Dr. Yurchenko DaniilAssociate Professor, School of Engineering & Physical Sciences, Heriot-Watt University, UK
Speech Title: Improving Flow-Induced Vibration Energy Harvesting Using Machine Learning
Abstract: Aims: To study how machine learning approach can improve the energy harvesting from wind induced vibrations Methods: Three different wake galloping piezoelectric energy harvesters are used to study their galloping response. Each harvester comprises a bluff body with square, triangular and circular cross-section. The bluff bodies are placed upstream of the air flow, which velocity spans within 2.9-14.5m/s. A rectangular parallelepiped bluff body mounted on a cantilever beam and attached with a piezoelectric sheet is placed downstream. Using machine learning technology, the present work selected different parameters as input features, and trained two machine learning models to predict the amplitude of the vortex-induced vibration of the two cylinders and the output voltage and vibration displacement of the piezoelectric energy harvester for wake galloping vibrations. Results: Three machine learning algorithms were tested, namely Decision Tree Regression, Random Forest and Gradient Boosted Regression Tree. The results indicate that the GBRT model exhibits the best performance in predicting both root mean square voltage and maximum displacement. Conclusions: This study demonstrates the promising application potential of ML in the ﬁeld of piezoelectric energy harvesting and how ML can help in predicting and optimizing the performance of such devices. Acknowledgements: This work was supported by the National Natural Science Foundation of China (Grant No.: 51977196), and China Postdoc-toral Science Foundation (2020T130557).
Biography: Dr. Yurchenko is an expert in the area of Nonlinear Stochastic Dynamics and Control, Vibrations mitigation and reliability, with focus on mathematical and experimental modelling of complex dynamic system with application in railway and automotive engineering, renewable wave and tidal energy, as well as energy harvesting using soft electroactive polymers. Obtained a PhD degree in Mechanical Engineering from Worcester Polytechnic Institute, USA on the development of stochastic optimal control theory for dynamic multi-degree-of- freedom systems and systems subjected to impact loading. The academic research resulted in numerous publications in highly prestigious peer-reviewed journals and conference proceedings. He is a member of the EUROMECH and IFToMM, Editorial board member of a number of prestigious journals in Dynamics and Mechanical Engineering, including Mechanical Systems and Signal Processing, Int. J. of Dynamics and Control, Vibrations. Co-Chair and Chair of a number of mini-symposia, Chair of the International Probabilistic Workshop 2019, held in Edinburgh and the Chair of the EUROMECH Colloquium of Mechanics of Soft Materials, 2022.