Physical and numerical modeling of seaweed in oceanic waters

Understanding the dynamics of the stress imposed by water motion on seaweeds in oceanic waters is crucial for effective coastal management and the sustainable development of seaweed-based industries. However, the dynamics of highly flexible seaweeds (e.g., Saccharina latissima or Laminiara digitata ), driven by its mechanical and morphological properties, poses challenges for conventional fluid-structure-interaction models. This paper provides a comprehensive review of physical and numerical modeling approaches for seaweed hydrodynamics, aiming to identify knowledge gaps and offer guidelines for future research. Methods for determining the physical properties of seaweeds are being reviewed. However, for living organisms, these properties change as a result of age, mechanical impact or environmental conditions. Therefore, we also review the impact of various influencing factors on the physical properties. Hydrodynamic properties of seaweeds (e.g., drag, reconfiguration, shading effects) and the connection with their physical properties will be discussed. Moreover, a comparison between physical and numerical models for the determination of hydrodynamic loads on seaweeds is presented, discussing their possibilities and limitations. Finally, the paper investigates differences in force determination between isolated seaweed, seaweed farms and forests, as well as variations between flow-dominated and wave-dominated environments. The review reveals the challenges in comparing physical and hydrodynamic properties of seaweeds across different studies. It also identifies knowledge gaps in the parametrization of these properties. The recommended guidelines proposed in this paper provide a first framework to address these challenges and minimize variability between studies, thereby offering valuable guidance for future research. • This paper reviews physical, hydrodynamic, and numerical aspects of seaweed modeling. • Guidelines for study comparability, ensuring consistency across research methodologies. • Expanding experimental data will improve numerical models and enhance understanding.