Vibration Energy Harvesting for the Development of autonomous monitoring Systems. Application to railway Bridges

This doctoral thesis investigates vibration-based energy harvesting as a power source for autonomous monitoring systems in railway infrastructure. The research focuses on the modelling, optimisation, and validation of piezoelectric (PEH) and magnetoelastic (MEH) energy harvesters, with particular emphasis on their application to railway bridges. Analytical lumped-parameter models are derived from a variational formulation for bimorph and unimorph cantilever configurations, enabling the development of a novel efficient design optimisation procedure. The models are validated through numerical simulations, laboratory testing, and in-field experiments on railway bridges on an in-service high-speed line. In addition, the study introduces a novel stochastic tuning strategy that accounts for uncertainties in bridge dynamics during train passages. By stochastic characterising harvested mechanical energy, the method identifies optimal tuning frequencies without requiring full experimental modal identification. Experimental validation demonstrates that this approach increased harvested energy by up to 300%compared to traditional tuning to bridge natural frequencies. In addition, the potential of additive manufacturing for the substructure of energy harvesters is investigated, offering the advantage of flexibility in design. Moreover, a comparative assessment of PEH and MEH devices interfaced with standard energy harvesting (SEH) circuits reveals that 3D-printed MEHs would achieve up to three times higher energy transfer than optimised PEHs under realistic operating conditions. These findings highlight the potential of MEHs as a promising alternative to conventional piezoelectric devices for low-frequency excitation in railway bridges. Overall, the thesis demonstrates the feasibility of employing piezoelectric and magnetoelastic energy harvesters to power low-consumption sensors in structural health monitoring and Internet of Things (IoT) networks, reducing reliance on batteries or wired connections. The contributions include new analytical formulations, experimental validation of design and tuning strategies, and practical guidelines for optimising energy harvesting in railway applications using 3D printed devices.

Author dc:creator

• Cámara Molina, Javier Cristóbal

Advisor dc:contributor.advisor

• Romero Ordóñez, Antonio

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/11441/185996

OAI identifier oai:identifier

oai:idus.us.es:11441/185996