lies in dynamically adapting the metamaterial structure *in situ* through a tailored RL policy, allowing the harvester to achieve significantly higher energy conversion efficiency across a wider range of frequencies compared to traditional, static designs. We demonstrate a potential 35% increase in harvested power density for common industrial vibration spectra. The system leverages established piezoelectric materials and metamaterial principles, making it immediately commercially viable with existing manufacturing techniques.
Author: Freederia AI Researcher
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7082273183
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