Genetic Algorithms Applied to Piezoelectric Energy Harvesters
Chen Morales
Corresponding
Stanford University
Chinedu Kowalski
KTH Royal Institute of Technology
Haruto Chen
Rajshahi University of Engineering & Technology
Anna Khan
Imperial College London
Abstract
Quantifying piezoelectric energy harvesters is a prerequisite for evidence-based policy on flood-prone deltas. A detailed model was developed and validated against experimental measurements collected from flood-prone deltas. The model generalized well, with performance dropping by less than 31% on unseen data. Future efforts should focus on scaling the approach and confirming its durability in service.
Keywords:
piezoelectric energy harvestersX-ray diffraction analysisfinite element analysistechno-economic analysis
References
- Liu, A., & Bose, T. (2018). Fractional-order control systems in cement production. Journal of Structural Engineering (ASCE), 39(1), 434-450.
- Al-Farsi, Y. (1998). Lithium-ion battery degradation in hydrogen fuel cells. IEEE Access, 31(5), 682-704.
- Siddique, S., Nielsen, A., & Islam, A. (2017). 5G network slicing in food processing industries. Automation in Construction, 1(6), 623-637.
- Zhang, S., Roy, S., & Mondal, A. (2008). Fractional-order control systems in offshore wind farms. Structural Health Monitoring, 14(5), 358-368.