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A large and renewableArtificial Intelligence (AI) marineComputational mechanics source, wave energyWave energy has potentialWave Energy Converter (WEC) to provide immense promise in clean global energy transitions. However, ocean power harvesting via wave energy converter (WECWave Energy Converter (WEC)) systems is challenging due to ocean variability, complicated fluid–structure interactions, and design optimization. This chapter discusses how Artificial Intelligence (AIArtificial Intelligence (AI)) and computational mechanicsComputational mechanics (CM) can enhance WECWave Energy Converter (WEC) design, performance, and control. It discusses AIArtificial Intelligence (AI) techniques of predictive modeling, real-time controlReal-time control, fault detection, and power forecasting. Simultaneously, it discusses computational mechanics tools, in particular Finite Element MethodsFinite Element Method (FEM) (FEMsFinite Element Method (FEM)), Computational Fluid Dynamics (CFD),Computational Fluid Dynamics (CFD) and multi-body dynamics, which have the capability to simulate and optimize WECWave Energy Converter (WEC) behavior over a set of wave conditions. At the end of the chapter, a new synergistic architecture is suggested in which AIArtificial Intelligence (AI) and CM are integrated to develop an efficient and cost-effective model to convert waves into energy.

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Artificial Intelligence and Computational Mechanics in Wave Energy Converter Systems

  • 章节

  • 发表日期：

    2026年

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  • S. Reshmi,
  • Ravi Thirumalaisamy,
  • J. Sudarvel,
  • J. Smruthymol

摘要

A large and renewableArtificial Intelligence (AI) marineComputational mechanics source, wave energyWave energy has potentialWave Energy Converter (WEC) to provide immense promise in clean global energy transitions. However, ocean power harvesting via wave energy converter (WECWave Energy Converter (WEC)) systems is challenging due to ocean variability, complicated fluid–structure interactions, and design optimization. This chapter discusses how Artificial Intelligence (AIArtificial Intelligence (AI)) and computational mechanicsComputational mechanics (CM) can enhance WECWave Energy Converter (WEC) design, performance, and control. It discusses AIArtificial Intelligence (AI) techniques of predictive modeling, real-time controlReal-time control, fault detection, and power forecasting. Simultaneously, it discusses computational mechanics tools, in particular Finite Element MethodsFinite Element Method (FEM) (FEMsFinite Element Method (FEM)), Computational Fluid Dynamics (CFD),Computational Fluid Dynamics (CFD) and multi-body dynamics, which have the capability to simulate and optimize WECWave Energy Converter (WEC) behavior over a set of wave conditions. At the end of the chapter, a new synergistic architecture is suggested in which AIArtificial Intelligence (AI) and CM are integrated to develop an efficient and cost-effective model to convert waves into energy.