Advanced Computational and Mathematical Techniques for Wave Energy Converter Systems

Springer, 2026

This book provides an in-depth examination of the advanced computational and mathematical techniques crucial for the development and optimization of Wave Energy Converter (WEC) systems. As global warming and climate change continue to impact ocean engineering, the need for sustainable and renewable energy sources becomes increasingly urgent. Wave energy, with its vast and largely untapped potential, stands out as a key player in clean power generation. This volume serves as a focal point in marine renewable energies, aligning with the Sustainable Development Goals (SDG-14). The chapters in this book cover a wide array of topics, showcasing the multifaceted applications of computation and hydrodynamic simulation in ocean engineering. Key concepts include the use of multi-criteria decision-making (MCDM) techniques combined with fuzzy logic theory for optimizing ocean wave energy. The book also presents a systematic assessment of WECs using spherical fuzzy multi-criteria group decision-making techniques. Readers will gain insights into the latest advancements in Computational Fluid Dynamics (CFD) and multi-objective optimization algorithms, which are essential for enhancing wave energy harvesting efficiency. Furthermore, the integration of Artificial Intelligence (AI) for predictive maintenance and fault diagnosis in hybrid solar-wind microgrid systems is thoroughly explored. This book is designed for researchers, engineers, and practitioners in the fields of marine renewable energies and ocean engineering. It offers cutting-edge insights into the computational and optimization strategies necessary for advancing sustainable maritime transportation and achieving the Sustainable Development Goals (SDG-14). Whether you are a seasoned professional or a newcomer to the field, this book provides the knowledge and tools needed to harness the power of wave energy and contribute to a sustainable future.

• Curatore: Tien Anh Tran, Pankaj Borah, Sasan Tavakoli
• Anno edizione: 2026
• Editore: Springer
• Formato: EPUB3 con Adobe DRM
• Lingua: Inglese
• Compatibilità: Tutti i dispositivi (eccetto Kindle)
• Layout: Reflowable
• EAN: 9783032108043